A comprehensive AI-powered learning platform that combines emotion detection, adaptive learning, gamification, and intelligent tutoring systems to create personalized educational experiences.
- AI Tutoring System: Intelligent adaptive tutoring with real-time feedback
- Emotion Detection: Real-time emotion recognition to adapt teaching methods
- Learning DNA: Unique learning profile creation and analysis
- Spaced Repetition: Science-backed retention optimization
- Gamification: Quest system, badges, and achievement tracking
- Co-learning: Collaborative learning experiences and peer interaction
- Knowledge Graph: Visual representation of learning progress and connections
- Story-driven Learning: Immersive narrative-based educational content
- Debate System: AI-powered debate facilitation and analysis
- Sandbox Environment: Safe experimental learning space
- Performance Analytics: Comprehensive learning analytics and insights
- Personalization Engine: ML-driven content recommendation system
NeuroLearn/
├── backend/ # Python Flask backend
│ ├── aitutor_backend/ # Main application package
│ │ ├── routes/ # API endpoints
│ │ ├── services/ # Business logic
│ │ ├── models.py # Database models
│ │ └── utils/ # Utility functions
│ ├── Face-Emotion-Detector/ # Emotion detection module
│ ├── scripts/ # Startup scripts
│ ├── requirements.txt # Python dependencies
│ └── aitutor.db # Development database
├── frontend/ # React.js frontend
│ ├── dashboard/ # Main dashboard application
│ │ ├── src/ # Source code
│ │ ├── package.json # Dependencies
│ │ └── vite.config.js # Build configuration
│ └── integration-examples/ # Integration examples
├── documentation/ # Comprehensive documentation
│ ├── api.md # API documentation
│ ├── backend.md # Backend architecture
│ ├── frontend.md # Frontend components
│ ├── emotion-detection.md # ML system guide
│ ├── deployment.md # Deployment guide
│ └── architecture-overview.md # System overview
├── deployment/ # Deployment configurations
│ ├── render.yaml # Render deployment
│ ├── netlify.toml # Netlify deployment
│ └── Procfile # Process configuration
├── package.json # Root package configuration
├── .gitignore # Git ignore rules
└── README.md # Project documentation
- Python 3.10+ - Core backend language
- Flask - Web framework
- SQLAlchemy - Database ORM
- OpenCV - Computer vision for emotion detection
- TensorFlow/PyTorch - ML model training
- WebSocket - Real-time communication
- React 18 - Frontend framework
- Vite - Build tool and dev server
- Tailwind CSS - Styling framework
- Framer Motion - Animations
- Three.js - 3D graphics for visualizations
- Socket.io - Real-time features
- Zustand - State management
- D3.js - Data visualization
- Monaco Editor - Code editor integration
- recharts - Chart components
Before setting up locally, check out the live deployment:
- Frontend: https://neurolearn1.netlify.app/dashboard
- Backend API: https://neurolearn-6c0k.onrender.com/
- Python 3.10+
- Node.js 16+
- npm/yarn
# Install all dependencies
npm run install:all
# Start full development environment
npm run devBackend Setup
# Navigate to backend directory
cd backend
# Install Python dependencies
pip install -r requirements.txt
# Initialize database
python -c "from aitutor_backend import create_app, db; app = create_app(); db.create_all()"
# Start backend server
python scripts/start_server.pyFrontend Setup
# Install root dependencies
npm install
# Start frontend development server
npm run frontend:devCreate .env files in respective directories:
Backend (.env)
FLASK_ENV=development
DATABASE_URL=sqlite:///aitutor.db
SECRET_KEY=your-secret-key
OPENAI_API_KEY=your-openai-key
Frontend (.env)
VITE_API_URL=http://localhost:5000
VITE_WEBSOCKET_URL=ws://localhost:5000
# Backend tests
python -m pytest backend/tests/
# Frontend tests
cd frontend/dashboard && npm test- routes/: API endpoint definitions
- services/: Business logic and ML services
- models/: Database models
- utils/: Utility functions and helpers
- components/: Reusable UI components
- pages/: Main application pages
- hooks/: Custom React hooks
- contexts/: React context providers
- utils/: Frontend utility functions
- Backend: Configure in
config.py - Frontend: Configure via environment variables
- Database: SQLite for development, PostgreSQL for production
The platform is now live and accessible at:
- 🎯 Frontend Dashboard: https://neurolearn1.netlify.app/dashboard
- ⚙️ Backend API: https://neurolearn-6c0k.onrender.com/
The platform supports multiple deployment options:
- Netlify: Frontend deployed via
netlify.toml - Render: Backend deployed via
render.yaml - Vercel: Alternative frontend deployment via
vercel.json
- Fork the repository
- Create a feature branch
- Commit your changes
- Push to the branch
- Create a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
Technical Excellence:
- Clean, modular architecture with separation of concerns
- RESTful API design with comprehensive documentation
- Real-time WebSocket implementation for collaborative features
- Advanced ML integration with emotion detection
- Scalable database design with SQLAlchemy ORM
User Experience:
- Responsive design with Tailwind CSS
- Smooth animations with Framer Motion
- Interactive 3D visualizations
- Real-time collaborative features with OpenCV
- Comprehensive analytics dashboard
Industry Standards:
- Modern React patterns (hooks, context, custom hooks)
- TypeScript-ready frontend architecture
- Comprehensive error handling
- Security best practices
- Testing framework integration
For questions about this project, please reach out to the development team.
Built with ❤️ for the future of education