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---
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:
```bash
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:
```bash
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.
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