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update docs

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  1. app.py +12 -4
app.py CHANGED
@@ -223,8 +223,6 @@ if __name__ == "__main__":
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  gr.Markdown(
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  """
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  # Generative Augmented Image Classifiers
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- This demo showcases the performance of image classifiers trained on various datasets as part of the project `Investigating the Effectiveness of Generative Diffusion Models in Synthesizing Images for Data Augmentation in Image Classification' dissertation.
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-
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  Main GitHub Repo: [Generative Data Augmentation](https://github.com/zhulinchng/generative-data-augmentation) | Generative Data Augmentation Demo: [Generative Data Augmented](https://huggingface.co/spaces/czl/generative-data-augmentation-demo).
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  """
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  )
@@ -267,9 +265,10 @@ Main GitHub Repo: [Generative Data Augmentation](https://github.com/zhulinchng/g
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  inputs=[methods, dataset_type],
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  outputs=[training_ds],
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  )
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- generate_button = gr.Button("Sample Random Image")
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  random_image_output = gr.Image(type="pil", label="Image to Classify")
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- classify_button_random = gr.Button("Classify")
 
 
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  with gr.Column():
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  output_label_random = gr.Label(num_top_classes=5)
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  download_model = gr.DownloadButton(
@@ -298,6 +297,15 @@ Main GitHub Repo: [Generative Data Augmentation](https://github.com/zhulinchng/g
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  inputs=[dataset_type, arch_type, methods, training_ds],
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  outputs=[download_model],
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  )
 
 
 
 
 
 
 
 
 
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  generate_button.click(
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  get_random_image,
 
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  gr.Markdown(
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  """
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  # Generative Augmented Image Classifiers
 
 
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  Main GitHub Repo: [Generative Data Augmentation](https://github.com/zhulinchng/generative-data-augmentation) | Generative Data Augmentation Demo: [Generative Data Augmented](https://huggingface.co/spaces/czl/generative-data-augmentation-demo).
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  """
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  )
 
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  inputs=[methods, dataset_type],
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  outputs=[training_ds],
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  )
 
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  random_image_output = gr.Image(type="pil", label="Image to Classify")
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+ with gr.Row():
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+ generate_button = gr.Button("Sample Random Image")
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+ classify_button_random = gr.Button("Classify")
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  with gr.Column():
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  output_label_random = gr.Label(num_top_classes=5)
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  download_model = gr.DownloadButton(
 
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  inputs=[dataset_type, arch_type, methods, training_ds],
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  outputs=[download_model],
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  )
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+ gr.Markdown(
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+ """
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+ This demo showcases the performance of image classifiers trained on various datasets as part of the project 'Investigating the Effectiveness of Generative Diffusion Models in Synthesizing Images for Data Augmentation in Image Classification' dissertation.
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+
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+ View the models and files used in this demo [here](https://huggingface.co/spaces/czl/generative-augmented-classifiers/tree/main).
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+
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+ Usage Instructions & Documentation [here](https://huggingface.co/spaces/czl/generative-augmented-classifiers/blob/main/README.md).
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+ """
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+ )
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  generate_button.click(
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  get_random_image,