Backend for the Emotion Detection application built with FastAPI and MongoDB.

• 🔐 User authentication with JWT tokens
• 🔄 Real-time updates with WebSockets
• 😀 Emotion tracking and storage
• 📱 RESTful API for emotion data access
• 🗄️ MongoDB for efficient document storage
• 🧠 AI-powered emotion detection from images

• Authentication

  • POST /api/auth/register - Register a new user
  • POST /api/auth/login - Login and get JWT token
  • GET /api/auth/me - Get current user profile
  • POST /api/auth/logout - Logout (clears auth cookies)

• Users

  • GET /api/users/me - Get current user profile
  • GET /api/users/{user_id} - Get user by ID
  • PATCH /api/users/me/emotion - Update current user's emotion

• Emotions

  • GET /api/emotions/ - Get emotions for current user
  • POST /api/emotions/ - Create a new emotion entry
  • GET /api/emotions/{emotion_id} - Get specific emotion entry
  • DELETE /api/emotions/{emotion_id} - Delete an emotion entry

• WebSockets

  • WebSocket /api/ws - Real-time emotion updates

• Emotion Detection (AI-powered)

  • POST /api/emotion-detection/detect - Detect emotions in an uploaded image (authenticated)
  • POST /api/emotion-detection/detect-anonymous - Detect emotions without authentication

• Python 3.9+
• MongoDB database (local or MongoDB Atlas)

1. Clone the repository

   git clone https://github.com/yourusername/emotion-detection-backend.git
   cd emotion-detection-backend

2. Create a virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables Create a .env file in the root directory with:

   DATABASE_URL=mongodb+srv://username:password@cluster.mongodb.net/dbname
   SECRET_KEY=your_secret_key
   ACCESS_TOKEN_EXPIRE_MINUTES=1440
   

5. Run the application

   uvicorn app.main:app --reload

6. Access the API documentation at http://localhost:8000/docs

This application is configured for deployment on Render.

• render.yaml - Render configuration for web service and database
• build.sh - Build script to install dependencies and run migrations
• main.py - Entry point for the application
• Procfile - Alternative startup command specification

1. Fork this repository to your GitHub account
2. Create a new Render account or log in
3. Click "New" and select "Blueprint" from your Render dashboard
4. Connect your GitHub account and select your fork of this repository
5. Render will automatically detect the render.yaml file and set up your services
6. Provide the required environment variables:
   • DATABASE_URL - MongoDB connection string (Render can provide a MongoDB instance)
   • SECRET_KEY - Secret key for JWT token signing
   • ACCESS_TOKEN_EXPIRE_MINUTES - Token expiration time in minutes (e.g., 1440 for 24 hours)
7. Deploy your application

See websocket_README.md for WebSocket implementation details.

The application uses MongoDB for data storage. In production, it's recommended to use MongoDB Atlas.

The API includes AI-powered emotion detection based on the Emotion Recognition model.

1. Upload an image with one or more faces
2. The API detects faces using OpenCV
3. Each face is processed through a pre-trained deep learning model
4. The API returns the detected emotion and confidence level

POST /api/emotion-detection/detect-anonymous
Content-Type: multipart/form-data

file: [binary image data]

{
  "emotion": "happy",
  "confidence": 0.92
}

If no face is detected, you'll get:

{
  "emotion": "no_face",
  "confidence": 0.0
}

Use the included test_emotion_detection.py script to test the API:

python test_emotion_detection.py path/to/your/image.jpg

• angry
• disgust
• scared
• happy
• sad
• surprised
• neutral

pytest

black app

This project is licensed under the MIT License - see the LICENSE file for details.