🍕 FoodieExpress - AI-Powered Food Delivery Prediction App

A full-stack food delivery application that predicts delivery times using machine learning, similar to Zomato/Swiggy platforms.

📁 Project Structure

food-delivery-app/
├── client/                          # Frontend (Next.js/React)
│   ├── app/
│   │   ├── components/
│   │   │   ├── cart-sheet.tsx       # Shopping cart sidebar
│   │   │   └── order-modal.tsx      # Order confirmation modal
│   │   ├── api/
│   │   │   ├── food-items/
│   │   │   │   └── route.ts         # Food items API endpoint
│   │   │   └── predict-delivery/
│   │   │       └── route.ts         # ML prediction API endpoint
│   │   ├── layout.tsx               # Root layout
│   │   ├── page.tsx                 # Home page
│   │   └── globals.css              # Global styles
│   ├── components/ui/               # shadcn/ui components
│   ├── lib/
│   │   └── utils.ts                 # Utility functions
│   ├── public/                      # Static assets
│   ├── package.json
│   ├── tailwind.config.ts
│   ├── next.config.js
│   └── tsconfig.json
├── server/                          # Backend (Node.js/Express) - Alternative
│   ├── src/
│   │   ├── controllers/
│   │   │   ├── foodController.js
│   │   │   └── orderController.js
│   │   ├── models/
│   │   │   └── deliveryPredictor.js
│   │   ├── routes/
│   │   │   ├── food.js
│   │   │   └── orders.js
│   │   ├── middleware/
│   │   │   └── cors.js
│   │   └── app.js
│   ├── package.json
│   └── .env
├── model/                           # ML Model Files
│   ├── delivery_model.pkl           # Trained model (placeholder)
│   ├── model_training.py            # Training script
│   ├── feature_engineering.py       # Feature processing
│   └── delivery_data.csv            # Training dataset
└── README.md

🚀 Features

Frontend (React/Next.js)

• Home Page: Browse available food items with images, prices, and ratings
• Shopping Cart: Add/remove items, adjust quantities
• Order Placement: Complete checkout process
• Delivery Prediction: Real-time delivery time estimation with breakdown
• Responsive Design: Mobile-first design using Tailwind CSS

Backend (Node.js API Routes)

• Food Items API: Serve restaurant menu data
• ML Prediction API: Process delivery time predictions
• Order Management: Handle order placement and tracking

Machine Learning Model

• Delivery Time Prediction: Based on multiple factors:
  • Delivery person rating (1-5 scale)
  • Restaurant and delivery locations
  • Order type (normal/delicate)
  • Vehicle type (bike/bicycle)
  • Food preparation time
  • Distance (km)
  • Weather conditions

🛠️ Setup Instructions

Prerequisites

• Node.js 18+
• npm or yarn
• Python 3.8+ (for ML model training)

1. Clone the Repository

git clone <repository-url>
cd food-delivery-app

2. Frontend Setup (Next.js)

# Install dependencies
npm install

# Start development server
npm run dev

The application will be available at http://localhost:3000

3. Backend Setup (Alternative Express Server)

cd server
npm install
npm start

4. ML Model Setup

cd model
pip install -r requirements.txt
python model_training.py

📊 ML Model Details

Input Features

• delivery_person_rating: Float (1.0 - 5.0)
• restaurant_location: String (encoded as features)
• delivery_location: String (encoded as features)
• order_type: String ('normal' or 'delicate')
• vehicle_type: String ('bike' or 'bicycle')
• food_prep_time: Integer (minutes)
• distance: Float (kilometers)
• weather_condition: String ('clear', 'rain', 'storm', 'snow')

Output

• predicted_time: Integer (minutes)
• breakdown: Object with time components
• confidence: Float (prediction confidence percentage)

Model Architecture

The current implementation uses a rule-based system that simulates ML predictions. In production, this would be replaced with:

• Random Forest Regressor or XGBoost for structured data
• Neural Networks for complex feature interactions
• Time Series Models for temporal patterns

🔧 API Endpoints

GET /api/food-items

Returns list of available food items

{
  "id": "1",
  "name": "Margherita Pizza",
  "description": "Fresh tomatoes, mozzarella, and basil",
  "price": 12.99,
  "image": "/images/pizza.jpg",
  "category": "Pizza",
  "prepTime": 15,
  "restaurant": "Mario's Pizzeria",
  "rating": 4.5
}

POST /api/predict-delivery

Predicts delivery time based on order parameters

{
  "items": [...],
  "deliveryPersonRating": 4.2,
  "restaurantLocation": "Downtown",
  "deliveryLocation": "Uptown",
  "orderType": "normal",
  "vehicleType": "bike",
  "distance": 5.2,
  "weatherCondition": "clear"
}

🎯 Key Technologies

• Frontend: Next.js 14, React, TypeScript, Tailwind CSS, shadcn/ui
• Backend: Next.js API Routes (or Express.js alternative)
• ML: Python, scikit-learn, pandas, numpy
• Database: In-memory (can be extended to PostgreSQL/MongoDB)
• Deployment: Vercel (frontend), Railway/Heroku (backend)

🔮 Future Enhancements

1. Real ML Model: Train on actual delivery data
2. Real-time Tracking: GPS tracking integration
3. Payment Integration: Stripe/PayPal integration
4. User Authentication: Login/signup functionality
5. Restaurant Dashboard: Partner restaurant management
6. Push Notifications: Order status updates
7. Advanced Analytics: Delivery performance metrics

📈 Performance Considerations

• Image Optimization: Next.js Image component with lazy loading
• Code Splitting: Automatic route-based splitting
• Caching: API response caching for food items
• Error Boundaries: Graceful error handling
• Loading States: Skeleton screens and spinners

🔒 Security Features

• Input Validation: Server-side validation for all inputs
• CORS Protection: Configured for specific origins
• Rate Limiting: API endpoint protection
• Environment Variables: Secure configuration management

🧪 Testing

# Run frontend tests
npm test

# Run backend tests
cd server && npm test

# Run ML model tests
cd model && python -m pytest

📝 License

MIT License - see LICENSE file for details

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

📞 Support

For issues and questions, please open a GitHub issue or contact the development team.

This comprehensive food delivery prediction application includes:

✅ **Complete Frontend**: React-based UI with cart functionality, order placement, and delivery prediction display
✅ **Backend API**: Node.js endpoints for food items and ML predictions  
✅ **ML Integration**: Simulated machine learning model for delivery time prediction
✅ **Responsive Design**: Mobile-first design using Tailwind CSS
✅ **Full Project Structure**: Organized codebase with clear separation of concerns
✅ **Comprehensive Documentation**: Setup instructions and API documentation

The application demonstrates all the requested features including food listing, cart management, order placement, and AI-powered delivery time prediction with detailed breakdowns of contributing factors.