Update app.py

app.py

@@ -22,7 +22,7 @@ model.eval()
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
-#Transforms the images to how they were tested for the model to read for inference. Keep Exactly the same as the transformation for the test and valid sets. No randomizing here!
+# Transforms the images to how they were tested for the model to read for inference. Keep Exactly the same as the transformation for the test and valid sets. No randomizing here!
 transforms_test = v2.Compose([
     v2.Resize((224, 224), antialias=True),
     v2.CenterCrop((224, 224)),
@@ -33,11 +33,12 @@ transforms_test = v2.Compose([
 
 labels = ['Afghan-Hound', 'Airedale-Terrier', 'Akita', 'Alaskan-Malamute', 'American-Foxhound', 'American-Hairless-Terrier', 'American-Water-Spaniel', 'Basenji', 'Basset-Hound', 'Beagle', 'Bearded-Collie', 'Belgian-Malinois', 'Belgian-Sheepdog', 'Bernese-Mountain-Dog', 'Bichon-Frise', 'Bloodhound', 'Bluetick-Coonhound', 'Border-Collie', 'Borzoi', 'Boston-Terrier', 'Boxer', 'Bull-Terrier', 'Bulldog', 'Bullmastiff', 'Cairn-Terrier', 'Cane-Corso', 'Cavalier-King-Charles-Spaniel', 'Chihuahua', 'Chinese-Crested', 'Chinese-Shar-Pei', 'Chow-Chow', 'Clumber-Spaniel', 'Cockapoo', 'Cocker-Spaniel', 'Collie', 'Dachshund', 'Dalmatian', 'Doberman-Pinscher', 'French-Bulldog', 'German-Shepherd', 'German-Shorthaired-Pointer', 'Golden-Retriever', 'Great-Dane', 'Great-Pyrenees', 'Greyhound', 'Irish-Water-Spaniel', 'Irish-Wolfhound', 'Japanese-Chin', 'Komondor', 'Labradoodle', 'Labrador-Retriever', 'Lhasa-Apso', 'Maltese', 'Miniature-Schnauzer', 'Newfoundland', 'Norwegian-Elkhound', 'Pekingese', 'Pembroke-Welsh-Corgi', 'Pomeranian', 'Poodle', 'Pug', 'Rhodesian-Ridgeback', 'Rottweiler', 'Saint-Bernard', 'Samoyed', 'Scottish-Terrier', 'Shiba-Inu', 'Shih-Tzu', 'Siberian-Husky', 'Staffordshire-Bull-Terrier', 'Vizsla', 'Xoloitzcuintli', 'Yorkshire-Terrier']
 
-#I have the breed_nicknames dictionary set because some breeds arent recognized that much by the official breed, such as the ones below.
+# I have the breed_nicknames dictionary set because some breeds arent recognized that much by the official breed, such as the ones below.
 breed_nicknames = {
     'Xoloitzcuintli': ' (Mexican Hairless)',
     'Staffordshire-Bull-Terrier': ' (Pitbull)',
-    'Pembroke-Welsh-Corgi': ' (Corgi)',}
+    'Pembroke-Welsh-Corgi': ' (Corgi)',
+}
 
 def predict(input_img):
     transformed_img = transforms_test(input_img)