autoexplainml 2.0.0

A modular Explainable AI framework for ML, Deep Learning, and Computer Vision

🧠 AutoExplainML

A production-ready machine learning explainability tool that converts complex ML model behavior into simple human-readable insights using SHAP.

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🌐 Live Demo

• Backend API: https://autoexplainml.onrender.com
• Frontend App: https://autoexplainml-ui.onrender.com

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pip install autoexplainml[dl] pip install autoexplainml[cv] pip install autoexplainml[ml] pip install autoexplainml[full]

🚀 Features

• Upload trained ML models (.pkl)
• Upload datasets (.csv)
• Get automatic feature importance explanations
• SHAP-based model interpretation
• Clean web UI (Streamlit)
• FastAPI backend
• Cloud deployment (Render)

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📌 Use Cases

🧠 Data Scientists

Understand which features influence model predictions.

🎓 Students

Learn how ML models behave internally.

🏢 Business Analysts

Gain transparency in AI-based decisions.

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🧠 Tech Stack

• Python
• FastAPI
• Streamlit
• SHAP
• Pandas, NumPy
• Scikit-learn
• Render (Deployment)

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📦 Architecture

Frontend (Streamlit UI) ↓ Backend API (FastAPI) ↓ SHAP Explainer Engine ↓ ML Model Output

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📸 Screenshots

⚙️ Run Locally

Backend

uvicorn backend.api:app --reload

Frontend

streamlit run frontend/app.py

Backend

👨‍💻 Author

Sidhant Narang