Enterprise Face Attendance System

A scalable, reliable, and secure face attendance system built with FastAPI (Backend) and OpenCV/Face_Recognition (Client). Designed for multi-camera environments and centralized data management.

🚀 Key Features

• Centralized Backend: A robust REST API powered by FastAPI handling all user data and logs.
• Scalable Architecture: Client-Server model allows multiple camera units ("Eyes") to sync with one central server ("Brain").
• Real-time Synchronization: Camera units automatically fetch updated employee data on startup.
• Spam Prevention: Intelligent client-side caching prevents duplicate attendance logs.
• Database: SQLite integration (easily upgradeable to PostgreSQL) using SQLAlchemy.

🛠️ Tech Stack

• Backend: Python 3.9, FastAPI, Uvicorn
• Database: Supabase (PostgreSQL)
• Storage: Supabase Storage (S3-compatible)
• AI/ML: dlib, face_recognition, OpenCV (Headless)
• Infrastructure: Docker, Docker Compose, Render (Cloud)

📂 Project Structure

├── backend/              # FastAPI Application (Dockerized)
│   ├── app/              # Source Code
│   └── Dockerfile        # Production Docker Config
├── frontend/             # React/Vite Application
├── documentation/        # Architecture & Flows
├── docker-compose.yml    # Local Orchestration
├── push_to_dockerhub.bat # Deployment Script
├── verify_deployment.py  # Health Check Script

📚 Detailed Documentation

• System Architecture: High-level overview of the system, including the problem statement and real-life impact.
• Workflow: Step-by-step breakdown of user registration, attendance marking, and monitoring flows.
• ER Diagram: Visual representation of the database schema and relationships.
• Infrastructure Flow: detailed data flow between the client, server, and cloud services.
• Tech Stack: In-depth look at the technologies used in the frontend, backend, and AI components.

⚡ Quick Start

1. Local Development (Docker)

The easiest way to run the entire stack (Backend + Frontend):

docker-compose up -d --build

This will start:

• Backend at http://localhost:8000
• Frontend at http://localhost:5173

2. Verify Health

Run the included Python script to ensure everything is connected:

python verify_deployment.py

☁️ Deployment

1. Push to Docker Hub

We build images locally to avoid memory issues on free cloud tiers. Run the script and follow the prompts:

.\push_to_dockerhub.bat

2. Deploy to Render

The render.yaml file configures the service to pull your image from Docker Hub.

• Connect your repo to Render Blueprints.
• Add Environment Variables: SUPABASE_URL, SUPABASE_KEY.
• That's it!

🤝 Contribution

Contributions are welcome! Please fork the repository and submit a pull request.

📄 License

MIT License