Update app.py

app.py

@@ -118,6 +118,19 @@ class ForgeryDetector:
             if features.ndim == 1:
                 features = features.reshape(1, -1)
             
+            # TEMPORARY FIX: Pad features to match classifier's expected count
+            expected_features = 526
+            current_features = features.shape[1]
+            if current_features < expected_features:
+                # Pad with zeros
+                padding = np.zeros((features.shape[0], expected_features - current_features))
+                features = np.hstack([features, padding])
+                print(f"Warning: Padded features from {current_features} to {expected_features}")
+            elif current_features > expected_features:
+                # Truncate
+                features = features[:, :expected_features]
+                print(f"Warning: Truncated features from {current_features} to {expected_features}")
+            
             # Classify
             predictions, confidences = self.classifier.predict(features)
             forgery_type = int(predictions[0])