Multiclass Lung Cancer Classification

A web application for classifying lung cancer types using machine learning.

Project Structure

• frontend/: Next.js frontend application
• backend/: FastAPI backend application

Running with Docker (Recommended)

The easiest way to run the application is using Docker Compose:

# Build and start the containers
docker-compose up --build

# Run in detached mode (background)
docker-compose up -d --build

# View logs
docker-compose logs -f

# View logs for a specific service
docker-compose logs -f frontend
docker-compose logs -f backend

# Stop the containers
docker-compose down

# Stop the containers and remove volumes
docker-compose down -v

The application will be available at:

• Frontend: http://localhost:3000
• Backend: http://localhost:5001

Docker Development Tips

• To rebuild a single service:

  docker-compose build frontend
  docker-compose build backend

• To restart a single service:

  docker-compose restart frontend
  docker-compose restart backend

• To view container status:

  docker-compose ps

• To check container health:

  docker-compose ps

Running Locally (Development)

Backend

1. Navigate to the backend directory:

   cd backend

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the server:

   python run.py

The backend will be available at http://localhost:5001

Frontend

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Run the development server:

   npm run dev

The frontend will be available at http://localhost:3000

File Upload

The application accepts CSV files for lung cancer classification. The CSV files should have the following format:

• Each row represents a sample
• Columns should contain the features used for classification
• The file should not include headers

After uploading, the file will be sent to the backend for processing, and the results will be displayed once processing is complete.

API Integration

The frontend communicates with the FastAPI backend through the following endpoints:

• /api/upload - POST endpoint for uploading CSV files
• /api/status/:fileId - GET endpoint for checking the status of a file
• /api/download/:fileId - GET endpoint for downloading the results

Technologies Used

• Next.js (with standalone output optimization)
• React
• FastAPI
• Docker & Docker Compose
• Tailwind CSS
• shadcn/ui components

Environment Variables

Frontend

• NEXT_PUBLIC_API_URL: Backend API URL (default: http://localhost:5001/api)

Backend

• HOST: Host to bind to (default: 0.0.0.0)
• PORT: Port to run on (default: 5001)