Update app.py

app.py

@@ -13,12 +13,10 @@ class_labels = joblib.load("class_labels.pkl")
 feature_extractor = VGG16(weights="imagenet", include_top=False, input_shape=(224, 224, 3))
 
 def extract_features(image):
-    """Extracts features using the same model used during training (VGG16 in this case)."""
-    image = cv2.resize(image, (224, 224))  # Resize image to 224x224
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
-    image = preprocess_input(image)  # Apply VGG16 preprocessing
-    features = feature_extractor.predict(image)
-    return features.flatten().reshape(1, -1)  # Reshape to match model input
+    """Manually reshape the image into the expected feature vector size."""
+    image = cv2.resize(image, (64, 32))  # Resize to the shape used in training
+    features = image.flatten().reshape(1, -1)  # Flatten to match model input size
+    return features
 
 def predict_animal(image):
     """Preprocess the image and make a prediction."""