Upload app.py

app.py

@@ -166,6 +166,8 @@ classes = ['antelope',
  'tiger',
  'zebra']
 
+
+
 def predict(img):
     # Add preprocessing and fix inference mode
     model.eval()
@@ -179,15 +181,18 @@ def predict(img):
     ])
     
     img_tensor = preprocess(img).unsqueeze(0)  # Fix spelling of unsqueeze
+
     with torch.inference_mode():  # Add parentheses
         logits = model(img_tensor)
         preds = logits.argmax(dim=1)
     return classes[preds.item()]
 
+
+
 """gradio interface"""
 demo = gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="pil", shape=(224, 224)),  # Input component
+    inputs=gr.Image(type="pil", width=244, height=244),  # Input component
     outputs="label",  # Output component
     title="Animal Classifier",
     description="Classify images into 30 animal categories"