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Update app.py
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app.py
CHANGED
@@ -387,12 +387,16 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css, title="PEEB") as demo:
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gt_class = gr.State("")
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with gr.Column():
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title_text = gr.Markdown("#
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gr.Markdown(
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"""
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"""
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@@ -401,7 +405,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=custom_css, title="PEEB") as demo:
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gr.Markdown("## Select an image to start!")
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image_gallery = gr.Gallery(value=IMAGE_GALLERY, label=None, preview=False, allow_preview=False, columns=10, height=250)
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gr.Markdown("### Custom
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with gr.Row():
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with gr.Column():
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gt_class = gr.State("")
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with gr.Column():
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title_text = gr.Markdown("# A classifier with Part-based Explainable and Editable Bottleneck (PEEB) | Demo")
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gr.Markdown(
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"""
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Here is a PEEB classifier pre-trained on Bird-11K and finetuned on CUB-200 (see our [NAACL 2024 paper](https://arxiv.org/abs/2403.05297) and [code](https://github.com/anguyen8/peeb/tree/inspect_ddp)).\n The demo shows how one runs PEEB on an existing image and edit descriptors to modify the classifier (without any re-training).
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Steps:
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1. **Select an image**. Then, PEEB will show its grounded explanations and the top-1 predicted label with associated softmax confidence score.
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2. **Hover mouse over text descriptors** to see the corresponding region used to match to each text descriptor.
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3. **Edit the text under [Custom Descriptions]()** which correspond to one extra, new class (i.e. 200+1 = 201). Further editing will overwrite this class' descriptors.
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4. **Click on Predict** to see the grounded explanations and the top-1 label for the newly modified CUB-201 classifier.
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"""
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)
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gr.Markdown("## Select an image to start!")
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image_gallery = gr.Gallery(value=IMAGE_GALLERY, label=None, preview=False, allow_preview=False, columns=10, height=250)
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gr.Markdown("### Custom descriptions: \n The first row should be **class name: {some name};**, the name of your 201th class. \n For the 12 part descriptors, please use **;** to separate the descriptions for each part, and use the format **{part name}: {descriptions}**. \n Note: you can delete a row for some part (e.g. *nape*) completely and that part will be removed from all 201 classes in the classifier.")
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with gr.Row():
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with gr.Column():
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