trans on

app.py

@@ -33,17 +33,19 @@ model = model.to('cpu')
 
 
 transform = transforms.Compose(
-    [transforms.ToPILImage(),transforms.Resize((128, 128)), transforms.ToTensor()]
+    [transforms.Resize((128, 128)), transforms.ToTensor()]
 )
 
 
 
 def predict(img):
-    # img = transform(img).to('cpu')
+    img = transform(img).to('cpu')
     # img = transform(img).unsqueeze(0)
     with torch.no_grad():
         out=  model(img)
     probability = torch.nn.functional.softmax(out[0],dim=0)
+    print(out)
+    print(type(img))
 
     values, indices = torch.topk(probability,k=2)
     return {LABELS[i]: v.item() for i,v in zip(indices,values)}