Update app.py

app.py

@@ -426,11 +426,47 @@ def predict_traffic_risk():
     data = request.json
     try:
         if model_traffic is None: return jsonify({"error": "Tabular model missing"}), 500
-        df = pd.DataFrame([{k: (v if v != "" else None) for k, v in data.items()}])
-        prob = float(model_traffic.predict_proba(df)[0][1] * 100)
+        
+        # 1. Enforce exact column names and order from the provided CSV file
+        csv_cols = [
+            'Weather', 'Road_Type', 'Time_of_Day', 'Traffic_Density', 'Speed_Limit',
+            'Number_of_Vehicles', 'Driver_Alcohol', 'Road_Condition', 'Vehicle_Type',
+            'Driver_Age', 'Driver_Experience', 'Road_Light_Condition'
+        ]
+        
+        # 2. Force correct data types to prevent silent DataFrame string failures
+        row_data = {}
+        for col in csv_cols:
+            val = data.get(col, "")
+            if col in ['Traffic_Density', 'Speed_Limit', 'Number_of_Vehicles', 'Driver_Alcohol', 'Driver_Age', 'Driver_Experience']:
+                try:
+                    row_data[col] = float(val)
+                except (ValueError, TypeError):
+                    row_data[col] = 0.0 # Strict numeric fallback 
+            else:
+                row_data[col] = val if val != "" else "Unknown"
+                
+        df = pd.DataFrame([row_data], columns=csv_cols)
+        
+        # 3. Predict dynamically based on the model's actual class array indices
+        probs = model_traffic.predict_proba(df)[0]
+        model_classes = list(model_traffic.classes_)
+        
+        if 'High' in model_classes:
+            prob = float(probs[model_classes.index('High')] * 100)
+        elif 'Major' in model_classes:
+            prob = float(probs[model_classes.index('Major')] * 100)
+        elif 1 in model_classes:
+            prob = float(probs[model_classes.index(1)] * 100)
+        else:
+            prob = float(probs[-1] * 100) # Safe fallback if classes are unnamed
+            
         status = "Major" if prob >= 70 else "Moderate" if prob >= 35 else "Minor"
+        
         return jsonify({"status": status, "risk_probability_percentage": prob})
-    except Exception as e: return jsonify({"error": str(e)}), 500
+    except Exception as e: 
+        print(f"Risk Predictor Error: {e}", flush=True)
+        return jsonify({"error": str(e)}), 500
 
 @app.route('/favicon.ico')
 @app.route('/app_logo')