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Update app.py
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app.py
CHANGED
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@@ -164,7 +164,7 @@ with gr.Blocks(title="Binding Challenges") as app:
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with gr.Tab("Antibody–Antigen Binding Challenge"):
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gr.Markdown("""
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# Antibody-Antigen Binding Challenge
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The goal of this challenge is to improve Boltz-2 accuracy for predicting the correct poses of a
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Accuracy will be measured through the Capri-Q docking assessment classification scores and the final winner will be determined based on the number of successful top-1 predictions on our *internal* test set. However, you are encouraged to submit results on the training set during the hack to see where you stack up.\n
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A prediction is deemed successful if the Capri-Q classification is either "high", "medium", or "acceptable".
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If multiple entries reach the same number of successful predictions, ties are broken by looking at the number of predictions with "High" classification, then with "Medium" classification and finally with "Acceptable" classification.
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@@ -222,7 +222,7 @@ with gr.Blocks(title="Binding Challenges") as app:
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)
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li_refresh.click(refresh_ligand, outputs=li_df)
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with gr.Tab("Final Submission"):
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fs_group = gr.Textbox(label="Group Name", placeholder="Your group name")
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fs_repo_url = gr.Textbox(label="Repository URL")
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fs_commit_sha = gr.Textbox(label="Commit SHA")
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@@ -245,7 +245,7 @@ with gr.Blocks(title="Binding Challenges") as app:
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inputs=[fs_group, fs_repo_url, fs_commit_sha, fs_challenge_type, fs_description_link],
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outputs=fs_df,
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)
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fs_refresh.click(refresh_final, outputs=fs_df)
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# For local dev: `python app.py`
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if __name__ == "__main__":
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with gr.Tab("Antibody–Antigen Binding Challenge"):
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gr.Markdown("""
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# Antibody-Antigen Binding Challenge
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+
The goal of this challenge is to improve Boltz-2 accuracy for predicting the correct poses of a Fv/Fab region binding to an antigen.\n
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Accuracy will be measured through the Capri-Q docking assessment classification scores and the final winner will be determined based on the number of successful top-1 predictions on our *internal* test set. However, you are encouraged to submit results on the training set during the hack to see where you stack up.\n
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A prediction is deemed successful if the Capri-Q classification is either "high", "medium", or "acceptable".
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If multiple entries reach the same number of successful predictions, ties are broken by looking at the number of predictions with "High" classification, then with "Medium" classification and finally with "Acceptable" classification.
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)
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li_refresh.click(refresh_ligand, outputs=li_df)
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'''with gr.Tab("Final Submission"):
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fs_group = gr.Textbox(label="Group Name", placeholder="Your group name")
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fs_repo_url = gr.Textbox(label="Repository URL")
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fs_commit_sha = gr.Textbox(label="Commit SHA")
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inputs=[fs_group, fs_repo_url, fs_commit_sha, fs_challenge_type, fs_description_link],
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outputs=fs_df,
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)
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fs_refresh.click(refresh_final, outputs=fs_df)'''
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# For local dev: `python app.py`
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if __name__ == "__main__":
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