🥗 FreshFit Backend

FreshFit – An intelligent, microservice-based health assistant system that integrates food recognition, personalized nutrition recommendations, and workout planning to empower users to lead healthier lifestyles.

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📖 Description

FreshFit is a comprehensive backend system designed for a smart health support application. It leverages microservice architecture to deliver scalable, maintainable, and modular services that cover all aspects of personal health management, including food recognition, fitness tracking, and custom nutrition plans.

Key features:

• 🔐 Authentication with JWT
• ⚙️ Microservice-based structure for scalability and modularity
• 📆 Rich service layer (nutrition, fitness, social, planning, recommendations, and more)
• 📈 Integrated monitoring and logging system with Grafana, Prometheus, Loki, and Jaeger
• 💇 Asynchronous messaging using RabbitMQ
• ⚡ Caching with Redis
• ☁️ Cloud-based storage via Cloudinary
• 📧 Email notifications with Nodemailer and Mustache
• ↩️ Background jobs with cron
• 🤖 AI integration: Gemini, Serper, FatSecret API
• 🐳 Containerized deployment using Docker on VPS (Vultr/DigitalOcean)

🧩 System Architecture

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🛠 Installation Instructions

1. Clone the Repository

git clone https://github.com/tuonghuynh11/FreshFit-API-Microservice.git

2. Environment Configuration

Create an .env file in the root of each service directory based on its respective .env.example:

cp .env.example .env

3. Start Services (Dev Mode)

Make sure Docker and Docker Compose are installed.

docker-compose up --build

This will launch all microservices, including:

• API Gateway
• All backend services (user, health, nutrition, fitness, etc.)
• RabbitMQ, Redis, MongoDB, PostgreSQL
• Monitoring stack (Grafana, Prometheus, Loki, Jaeger)

4. API Access

• Gateway: http://localhost:8080
• Each microservice runs on internal ports through the gateway.

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📚 Learn More

📌 Microservices Overview

Core Stack

• Common Stack for most services: Express.js, TypeScript, MongoDB, express-validator, formidable

Individual Services:

Service	Description
gateway	Routing, authentication, rate limiting, caching
users-service	User management, profile, authentication
appointments-service	Appointment scheduling and history (PostgreSQL, TypeORM)
health-service	Health records and biometric tracking
nutrition-service	Food logging, nutrition recommendation
fitness-service	Exercise tracking, fitness recommendations
plan-service	Daily and weekly planning
challenge-service	Workout/diet challenges
social-service	User interactions and social feed
notification-service	Push and real-time notifications (Expo, Socket.IO)
reporting-service	User reports and analytics
transaction-service	Payment and transaction management
recommendation-service	AI-based recommendation engine (Python, FastAPI)

🧐 AI & Integrations

• Gemini – AI assistant for recommendations
• Serper – Search engine integration
• FatSecret – Nutrition API
• Cloudinary – Image and video storage

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🔄 CI/CD

The project uses GitHub Actions for CI/CD. The workflow is defined in .github/workflows We use GitHub Actions for continuous integration and delivery.

• Build and Test: Automatic build and lint check on PRs
• Dockerized Deployment: Auto-build Docker images for each service
• VPS Deployment: Manual or script-based deployment via SSH to Vultr/DigitalOcean instances
• Health Checks: Monitored by Prometheus and displayed on Grafana dashboards
• Logs & Tracing: Centralized logging via Loki and distributed tracing with Jaeger

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Feel free to contribute, raise issues, or fork the repo to improve the future of health tech! 🚀