Update app.py

app.py

@@ -67,17 +67,23 @@ carTransforms = transforms.Compose([transforms.Resize(224),
                                     transforms.Normalize(mean=MEAN, std=STD)])
 
 def classifyCar(im):
-  #im = Image.fromarray(im.astype('uint8'), 'RGB')
+  try:
   im = cv2.imread(im)
-  outputs = predictor(im)
-  v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
-  out = v.draw_instance_predictions(outputs["instances"])
+  except:
+    label = "fail"
+  try:
+    outputs = predictor(im)
+    v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
+    out = v.draw_instance_predictions(outputs["instances"])
+    label = "success"
+  except:
+    label = "fail2"
     #im2 = carTransforms(im).unsqueeze(0)  # transform and add batch dimension
     #with torch.no_grad():
     #  scores = torch.nn.functional.softmax(DesignModernityModel(im2)[0])
       #{LABELS[i]: float(scores[i]) for i in range(n_labels)}
       #Image.fromarray(np.uint8(out.get_image())).convert('RGB')
-  return im, "test"
+  return label
 
 #examples = [[example_img.jpg], [example_img2.jpg]]  # must be uploaded in repo