File size: 1,952 Bytes
31e6f11 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | """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()
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