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.env.example

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+# Environment variables template
+# Copy this file to .env and fill in your actual values
+
+# MongoDB Connection
+MONGO_URI=mongodb+srv://username:password@cluster.mongodb.net/database?retryWrites=true&w=majority
+
+# Google Gemini API
+GEMINI_API_KEY=your_gemini_api_key_here
+
+# Node.js Environment
+NODE_ENV=production
+
+# Next.js Environment
+NEXT_PUBLIC_API_URL=http://localhost:8001
+NEXT_PUBLIC_FASTAPI_URL=http://localhost:8000

.gitignore

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+.vscode
+.idx
+.github
+.env
+.

.vercelignore

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+# Exclude other services from Vercel deployment
+server/
+FastAPI/
+.github/
+docker-compose.yml
+**/Dockerfile
+**/.dockerignore
+railway.yml
+
+# Standard ignores
+node_modules
+.next
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+*.log

FastAPI/.dockerignore

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+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+.git
+.gitignore
+README.md
+Dockerfile
+.dockerignore
+.env
+.pytest_cache
+.coverage
+htmlcov/
+.venv
+venv/

FastAPI/.gitignore

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+.env
+client_secret.json
+token.json

FastAPI/Dockerfile

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+# Use Python 3.11 slim image
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    gcc \
+    g++ \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY . .
+
+# Create non-root user for security
+RUN useradd -m -u 1000 appuser && chown -R appuser:appuser /app
+USER appuser
+
+# Expose port
+EXPOSE 8000
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:8000/health || exit 1
+
+# Run the application
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

FastAPI/README.md

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+# Doubt Solver Agent
+
+
+## Setup Instructions
+
+1. **Clone the repository:**
+   ```bash
+   git clone <repository-url>
+   cd <repository-name>
+   ```
+
+2. **Install dependencies:**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Create a `.env` file in the root directory and add your Gemini API key:**
+   ```
+   GEMINI_API_KEY=your_api_key_here
+   MONGO_URI = "mongodb://localhost:27017"
+   ```
+4. **Paste Google Oauth2 file as `client_secret.json` and `token.json` in the root directory (Required while  Google Meet Creation)**
+
+5. **Run the application locally:**
+   ```bash
+   uvicorn main:app --reload --port 5000
+   ```
+
+6. **Access the application at** `http://localhost:5000`
+
+## Project Structure
+
+```
+.
+├── README.md
+├── requirements.txt
+├── .env
+├── main.py
+├── src/
+│   ├── db.py
+│   ├── chat.py
+│   ├── crud.py
+│   ├── meet.py
+│   └── models.py
+```
+
+## API Endpoints
+
+- `POST /api/query`: Submit a question to the tutoring system
+  - Request body: `{"query": "your question here"}`
+  - Response: `{"response": "system's response"}`
+
+## MongoDB Initialization
+
+To use this application, you must have MongoDB running locally. Follow these steps to ensure MongoDB is set up correctly:
+
+1. **Install MongoDB**
+   - Download and install MongoDB Community Edition from: https://www.mongodb.com/try/download/community
+   - Follow the installation instructions for your operating system.
+
+2. **Start the MongoDB server**
+   - On most systems, you can start MongoDB with:
+     ```bash
+     mongod
+     ```
+   - By default, MongoDB will run on `mongodb://localhost:27017`.
+
+3. **Automatic Collection Creation**
+   - When you start the FastAPI server, the application will automatically check for and create the following collections if they do not exist:
+     - `teachers`
+     - `students`
+     - `chats`
+   - This logic is handled in `src/db.py`.
+
+4. **Verify Connection**
+   - Ensure your MongoDB server is running **before** starting the FastAPI server. If MongoDB is not running, the application will not be able to connect to the database.
+
+If you need to change the MongoDB connection URI, update the `MONGO_DETAILS` variable in `src/db.py`. 

FastAPI/main.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from typing import List
+from src.models import TeacherModel, StudentModel, ChatModel, ChatEntry
+from src.crud import (
+    get_all_teachers, 
+    get_all_students, 
+    get_chat_by_roll, 
+    update_chat_by_roll,
+    add_chat_entry
+)
+from pydantic import BaseModel
+from src.chat import DoubtAgent
+from dotenv import load_dotenv
+load_dotenv()
+import os
+
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+app = FastAPI()
+
+# Health check endpoint
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy", "service": "NeuroGrade FastAPI"}
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:3000", "http://127.0.0.1:3000"],  # Add your frontend URL
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.get("/")
+def read_root():
+    return {"message": "NeuroGrade Chat API is running"}
+
+@app.get("/teachers", response_model=List[TeacherModel])
+def read_teachers():
+    return get_all_teachers()
+
+@app.get("/students", response_model=List[StudentModel])
+def read_students():
+    return get_all_students()
+
+@app.get("/chats/{stud_roll}", response_model=ChatModel)
+def read_chat(stud_roll: str):
+    chat = get_chat_by_roll(stud_roll)
+    if chat:
+        return chat
+    raise HTTPException(status_code=404, detail="Chat not found")
+
+@app.put("/chats/{stud_roll}", response_model=bool)
+def update_chat(stud_roll: str, chats: List[ChatEntry]):
+    updated = update_chat_by_roll(stud_roll, chats)
+    if updated:
+        return True
+    raise HTTPException(status_code=404, detail="Chat not found or not updated")
+
+# Request model for agent endpoint
+class AgentRequest(BaseModel):
+    query: str
+    student_roll: str
+
+@app.post("/agent/ask")
+def agent_ask(request: AgentRequest):
+    # 1. Get history
+    chat_history_doc = get_chat_by_roll(request.student_roll)
+    chat_history = chat_history_doc.chats if chat_history_doc else []
+
+    # 2. Call agent with history
+    agent = DoubtAgent(
+        api_key=GEMINI_API_KEY, 
+        student_roll=request.student_roll, 
+        chat_history=chat_history
+    )
+    response = agent.get_response(request.query)
+
+    # 3. Store new entry
+    add_chat_entry(stud_roll=request.student_roll, query=request.query, answer=response)
+
+    return {"response": response}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=5000)
+
+

FastAPI/requirements.txt

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+fastapi==0.115.14
+google_api_python_client==2.172.0
+google_auth_oauthlib==1.2.2
+langchain_core==0.3.68
+langchain_google_genai==2.1.6
+langgraph==0.5.1
+protobuf==6.31.1
+pydantic==2.11.7
+pymongo==4.13.2
+python-dotenv==1.1.1
+typing_extensions==4.14.1
+uvicorn==0.32.1

FastAPI/src/__init__.py

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+"""
+Multi-agent tutoring system agents package.
+"""