app.py

import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
"""Sentinel — causal sepsis early-warning demo (Gradio)."""

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import gradio as gr

from sentinel_core import SepsisPredictor, derive_alerts, LABEL

THRESHOLD, PERSISTENCE, REFRACTORY = 0.70, 2, 6
predictor = SepsisPredictor("sentinel_causal.pt", "scaler.csv", threshold=THRESHOLD)

EXAMPLES = {
    "Septic patient (deteriorates to sepsis)": "examples/septic_patient.psv",
    "Non-septic patient (stable)": "examples/nonseptic_patient.psv",
}


def _run(df):
    onset = None
    if LABEL in df.columns and (df[LABEL] == 1).any():
        onset = int((df[LABEL] == 1).argmax()) + 6   # label shifted 6h pre-onset
    feats = df.drop(columns=[LABEL]) if LABEL in df.columns else df
    risk = predictor.predict(feats)
    alert_hours, _ = derive_alerts(risk, THRESHOLD, PERSISTENCE, REFRACTORY)
    return risk, alert_hours, onset


def _plot(risk, alert_hours, onset):
    hours = list(range(len(risk)))
    fig, ax = plt.subplots(figsize=(10, 4))
    ax.plot(hours, risk, color="#BD3F36", lw=2, label="Sepsis risk")
    ax.axhline(THRESHOLD, color="grey", ls="--", lw=1, label=f"Threshold ({THRESHOLD})")
    if onset is not None and onset <= len(risk):
        ax.axvline(onset, color="black", ls=":", lw=1.5, label=f"Onset (h{onset})")
    if alert_hours:
        ax.scatter(alert_hours, [risk[h] for h in alert_hours], color="#BD3F36",
                   s=90, marker="v", zorder=5, label="Alert fired")
    ax.fill_between(hours, 0, risk, color="#BD3F36", alpha=0.08)
    ax.set(xlabel="ICU hour", ylabel="Predicted sepsis risk", ylim=(0, 1),
           title="Streaming sepsis risk (causal, real-time simulation)")
    ax.legend(loc="upper left", fontsize=9)
    fig.tight_layout()
    return fig


def analyze(example_choice, uploaded):
    if uploaded is not None:
        df = pd.read_csv(uploaded.name, sep="|")
        source = "uploaded patient"
    else:
        df = pd.read_csv(EXAMPLES[example_choice], sep="|")
        source = example_choice
    risk, alert_hours, onset = _run(df)
    fig = _plot(risk, alert_hours, onset)

    peak = float(np.max(risk))
    first = alert_hours[0] if alert_hours else None
    lines = [f"**{source}** — {len(risk)} ICU hours"]
    lines.append(f"Peak risk: {peak:.2f}")
    if onset is not None:
        lines.append(f"Documented clinical onset: ~hour {onset}")
    if first is not None:
        lead = f", ~{onset - first}h before onset" if onset else ""
        lines.append(f"First alert: hour {first}{lead}")
        lines.append(f"Total alerts: {len(alert_hours)} (at hours {alert_hours})")
    else:
        lines.append("No alerts fired (risk stayed below the sustained-alert policy).")
    return fig, "\n\n".join(lines)


with gr.Blocks(title="Sentinel — Sepsis Early Warning") as demo:
    gr.Markdown(
        "# Sentinel — Causal Sepsis Early Warning\n"
        "Predicts hour-by-hour sepsis risk from streaming ICU vitals (PhysioNet 2019), "
        "with a fatigue-aware alerting policy. Risk at each hour uses only data up to "
        "that hour (a true causal simulation).\n\n"
        "*Research demonstration on public data. **Not for clinical use.***"
    )
    with gr.Row():
        with gr.Column(scale=1):
            choice = gr.Dropdown(list(EXAMPLES.keys()), value=list(EXAMPLES.keys())[0],
                                 label="Example patient")
            upload = gr.File(label="…or upload a PhysioNet .psv", file_types=[".psv"])
            btn = gr.Button("Analyze", variant="primary")
        with gr.Column(scale=2):
            plot = gr.Plot(label="Risk trajectory")
            summary = gr.Markdown()
    btn.click(analyze, [choice, upload], [plot, summary])
    demo.load(analyze, [choice, upload], [plot, summary])

if __name__ == "__main__":
    demo.launch()