minor changes

model/pothole_model.py

@@ -34,39 +34,28 @@ def load_image_model(image):
     np.set_printoptions(suppress=True)
 
     # Load the model from the URL
-    model_url = "https://huggingface.co/spaces/Soham0708/pothole_detect/blob/main/keras_model.h5"
-    response = requests.get(model_url)
-    response.raise_for_status()  # Raise an exception if the download fails
-    model_data = BytesIO(response.content)
+    model_url = "https://huggingface.co/spaces/Soham0708/pothole_detect/resolve/main/keras_model.h5"
+    model_path = tf.keras.utils.get_file("keras_model.h5", model_url)
 
-    # Load the model from the in-memory bytes
-    model = tf.keras.models.load_model(model_data)
+    # Load the model
+    model = tf.keras.models.load_model(model_path)
 
     # Load the labels
     class_names = open("labels.txt", "r").readlines()
 
     # Create the array of the right shape to feed into the keras model
-    # The 'length' or number of images you can put into the array is
-    # determined by the first position in the shape tuple, in this case 1
     data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
 
     # Replace this with the path to your image
     image = Image.open(image).convert("RGB")
 
-    # resizing the image to be at least 224x224 and then cropping from the center
-    size = (224, 224)
-    image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
-
-    # turn the image into a numpy array
+    # Resize and preprocess the image
+    image = ImageOps.fit(image, (224, 224), Image.ANTIALIAS)
     image_array = np.asarray(image)
-
-    # Normalize the image
     normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
-
-    # Load the image into the array
     data[0] = normalized_image_array
 
-    # Predicts the model
+    # Make prediction
     prediction = model.predict(data)
     index = np.argmax(prediction)
     class_name = class_names[index]