Create app.py

app.py

@@ -28,20 +28,19 @@ cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
 cfg.MODEL.ROI_HEADS.NUM_CLASSES = 19
 cfg.MODEL.WEIGHTS = model_path
 
+
 if not torch.cuda.is_available():
     cfg.MODEL.DEVICE='cpu'
     
 predictor = DefaultPredictor(cfg)
 my_metadata = MetadataCatalog.get("car_parts_dataset_val")
-my_metadata.thing_classes = ["_background_","back_bumper","back_glass","back_left_door","back_left_light","back_right_door","back_right_light","front_bumper","front_glass","front_left_door","front_left_light","front_right_door","front_right_light","hood","left_mirror","right_mirror","tailgate","trunk","wheel"]
 
 def inference(image):
     print(image.height)
 
     height = image.height
 
-    #img = np.array(image.resize((500, height)))
-    
+    # img = np.array(image.resize((500, height)))
     img = np.array(image)
     outputs = predictor(img)
     v = Visualizer(img[:, :, ::-1],