IntelliFit is a scalable, full-stack, AI-powered fitness application built using a microservices architecture.
It leverages Spring Boot, React, and modern cloud-native tools to deliver personalized health recommendations powered by AI.

This project demonstrates the development of a modern, distributed fitness platform using Spring Cloud Microservices.
It integrates AI, containerization, and secure authentication to provide a real-world, production-ready fitness solution.

• Fully featured fitness application using Microservices Architecture
• AI Integration using Google Gemini API
• User & Activity Management with PostgreSQL and MongoDB
• Secure Authentication using Keycloak
• Scalable Communication with RabbitMQ
• Centralized Configuration with Spring Cloud Config Server
• Service Discovery using Eureka
• Containerized Deployment with Docker & Kubernetes

Backend:

• Spring Boot (Microservices)
• Spring Cloud Gateway
• Eureka Server (Service Registry)
• RabbitMQ (Async Messaging)
• Spring Cloud Config Server
• Keycloak (Authentication & Authorization)
• PostgreSQL / MySQL
• MongoDB
• Google Gemini API (AI Service)

Frontend:

• React.js

DevOps:

• Docker
• Kubernetes
• AWS (for cloud deployment)
• Prometheus + Grafana (Monitoring)

(Placeholder – Will be added soon)
The architecture follows a Spring Cloud Microservices pattern with Eureka, API Gateway, Config Server, and independent services for user, activity, and AI modules.

• Add AI model fine-tuning for improved recommendations
• Build admin dashboard for analytics
• Enable CI/CD pipeline using GitHub Actions

AK
MCA Final Year Project, SRM University
Guided by [Guide]

This project is for academic and learning purposes only.
All trademarks and technologies belong to their respective owners.

• AI Service Microservice

  • Created new AI-Service (Spring Boot + MongoDB + Eureka Client).
  • Configured RabbitMQ integration with custom exchange, queue, and routingKey.
  • Implemented RabbitMqConfig and @RabbitListener to consume activity messages.
  • Verified successful end-to-end asynchronous communication:
    User → Activity → RabbitMQ → AI-Service.
  • Fixed Eureka IP resolution issues using prefer-ip-address: true.
  • Confirmed service registration in Eureka Server and live message flow in RabbitMQ UI.
  • Connected MongoDB (fitnessrecommendation) and verified data persistence.

• Activity Service Enhancements

  • Integrated RabbitTemplate for publishing activity data asynchronously.
  • Configured exchange and routing properties via application.yml.
  • Added Jackson2JsonMessageConverter in RabbitMqConfig.
  • Verified message publishing to RabbitMQ on new activity events.

• Eureka & Interservice Communication

  • Completed Eureka Service Discovery setup for all microservices.
  • Fixed DNS/NXDOMAIN issue and validated service registration using localhost IPs.
  • Ensured seamless communication among User Service, Activity Service, and AI Service.

• AI Service Recommendation Logic
  • Implementing logic to store and analyze activity data for personalized recommendations.
  • Adding persistence with RecommendationRepository.save() and idempotency via existsByActivityId.
  • Planning retry and DLQ (Dead Letter Queue) handling for message processing.

• Complete AI recommendation generation and persistence layer.
• Add observability using Micrometer / OpenTelemetry for message flow tracking.
• Begin Docker containerization for all microservices.
• Prepare System Architecture Diagram and update documentation.
• Develop React Frontend for user activity visualization and AI recommendations.

📌 Next milestone: Full AI-driven recommendation workflow with persistence and analytics integration.

• User Service Microservice

  • Implemented user registration, authentication, and profile management.
  • Integrated PostgreSQL for persistent data storage.
  • Secured endpoints using Keycloak authentication.
  • Successfully tested all endpoints.

• Activity Service Microservice

  • Developed RESTful APIs for logging, updating, and fetching user fitness activities.
  • Integrated MongoDB for flexible activity data storage.
  • Connected with User Service via REST calls and RabbitMQ for asynchronous event handling.
  • Completed inter-service communication testing using Eureka Service Registry.

• Project Documentation

  • Completed detailed sections:
    • Introduction
    • Analysis & Requirement
    • Problem Description / Modules Description
  • Ready for inclusion in First/Second Review submission.

• AI Service Microservice
  • Building service to process activity data and generate personalized recommendations.
  • Integrating with Google Gemini API for AI-driven analysis.

• Complete AI Service implementation and connect with Activity Service.
• Prepare System Architecture Diagram for documentation.
• Begin Docker containerization for all microservices.
• Develop React Frontend for user dashboard and AI recommendations.

📌 Next milestone: Deploy all services locally with Docker and connect frontend to backend microservices.