Update Streamlit app

app.py

@@ -37,44 +37,44 @@ def load_model():
 # FEATURE ENGINEERING FUNCTION
 # ============================================
 def engineer_features(df):
-    """Apply feature engineering to match training pipeline"""
+    """Apply feature engineering to match training pipeline exactly"""
     df_enhanced = df.copy()
     
-    # First, rename columns to match training data convention
-    rename_mapping = {
-        "Lub oil pressure": "Lube Oil Pressure",
-        "lub oil temp": "Lube Oil Temperature",
-        "Coolant temp": "Coolant Temperature",
-        "Engine rpm": "Engine RPM",
-        "Fuel pressure": "Fuel Pressure",
-        "Coolant pressure": "Coolant Pressure"
-    }
+    # Ensure we have the correct raw features in the correct order
+    # Raw features from input (keeping original names as they come from user input)
+    required_features = [
+        'Engine rpm',
+        'Lub oil pressure', 
+        'Fuel pressure',
+        'Coolant pressure',
+        'lub oil temp',
+        'Coolant temp'
+    ]
     
-    for old_name, new_name in rename_mapping.items():
-        if old_name in df_enhanced.columns:
-            df_enhanced.rename(columns={old_name: new_name}, inplace=True)
+    # Verify all required features exist
+    for feature in required_features:
+        if feature not in df_enhanced.columns:
+            raise ValueError(f"Missing required feature: {feature}")
     
-    # Create sensor interactions (matching training pipeline)
-    sensor_columns = [col for col in df_enhanced.columns]
+    # Create engineered features (matching training pipeline)
+    df_enhanced['Lub_Stress_Index'] = df_enhanced['Lub oil pressure'] * df_enhanced['lub oil temp']
+    df_enhanced['Thermal_Efficiency'] = df_enhanced['Coolant pressure'] / (df_enhanced['Coolant temp'] + 1e-5)
+    df_enhanced['Power_Load_Index'] = df_enhanced['Engine rpm'] * df_enhanced['Fuel pressure']
     
-    # Add ratio features
-    if 'Lube Oil Pressure' in df_enhanced.columns and 'Coolant Pressure' in df_enhanced.columns:
-        df_enhanced['Oil_Coolant_Pressure_Ratio'] = (
-            df_enhanced['Lube Oil Pressure'] / (df_enhanced['Coolant Pressure'] + 1)
-        )
-    
-    if 'Lube Oil Temperature' in df_enhanced.columns and 'Coolant Temperature' in df_enhanced.columns:
-        df_enhanced['Oil_Coolant_Temp_Diff'] = (
-            df_enhanced['Lube Oil Temperature'] - df_enhanced['Coolant Temperature']
-        )
-    
-    # Add squared features for each sensor
-    for col in ['Lube Oil Pressure', 'Lube Oil Temperature', 'Coolant Pressure', 
-                'Coolant Temperature', 'Engine RPM', 'Fuel Pressure']:
-        if col in df_enhanced.columns:
-            df_enhanced[f'{col}_Squared'] = df_enhanced[col] ** 2
+    # Return features in the EXACT order the model was trained with
+    feature_order = [
+        'Engine rpm',
+        'Lub oil pressure',
+        'Fuel pressure',
+        'Coolant pressure',
+        'lub oil temp',
+        'Coolant temp',
+        'Lub_Stress_Index',
+        'Thermal_Efficiency',
+        'Power_Load_Index'
+    ]
     
-    return df_enhanced
+    return df_enhanced[feature_order]
 
 # ============================================
 # MAIN APP