Pneunet - Cloud Computing Project

A FastAPI-based web application for medical image analysis, specifically focused on pneumonia detection using deep learning models.

Project Structure

.
├── app/                    # Main application directory
│   ├── main.py            # FastAPI application entry point
│   ├── logger.py          # Logging configuration
│   └── templates/         # HTML templates
├── tests/                 # Test directory
│   └── test_main.py       # Application tests
├── data/                  # Data directory for storing images and models
├── logs/                  # Application logs
├── .github/              # GitHub configuration
│   └── workflows/        # CI/CD workflows
├── dockerfile            # Docker configuration
├── requirements.txt      # Python dependencies
├── pytest.ini           # PyTest configuration
├── .env                 # Environment variables
└── README.md            # Project documentation

Features

• FastAPI-based REST API
• Web interface for image upload and analysis
• Deep learning model integration for pneumonia detection
• Docker support for containerized deployment
• Automated testing with PyTest
• CI/CD pipeline with GitHub Actions
• Comprehensive logging system

Prerequisites

• Python 3.10 or higher
• Docker (optional, for containerized deployment)

Installation

1. Clone the repository:

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

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

Running the Application

Local Development

1. Start the FastAPI server:

uvicorn app.main:app --reload

2. Open your browser and navigate to http://localhost:8000

Docker Deployment

1. Build the Docker image:

docker build -t pneunet .

2. Run the container:

docker run -p 8000:8000 pneunet

Testing

The project uses PyTest for testing. To run the tests:

pytest tests/ -v

Test logs are automatically generated in the logs/ directory.

CI/CD Pipeline

The project includes a GitHub Actions workflow that:

• Runs on push to main branch and pull requests
• Sets up Python 3.10
• Installs dependencies
• Runs tests
• Uploads test logs as artifacts

API Endpoints

• GET /: Web interface
• GET /api/v1/: API root endpoint

Development

The project uses:

• FastAPI for the backend
• Jinja2 for templating
• PyTest for testing
• GitHub Actions for CI/CD
• Custom logging configuration

Contributing

Write me edgarabasov1@gmail.com