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Clone the repository:\n```bash\ngit clone https://github.com/oldhero5/talent_track.git\ncd talent_track\n```\n\n2. Create and activate a virtual environment:\n```bash\nuv venv\nsource .venv/bin/activate  # On Unix/macOS\n# or\n.venv\\Scripts\\activate  # On Windows\n```\n\n3. Install dependencies:\n```bash\nuv pip install -r requirements.txt\n```\n\n## Project Structure\n```\ntalent_track/\n├── talent_track/\n│   ├── __init__.py\n│   ├── app.py\n│   ├── functions.py\n│   ├── static/\n│   │   ├── css/\n│   │   │   └── style.css\n│   │   └── js/\n│   │       ├── talent_pool.js\n│   │       ├── model_analysis.js\n│   │       └── model_monitoring.js\n│   └── templates/\n│       ├── index.html\n│       ├── talent_pool.html\n│       ├── model_analysis.html\n│       └── model_monitoring.html\n├── pyproject.toml\n└── requirements.txt\n```\n\n## Running the Application\n\n1. Make sure you're in the project root directory and your virtual environment is activated\n\n2. Run the Flask application:\n```bash\npython -m talent_track.app\n```\n\n3. Open your browser and navigate to:\n```\nhttp://localhost:5000\n```\n\n## Features in Detail\n\n### Talent Pool Visualization\n- Interactive scatter plot showing employee and candidate clustering\n- Filter by status and experience level\n- Hover for detailed information\n- Click for candidate details\n\n### Model Analysis\n- Candidate similarity scoring\n- SHAP value explanations\n- Feature importance visualization\n- Individual candidate analysis\n\n### Model Monitoring\n- Data drift detection\n- Confusion matrix\n- ROC curve analysis\n- Performance metrics tracking\n\n## Development\n\n### Prerequisites\n- Python 3.8+\n- uv package manager\n- Node.js (for DataTables)\n\n### Setup Development Environment\n```bash\n# Clone repository\ngit clone https://github.com/yourusername/talent_track.git\ncd talent_track\n\n# Create virtual environment\nuv venv\nsource .venv/bin/activate\n\n# Install dependencies\nuv pip install -r requirements.txt\n```\n\n### Running Tests\n```bash\npytest tests/\n```\n\n## Contributing\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`)\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)\n4. Push to the branch (`git push origin feature/AmazingFeature`)\n5. Open a Pull Request\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details\n\n## Acknowledgments\n\n- FAISS library by Facebook Research\n- SHAP (SHapley Additive exPlanations)\n- Flask web framework\n- DataTables for interactive tables\n- Plotly for interactive visualizations\n\n## Troubleshooting\n\n### Common Issues\n\n1. Template Not Found\n```bash\n# Make sure your template folder path is correct in app.py\napp = Flask(__name__,\n            template_folder='/path/to/your/templates',\n            static_folder='static')\n```\n\n2. Data Initialization Errors\n```bash\n# Check the console output for initialization errors\n# Make sure all dependencies are installed\nuv pip install -r requirements.txt\n```\n\n3. Visualization Errors\n```bash\n# If visualizations aren't showing:\n# - Check browser console for JavaScript errors\n# - Verify that static files are being served correctly\n# - Check that matplotlib backend is set to 'Agg'\n```\n\n## Support\n\nFor support, please open an issue in the GitHub repository or contact the maintainers.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Foldhero5%2Ftalent_track","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Foldhero5%2Ftalent_track","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Foldhero5%2Ftalent_track/lists"}