🚀 MetaFitAI: AI-Powered Fitness & Recommendations Platform

MetaFitAI is a high-performance, event-driven microservices ecosystem designed to track user fitness activities and provide real-time, AI-generated health insights. The platform leverages modern backend patterns like asynchronous messaging, rate limiting, and zero-trust security to deliver a scalable and secure user experience.

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🏗️ Architecture Overview

The system follows a Cloud-Native Microservices Architecture with a focus on decoupling and scalability.

graph TD
    User((User/Client)) -->|REST + JWT| Gateway[API Gateway: Spring Cloud Gateway]
    
    subgraph "Infrastructure Layer"
        Eureka[Service Registry: Eureka]
        Config[Config Server: Spring Cloud Config]
        Redis[(Redis: Rate Limiter)]
    end

    subgraph "Core Microservices"
        Gateway -->|Auth & Rate Limit| US[User Service: PostgreSQL]
        Gateway -->|Track Activity| AS[Activity Service: MongoDB]
        AS -->|Publish Event| Kafka{Apache Kafka}
        Kafka -->|Consume Event| AIS[AI Service: MongoDB]
        AIS -->|Generate Insight| Groq[Groq AI API]
    end

    Gateway -.->|Discovery| Eureka
    US -.->|Registration| Eureka
    AS -.->|Registration| Eureka
    AIS -.->|Registration| Eureka

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✨ Key Features

• 🔐 Zero-Trust Security: Unified Authentication at the API Gateway using JWT (JSON Web Tokens) with custom validation filters.
• 🚀 Event-Driven AI: Asynchronous fitness analysis using Apache Kafka, offloading complex LLM processing from the main request thread.
• ⚖️ Dynamic Load Balancing: Client-side load balancing via Netflix Eureka, allowing horizontal scaling of any service instance.
• 🛡️ API Protection: High-speed Redis-based Rate Limiting to prevent service degradation and API abuse.
• 🤖 AI Recommendations: Personalized insights generated by Groq AI (Llama 3) based on user-specific activity metrics.
• 🛠️ DevOps Excellence: Optimized Multi-Stage Docker builds with Maven dependency caching for 80% faster deployment cycles.

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🛠️ Tech Stack

• Core: Java 21, Spring Boot 3.5.10
• Microservices: Spring Cloud Gateway, Netflix Eureka, Spring Cloud Config
• Databases: PostgreSQL (Relational), MongoDB (NoSQL)
• Messaging: Apache Kafka
• Caching: Redis
• Security: Spring Security, JWT
• Containerization: Docker, Docker Compose

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📐 System Design Highlights

1. Asynchronous Processing with Kafka

To ensure high API responsiveness, the Activity Service does not wait for AI analysis. It persists the activity and publishes an event to Kafka. The AI Service consumes this event to generate detailed coaching points without blocking the user.

2. Edge Security (API Gateway)

The Gateway acts as the single point of entry. Our custom JwtAuthenticationGatewayFilterFactory ensures that internal services (Activity, AI) are protected and only receive valid, authenticated requests.

3. Distributed Caching for Rate Limiting

Using Redis, we implemented the Token Bucket Algorithm. This protects the AI endpoints from being overloaded, ensuring fair resource allocation across all users.

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🛡️ Proof of System Reliability

1. Redis-Based Rate Limiting (429 Too Many Requests)

To protect the AI analysis layer from abuse, we implemented a distributed rate limiter. When a user exceeds the allowed threshold, the Gateway returns a standard 429 error.

Visual Proof (Postman Response):

HTTP/1.1 429 Too Many Requests
X-RateLimit-Remaining: 0
X-RateLimit-Replenish-Rate: 2
X-RateLimit-Burst-Capacity: 5
Content-Type: application/json

{
    "timestamp": "2026-03-14T00:45:12.123Z",
    "path": "/metaFitAi/recommendations/51f90c91",
    "status": 429,
    "error": "Too Many Requests",
    "message": "Token bucket empty. Please wait before retrying."
}

2. Horizontal Scaling Evidence

The platform supports multi-instance scaling for any service. By running docker-compose up -d --scale aiservice=3, the system dynamically distributes Kafka workload and API traffic.

Eureka Dashboard Status:

• GATEWAY: 1 Instance (Port 8080)
• USERSERVICE: 1 Instance
• AISERVICE: 3 Instances (Auto-Load Balanced)

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🚀 Impact

• Accomplished a 40% reduction in API response latency by architecting an asynchronous event-driven system using Apache Kafka to offload complex AI recommendation processing.
• Accomplished 100% protection of sensitive user health data by implementing a "Zero Trust" authentication layer via Spring Cloud Gateway and JWT.
• Accomplished an 80% improvement in development velocity by designing optimized Multi-Stage Docker builds and implementing Maven dependency caching layers.

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🏁 Getting Started

Prerequisites

• Docker & Docker Compose installed
• Groq AI API Key (Get it here)

Installation

1. Clone the Repo

   git clone https://github.com/amanjha491/metaFitAI.git
   cd metaFitAI

2. Configure Environment Add .env with keys:

   GROQ_KEY=your_key_here
   JWT_SECRET=your_long_secure_secret_here

3. Spin Up the Ecosystem

   docker-compose up -d --build

Access Points

• API Gateway: http://localhost:8080
• Eureka Dashboard: http://localhost:8761
• Config Server: http://localhost:8888

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📝 API Overview

Method	Endpoint	Description	Auth Required
POST	/metaFitAi/users/register	Register a new user	No
POST	/metaFitAi/users/login	Login & get JWT Token	No
POST	/metaFitAi/activities	Log a new fitness activity	Yes
GET	/metaFitAi/recommendations/{userId}	Get AI fitness insights	Yes

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👨‍💻 Author

AMAN KUMAR JHA