🚀 QC Application

This is a Quality Control (QC) AI application built with a modular, Docker-based architecture. The setup includes a frontend UI, a backend server, an AI microservice, Redis for caching, and PostgreSQL for database storage.

📦 Project Structure

qc_application/
├── backend/         # FastAPI backend
├── frontend/        # React frontend
├── ai_service      # AI service container
├── redis_data      # Redis storage
├── database
│   └── postgres_data/
├── docker-compose.yaml
├── .gitignore
└── README.md

🛠 Technologies & Containers Used

Container	Technology	Port	Purpose
Frontend	React.js (Vite)	3000	User Interface
Backend	FastAPI	8000	Backend API
AI Service	FastAPI & Python	5001	AI Model Training/Inference
Redis	Redis (latest)	6379	Caching & Queuing
Postgres	PostgreSQL (latest)	5432	Database storage

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✅ How to Run the Project (Clearly Documented)

1. Build & Run Docker Containers:

Run from the project root directory:

docker-compose down
docker-compose build
docker-compose up -d

Check Running Containers:

docker-compose ps

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🌐 Service URLs

• Frontend: http://localhost:3000
• Backend API Docs: http://localhost:8000/docs
• AI Service API Docs: http://localhost:5001/docs
• Redis: redis://localhost:6379
• PostgreSQL: postgres://postgres:postgres@localhost:5432/qcdb

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🚨 Important Commands

🔄 Rebuilding Containers

To rebuild all containers explicitly:

docker-compose down
docker-compose build --no-cache
docker-compose up -d

To rebuild specific container explicitly (frontend example):

docker-compose build --no-cache frontend
docker-compose up -d frontend

🐞 Debugging Containers

Check container logs explicitly:

docker-compose logs frontend
docker-compose logs backend
docker-compose logs ai_service

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⚙️ Git Setup

Your git user details:

git config --global user.name "name"
git config --global user.email "nmae@example.com"

📌 Development Workflow

• Start by developing frontend UI components and integration with backend APIs.
• Implement backend logic clearly to communicate with the AI microservice.
• Implement your AI inference/training logic in the AI microservice container.
• Use Redis for caching intermediate data.
• Store structured data in PostgreSQL database.

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📝 Notes

• Ensure to keep your .env files updated clearly with the correct container URLs and credentials.
• Regularly clear and rebuild your Docker containers if you're making major configuration changes.

🚀 Next Steps

• Implement actual AI logic.
• Add comprehensive frontend features, including image upload and display.
• Enhance backend and AI integration clearly for robust communication.

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📚 References

• FastAPI Documentation
• React.js
• Docker
• Vite Documentation

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Happy coding! 🎉