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It is containerized using Docker for easy deployment and reproducibility.\n\n## Features\n\n- Accepts structured input data and returns SHAP, LIME, or Integrated Gradients explanations\n- Supports tabular, text, and image models (with extensibility for others)\n- Dockerized for scalable and environment-agnostic deployment\n- Saves plots to disk and serves results via API and CLI\n- Extensible: plug in your own models and explanation methods\n- Batch and single-row explanation support\n- Human-friendly, narrative-rich JSON and HTML report outputs\n\n---\n\n## Tech Stack\n\n- Python 3.10+\n- FastAPI\n- SHAP, LIME, Captum (Integrated Gradients)\n- Scikit-learn / XGBoost / LightGBM / PyTorch / TensorFlow (optional)\n- Matplotlib / Plotly\n- Docker \u0026 Docker Compose\n\n---\n\n## Setup\n\n### 1. Clone the Repository\n\n```bash\ngit clone https://github.com/your-username/shap-explainer-backend.git\ncd shap-explainer-backend\n```\n\n### 2. Install dependencies\n\n```bash\npip install -r requirements.txt\n```\n\n### 3. Run the API server\n\n```bash\nuvicorn main:app --reload\n```\n\n### 4. Run CLI explanations\n\n```bash\npython cli_explain.py --model \u003cmodel_file\u003e --data \u003cdata_file\u003e --method shap|lime|integrated_gradients\n```\n\n## License\n\nThis project is licensed under the MIT License. See the LICENSE file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fselasie5%2Fexplainable-backend","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fselasie5%2Fexplainable-backend","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fselasie5%2Fexplainable-backend/lists"}