Update main.py

main.py

@@ -2,6 +2,7 @@ from flask import Flask, jsonify, request
 from flask_cors import CORS
 from pymongo.mongo_client import MongoClient
 from pymongo.server_api import ServerApi
+from bson.objectid import ObjectId
 import google.generativeai as genai
 import urllib.parse
 from models import UserSchema
@@ -10,13 +11,17 @@ from flask_jwt_extended import JWTManager, create_access_token
 from middleware.authUser import auth_user
 from datetime import timedelta 
 from controllers.demo import get_initial_data
+from controllers.mindmap import saveMindmap, getMindmap, deleteMindmap, getMindmapByid
+import json
+from dotenv import load_dotenv
 import os
 
+load_dotenv()
+
 app = Flask(__name__)
 
 bcrypt = Bcrypt(app)
 jwt = JWTManager(app)
-
 CORS(app)
 
 app.config['JWT_SECRET_KEY'] = os.getenv('JWT_SECRET')
@@ -24,13 +29,15 @@ app.config['JWT_SECRET_KEY'] = os.getenv('JWT_SECRET')
 # MongoDB configuration
 username = urllib.parse.quote_plus(os.getenv('MONGO_USERNAME'))
 password = urllib.parse.quote_plus(os.getenv('MONGO_PASSWORD'))
-restUri = os.getenv('REST_URI')
+restUri = os.getenv('REST_URI');
 
-uri = f'mongodb+srv://{username}:{password}@cluster0.iidzcbc.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0'
+uri = f'mongodb+srv://{username}:{password}{restUri}'
 
-client = MongoClient(uri)
+client = MongoClient(uri, server_api=ServerApi('1'))
 db = client.GenUpNexus
-users_collection = db["users3"]
+users_collection = db["users"]
+interviews_collection = db["interviews"]
+savedMindmap = db["savedMindmap"]
 
 # Send a ping to confirm a successful connection
 try:
@@ -45,145 +52,412 @@ GOOGLE_API_KEY=os.getenv('GOOGLE_API_KEY')
 genai.configure(api_key=GOOGLE_API_KEY)
 model = genai.GenerativeModel('gemini-pro')
 
+# Caches to reduce no of queries to MongoDB...
+user_id_ping = {'current': 0}
+user_chats = {}
+
 @app.route('/')
 def index():
     return "Server is Running..."
 
-@app.route('/tree', methods=["POST", "GET"])
+@app.route('/index')
+def index2():
+    return "routes checking..."
+
+@app.route('/trees', methods=["POST", "GET"])
 def tree():
     if request.method == 'POST':
         data = request.get_json()
         query = data.get('query')
         print(query)
-        response = model.generate_content('''I will give you a topic and you have to generate an explanation of the topic and respond with JSON structure as follows as i want to use this json are Nodes & Edges and visualise this using ReactFlow library, the json structure will be :
-        nodes = [
-            {
-                id: "1",
-                type: "input",
-                data: {
-                label: "Input Node",
-                },
-                position: { x: 250, y: 0 },
-            },
-            {
-                id: "2",
-                data: {
-                label: "Default Node",
-                },
-                position: { x: 100, y: 100 },
-            },
-            {
-                id: "3",
-                type: "output",
-                data: {
-                label: "Output Node",
-                },
-                position: { x: 400, y: 100 },
-            },
-            {
-                id: "4",
-                type: "custom",
-                position: { x: 100, y: 200 },
-                data: {
-                selects: {
-                    "handle-0": "smoothstep",
-                    "handle-1": "smoothstep",
-                },
-                },
-            },
-            {
-                id: "5",
-                type: "output",
-                data: {
-                label: "custom style",
-                },
-                className: "circle",
-                style: {
-                background: "#2B6CB0",
-                color: "white",
-                },
-                position: { x: 400, y: 200 },
-                sourcePosition: Position.Right,
-                targetPosition: Position.Left,
-            },
-            {
-                id: "6",
-                type: "output",
-                style: {
-                background: "#63B3ED",
-                color: "white",
-                width: 100,
-                },
-                data: {
-                label: "Node",
-                },
-                position: { x: 400, y: 325 },
-                sourcePosition: Position.Right,
-                targetPosition: Position.Left,
-            },
-            {
-                id: "7",
-                type: "default",
-                className: "annotation",
-                data: {
-                label: (
-                    <>
-                    On the bottom left you see the <strong>Controls</strong> and the
-                    bottom right the <strong>MiniMap</strong>. This is also just a node 🥳
-                    </>
-                ),
+        response = model.generate_content('''I will give you a topic and you have to generate an explanation of the topic in points in hierarchical tree structure and respond with JSON structure as follows:
+        {
+            "name": "Java",
+            "children": [
+                {
+                "name": "Development Environment",
+                "children": [
+                    {
+                    "name": "Java Source Code",
+                    "value": ".java files",
+                    "description": "Human-readable code written with Java syntax."
+                    },
+                    {
+                    "name": "Java Development Kit (JDK)",
+                    "children": [
+                        {
+                        "name": "Compiler",
+                        "value": "translates to bytecode",
+                        "description": "Transforms Java source code into bytecode instructions understood by the JVM."
+                        },
+                        {
+                        "name": "Java Class Library (JCL)",
+                        "value": "predefined classes and functions",
+                        "description": "Provides a collection of reusable code for common functionalities."
+                        }
+                    ]
+                    }
+                ]
                 },
-                draggable: false,
-                selectable: false,
-                position: { x: 150, y: 400 },
-            },
-            ];
-
-            edges = [
-                { id: "e1-2", source: "1", target: "2", label: "this is an edge label" },
-                { id: "e1-3", source: "1", target: "3", animated: true },
                 {
-                    id: "e4-5",
-                    source: "4",
-                    target: "5",
-                    type: "smoothstep",
-                    sourceHandle: "handle-0",
-                    data: {
-                    selectIndex: 0,
+                "name": "Execution",
+                "children": [
+                    {
+                    "name": "Java Runtime Environment (JRE)",
+                    "children": [
+                        {
+                        "name": "Java Virtual Machine (JVM)",
+                        "value": "executes bytecode",
+                        "description": "Software program that interprets and executes bytecode instructions."
+                        },
+                        {
+                        "name": "Class Loader",
+                        "value": "loads bytecode into memory",
+                        "description": "Loads .class files containing bytecode into JVM memory for execution."
+                        }
+                    ]
                     },
-                    markerEnd: {
-                    type: MarkerType.ArrowClosed,
+                    {
+                    "name": "Bytecode",
+                    "value": ".class files (platform-independent)",
+                    "description": "Machine-independent instructions generated by the compiler, executable on any system with JVM."
                     },
+                    {
+                    "name": "Just-In-Time (JIT) Compilation (optional)",
+                    "value": "improves performance by translating bytecode to machine code",
+                    "description": "Technique that translates frequently used bytecode sections into native machine code for faster execution."
+                    }
+                ]
                 },
                 {
-                    id: "e4-6",
-                    source: "4",
-                    target: "6",
-                    type: "smoothstep",
-                    sourceHandle: "handle-1",
-                    data: {
-                    selectIndex: 1,
+                "name": "Key Features",
+                "children": [
+                    {
+                    "name": "Object-Oriented Programming",
+                    "value": "uses objects and classes",
+                    "description": "Programs are structured around objects that encapsulate data and behavior."
                     },
-                    markerEnd: {
-                    type: MarkerType.ArrowClosed,
+                    {
+                    "name": "Platform Independent (write once, run anywhere)",
+                    "value": "bytecode runs on any system with JVM",
+                    "description": "Java code can be compiled once and run on any platform with a JVM installed."
                     },
-                },
-            ];
+                    {
+                    "name": "Garbage Collection",
+                    "value": "automatic memory management",
+                    "description": "JVM automatically reclaims memory from unused objects, simplifying memory management for developers."
+                    }
+                ]
+                }
+            ]
+            }
             Topic is: ''' + query)
         
         # print(response.text)
         return jsonify({'success': True, 'data': response.text})
         # return temp 
 
+
+
+
+@app.route('/tree/demo', methods=["POST"])
+def treeDemo():
+    if request.method == 'POST':
+        data = request.get_json()
+        query = data.get('query')
+        print(query)
+        response = model.generate_content('''Generate a comprehensive knowledge map representing the user's query, suitable for ReactFlow visualization.
+
+        **Prompt:** {query}
+
+        **Structure:**
+
+        - Top-level node: Represent the user's query.
+        - Sub-nodes branching out based on the query's relevance:
+            - Leverage external knowledge sources (e.g., Wikipedia, knowledge graphs, domain-specific APIs) to identify relevant sub-concepts, related entities, and potential relationships.
+        - Consider including different categories of sub-nodes:
+            - **Concepts:** Core ideas or principles related to the query.
+            - **Subfields:** Specialized areas within the main topic.
+            - **Applications:** Practical uses of the concept or subfield.
+            - **Tools and Technologies:** Software or platforms used to implement the concepts.
+            - **Examples:** Illustrative instances or use cases.
+            - **Historical Context:** Milestones or key figures in the topic's development.
+            - **See Also:** Links to broader concepts or related areas for the further exploration.
+            
+                                                
+        **Content:**
+
+        - Each node should have a label describing the concept, entity, or tool.
+        - Optionally, include brief descriptions, definitions, or key points within the nodes or as tooltips.
+        - Consider using icons to visually represent different categories of nodes (e.g., light bulb for concepts, gear for tools, calendar for historical context, puzzle piece for subfields).
+        - there should be atmax 10 nodes in the knowledge map.
+        - Also follow the n-ary tree structure for better visualization.
+        - Ensure the knowledge map is visually appealing, well-organized, and easy to navigate.
+
+        **Desired Format:**
+
+        - JSON structure compatible with ReactFlow:
+            - nodes (list): id, position, data (label, description, icon(if required), category), type(input, output or custom), style (background, color).
+            - edges (list): id, source, target, label(if required), animated (true or false), style (stroke).
+        - keep the position of nodes spaced out for better visualization.
+        - always keep the top-level node at the center of the visualization.
+        - keep atleast 2 edges "animated":true.
+        - Strictly keep the first node with style having color property with value blue and background property with value #0FFFF0.
+        - Strictly keep the second node with type property value as custom.
+        - You can style the nodes with different colors and edges with different colors. 
+        - to edit edges add style with stroke property and a hexcode value to it.
+                                        
+        Topic is: ''' + query)
+        
+        # response.text(8,)
+        print(response.text)
+        json_data = response.text
+        modified_json_data = json_data[8:-3]
+        return jsonify({'success': True, 'data': modified_json_data})
+        # return temp 
+
+
+def res(user_id):
+    avg_text = 0        
+    avg_code = 0
+    count = 0
+    for i, ele in enumerate(user_chats[user_id]['chat'].history):
+        if i == 0:
+            continue
+
+        if ele.role == 'model':
+            temp = json.loads(ele.parts[0].text)
+            print(temp)
+            if 'question' in temp.keys():
+                continue
+            elif 'next_question' in temp.keys() or 'end' in temp.keys():
+                count += 1
+                avg_text += temp['text_correctness']
+                if temp['code_correctness']:
+                    avg_code += temp['code_correctness']
+            print(json.loads(ele.parts[0].text), end='\n\n')
+
+    avg_text /= count
+    avg_code /= count
+
+    user_chats[user_id]['test_results'] = {'avg_text': avg_text, 'avg_code': avg_code}
+
+    return True
+
+
 @app.route('/interview', methods=["POST", "GET"])
 def interview():
     if request.method == 'POST':
         data = request.get_json()
+        print(data)
         if data.get('from') == 'client':
-            return "Success"
+            user_id = data.get('user_id')
+            request_type = data.get('type')
+
+            if request_type == 1:           # Initialize Questionarrie.
+                chat = model.start_chat(history=[])
+                user_chats[user_id] = {}
+                user_chats[user_id]['chat'] = chat
+                user_chats[user_id]['processed'] = False
+
+                position = data.get('position')
+                round = data.get('round')
+                difficulty_level = data.get('difficulty_level')
+                company_name = data.get('company_name')
+
+                user_chats[user_id]['position'] = position
+                user_chats[user_id]['round'] = round
+                user_chats[user_id]['difficulty_level'] = difficulty_level
+                user_chats[user_id]['company_name'] = company_name
+                
+                response = chat.send_message('''You are a Interviewer. I am providing you with the the position for which the inerview is, Round type, difficulty level of the interview to be conducted, Company name.
+                                             You need to generate atmost 2 interview questions one after another.
+                                             The questions may consists of writing a small code along with text as well.
+                                             
+                                             Now generate first question in following JSON format: 
+                                             {
+                                                "question": "What is ...?"
+                                             }
+
+                                             I will respond to the question in the following JSON format:
+                                             {
+                                                "text_answer": "answer ...",
+                                                "code": "if any...."
+                                             }
+                                             
+                                             Now after evaluating the answers you need to respond in the following JSON format:
+                                             {
+                                                "next_question": "What is ...?",
+                                                "text_correctness": "Test the correctness of text and return a range from 1 to 5 of correctness of text.",
+                                                "text_suggestions": "Some suggestions regarding the text_answer.... in string format."
+                                                "code_correctness": "Test the correctness of code and return a range from 1 to 5 of correctness of code",
+                                                "code_suggestions": "Any suggestions or optimizations to the code...in string format.",
+                                             }
+
+                                             At the end of the interview if no Questions are required then respond in the following format:
+                                             {
+                                                "text_correctness": "Test the correctness of text and return a range from 1 to 5 of correctness of text.",
+                                                "text_suggestions": "Some suggestions regarding the text_answer...."
+                                                "code_correctness": "Test the correctness of code and return a range from 1 to 5 of correctness of code",
+                                                "code_suggestions": "Any suggestions or optimizations to the code...",
+                                                "end": "No more Questions thanks for your time."
+                                             }
+                                             
+                                             Here are the details:
+                                             Position : '''+ position + '''
+                                             Round: '''+ round + '''
+                                             Difficullty Level : '''+ difficulty_level + '''
+                                             Company Interview : ''' + company_name)
+                print(response.text)
+                temp = json.loads(response.text)
+                user_chats[user_id]['qa'] = [{'question': temp['question']}]
+                return jsonify({'success': True, 'data': response.text})
+            
+            if request_type == 2:
+                text_data = data.get('text_data')
+                code = data.get('code')
+
+                chat = user_chats[user_id]['chat']
+                response = chat.send_message('''{"text_answer": "''' + text_data + '''", "code": "''' + code + '''"}''')
+                
+                print(response.text)
+
+                json_text =  json.loads(response.text)
+                
+                for i, ele in enumerate(user_chats[user_id]['qa']):
+                    if i == len(user_chats[user_id]['qa'])-1:
+                        ele['text_answer'] = text_data
+                        ele['code_answer'] = code
+                        ele['text_correctness'] = json_text['text_correctness']
+                        ele['text_suggestions'] = json_text['text_suggestions']
+                        ele['code_correctness'] = json_text['code_correctness']
+                        ele['code_suggestions'] = json_text['code_suggestions']
+                
+                try:
+                    if json_text['end']:
+                        user_id_ping['current'] = user_id
+                        if res(user_id):
+                            print(user_chats[user_id])
+                        return jsonify({'success': True, 'data': response.text, 'end': True})
+                except Exception as e:
+                    print(e)
+
+                user_chats[user_id]['qa'].append({'question': json_text['next_question']})
+            
+                return jsonify({'success': True, 'data': response.text, 'end': False})
+
+        
         elif data.get('from') == 'gradio':
             print(data)
-            return "Success"
+            user_id = data.get('user_id')
+            user_chats[user_id]['processed'] = True
+            user_chats[user_id]['gradio_results'] = {'total_video_emotions': data.get('total_video_emotions'), 'emotions_final': data.get('emotions_final'), 'body_language': data.get('body_language'), 'distraction_rate': data.get('distraction_rate'), 'formatted_response': data.get('formatted_response'), 'total_transcript_sentiment': data.get('total_transcript_sentiment')}
+            
+            emotion_weights = {
+                'admiration': 0.8,
+                'amusement': 0.7,
+                'angry': -0.8,
+                'annoyance': -0.7,
+                'approval': 0.9,
+                'calm': 0.8,
+                'caring': 0.8,
+                'confusion': -0.5,
+                'curiosity': 0.6,
+                'desire': 0.7,
+                'disappointment': -0.8,
+                'disapproval': -0.9,
+                'disgust': -0.9,
+                'embarrassment': -0.7,
+                'excitement': 0.8,
+                'fear': -0.8,
+                'fearful': -0.8,
+                'gratitude': 0.9,
+                'grief': -0.9,
+                'happy': 0.9,
+                'love': 0.9,
+                'nervousness': -0.6,
+                'optimism': 0.8,
+                'pride': 0.9,
+                'realization': 0.7,
+                'relief': 0.8,
+                'remorse': -0.8,
+                'sad': -0.9,
+                'surprise': 0.7,
+                'surprised': 0.7,
+                'neutral': 0.0
+            }
+            
+            temp = data.get('total_video_emotions')
+            temp2 = data.get('emotions_final')
+            temp3 = data.get('formatted_response')
+            temp4 = data.get('total_transcript_sentiment')
+
+            total_video_emotion_score = sum(temp[emotion] * emotion_weights[emotion] for emotion in temp)
+            total_video_emotion_normalized_score = ((total_video_emotion_score + 1) / 2) * 9 + 1
+
+            emotion_final_score = sum(temp2[emotion] * emotion_weights[emotion] for emotion in temp2)
+            emotion_final_normalized_score = ((emotion_final_score + 1) / 2) * 9 + 1
+
+            speech_sentiment_score = sum(temp3[emotion] * emotion_weights[emotion] for emotion in temp3)
+            speech_sentiment_normalized_score = ((speech_sentiment_score + 1) / 2) * 9 + 1
+
+            total_transcript_sentiment_score = sum(temp4[emotion] * emotion_weights[emotion] for emotion in temp4)
+            total_transcript_sentiment_normalized_score = ((total_transcript_sentiment_score + 1) / 2) * 9 + 1
+
+            body_language_score = data.get('body_language')['Good'] * 10
+            distraction_rate_score = data.get('distraction_rate') * 10
+
+            avg_text_score = user_chats[user_id]['test_results']['avg_text']
+            avg_code_score = user_chats[user_id]['test_results']['avg_code']
+
+            interview_score = (total_video_emotion_normalized_score + emotion_final_normalized_score + speech_sentiment_normalized_score + total_transcript_sentiment_normalized_score + body_language_score + distraction_rate_score + avg_text_score + avg_code_score)/7
+
+            print(interview_score)
+            user_chats[user_id]['interview_score'] = interview_score
+            
+            user_chats[user_id]['user_id'] = user_id
+            print(user_chats[user_id])
+
+            # Store user_chats[user_id] into MongoDB...
+            del user_chats[user_id]["chat"]
+            result = interviews_collection.insert_one(user_chats[user_id])
+            print(result)
+
+            return jsonify({'success': True})
+        
+
+@app.route('/result', methods=['POST', 'GET'])
+def result():
+    if request.method == 'POST':
+        data = request.get_json()
+        user_id = data.get('user_id')
+
+        if data.get('type') == 1:
+            result = interviews_collection.find({ "user_id" : user_id}, {"_id" : 1, "company_name" : 1, "difficulty_level" : 1, "interview_score" : 1, "position" : 1, "round" : 1 })
+            temp = []
+            for ele in result:
+                ele['_id'] = str(ele['_id'])
+                temp.append(ele)
+
+            return jsonify({'success': True, 'data': temp})
+        
+        elif data.get('type') == 2:
+            result2 = interviews_collection.find_one({ "_id": ObjectId(data.get("_id")), "user_id": user_id })
+            if result2:
+                result2['_id'] = str(result2['_id'])
+                print(result2)
+                return jsonify({'success': True, 'data': result2})
+            else:
+                if not user_chats[user_id]['processed']:
+                    return jsonify({'processing': True})
+                else:
+                    return jsonify({'error': "No such record found."})
+
+
+@app.route('/useridping', methods=['GET'])
+def useridping():
+    if request.method == 'GET':
+        return jsonify(user_id_ping)
 
 
 # User Routes
@@ -236,7 +510,7 @@ def signin():
     expires = timedelta(days=7)
     access_token = create_access_token(identity={"email": user['email'], "id": str(user['_id'])}, expires_delta=expires)
 
-    res = {"name": user['name'], "email": user['email']}
+    res = {"name": user['name'], "email": user['email'], "user_id": str(user['_id'])}
 
     return jsonify({"result": res, "token": access_token}), 200
 
@@ -256,10 +530,38 @@ def delete_account():
         print(e)
         return jsonify({"message": "Something went wrong"}), 500
 
+# mindmap routes
+@app.route('/mindmap/save', methods=['POST'])
+@auth_user
+def mindmapSave():
+    userId = request.userId
+    data = request.json
+    return saveMindmap(data, userId, savedMindmap)
+
+@app.route('/mindmap/get', methods=['GET'])
+@auth_user
+def mindmapGet():
+    userId = request.userId
+    return getMindmap(userId, savedMindmap)
+
+@app.route('/mindmap/get/<id>', methods=['GET'])
+@auth_user
+def mindmapGetById(id):
+    userId = request.userId
+    return getMindmapByid(userId, id, savedMindmap)
+
+@app.route('/mindmap/delete', methods=['POST'])
+@auth_user
+def mindmapDelete():
+    userId = request.userId
+    data = request.json
+    return deleteMindmap(userId, data, savedMindmap)
+
+
 @app.route('/mindmap/demo', methods=['POST'])
 def mindmapDemo():
     data = request.json
-    print(data)
+    print(data);
     return get_initial_data(), 200