load breeds from model/config.json instead of hard-coded list

app.py

@@ -1,132 +1,18 @@
+import json
 import tensorflow as tf
 from huggingface_hub import hf_hub_download
 import gradio as gr
 
 tf_model = hf_hub_download(repo_id='mikachou/dog-breed-classifier', filename='tf_model.h5')
+config_json = hf_hub_download(repo_id='mikachou/dog-breed-classifier', filename='config.json')
 
 model = tf.keras.models.load_model(tf_model)
 print(model.summary())
 
-dogs_breeds = ['Chihuahua',
- 'Japanese spaniel',
- 'Maltese dog',
- 'Pekinese',
- 'Shih-Tzu',
- 'Blenheim spaniel',
- 'papillon',
- 'toy terrier',
- 'Rhodesian ridgeback',
- 'Afghan hound',
- 'basset',
- 'beagle',
- 'bloodhound',
- 'bluetick',
- 'black-and-tan coonhound',
- 'Walker hound',
- 'English foxhound',
- 'redbone',
- 'borzoi',
- 'Irish wolfhound',
- 'Italian greyhound',
- 'whippet',
- 'Ibizan hound',
- 'Norwegian elkhound',
- 'otterhound',
- 'Saluki',
- 'Scottish deerhound',
- 'Weimaraner',
- 'Staffordshire bullterrier',
- 'American Staffordshire terrier',
- 'Bedlington terrier',
- 'Border terrier',
- 'Kerry blue terrier',
- 'Irish terrier',
- 'Norfolk terrier',
- 'Norwich terrier',
- 'Yorkshire terrier',
- 'wire-haired fox terrier',
- 'Lakeland terrier',
- 'Sealyham terrier',
- 'Airedale',
- 'cairn',
- 'Australian terrier',
- 'Dandie Dinmont',
- 'Boston bull',
- 'miniature schnauzer',
- 'giant schnauzer',
- 'standard schnauzer',
- 'Scotch terrier',
- 'Tibetan terrier',
- 'silky terrier',
- 'soft-coated wheaten terrier',
- 'West Highland white terrier',
- 'Lhasa',
- 'flat-coated retriever',
- 'curly-coated retriever',
- 'golden retriever',
- 'Labrador retriever',
- 'Chesapeake Bay retriever',
- 'German short-haired pointer',
- 'vizsla',
- 'English setter',
- 'Irish setter',
- 'Gordon setter',
- 'Brittany spaniel',
- 'clumber',
- 'English springer',
- 'Welsh springer spaniel',
- 'cocker spaniel',
- 'Sussex spaniel',
- 'Irish water spaniel',
- 'kuvasz',
- 'schipperke',
- 'groenendael',
- 'malinois',
- 'briard',
- 'kelpie',
- 'komondor',
- 'Old English sheepdog',
- 'Shetland sheepdog',
- 'collie',
- 'Border collie',
- 'Bouvier des Flandres',
- 'Rottweiler',
- 'German shepherd',
- 'Doberman',
- 'miniature pinscher',
- 'Greater Swiss Mountain dog',
- 'Bernese mountain dog',
- 'Appenzeller',
- 'EntleBucher',
- 'boxer',
- 'bull mastiff',
- 'Tibetan mastiff',
- 'French bulldog',
- 'Great Dane',
- 'Saint Bernard',
- 'Eskimo dog',
- 'malamute',
- 'Siberian husky',
- 'affenpinscher',
- 'basenji',
- 'pug',
- 'Leonberg',
- 'Newfoundland',
- 'Great Pyrenees',
- 'Samoyed',
- 'Pomeranian',
- 'chow',
- 'keeshond',
- 'Brabancon griffon',
- 'Pembroke',
- 'Cardigan',
- 'toy poodle',
- 'miniature poodle',
- 'standard poodle',
- 'Mexican hairless',
- 'dingo',
- 'dhole',
- 'African hunting dog']
+with open(config_json) as f:
+    config = json.load(f)
+
+dogs_breeds = list(config['id2label'].values())
 
 def predict(filepath):
     img = tf.io.read_file(filepath)