update

app.py

@@ -48,15 +48,6 @@ def inference(img):
     prediction = model(image)
     prediction = F.softmax(prediction, dim=1).flatten()
 
-    # 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 res
-
     return {imagenet_id_to_classname[str(i)]: float(prediction[i]) for i in range(1000)}
 
 def set_example_image(example: list) -> dict:
@@ -76,7 +67,7 @@ with demo:
         with gr.Row():
                 with gr.Column():
                     with gr.Row():
-                        input_image = gr.Image(label='Input Image', type='numpy')
+                        input_image = gr.Image(label='Input Image', type='pil')
                     with gr.Row():
                         submit_button = gr.Button('Submit')
                 with gr.Column():
@@ -93,19 +84,4 @@ with demo:
     submit_button.click(fn=inference, inputs=input_image, outputs=label)
     example_images.click(fn=set_example_image, inputs=example_images, outputs=example_images.components)
 
-demo.launch(enable_queue=True)
-
-
-
-# inputs = gr.inputs.Image(type='pil')
-# label = gr.outputs.Label(num_top_classes=5)
-
-# title = "UniFormer-S"
-# description = "Gradio demo for UniFormer: To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-# article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.09450' target='_blank'>UniFormer: Unifying Convolution and Self-attention for Visual Recognition</a> | <a href='https://github.com/Sense-X/UniFormer' target='_blank'>Github Repo</a></p>"
-
-# gr.Interface(
-#     inference, inputs, outputs=label, 
-#     title=title, description=description, article=article, 
-#     examples=[['library.jpeg'], ['cat.png'], ['dog.png'], ['panda.png']]
-#     ).launch(enable_queue=True, cache_examples=True)
+demo.launch(enable_queue=True)