Chucaflu11/uci-predict

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Deploy to Hugging Face Spaces

Quick steps to deploy this repository as a Gradio Space:

1. 1.Make sure best_cnn_only_model.h5, best_rnn_only_model.h5 and feature_names.json are present in the repo root. If models are large, consider hosting them on the Hugging Face Hub and downloading at runtime or using Git LFS.
2. 2.Ensure requirements.txt includes gradio (this repo now contains it).
3. 3.Create a new Space on Hugging Face: choose "Gradio" as the SDK and "Public" or "Private" depending on your needs.
4. 4.Push this repository to the Space's Git remote. Example (after creating the Space):

git init git add . git commit -m "Initial Space" git remote add origin https://huggingface.co/spaces/<your-username>/<your-space-name> git push origin main

1. 1.The Space will detect app.py (Gradio) and install packages from requirements.txt. The app will be available after build completes.

Notes and tips:

• —If your model files are > 50 MB, either enable Git LFS for the Space or upload models to the Hugging Face Hub and change app.py to download them at startup.
• —If you prefer smaller install footprint, use tensorflow-cpu in requirements.txt.
• —For debugging, check the Space logs on the web UI (Build logs / Server logs).