Update app.py

app.py

@@ -68,33 +68,16 @@ carTransforms = transforms.Compose([transforms.Resize(224),
 
 def classifyCar(im):
   #im = Image.fromarray(im.astype('uint8'), 'RGB')
-  try:
-    im = cv2.imread(im)
-  except:
-    return im, {"error0": im.shape}
-  try:
-    with torch.no_grad():
-      outputs = predictor(im)
-  except:
-    return im, {"error1": 0.5}
-  try:
-      v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
-  except Exception as err:
-    return im, {"error2": 0.5}
-  try:
-      out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
-  except Exception as err:
-    return im, {"error3": 0.5}
-  try:
-    im2 = carTransforms(im).unsqueeze(0)  # transform and add batch dimension
-  except:
-    return Image.fromarray(np.uint8(out.get_image())).convert('RGB'), {"error4": 0.5}
-  try:
-    with torch.no_grad():
-      scores = torch.nn.functional.softmax(DesignModernityModel(im2)[0])
-  except:
-    return Image.fromarray(np.uint8(out.get_image())).convert('RGB'), {"error5": 0.5}
-  return Image.fromarray(np.uint8(out.get_image())).convert('RGB'), {LABELS[i]: float(scores[i]) for i in range(n_labels)}
+  im = cv2.imread(im)
+  with torch.no_grad():
+    outputs = predictor(im)
+  v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
+  out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+    #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)}
+  return Image.fromarray(np.uint8(out.get_image())).convert('RGB'), "test"
 
 #examples = [[example_img.jpg], [example_img2.jpg]]  # must be uploaded in repo