README.md

metadata

title: Inference Studio
emoji: ⚡
colorFrom: red
colorTo: blue
sdk: docker
app_port: 7860

ML Lab Assignment | Inference Studio

A premium, highly asynchronous web interface for machine learning model inference. This project serves as a comprehensive playground for testing and interpreting three distinct predictive models using FastAPI, Jinja2, and SHAP explainability.

Features

• Multi-Model Support: Seamlessly switch between Consumer Trends, F1 Pit Strategy, and Sleep Health models.
• Deep Interpretability: Real-time SHAP Diverging Charts visualizing feature contributions.
• Neural UI/UX: Cyber-Industrial aesthetic with glassmorphism, fluid animations, and dark-mode optimization.
• Data Injection: "Auto-Inject" feature to quickly test models with real samples from the datasets.
• HuggingFace Ready: Fully containerized and optimized for deployment on HF Spaces.

Project Structure

.
├── dataset/                # Source CSV files from Kaggle
├── inference/
│   ├── app.py              # FastAPI Backend with SHAP Integration
│   └── template/           # Frontend (HTML, CSS, JS)
├── notebooks/              # Jupyter Notebooks for model training
├── outputs/                # Trained model artifacts (.joblib) and performance metrics
├── Dockerfile              # Multi-stage Docker configuration
├── .gitattributes          # LFS tracking for datasets and models

Datasets & Models

Model	Target	Dataset Source
Consumer Trends	Category Prediction	Kaggle Link
F1 Pit Strategy	Pit Stop Next Lap	Kaggle Link
Sleep Health	Sleep Disorder Classifier	Kaggle Link

Local Development

1. Setup Environment:

   python -m venv .venv
   source .venv/bin/activate  # or .venv\Scripts\activate on Windows
   pip install -r requirements.txt
   

2. Run Server:

   uvicorn inference.app:app --reload
   

3. Access UI: Open http://127.0.0.1:8000

Technology Stack

• Backend: FastAPI (Python 3.11)
• Explainability: SHAP (SHapley Additive exPlanations)
• Frontend: Vanilla CSS, JS (ES6+), Jinja2 Templates
• Containerization: Docker
• ML Engine: Scikit-Learn, XGBoost, Joblib

License

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

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Created as part of the B.Tech Semester 04 Machine Learning Lab Assignment.