Initial Gradio demo for epibarrett

README.md

@@ -0,0 +1,6 @@
+# epibarrett demo
+
+A Gradio interface for the epibarrett BE/EAC methylation classifier.
+
+Upload a CSV of HM450-style beta values (samples × probes) and get calibrated
+BE/EAC probabilities from the genome-wide LASSO panel.

app.py

@@ -0,0 +1,62 @@
+"""Gradio demo for epibarrett BE/EAC methylation classifier."""
+
+from __future__ import annotations
+
+import tempfile
+from pathlib import Path
+
+import gradio as gr
+import joblib
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+REPO_ID = "kmlyyll/epibarrett-model"
+BUNDLE_PATH = Path("epibarrett_model.joblib")
+
+
+def _load_bundle():
+    if not BUNDLE_PATH.exists():
+        hf_hub_download(REPO_ID, filename="epibarrett_model.joblib", local_dir=".")
+    return joblib.load(BUNDLE_PATH)
+
+
+BUNDLE = _load_bundle()
+LASSO = BUNDLE["lasso"]
+PREPROCESSOR = BUNDLE["preprocessor"]
+PROBE_NAMES = BUNDLE["probe_names"]
+CLINICAL_FEATURES = BUNDLE["clinical_features"]
+
+
+def predict(csv_file):
+    X = pd.read_csv(csv_file.name, index_col=0)
+    missing = [p for p in PROBE_NAMES if p not in X.columns]
+    if missing:
+        raise gr.Error(
+            f"Missing {len(missing)} expected probe columns (e.g. {missing[:5]})."
+        )
+    M = PREPROCESSOR.transform(X[PROBE_NAMES])
+    proba = LASSO.predict_proba(M.to_numpy())[:, 1]
+    out = pd.DataFrame(
+        {"sample_id": X.index, "BE_EAC_probability": proba, "risk_call": (proba >= 0.5).astype(int)}
+    )
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
+    out.to_csv(tmp.name, index=False)
+    return out, tmp.name
+
+
+with gr.Blocks(title="epibarrett BE/EAC classifier") as demo:
+    gr.Markdown(
+        """
+        # epibarrett demo
+        Upload a CSV of HM450-style beta values (rows = samples, columns = CpG probes).
+        The model returns a calibrated probability of Barrett's esophagus / EAC for each sample.
+        """
+    )
+    file_in = gr.File(label="Upload beta-value CSV", file_types=[".csv"])
+    btn = gr.Button("Predict")
+    table_out = gr.Dataframe(label="Predictions")
+    file_out = gr.File(label="Download predictions CSV")
+    btn.click(fn=predict, inputs=file_in, outputs=[table_out, file_out])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,7 @@
+epibarrett @ git+https://github.com/lynchaos/epibarrett.git@main
+gradio>=4.0
+huggingface-hub>=0.20
+joblib>=1.3
+numpy>=1.24
+pandas>=2.0
+scikit-learn>=1.3,<1.7