Classroom Face Prototype

This project is a multi-service application that integrates a machine learning service, a frontend, a database, and a camera service for classroom face recognition.

Services

• ml_service: Provides ML-based face detection/recognition (exposes HTTP endpoints on port 8000).
• frontend: Hosts the user interface and classroom updates (exposes HTTP endpoints on port 3000).
• database: Manages student data (exposes HTTP endpoints on port 5002).
• camera: A one-shot service that captures an image, processes it, and triggers updates in the other services.

Prerequisites

• Docker installed and running.
• Basic familiarity with Docker and Docker Compose.

Getting Started

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>

2. Build and run all services:

   There are multiple ways to build and run the services:

   Option 1: Using Docker Compose directly (recommended):

   # Build and start all services
   docker-compose up
   
   # Or to rebuild from scratch:
   docker-compose up --build
   
   # To run in background mode:
   docker-compose up -d

   Option 2: Using Makefiles:

   make all

   Option 3: Run camera service which triggers the pipeline:

   docker-compose run camera

3. Running individual services:

   You can also build or run services individually:

   • Using Docker Compose:

     docker-compose up ml-service
     docker-compose up database
     docker-compose up frontend

   • Using Makefiles:

     make ml_service
     make database
     make frontend
     make camera

Testing the Services

After running docker-compose up, you can test each service individually using curl commands:

ML Service Tests

# Test the ML service health endpoint
curl http://localhost:8000/api/health

# Test face recognition with a sample image
curl -X POST \
  -H "Content-Type: application/json" \
  -d "{\"Image\": {\"Bytes\": \"$(base64 -i ./database/db_images/jayvin.jpg)\"} }" \
  http://localhost:8000/api/predict

Database Service Tests

# Check database health
curl http://localhost:5002/api/health

# Get a student by ID
curl http://localhost:5002/api/student?studentId=jayvin

# Add a new student
curl -X POST \
  -H "Content-Type: application/json" \
  -d '{"studentId": "newstudent", "name": "New Student", "email": "new@example.com", "photoReference": "newstudent.jpg"}' \
  http://localhost:5002/api/student

Stopping the Services

# Stop all services but keep volumes
docker-compose down

# Stop services and remove volumes (complete cleanup)
docker-compose down -v