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backend/database.py

@@ -82,11 +82,13 @@ async def init_db() -> None:
 
 async def log_prediction(data: Any, prediction_label: str, confidence: float) -> None:
     async with aiosqlite.connect(DB_PATH) as db:
+        from datetime import timezone, timedelta
+        ist = timezone(timedelta(hours=5, minutes=30))
         await db.execute('''
             INSERT INTO predictions (timestamp, heart_rate, spo2, sys_bp, dia_bp, temp, fall_detection, prediction_label, confidence)
             VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
         ''', (
-            datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S"),
+            datetime.now(ist).strftime("%Y-%m-%d %H:%M:%S"),
             data.Heart_Rate,
             data.SpO2_Level,
             data.Systolic_BP,

backend/ensemble.py

@@ -50,4 +50,8 @@ class EnsembleModel:
             for i, prob in enumerate(weighted_probs)
         }
         
-        return final_pred, confidence, individual_preds, class_probs, self.weights
+        individual_conf = {
+            name: float(np.max(probs)) for name, probs in individual_probs.items()
+        }
+        
+        return final_pred, confidence, individual_preds, class_probs, self.weights, individual_conf

backend/inference.py

@@ -31,7 +31,7 @@ def evaluate(ensemble_model, data) -> Dict[str, Any]:
         return {"error": "Ensemble model not loaded"}
 
     # Ensemble Prediction
-    disease_label, conf, ind_preds, class_probs, weights = ensemble_model.predict(processed_df)
+    disease_label, conf, ind_preds, class_probs, weights, ind_conf = ensemble_model.predict(processed_df)
     is_at_risk = (disease_label.lower() not in ["healthy", "normal", "none"])
 
     return {
@@ -40,6 +40,7 @@ def evaluate(ensemble_model, data) -> Dict[str, Any]:
         "probability": float(conf),
         "ensemble_prediction": is_at_risk,
         "model_outputs": ind_preds,
+        "model_probs": ind_conf,
         "disease_probs": class_probs,
         "weights": weights
     }

backend/main.py

@@ -77,8 +77,11 @@ def health_check():
 
 def log_alert_sync(data: PatientData, conf: float):
     try:
+        from datetime import datetime, timezone, timedelta
+        ist = timezone(timedelta(hours=5, minutes=30))
+        timestamp = datetime.now(ist).strftime("%Y-%m-%d %H:%M:%S")
         with open("alerts.log", "a") as f:
-            f.write(f"ALERT: Patient at risk! HR: {data.Heart_Rate}, SpO2: {data.SpO2_Level}, Confidence: {conf:.2f}\n")
+            f.write(f"[{timestamp}] ALERT: Patient at risk! HR: {data.Heart_Rate}, SpO2: {data.SpO2_Level}, Confidence: {conf:.2f}\n")
     except Exception:
         pass