Fitness-Using-AI

A modern, AI-powered fitness tracking platform built with a microservices architecture using Spring Boot, integrated with advanced user authentication, RabbitMQ event messaging, scalable data storage, and a React-based web frontend.

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🚀 Features

• User Registration & Login: Secure authentication via Keycloak (OIDC), supporting both sign up and sign in.
• Activity Tracking: Log activities with full details (activity name, duration, calories burned).
• AI-Powered Suggestions: Get personalized workout and nutrition suggestions using Gemini AI APIs.
• Microservices Architecture: 6 Spring Boot microservices, each handling a separate business domain for modularity and scalability.
• Real-time Messaging: Services communicate asynchronously using RabbitMQ.
• Data Persistence: Data stored across MongoDB and PostgreSQL databases, managed via Docker.
• Robust API Gateway: All backend endpoints are accessed securely through a centralized Spring Cloud Gateway.
• Modern Frontend: Responsive, intuitive React web interface, built with npm modules.
• Fully Containerized: All components are dockerized for easy deployment with Docker Compose.

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🖥️ How It Works

1. Sign Up / Sign In
   • New users register or log in through Keycloak on the frontend.
2. Add Activity
   • After authentication, users can add activities (e.g., "Jogging", 30min, 250 calories).
3. AI Suggestions
   • The AI service uses Gemini AI to provide fitness and nutrition recommendations.
   • Suggestions are displayed to the user and saved for future reference.
4. Persist & View
   • All user data, activities, and AI suggestions are stored securely in MongoDB and PostgreSQL, accessible across devices.
5. Gateway & Security
   • All API requests are routed through the API Gateway for security and access control via Keycloak.

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🏗️ Tech Stack

Layer	Technology
Frontend	React, npm, Axios, CSS Modules
API Gateway	Spring Cloud Gateway
Authentication	Keycloak (OIDC), Spring Security
Microservices	Spring Boot (Java)
Messaging	RabbitMQ
Databases	MongoDB, PostgreSQL
AI Integration	Gemini AI API
DevOps	Docker, Docker Compose

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🐳 Quick Start (Local/Remote)

Prerequisites

• Docker & Docker Compose
• Node.js and npm
• (For remote setup: access to a cloud VM, e.g., Ubuntu)

1. Clone & Configure

git clone https://github.com/DeshveerBatth/Fitness-Using-AI.git

cd Fitness-Using-AI

2. Environment Variables

• Copy .env.example to .env in each microservice (examples provided).
• Update variables: database URIs, RabbitMQ credentials, Gemini API keys, etc.

3. Build Frontend

cd frontend

npm install

npm run build

cd ..

4. Launch All Services

docker-compose up --build -d

5. Access the App

• Go to http://<server-ip>:8080 (replace with your actual server IP).

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✨ Screenshots

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📚 Project Structure

Fitness-Using-AI/

├── user-service/

├── activity-service/

├── ai-service/

├── gateway-service/

├── config-server/

├── eureka-server/

├── frontend/

├── docker-compose.yml

└── README.md

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📝 API Usage

All endpoints require valid authentication via Keycloak.

• Register a User
  POST http://localhost:8080/api/users/register
  Body: user details JSON

• Get User by ID
  GET http://localhost:8080/api/users/{userId}

• Validate User
  GET http://localhost:8081/api/users/{userId}/validate

• Register Activity
  POST http://localhost:8082/api/activities
POST http://localhost:8080/api/activities

• Get Activity by Activity ID
  GET http://localhost:8080/api/activities/{activityId}

• Get AI Recommendations for User
  GET http://localhost:8080/api/recommendations/user/{userId}

• Get Gemini AI Response for Activity
  GET http://localhost:8083/api/recommendations/activity/{activityId}

• (Many more API endpoints are available; see code for details.)

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👤 Author

Deshveer Singh

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🤝 Contributing

Pull requests are welcome! Please open an issue to discuss big changes or new features before submitting code.

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⚖️ License

MIT — Free to use, modify, and build on!

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_Empowering everyone to get healthier, using modular open-source tech + AI!