FoodPort - Next Generation Food Delivery Platform

FoodPort is a state-of-the-art online food delivery platform designed using a robust microservices architecture and advanced AI Recommendation Service. This project demonstrates deep full‑stack engineering expertise—from scalable backend services and microservices to a dynamic, responsive React frontend. It is built to deliver a seamless user experience while leveraging cutting‑edge AI recommendation systems to personalize food suggestions.

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Table of Contents

• Demo
• Key Features
• Architecture & Technologies
• AI Recommendation Service
• Microservices & Scalability
• Installation & Setup
• Usage
• Demo
• Project Structure
• Future Enhancements
• Contributing
• License
• Author

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Demo

Watch our demo video to see FoodPort in action:

Click the image above to watch the demo video.

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Key Features

• AI-Driven Personalized Recommendations: Tailored food suggestions based on user preferences, dietary restrictions, and order history.
• End-to-End Microservices Architecture: Scalable services handling authentication, ordering, and AI recommendations.
• Modern UI/UX: A sleek, intuitive front-end built with React, Redux, and Tailwind CSS.
• Secure & Optimized Backend: Robust Express.js and MongoDB stack with authentication, rate limiting, and best security practices.
• Cloud Deployment: Hosted on Vercel (frontend), Render/Railway (backend & AI service), and MongoDB Atlas (database).

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Architecture & Technologies

Backend

• Node.js & Express.js: Build a robust RESTful API for handling orders, user management, and more.
• MongoDB & Mongoose: A flexible NoSQL database that supports rapid prototyping and scalable data storage.
• Redis & Express-Session: Efficient session management and caching mechanisms.
• FastAPI (Python): High-performance API for our AI Recommendation Service.
• TensorFlow & FAISS: Advanced machine learning and similarity search frameworks for real‑time personalized recommendations.
• Microservices Architecture: Deployed as independent, containerized microservices ensuring high scalability and maintainability.

Frontend

• React & Redux Toolkit: Modern, component-based UI with centralized state management.
• React Router: Seamless navigation between various application views.
• Tailwind CSS & MUI: Utility‑first styling for a modern, responsive design.
• Axios & React Hot Toast: Efficient HTTP requests and user-friendly notifications.

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AI Recommendation Service

The AI Recommendation Service is the heart of FoodPort’s personalization. It uses advanced deep learning models (TensorFlow) and FAISS for high‑performance similarity search, enabling the following:

• Dynamic Embedding Generation: Utilize a trained deep learning model to generate product embeddings.
• Efficient Similarity Search: Leverage FAISS to quickly retrieve relevant products from a vast dataset.
• Personalized Filtering: Filters recommendations based on user-specific dietary preferences and preferred cuisine.
• Scalable Microservice: Built with FastAPI, designed for low latency and high concurrency, making it perfect for deployment in cloud environments and Kubernetes clusters.

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Microservices & Scalability

• Independent Deployment: Each service (backend APIs, AI recommendation service) can be developed, deployed, and scaled independently.
• Containerization: Leverage Docker (and Kubernetes) to ensure consistent deployment across environments.
• CI/CD Pipeline: Automated testing, linting, and continuous deployment ensure rapid and reliable releases.
• Monitoring & Logging: Robust error handling, logging (PM2, centralized logging), and performance monitoring are integrated to maintain system resilience.

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Screenshot

Home

Personalized AI Recommedation

Restaurants

Cart

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Installation & Setup

Prerequisites

• Node.js (v14+)
• Python (3.8+)
• MongoDB
• Redis
• Docker (optional, for containerized deployment)

Backend Setup

1. Clone the repository:

   git clone https://github.com/imunreal7/FoodPort.git
   cd FoodPort/backend

2. Install dependencies:

   npm install

3. Configure Environment Variables:

   Create a .env file with variables such as:

   # Database Configuration
   DB_URI=your_database_uri
   
   # JWT Authentication
   JWT_SECRET=your_jwt_secret
   JWT_EXPIRES_IN=7d  # Token expiry duration
   
   # Server Configuration
   PORT=5000
   # NODE_ENV=production  # Uncomment for production mode
   
   # API Rate Limiting
   RATE_LIMIT_MAX=100  # Maximum requests allowed per 15 minutes
   RATE_LIMIT_WINDOW=15m
   
   # External API URLs
   RECOMMENDATION_SERVICE_URL=your_recommendation_service_url
   
   # Redis Configuration
   REDIS_HOST=127.0.0.1
   REDIS_PORT=6379
   REDIS_TTL=3600  # Cache expiration time in seconds (1 hour)
   SESSION_SECRET=your_session_secret
   
   

4. Start the Backend Server:

   npm start

   for dev:

    npm run dev

AI Recommendation Service Setup

1. Navigate to the recommendation service folder:

   cd ../recommendation_service

2. Install Python dependencies:

   pip install -r requirements.txt

3. Ensure model and index files are in place, then run:

   uvicorn main:app --host 0.0.0.0 --port 8000

Frontend Setup

1. Navigate to the client directory:

   cd ../client

2. Install dependencies:

   npm install

3. Start the Frontend Application:

   npm start

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Usage

• Restaurant & Menu Exploration: Browse highly rated restaurants and detailed menus.
• Personalized Recommendations: Leverage the AI-powered recommendation engine for curated food suggestions.
• Easy Order Placement: Add items to your cart, proceed to checkout, and track orders seamlessly.
• User Profile Management: Update personal information, dietary preferences, and more.

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Future Enhancements

• Advanced Analytics: Incorporate user behavior tracking and advanced analytics to refine AI recommendations further.
• Enhanced Security: Integrate multi-factor authentication and secure payment gateways.
• Jest Unit Test Cases: Implement Jest unit tests with 80%+ code coverage, which helps us reduce bug occurrence.
• Global Support: Implement internationalization (i18n) and multi-language support.
• Progressive Web App (PWA): Enable offline support and improved mobile performance with PWA features.

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Contributing

Contributions, improvements, and bug fixes are welcome! Please fork the repository and open a pull request with your proposed changes.

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License

This project is licensed under the ISC License.

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Author

Aman Dubey Senior Software Engineer | Full-Stack Developer | AI Enthusiast GitHub | LinkedIn

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Thank you for checking out FoodPort – a showcase of cutting-edge microservices, AI, and full-stack development expertise.