Update app.py

app.py

@@ -13,18 +13,18 @@ model = tf.keras.models.load_model(path_to_model)
 IMG_HEIGHT = 180
 IMG_WIDTH = 180
 
+# Define a function to preprocess the image
 # Define a function to preprocess the image
 def preprocess_image(image):
     # Resize the image to the expected input size of the model
     img = image.resize((IMG_HEIGHT, IMG_WIDTH))
-    # Convert the image to a numpy array
-    img_array = img_to_array(img)
+    # Reshape the image array to match the input shape of the model
+    img_array = np.array(img).reshape((1, IMG_HEIGHT, IMG_WIDTH, 3))
     # Normalize the pixel values
     img_array = img_array / 255.0
-    # Reshape the image array to match the input shape of the model
-    img_array = np.reshape(img_array, (1, IMG_HEIGHT, IMG_WIDTH, 3))
     return img_array
 
+
 # Create the Streamlit app
 def main():
     # Set the title and a brief description