Script+ Reader — handwritten Indian prescription recognition

Deploying (one-time, ~20 min)

1. Create the Space: huggingface.co/new-space → SDK: Gradio. Upload app.py, rx_pipeline.py, requirements.txt, README.md.
2. Create a separate model repo: huggingface.co/new → type Model, e.g. YOURNAME/scriptplus-checkpoints (private is fine).
3. Push your checkpoints there from Colab (Drive already mounted):
   python

   1!pip -q install huggingface_hub 2from huggingface_hub import HfApi 3api = HfApi(token="hf_...your write token...") 4api.upload_folder(folder_path="/content/drive/MyDrive/rx_system/m_htr_merged", 5 repo_id="YOURNAME/scriptplus-checkpoints", repo_type="model", 6 path_in_repo="m_htr_merged") 7api.upload_folder(folder_path="/content/drive/MyDrive/rx_system/m2_model", 8 repo_id="YOURNAME/scriptplus-checkpoints", repo_type="model", 9 path_in_repo="m2_model") 10api.upload_file(path_or_fileobj="/content/drive/MyDrive/rx_system/abstention_gate.joblib", 11 repo_id="YOURNAME/scriptplus-checkpoints", repo_type="model", 12 path_in_repo="abstention_gate.joblib")
4. On the Space page → Settings → Variables and secrets:
   • Add secret HF_TOKEN = a read-scope token (needed if your model repo is private).
   • Add variable RX_CKPT_REPO = YOURNAME/scriptplus-checkpoints (skip this if you kept the default shadowfurynon/scriptplus-checkpoints).
5. Restart the Space. On first boot rx_pipeline.py downloads the checkpoints from the model repo into local disk automatically — watch the Logs tab, this takes a few minutes the first time only.

Hardware

Env knobs

• RX_EXPAND_VOCAB=0 → curated-only vocabulary (recall ceiling-limited)
• RX_GATE_TH → abstention gate threshold (default 0.5)
• RX_CKPT_REPO → HF model repo holding checkpoints (default shadowfurynon/scriptplus-checkpoints)
• HF_TOKEN → set as a Space secret if the checkpoint model repo is private