update

README.md

@@ -1,46 +1,13 @@
 ---
 title: Uniformer_image_demo
-emoji: 🏢
-colorFrom: blue
-colorTo: indigo
+emoji: 📷
+colorFrom: pink
+colorTo: green
 sdk: gradio
+sdk_version: 3.0.3
 app_file: app.py
 pinned: false
 license: mit
 ---
 
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

@@ -49,17 +49,39 @@ def inference(img):
     prediction = F.softmax(prediction, dim=1).flatten()
 
     return {imagenet_id_to_classname[str(i)]: float(prediction[i]) for i in range(1000)}
-    
 
-inputs = gr.inputs.Image(type='pil')
-label = gr.outputs.Label(num_top_classes=5)
+def set_example_image(example: list) -> dict:
+    return gr.Image.update(value=example[0])
+
+
+demo = gr.Blocks()
+with demo:
+    gr.Markdown(
+        """
+        # UniFormer-S
+        Gradio demo for <a href='https://github.com/Sense-X/UniFormer' target='_blank'>UniFormer</a>: To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.
+        """
+    )
+
+    with gr.Box():
+        with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        input_image = gr.Image(label='Input Image', type='pil')
+                    with gr.Row():
+                        submit_button = gr.Button('Submit')
+                with gr.Column():
+                    label = gr.Label(num_top_classes=5)
+        with gr.Row():
+            example_images = gr.Dataset(components=[input_image], samples=[['library.jpeg'], ['cat.png'], ['dog.png'], ['panda.png']])
+
+    gr.Markdown(
+        """
+        <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>
+        """
+    )
 
-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>"
+    submit_button.click(fn=inference, inputs=input_image, outputs=label)
+    example_images.click(fn=set_example_image, inputs=example_images, outputs=example_images.components)
 
-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)