Spaces:
Sleeping
Sleeping
File size: 2,806 Bytes
22032c4 a1099a1 22032c4 a1099a1 22032c4 a1099a1 22032c4 a1099a1 22032c4 a1099a1 22032c4 a1099a1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | ---
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
```text
.
├── 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](https://www.kaggle.com/datasets/minahilfatima12328/consumer-shopping-trends-analysis) |
| **F1 Pit Strategy** | Pit Stop Next Lap | [Kaggle Link](https://www.kaggle.com/datasets/aadigupta1601/f1-strategy-dataset-pit-stop-prediction) |
| **Sleep Health** | Sleep Disorder Classifier | [Kaggle Link](https://www.kaggle.com/datasets/mohankrishnathalla/sleep-health-and-daily-performance-dataset) |
## Local Development
1. **Setup Environment**:
```bash
python -m venv .venv
source .venv/bin/activate # or .venv\Scripts\activate on Windows
pip install -r requirements.txt
```
2. **Run Server**:
```bash
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](LICENSE) file for details.
---
*Created as part of the B.Tech Semester 04 Machine Learning Lab Assignment.*
|