Update app.py

app.py

@@ -13,25 +13,30 @@ import pathlib
 temp = pathlib.WindowsPath
 pathlib.WindowsPath = pathlib.PosixPath
 
-model_dir = "models/parrot-recognizer-v3.pkl"
+model_dir = "models/parrot-recognizer-v10.pkl"
 
 model = load_learner(model_dir)
 
 parrot_species = ['african grey parrot',
  'australian king parrot',
+ 'australian night parrot',
+ 'bare eyed cockatoo',
+ 'blue and yellow macaw',
+ 'blue headed parrot',
  'blue lorikeet',
- 'blue-and-yellow macaw',
- 'blue-headed parrot',
+ 'brown hooded parrot',
  'budgerigar',
  'burrowing parrot',
  'caique parrot',
  'catalina macaw',
  'chestnut-fronted macaw',
+ 'citron cockatoo',
  'cockatiels',
  'crimson rosella',
  'cuban amazon',
  'eclectus parrot',
- 'galah',
+ 'galah cockatoo',
+ 'gang gang cockatoo',
  'golden parakeet',
  'great green macaw',
  'great hanging parrot',
@@ -42,31 +47,36 @@ parrot_species = ['african grey parrot',
  'kea',
  'kākāpō',
  'lovebirds',
+ 'major mitchell_s cockatoo',
  'monk parakeet',
- 'orange-winged amazon',
+ 'musk lorikeet',
  'palm cockatoo',
  'parrotlet',
- 'plum-headed parakeet',
+ 'plum headed parakeet',
  'puerto rican amazon',
  'rainbow lorikeet',
- 'red-breasted parakeet',
- 'red-crowned amazon',
- 'red-crowned parakeet',
- 'red-fan parrot',
- 'red-shouldered macaw',
- 'red-tailed black cockatoos',
- 'rose-ringed parakeet',
+ 'red breasted parakeet',
+ 'red crowned amazon',
+ 'red crowned parakeet',
+ 'red fan parrot',
+ 'red lory',
+ 'red rumped parrot',
+ 'red shouldered macaw',
+ 'red tailed black cockatoos',
+ 'rose ringed parakeet',
  'saint vincent amazon',
+ 'salmon crested cockatoo',
  'scarlet macaw',
  'senegal parrot',
  'spixs macaw',
+ 'sulpher crested cockatoo',
  'sun conure',
- 'thick-billed parrot',
- 'turquoise-fronted amazon',
+ 'thick billed parrot',
+ 'turquoise fronted amazon',
+ 'umbrella cockatoo',
  'vernal hanging parrot',
- 'white cockatoo',
- 'yellow-collared macaws',
- 'yellow-headed amazon']
+ 'yellow collared macaws',
+ 'yellow headed amazon']
 
 def recognize_image(image):
   pred, idx, probs = model.predict(image)
@@ -101,6 +111,11 @@ examples = [
     "test_images/unknown_12.jpg",
     "test_images/unknown_13.jpg",
     "test_images/unknown_14.jpg",
+    "test_images/unknown_15.jpg",
+    "test_images/unknown_16.jpg",
+    "test_images/unknown_17.jpg",
+    "test_images/unknown_18.jpg",
+    "test_images/unknown_19.jpg",
     ]
 
 iface = gr.Interface(fn=recognize_image, inputs=image, outputs=label, examples=examples)