Update app.py

app.py

@@ -26,8 +26,6 @@ model = model.eval()
 imagenet_id_to_classname = {}
 for k, v in imagenet_classnames.items():
     imagenet_id_to_classname[k] = v[1] 
-    
-os.system("wget https://upload.wikimedia.org/wikipedia/commons/thumb/c/c5/13-11-02-olb-by-RalfR-03.jpg/800px-13-11-02-olb-by-RalfR-03.jpg -O library.jpg")
 
 def inference(img):
     image = img
@@ -47,13 +45,6 @@ def inference(img):
     prediction = model(image)
     prediction = F.softmax(prediction, dim=1)
 
-    # pred_classes = prediction.topk(k=5).indices
-    # pred_class_names = [imagenet_id_to_classname[str(i.item())] for i in pred_classes[0]]
-    # pred_class_probs = [prediction[0][i.item()].item() * 100 for i in pred_classes[0]]
-    # res = "Top 5 predicted labels:\n"
-    # for name, prob in zip(pred_class_names, pred_class_probs):
-    #     res += f"[{prob:2.2f}%]\t{name}\n"
-
     return {imagenet_id_to_classname[str(i)]: float(prediction[0][i]) for i in range(1000)}
     
 inputs = gr.inputs.Image(type='pil')