# tools.py

import pandas as pd

from pathlib import Path
import requests
import regex as re
import time
import os
from duckduckgo_search import DDGS
from langchain_core.tools import tool
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"


def _download_file_for_task(task_id: str, ext: str) -> str:
    """
    Helper: attempt to GET the remote file for a given task_id.
    Saves under ./hf_files/{task_id}.{ext}. Returns the local path if successful,
    or an empty string if no file / download failed.
    """
    
    print("reached _download_file_for_task")
    os.makedirs("hf_files", exist_ok=True)
    local_path = os.path.join("hf_files", f"{task_id}.{ext}")
    url = f"{DEFAULT_API_URL}/files/{task_id}"
    
    try:
        resp = requests.get(url, timeout=10)
        if resp.status_code == 200 and resp.content:
            print(f"Downloaded file from {url} to {local_path}")
            with open(local_path, "wb") as f:
                f.write(resp.content)
            return local_path
    except Exception:
        print(f"Error downloading file from {url} to {local_path}")
        pass

    # If we get here, either 404 or download error
    return ""

@tool
def image_tool(task_id: str) -> str:
    """
    Expects: task_id is a string
    Returns: "OCR text + brief caption or an error message"
      
    """
    print("reached image_tool")
    # path_or_id = state.get("ocr_path", "")
    for ext in ("png", "jpg", "jpeg"):
            candidate = _download_file_for_task(task_id, ext)
            if candidate:
                local_img = candidate
                break

    if not local_img or not os.path.exists(local_img):
        return {
            "ocr_path": None,
            "ocr_result": "Error: No image file found (local nonexistent or download failed)."
        }

    # 2) Read raw bytes
    try:
        with open(local_img, "rb") as f:
            image_bytes = f.read()
    except Exception as e:
        return  f"Error reading image file: {e}"
        

    # 3) Prepare HF Inference headers
    hf_token = os.getenv("HF_TOKEN")
    if not hf_token:
        return "Error: HUGGINGFACE_API_KEY not set in environment."
        

    headers = {"Authorization": f"Bearer {hf_token}"}

    # 4) Call HF’s vision-ocr to extract text
    ocr_text = ""
    try:
        ocr_resp = requests.post(
            "https://api-inference.huggingface.co/models/google/vit-ocr",
            headers=headers,
            files={"file": image_bytes},
            timeout=30
        )
        ocr_resp.raise_for_status()
        ocr_json = ocr_resp.json()

        # The JSON has “pages” → list of blocks → “lines” → each line has “text”
        lines = []
        for page in ocr_json.get("pages", []):
            for line in page.get("lines", []):
                lines.append(line.get("text", "").strip())
        ocr_text = "\n".join(lines).strip() or "(no visible text)"
    except Exception as e:
        ocr_text = f"Error during HF OCR: {e}"

    # 5) Call HF’s image-captioning to get a brief description
    caption = ""
    try:
        cap_resp = requests.post(
            "https://api-inference.huggingface.co/models/Salesforce/blip-image-captioning-base",
            headers=headers,
            files={"file": image_bytes},
            timeout=30
        )
        cap_resp.raise_for_status()
        cap_json = cap_resp.json()
        # The response looks like: {"generated_text": "...caption..."}
        caption = cap_json.get("generated_text", "").strip()
        if not caption:
            caption = "(no caption returned)"
    except Exception as e:
        caption = f"Error during HF captioning: {e}"

    # 6) Combine OCR + caption
    combined = f"OCR text:\n{ocr_text}\n\nImage caption:\n{caption}"
    print("combined: ")
    return combined

@tool
def excel_tool(task_id: str) -> str:
    """
    Downloads <task_id>.xlsx (if any) and returns a stringified list of
    records from the specified sheet.  No fallback to user-supplied tables.
    Expected keys in `task_id`:
        • task_id            – required (used to download the file)

    returns: stringified list of records from the specified sheet
    """
    print("reached excel_tool")
    sheet   = "Sheet1"

    local_xlsx = _download_file_for_task(task_id, "xlsx")
    if not local_xlsx or not os.path.exists(local_xlsx):
        return "Error: Excel file not found for this task."

    try:
        xls = pd.ExcelFile(local_xlsx)
        df  = pd.read_excel(
            xls,
            sheet_name=sheet if sheet and sheet in xls.sheet_names else xls.sheet_names[0]
        )
        print(f"Excel file read successfully: {str(df.to_dict(orient='records'))}")
        return str(df.to_dict(orient="records"))
    except Exception as e:
        return f"Error reading Excel file: {e}"
    

import openai
@tool
def audio_transcriber_tool(task_id: str) -> str:
    """
    LangGraph tool for transcribing audio via OpenAI's Whisper API.
    Expects: task_id is a string
    Returns:
    "<text or error message>"
    Always attempts to download the file for the given path or task ID.
    """
    print("reached audio_transcriber_tool")


    # Always attempt to download the file, regardless of local existence
    local_audio = ""
    for ext in ("mp3", "wav", "m4a"):
        candidate = _download_file_for_task(task_id, ext)
        if candidate:
            local_audio = candidate
            break

    if not local_audio or not os.path.exists(local_audio):
        return "Error: No audio file found (download failed)."
        

    # Send to OpenAI Whisper
    try:
        openai.api_key = os.getenv("OPENAI_API_KEY")
        if not openai.api_key:
            raise RuntimeError("OPENAI_API_KEY is not set in environment.")

        with open(local_audio, "rb") as audio_file:
            print("reached openai.audio.transcriptions.create")
            response = openai.audio.transcriptions.create(
                model="whisper-1", 
                file=audio_file,
            )
            print("reached response")
        text = response.text.strip()
    except Exception as e:
        text = f"Error during transcription: {e}"
    print(f"Transcripted as transcript: {text}")
    return text
# tools.py

import re
import requests

@tool
def wikipedia_search_tool(wiki_query: str) -> str:
    """
    LangGraph wrapper for searching Wikipedia.
    Expects: wiki_query is a non‐empty string.
    Returns: text summary of first matching page or an error message>"
      
    If no valid wiki_query is provided, returns {}.
    """
    print("reached wikipedia search tool")
    query = wiki_query
    if not query:
        return {}

    try:
        # 1) Use the MediaWiki API to search for page titles matching the query
        search_params = {
            "action": "query",
            "list": "search",
            "srsearch": query,
            "format": "json",
            "utf8": 1
        }
        search_resp = requests.get("https://en.wikipedia.org/w/api.php", params=search_params, timeout=10)
        search_resp.raise_for_status()
        search_data = search_resp.json()

        search_results = search_data.get("query", {}).get("search", [])
        # print("wikipedia: search_results",search_results)
        if not search_results:
            print(f"No Wikipedia page found for '{query}'.")
            return f"No Wikipedia page found for '{query}'."

        # 2) Take the first search result's title
        first_title = search_results[0].get("title", "")
        if not first_title:
            print("Unexpected format from Wikipedia search.")
            return "Unexpected format from Wikipedia search."

        # 3) Fetch the page summary for that title via the REST summary endpoint
        title_for_url = requests.utils.requote_uri(first_title)
        summary_url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{title_for_url}"
        summary_resp = requests.get(summary_url, timeout=10)
        summary_resp.raise_for_status()
        summary_data = summary_resp.json()

        # 4) Extract either the "extract" field or a fallback message
        summary_text = summary_data.get("extract")
        if not summary_text:
            summary_text = summary_data.get("description", "No summary available.")
        print(f"Title: {first_title}\n\n{summary_text}")
        return f"Title: {first_title}\n\n{summary_text}"
        

    except requests.exceptions.RequestException as e:
        return f"Wikipedia search error: {e}"
    except Exception as e:
        return f"Unexpected error in wikipedia_search_tool: {e}"


from langchain_openai import ChatOpenAI
from langchain.schema import SystemMessage, HumanMessage
LLM = ChatOpenAI(model_name="gpt-4.1-mini", temperature=0.2)

@tool
def analyze_code_tool(task_id: str) -> str:
    """
    Either task_id  OR  (file + task_id)
    Reads the code (max 400 lines / 10 kB) and asks the LLM for:
        • plain-language summary
        • list of key functions/classes
        • obvious bugs or style smells
    Returns that analysis as a string.
    """
    print("reached analyze_code_tool")
    code_txt = ""
    if not task_id:
        code_txt = "No code provided."
    else:
        path = _download_file_for_task(task_id, "py")
        if not path:
            return "Error: .py file not found for this task."
        code_txt = Path(path).read_text(encoding="utf-8", errors="ignore")
    # else:
    #     return "Error: neither snippet nor file provided."

    # Truncate for safety
    lines = code_txt.splitlines()[:400]
    code_sample = "\n".join(lines)[:10_000]

    prompt = [
        SystemMessage(content="You are a senior Python code reviewer."),
        HumanMessage(content=(
            "Please analyse the following code. "
            "Summarise what it does, list key functions/classes, "
            "and point out any obvious bugs, performance issues or style problems.\n\n"
            f"```python\n{code_sample}\n```"
            "If you can then find the output of the code and return it in the output."
        ))
    ]
    return LLM.invoke(prompt).content.strip()


# def web_search_tool(state: AgentState) -> AgentState:
#     """
#     Expects: state["web_search_query"] is a non‐empty string.
#     Returns: {"web_search_query": None, "web_search_result": <string>}.
#     Retries up to 5 times on either a DuckDuckGo “202 Ratelimit” response or any exception (e.g. timeout).
#     """
#     print("reached web_search_tool")
#     query = state.get("web_search_query", "")
#     if not query:
#         return {}  # nothing to do

#     ddg = DDGS()
#     max_retries = 5
#     result_text = ""

#     for attempt in range(1, max_retries + 1):
#         try:
#             result_text = str(ddg.text(query, max_results=5))
#         except Exception as e:
#             # Network error or timeout—retry up to max_retries
#             if attempt < max_retries:
#                 print(f"web_search_tool: exception '{e}', retrying in 4 seconds ({attempt}/{max_retries})")
#                 time.sleep(4)
#                 continue
#             else:
#                 # Final attempt failed
#                 return {
#                     "web_search_query": None,
#                     "web_search_result": f"Error during DuckDuckGo search: {e}"
#                 }

#         # Check for DuckDuckGo rate‐limit indicator
#         if "202 Ratelimit" in result_text:
#             if attempt < max_retries:
#                 print(f"web_search_tool: received '202 Ratelimit', retrying in 4 seconds ({attempt}/{max_retries})")
#                 time.sleep(4)
#                 continue
#             else:
#                 # Final attempt still rate‐limited
#                 break

#         # Successful response (no exception and no rate‐limit text)
#         break

#     return {
#         "web_search_query": None,
#         "web_search_result": result_text
#     }