HandPlay — Rock, Paper, Scissors

A Streamlit web app that recognises rock, paper, and scissors hand gestures from a camera snapshot or uploaded image.

Run locally

pip install -r requirements.txt
streamlit run app.py

Deploy on Streamlit Community Cloud

1. Create a GitHub repository and upload this folder's contents.
2. At share.streamlit.io, select Create app and connect the repository.
3. Select app.py as the main file and click Deploy.

During Create app, open Advanced settings and choose Python 3.11. (The Python version is selected in Streamlit Cloud's deployment UI.) The packages.txt file supplies the Linux libgl1 and GLib runtime dependencies required by OpenCV. The training dataset is not required for deployment.

How it works

MediaPipe detects 21 hand landmarks. Their coordinates are centered on the wrist and scaled to hand size, then a logistic-regression model predicts the gesture.