Statistical Model Suggester

A web application that helps users choose appropriate statistical models based on their data characteristics and analysis goals.

Features

• Model Recommendations: Get suggestions for statistical models based on your data type and analysis goals
• User Accounts: Save analysis history and track previous recommendations
• Expert Mode: Advanced features for experienced statisticians
• PDF/Word Export: Export analysis results and recommendations
• Admin Dashboard: User management and system monitoring
• Optional AI Chat: Enhanced recommendations with AI assistance (requires heavy ML dependencies)

Quick Start

1. Clone and setup:

git clone https://github.com/codoom1/statistical-model-suggester.git
cd statistical-model-suggester
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt        # Core app (~100MB)
pip install -r requirements-dev.txt    # + Development tools

3. Run the app:

export FLASK_SECRET_KEY='your-secret-key-here'
python app.py
# Visit: http://localhost:8084

Dependencies

• requirements.txt: Core production dependencies (Flask, SQLAlchemy, scikit-learn, basic plotting) The app gracefully handles missing optional dependencies (like PDF export libraries).

Deployment

Local Development

export FLASK_SECRET_KEY='your-secret-key-here'
python app.py
# Visit: http://localhost:8084

Production (Render.com)

1. Create a Web Service on Render and connect your GitHub repository
2. Build Command: pip install -r requirements.txt && python render_build.py
3. Start Command: gunicorn app:app
4. Add a PostgreSQL database and Render will set DATABASE_URL automatically

Required Environment Variables:

FLASK_ENV=production
SECRET_KEY=<your-secure-random-key>
ADMIN_USERNAME=<your-admin-username>
ADMIN_EMAIL=<your-admin-email>
ADMIN_PASSWORD=<your-secure-admin-password>

Optional (for email notifications):

MAIL_SERVER=<smtp-server>
MAIL_USERNAME=<email>
MAIL_PASSWORD=<password>

Optional (for AI features):

AI_ENHANCEMENT_ENABLED=true
HUGGINGFACE_API_KEY=<your-key>

Project Structure

├── app.py                 # Main Flask application
├── models.py             # Database models
├── requirements.txt      # Core dependencies
├── requirements-dev.txt  # Development tools
├── routes/              # Route handlers
├── templates/           # HTML templates
├── static/             # CSS, JS, images
├── utils/              # Helper functions
├── tests/              # Test suite
└── data/               # Model database

Testing

pip install -r requirements-dev.txt
python -m pytest tests/ -v

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

MIT License - see LICENSE file for details.