clean up class result

app.py

@@ -20,8 +20,6 @@ def predict(img: np.ndarray) -> str:
     # input: numpy array of image in RGB (see defaults for https://www.gradio.app/docs/#image)
     
     # The MobileNet model expects images in RGB format.
-    #image = cv2.cvtColor(img, code=cv2.COLOR_BGR2RGB)
-    #image = cv2.cvtColor(img, code=cv2.COLOR_BGR2RGB)
     # Resize to MobileNet image shape.
     input_image = cv2.resize(src=img, dsize=(224, 224))
     # Reshape to model input shape.
@@ -38,6 +36,8 @@ def predict(img: np.ndarray) -> str:
     # Therefore, a background must be added at the beginning of imagenet_classes.
     imagenet_classes = ['background'] + imagenet_classes
     best_class = imagenet_classes[result_index]
+    # clean up
+    best_class = best_class.partition(' ')[2])
     # TODO: get n best results with corresponding probabilities?
     return best_class