Update app.py

app.py

@@ -9,8 +9,23 @@ utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_
 efficientnet.eval().to(device)
 
 def inference(img):
+
+  img_transforms = transforms.Compose(
+                [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor()]
+            )
+
+  img = img_transforms(img)
+  with torch.no_grad():
+    # mean and std are not multiplied by 255 as they are in training script
+    # torch dataloader reads data into bytes whereas loading directly
+    # through PIL creates a tensor with floats in [0,1] range
+    mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
+    std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
+    img = img.float()
+    img = img.unsqueeze(0).sub_(mean).div_(std)
+
   batch = torch.cat(
-    [utils.prepare_input_from_uri(img)]
+    [img]
   ).to(device)
   with torch.no_grad():
       output = torch.nn.functional.softmax(efficientnet(batch), dim=1)