Added ALL THE BIRDS classes to the list

app.py

@@ -61,9 +61,18 @@ def classify_image(image):
     classification = labels[predicted.item()]
 
     # Check if the predicted class is a bird
-    bird_categories = ['bird', 'fowl', 'hen', 'cock', 'rooster', 'peacock', 'parrot', 'eagle', 'owl', 'penguin']
+    bird_categories = [
+        'cock', 'hen', 'ostrich', 'brambling', 'goldfinch', 'house finch', 'junco', 'indigo bunting', 'robin',
+        'bulbul', 'jay', 'magpie', 'chickadee', 'water ouzel', 'kite', 'bald eagle', 'vulture', 'great grey owl',
+        'European fire salamander', 'ptarmigan', 'ruffed grouse', 'prairie chicken', 'peacock', 'quail', 'partridge',
+        'African grey', 'macaw', 'sulphur-crested cockatoo', 'lorikeet', 'coucal', 'bee eater', 'hornbill',
+        'hummingbird', 'jacamar', 'toucan', 'drake', 'red-breasted merganser', 'goose', 'black swan', 'white stork',
+        'black stork', 'spoonbill', 'flamingo', 'little blue heron', 'American egret', 'bittern', 'crane', 'limpkin',
+        'European gallinule', 'American coot', 'bustard', 'ruddy turnstone', 'red-backed sandpiper', 'redshank',
+        'dowitcher', 'oystercatcher', 'pelican', 'king penguin', 'albatross'
+    ]
     is_bird = ('bird' in classification.lower()) or any(category in classification.lower() for category in bird_categories)
-
+# 
     # Get the confidence score
     confidence_score = torch.nn.functional.softmax(output[0], dim=0)[predicted].item()
     confidence_percentage = f"{confidence_score:.2%}"