README.md

metadata

title: Polyreactivity Benchmark
emoji: 🧬
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 4.21.0
app_file: app.py
pinned: false
license: mit

Polyreactivity Space

Interactive Gradio interface for scoring antibody sequences with the trained polyreactivity model.

Usage

1. Train a model (see project README) and ensure the resulting artifact is accessible at artifacts/model.joblib, or upload the file through the UI.
2. Launch locally:

   python space/app.py
   

3. Provide a heavy-chain sequence (optional light chain) and click Predict, or upload a CSV with columns id, heavy_seq[, light_seq] for batch scoring.

Benchmark mode

• Include a binary label column to obtain accuracy, F1, ROC-AUC, PR-AUC, and Brier score against your ground truth.
• Include a reactivity_count column to compute Spearman correlation between predicted probabilities and graded ELISA flag counts.
• The app writes merged inputs + predictions to polyreact_predictions.csv for downstream analysis.
• A four-row demo file lives in space/examples/sample_batch.csv.

Environment Variables

• POLYREACT_MODEL_PATH — default path to the trained model artifact.
• POLYREACT_CONFIG_PATH — default YAML configuration for inference overrides.

Both variables are optional; when unset, the app looks for artifacts/model.joblib and configs/default.yaml relative to the project root.

Deploying to Hugging Face Spaces

Automate deployment with the helper script once you have set HF_TOKEN (or another environment variable of your choice) with a Hugging Face write token:

export HF_TOKEN=hf_your_write_token
python space/deploy.py --space-id your-username/polyreactivity-space

Add --private if you prefer a private Space or use --token-env when the token lives under a different variable name. The script uploads the package, configuration, and Space assets — including the default artifacts/model.joblib — so the interface is ready immediately after the build completes.