Update app.py

app.py

@@ -277,12 +277,16 @@ def predict(image):
     # Postprocess output image
     
     annotated_img = output[0]
+
+    # Reshape the image to match the PIL Image input shape
+    annotated_img = annotated_img.reshape(640, 640, 3)
+    print("annotated_img shape after reshape:", annotated_img.shape)
     
     # annotated_img = (output[0] / 255.0 - mean)/std
     # 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_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))