Add final Guardian Monitor pipeline and backend alert bridge

guardian_backend_bridge.py

@@ -0,0 +1,191 @@
+
+"""
+Guardian Monitor Backend Bridge
+--------------------------------
+Keeps guardian_pipeline_final.py unchanged.
+
+What this script does:
+1. Runs guardian_pipeline_final.py as-is
+2. Reads predictions_baseline.csv
+3. Logs into the Guardian backend
+4. Sends Medium/High alerts automatically
+5. Optionally fetches alerts back for confirmation
+
+How to use:
+- Put this file in the same folder as guardian_pipeline_final.py
+- Make sure requests, pandas are installed
+- Run: python3 guardian_backend_bridge.py
+
+Notes:
+- Update EMAIL/PASSWORD only if your backend credentials change
+- Adjust SEND_LEVELS if you only want High alerts
+"""
+
+import os
+import sys
+import subprocess
+from pathlib import Path
+
+import pandas as pd
+import requests
+
+# ── Backend Configuration ─────────────────────────────────────────────────────
+
+BASE_URL = "https://guardian-backend-ashen.vercel.app/api/v1"
+API_EMAIL = "nurse@guardian.com"
+API_PASSWORD = "Password123!"
+SEND_LEVELS = {"High", "Medium"}   # Change to {"High"} if needed
+FETCH_ALERTS_AFTER_SEND = True
+
+# ── Paths ─────────────────────────────────────────────────────────────────────
+
+HERE = Path(__file__).resolve().parent
+PIPELINE_FILE = HERE / "guardian_pipeline_final.py"
+PREDICTIONS_FILE = HERE / "predictions_baseline.csv"
+
+
+def run_pipeline() -> None:
+    """Run the existing Guardian Monitor pipeline unchanged."""
+    if not PIPELINE_FILE.exists():
+        raise FileNotFoundError(
+            f"Could not find {PIPELINE_FILE.name} in {HERE}. "
+            "Put this bridge file in the same folder as guardian_pipeline_final.py."
+        )
+
+    print(f"[BRIDGE] Running existing pipeline: {PIPELINE_FILE.name}")
+    result = subprocess.run(
+        [sys.executable, str(PIPELINE_FILE)],
+        cwd=str(HERE),
+        capture_output=False,
+        text=True
+    )
+    if result.returncode != 0:
+        raise RuntimeError(f"Pipeline failed with exit code {result.returncode}")
+
+
+def get_auth_headers():
+    """Login and return Bearer auth headers."""
+    print("[BRIDGE] Logging into backend...")
+    login_res = requests.post(
+        f"{BASE_URL}/auth/login",
+        json={
+            "email": API_EMAIL,
+            "password": API_PASSWORD
+        },
+        timeout=20
+    )
+
+    print(f"[BRIDGE] Login status: {login_res.status_code}")
+    try:
+        login_json = login_res.json()
+    except Exception:
+        raise RuntimeError(f"Login response was not JSON: {login_res.text}")
+
+    if login_res.status_code != 200:
+        raise RuntimeError(f"Login failed: {login_json}")
+
+    token = login_json.get("token")
+    if not token:
+        raise RuntimeError(f"Login succeeded but no token returned: {login_json}")
+
+    return {"Authorization": f"Bearer {token}"}
+
+
+def build_alert_message(row: pd.Series) -> tuple[str, str]:
+    """
+    Convert a pipeline row into:
+    - alert_type
+    - message
+    """
+    final_alert = str(row.get("final_alert", "unknown")).strip()
+    patient_id = row.get("subject_id", "unknown")
+    explanation = str(row.get("explanation", "No explanation provided.")).strip()
+    anomaly_type = str(row.get("anomaly_type", "none")).strip()
+
+    alert_type = f"{final_alert.lower()}_alert"
+
+    if anomaly_type and anomaly_type != "none":
+        message = f"Patient {patient_id}: {explanation} (Anomaly: {anomaly_type})"
+    else:
+        message = f"Patient {patient_id}: {explanation}"
+
+    return alert_type, message
+
+
+def send_alerts(headers) -> int:
+    """Read predictions_baseline.csv and POST selected alerts."""
+    if not PREDICTIONS_FILE.exists():
+        raise FileNotFoundError(
+            f"{PREDICTIONS_FILE.name} not found. "
+            "Run guardian_pipeline_final.py first or let this bridge run it."
+        )
+
+    df = pd.read_csv(PREDICTIONS_FILE)
+
+    required_cols = {"subject_id", "final_alert", "explanation"}
+    missing = required_cols - set(df.columns)
+    if missing:
+        raise ValueError(f"Predictions file is missing required columns: {missing}")
+
+    sent_count = 0
+    candidates = df[df["final_alert"].isin(SEND_LEVELS)].copy()
+
+    print(f"[BRIDGE] Alerts eligible for send: {len(candidates)}")
+
+    for _, row in candidates.iterrows():
+        alert_type, message = build_alert_message(row)
+
+        res = requests.post(
+            f"{BASE_URL}/alerts",
+            headers=headers,
+            json={
+                "alert_type": alert_type,
+                "message": message
+            },
+            timeout=20
+        )
+
+        print(f"[BRIDGE] POST /alerts -> {res.status_code}")
+
+        try:
+            payload = res.json()
+        except Exception:
+            payload = res.text
+
+        print(f"[BRIDGE] Response: {payload}")
+
+        if res.status_code in (200, 201):
+            sent_count += 1
+
+    return sent_count
+
+
+def fetch_alerts(headers):
+    """Fetch alerts from backend for confirmation."""
+    print("[BRIDGE] Fetching alerts from backend...")
+    res = requests.get(f"{BASE_URL}/alerts", headers=headers, timeout=20)
+    print(f"[BRIDGE] GET /alerts -> {res.status_code}")
+    try:
+        print(res.json())
+    except Exception:
+        print(res.text)
+
+
+def main():
+    print("=" * 72)
+    print("GUARDIAN MONITOR — BACKEND ALERT BRIDGE")
+    print("=" * 72)
+
+    run_pipeline()
+    headers = get_auth_headers()
+    sent = send_alerts(headers)
+    print(f"[BRIDGE] Successful sends: {sent}")
+
+    if FETCH_ALERTS_AFTER_SEND:
+        fetch_alerts(headers)
+
+    print("[BRIDGE] Done.")
+
+
+if __name__ == "__main__":
+    main()