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Update app.py
Browse files
app.py
CHANGED
@@ -45,6 +45,18 @@ def refresh_data():
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benchmark_types = ["similarity", "function", "family", "affinity"]
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download_from_hub(benchmark_types)
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block = gr.Blocks()
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with block:
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@@ -59,13 +71,27 @@ with block:
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metric_names = leaderboard.columns.tolist()
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metrics_with_method = metric_names.copy()
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metric_names.remove('Method') # Remove method_name from the metric options
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# Leaderboard section with method and metric selectors
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leaderboard_method_selector = gr.CheckboxGroup(
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choices=method_names, label="Select Methods for the Leaderboard", value=method_names, interactive=True
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)
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leaderboard_metric_selector = gr.CheckboxGroup(
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choices=metric_names, label="Select Metrics for the Leaderboard", value=
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)
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# Display the filtered leaderboard
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@@ -89,6 +115,14 @@ with block:
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inputs=[leaderboard_method_selector, leaderboard_metric_selector],
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outputs=data_component
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)
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leaderboard_metric_selector.change(
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get_baseline_df,
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inputs=[leaderboard_method_selector, leaderboard_metric_selector],
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benchmark_types = ["similarity", "function", "family", "affinity"]
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download_from_hub(benchmark_types)
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# Define a function to update metrics based on benchmark type selection
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def update_metrics(selected_benchmarks):
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updated_metrics = set()
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for benchmark in selected_benchmarks:
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updated_metrics.update(benchmark_metric_mapping.get(benchmark, []))
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return list(updated_metrics)
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# Define a function to update the leaderboard
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def update_leaderboard(selected_methods, selected_metrics):
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updated_df = get_baseline_df(selected_methods, selected_metrics)
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return updated_df
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block = gr.Blocks()
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with block:
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metric_names = leaderboard.columns.tolist()
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metrics_with_method = metric_names.copy()
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metric_names.remove('Method') # Remove method_name from the metric options
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benchmark_metric_mapping = {
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"similarity": [metric for metric in metric_names if metric.startswith('sim_')],
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"function": [metric for metric in metric_names if metric.startswith('func')],
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"family": [metric for metric in metric_names if metric.startswith('fam_')],
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"affinity": [metric for metric in metric_names if metric.startswith('aff_')],
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}
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# Leaderboard section with method and metric selectors
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leaderboard_method_selector = gr.CheckboxGroup(
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choices=method_names, label="Select Methods for the Leaderboard", value=method_names, interactive=True
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)
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benchmark_type_selector = gr.CheckboxGroup(
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choices=list(benchmark_metric_mapping.keys()),
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label="Select Benchmark Types",
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value=None, # Initially select all benchmark types
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interactive=True
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)
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leaderboard_metric_selector = gr.CheckboxGroup(
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choices=metric_names, label="Select Metrics for the Leaderboard", value=None, interactive=True
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)
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# Display the filtered leaderboard
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inputs=[leaderboard_method_selector, leaderboard_metric_selector],
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outputs=data_component
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)
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# Update metrics when benchmark type changes
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benchmark_type_selector.change(
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lambda selected_benchmarks: update_metrics(selected_benchmarks),
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inputs=[benchmark_type_selector],
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outputs=leaderboard_metric_selector
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)
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leaderboard_metric_selector.change(
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get_baseline_df,
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inputs=[leaderboard_method_selector, leaderboard_metric_selector],
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