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| title: JAI | |
| emoji: 🚀 | |
| colorFrom: indigo | |
| colorTo: gray | |
| sdk: docker | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # 🎥 Multi-Face Attention Detector (YOLOv7, Streamlit, GPU) | |
| This app detects attention in multiple faces using YOLOv7 + mediapipe + a custom ML model. | |
| ## 🚀 Setup on Hugging Face | |
| 1. Create a Space → choose Blank Docker template. | |
| 2. Upload: | |
| - app.py | |
| - requirements.txt | |
| - Dockerfile | |
| - model.pkl (your ML model) | |
| - example_input.jpg (optional test image) | |
| 3. Enable GPU hardware. | |
| 4. Deploy! | |
| ## ✅ Features | |
| - YOLOv7 GPU face + phone detection | |
| - Mediapipe fallback (via mediapipe-nightly) | |
| - OpenCV video processing | |
| - Streamlit dashboard with charts, logs, CSV download | |
| ## 💡 Notes | |
| - Base image: `nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04` | |
| - Compatible with `torch==2.1.2+cu118` | |
| - Includes placeholder model.pkl for demo | |