GoalSight

⚽️ GoalSight — a platform for football match analytics and prediction powered by machine learning. A convenient tool for coaches, journalists, and fans!

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Demo

Link to our website

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About the Project

GoalSight is a modern platform for collecting, visualizing, and analyzing football statistics, as well as predicting match outcomes using ML. The project includes:

• Django backend with REST API and ML model integration
• React/TypeScript frontend
• Data import from CSV and external APIs
• Documentation and automation via Docker

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Quick Start

Using Docker

1. Clone the repository:

   git clone https://github.com/IU-Capstone-Project-2025/GoalSight.git
   cd GoalSight

2. Decrypt the environment variables file:

   cd docker/local
   gpg --decrypt .env.gpg > .env

   You will need a password to decrypt. If you don't have it, contact the team lead.

3. Start the services:

   docker-compose up --build

• Backend: http://localhost:8000/
• Frontend: http://localhost:3000/

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Testing

The project includes tests for the backend (Django), ML (separately via pytest), and frontend (React):

Backend (Django)

• Unit and integration tests for models, serializers, API, and management commands.
• All tests are located in tests.py files and tests/ subfolders inside each app.
• Run all tests:

  python backend/goalsight/manage.py test

ML tests (pytest)

• ML service and model tests are located in backend/goalsight/predictions/tests/
• Run ML tests separately using pytest:

  cd backend/goalsight/predictions
  pytest tests/

Frontend (React)

• Unit tests for components (Jest)

• Integration tests for API (Jest)

• E2E tests for user scenarios (Cypress)

• Tests are located in frontend/__tests__/

• Run unit and integration tests:

  npm run test:components
  npm run test:api

• Run e2e tests (Cypress):

  npm run test:e2e

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Development

Requirements

• Python 3.11+
• Node.js 18+
• Docker (for quick setup)

Environment

• All environment variables for local development are stored in docker/local/.env.gpg
• ML models and artifacts: backend/goalsight/ml_models/
• Jupyter notebooks: notebooks/

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File Structure

GoalSight/
├── backend/                  # Django backend
│   ├── goalsight/            # Django project and apps
│   │   ├── goalsight/        # Django settings (settings, urls, wsgi, asgi)
│   │   ├── matches/          # Matches app (models, serializers, views, management commands)
│   │   ├── teams/            # Teams app (models, serializers, views, management commands)
│   │   ├── tournaments/      # Tournaments app (models, serializers, views, management commands)
│   │   ├── predictions/      # ML service, prediction API, ML tests
│   │   ├── ml_models/        # Trained ML models and preprocessors
│   │   ├── staticfiles/      # Django static files
│   │   └── manage.py         # Django management script
│   ├── requirements.txt      # Python dependencies
│   └── entrypoint.sh         # Docker entrypoint script
├── frontend/                 # React/TypeScript frontend
│   ├── src/                  # Application source code
│   │   ├── components/       # UI components
│   │   ├── pages/            # Application pages
│   │   ├── styles/           # Styles
│   │   └── ...
│   ├── __tests__/            # Frontend tests (Jest, Cypress)
│   ├── public/               # Static files (index.html, etc.)
│   ├── package.json          # Node.js dependencies
│   └── ...
├── docker/                   # Docker configs for different environments
│   ├── local/                # Local development
│   ├── production/           # Production
│   └── staging/              # Staging
├── notebooks/                # Jupyter notebooks for ML and data analysis
├── openapi.yaml              # OpenAPI schema for API documentation
└── README.md                 # Project documentation

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Documentation

• Swagger: http://localhost:8000/swagger/ (when backend is running)
• ML notebooks: notebooks/ (folder)

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Branches

• main — stable version
• staging — development branch

License

This project is licensed under the MIT License. See LICENSE for details.