Update app.py

app.py

@@ -252,7 +252,7 @@ def predict(image):
     # Reshape the image to match the model's input shape
     image = image.reshape(3, 640, 640)
 
-    #  Normalize output image using ImageNet-style normalization
+    # Normalize output image using ImageNet-style normalization
     mean = [0.485, 0.456, 0.406]
     std = [0.229, 0.224, 0.225]
     mean = np.expand_dims(mean, axis=(1,2))
@@ -279,6 +279,7 @@ def predict(image):
     annotated_img = output[0]
 
     # Reshape the image to match the PIL Image input shape
+    # print("annotated_img shape before reshape:", annotated_img.shape)
     annotated_img = annotated_img.reshape(640, 640, 3)
     print("annotated_img shape after reshape:", annotated_img.shape)
     
@@ -286,7 +287,6 @@ def predict(image):
     # annotated_img = classes[output[0][0].argmax(0)]
     
     print("Annotated image type before normalization:", type(annotated_img))
-    # print("annotated_img shape before normalization:", annotated_img.shape)
     # print("Annotated image before normalization:", annotated_img)
     print("Min value of image before normalization:", np.min(annotated_img))
     print("Max value of image before normalization:", np.max(annotated_img))