add model transforms

app.py

@@ -12,10 +12,11 @@ categories = ("Aculus Olearius", "Healthy", "Peacock Spot")
 
 
 def classify_health(input_img):
-    input_img = torch.from_numpy(input_img)
-    image = transforms(input_img).unsqueeze(0) 
-    probs = model(image)
-    idx = probs.argmax()
+    input_img = transforms.ToTensor()(input_img)
+    with torch.no_grad():
+        image = transforms(input_img).unsqueeze(0) 
+        probs = model(image)
+        idx = probs.argmax()
     return dict(zip(categories, map(float, probs)))