Update app.py

app.py

@@ -5,21 +5,25 @@ import gradio as gr
 # Load the trained model
 model = joblib.load("anomaly_detector_rf_model.pkl")
 
-# Define feature columns (exclude non-numeric ones if needed)
-feature_cols = ['hour', 'day_of_week', 'is_weekend', 'amount_zscore', 'log_amount',
-                'type_atm_withdrawal', 'type_credit', 'type_debit', 'merchant_encoded']
+# Define feature columns (include all used during training)
+feature_cols = ['hour', 'day_of_week', 'is_weekend', 'amount', 'merchant_avg_amount',
+                'amount_zscore', 'log_amount', 'type_atm_withdrawal', 'type_credit',
+                'type_debit', 'merchant_encoded']
 
 def detect_anomalies(file_path):
     # Read the dataset
     df = pd.read_csv(file_path)
     
     # Ensure all features exist in the dataframe
-    if not all(col in df.columns for col in feature_cols):
-        missing_cols = [col for col in feature_cols if col not in df.columns]
+    missing_cols = [col for col in feature_cols if col not in df.columns]
+    if missing_cols:
         return f"Missing columns in dataset: {missing_cols}"
     
+    # Align feature order with model training
+    df = df[feature_cols]
+    
     # Make predictions
-    df['is_anomalous'] = model.predict(df[feature_cols])
+    df['is_anomalous'] = model.predict(df)
     
     # Filter anomalous transactions
     anomalies = df[df['is_anomalous'] == 1][['transaction_id', 'merchant', 'location', 'amount']]