minor changes

model/pothole_model.py

@@ -23,22 +23,30 @@
 #     return predicted_class
 
 
-from keras.models import load_model  # TensorFlow is required for Keras to work
-from PIL import Image, ImageOps  # Install pillow instead of PIL
+
+from keras.models import load_model
+from PIL import Image, ImageOps
 import numpy as np
-from io import BytesIO
 import requests
-
+import tempfile
+import os
 
 def load_image_model(image):
     # Disable scientific notation for clarity
     np.set_printoptions(suppress=True)
 
-    # Load the model
+    # 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 = load_model(BytesIO(response.content))
+    
+    # Save the model to a temporary file
+    with tempfile.NamedTemporaryFile(suffix=".h5", delete=False) as tmp_file:
+        tmp_file.write(response.content)
+        tmp_file_path = tmp_file.name
+
+    # Load the model from the temporary file
+    model = load_model(tmp_file_path)
 
     # Load the labels
     class_names = open("labels.txt", "r").readlines()
@@ -49,7 +57,7 @@ def load_image_model(image):
     data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
 
     # Replace this with the path to your image
-    image = Image.open(BytesIO(image)).convert("RGB")
+    image = Image.open(by(image)).convert("RGB")
 
     # resizing the image to be at least 224x224 and then cropping from the center
     size = (224, 224)
@@ -74,3 +82,5 @@ def load_image_model(image):
     print("Class:", class_name[2:], end="")
     print("Confidence Score:", confidence_score)
 
+    # Clean up temporary file
+    os.remove(tmp_file_path)