Upload app.py

app.py

@@ -180,9 +180,9 @@ def predict(img):
         transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
     ])
     
-    img_tensor = preprocess(img).unsqueeze(0)  # Fix spelling of unsqueeze
+    img_tensor = preprocess(img).unsqueeze(0)  
 
-    with torch.inference_mode():  # Add parentheses
+    with torch.inference_mode():  
         logits = model(img_tensor)
         preds = logits.argmax(dim=1)
     return classes[preds.item()]
@@ -192,10 +192,10 @@ def predict(img):
 """gradio interface"""
 demo = gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="pil", width=244, height=244),  # Input component
-    outputs="label",  # Output component
+    inputs=gr.Image(type="pil", width=244, height=244),  
+    outputs="label", 
     title="Animal Classifier",
-    description="Classify images into 30 animal categories"
+    description="Classify 30 animal categories: antelope, buffalo, chimpanzee, cow, deer, dolphin, elephant, fox, giant+panda, giraffe, gorilla, grizzly+bear, hamster, hippopotamus, horse, humpback+whale, leopard, lion, moose, otter, ox, pig, polar+bear, rabbit, rhinoceros, seal, sheep, squirrel, tiger, zebra"
 )
 
 """launch interface"""