Update app.py
Browse files
app.py
CHANGED
@@ -62,13 +62,6 @@ demo = gr.Blocks()
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with demo:
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gr.Markdown("# π WebApp1K Models Leaderboard")
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model_input = gr.Dropdown(choices=models, label="Select Model")
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scenario_input = gr.Dropdown(choices=scenarios, label="Select Category")
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output = gr.DataFrame(headers=["pass@1", "pass@5", "pass@10"])
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filter_button = gr.Button("Filter")
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filter_button.click(filter_data, inputs=[model_input, scenario_input], outputs=output)
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# Initialize leaderboard with the complete DataFrame
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complete_pass_at_k = df.groupby('Model')[['Runs', 'Successes']].apply(lambda x: pd.Series({
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'pass@1': estimate_pass_at_k(x['Runs'].values, x['Successes'].values, 1).mean(),
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@@ -78,5 +71,12 @@ with demo:
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leaderboard = init_leaderboard(complete_pass_at_k)
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# Launch the Gradio interface
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demo.launch()
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with demo:
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gr.Markdown("# π WebApp1K Models Leaderboard")
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# Initialize leaderboard with the complete DataFrame
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complete_pass_at_k = df.groupby('Model')[['Runs', 'Successes']].apply(lambda x: pd.Series({
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'pass@1': estimate_pass_at_k(x['Runs'].values, x['Successes'].values, 1).mean(),
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leaderboard = init_leaderboard(complete_pass_at_k)
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model_input = gr.Dropdown(choices=models, label="Select Model")
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scenario_input = gr.Dropdown(choices=scenarios, label="Select Category")
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output = gr.DataFrame(headers=["pass@1", "pass@5", "pass@10"])
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filter_button = gr.Button("Filter")
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filter_button.click(filter_data, inputs=[model_input, scenario_input], outputs=output)
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# Launch the Gradio interface
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demo.launch()
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