Gamble Comment Detector

An intelligent machine learning system with automated MLOps pipeline for detecting gambling-related content in user comments using natural language processing techniques.

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Automated ML Pipeline (MLOps)

This project implements a complete MLOps pipeline using GitHub Actions that automatically handles the entire machine learning lifecycle:

Pipeline Stages

Stage	Description	Tools Used
Environment Setup	Python 3.9, dependencies installation	setup-python@v4, pip
Data Management	Auto-generate dummy data if missing	Custom Python script
Model Training	Train ML model with latest data	scikit-learn, joblib
Model Evaluation	Generate performance metrics and reports	Custom evaluation script
Artifact Management	Version control for model files	Git commits, upload-artifact@v4
Auto Deployment	Deploy to Hugging Face Spaces	Git LFS, HF Spaces API

Pipeline Triggers

• Automatic : Every push to main branch
• Manual : Workflow dispatch for on-demand runs

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Project Structure

gamble-comment-detector/
├── 📁 .github/
│   └── workflows/
│       └── pipeline.yml   # Pipeline Configuration
├── 📁 app/
│   ├── __init__.py
│   └── main.py            # FastAPI application
├── 📁 data/
│   └── comments.csv       # Training dataset
├── 📁 model/
│   ├── eval_report.json   # Automated evaluation metrics
│   ├── saved_model.joblib # Auto-generated ML model
│   └── vectorizer.joblib  # Auto-generated text vectorizer
├── 📁 notebooks/
│   └── Baseline.ipynb     # Jupyter notebook for experimentation
├── 📁 scripts/
│   └── generate_dummy_data.py  # Auto data generation
├── 📁 src/
│   ├── __init__.py
│   ├── evaluate.py        # Automated model evaluation
│   ├── inference.py       # Prediction logic
│   ├── preprocessing.py   # Data preprocessing
│   └── train.py          # Automated model training
├── app.py                # Main application entry point
├── requirements.txt      # Python dependencies
└── README.md            # This file

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API Endpoints

Method	Endpoint	Description
POST	/predict	Analyze a single comment
POST	/predict/batch	Analyze multiple comments
GET	/health	Health check endpoint
GET	/model/info	Get model information and version

Example API Usage

Single Prediction:

POST /predict
{
    "text": "I just won big at the casino last night!"
}

Response:
{
    "is_gambling": true,
    "confidence": 0.87,
    "model_version": "v1.2.3",
    "timestamp": "2024-01-15T10:30:00Z"
}

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MLOps Workflow Details

GitHub Actions Pipeline Configuration

The automated pipeline (pipeline.yml) includes:

# Key Pipeline Steps
1. Environment Setup (Python 3.9)
2. Dependency Installation
3. Automated Model Training
4. Performance Evaluation
5. Model Artifact Versioning
6. Auto-Deployment to HF Spaces

Required Secrets

For the pipeline to work, set these GitHub repository secrets:

• HF_TOKEN: Hugging Face API token for deployment

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Development

Local Development

# Run tests
pytest tests/ -v

# Code formatting
black src/ app/
flake8 src/ app/

# Generate dummy data
python scripts/generate_dummy_data.py

Pipeline Testing

# Test pipeline locally (requires Act)
act -j train-and-deploy

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Contributing

We welcome contributions! The automated pipeline will test your changes automatically.

How to Contribute

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

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Roadmap

• ✅ Basic ML model implementation
• ✅ FastAPI REST API
• ✅ Automated MLOps pipeline with GitHub Actions
• ✅ Auto-deployment to Hugging Face Spaces
• ✅ Model evaluation metrics and reporting
• 🔄 A/B testing framework
• 🔄 Model performance monitoring dashboard
• 🔄 Advanced deep learning models
• 🔄 Multi-language support
• 🔄 Docker containerization
• 🔄 Kubernetes deployment

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Live Demo

Try the live model: Hugging Face Spaces

Updated automatically with every model improvement!

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Acknowledgments

• Thanks to all contributors who helped build this project
• Built with using FastAPI, scikit-learn, GitHub Actions, and Hugging Face

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Support

• Found a bug? Open an issue
• Have a feature request? Start a discussion
• Pipeline Issues? Check Actions tab

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⭐ Don't forget to star this repository if you found it helpful!