Update app.py

app.py

@@ -244,32 +244,27 @@ def predict(image):
     input_name = model.get_inputs()[0].name
     input_shape = model.get_inputs()[0].shape
 
-    print("start", image.shape) #start (720, 1280, 3)
-
-
     # Resize the image to the model's input shape
     image = cv2.resize(image, (input_shape[2], input_shape[3]))
-    
-    print("after resize", image.shape) # after resize (640, 640, 3)
-
     image = image.reshape(3, 640, 640)
 
-    print("after reshape", image.shape) # after reshape
-
     # Convert the image to a numpy array and add a batch dimension
     if len(input_shape) == 4 and input_shape[0] == 1:
         image = np.expand_dims(image, axis=0)
     
     image = image.astype(np.float32) # after expands/astype (1, 640, 640, 3)
 
-    print("after expands/astype", image.shape)
-
+    # Normalize the image
+    mean = [0.485, 0.456, 0.406]
+    std = [0.229, 0.224, 0.225]
+    image = (image / 255.0 - mean)/std
+    
     # Perform inference
     output = model.run(None, {input_name: image})
-    print(type(output))
-    print(output)
+    # print(type(output))
+    # print(output)
 
-    return image
+    return output
     
 # Gradio interface
 demo = gr.Interface(