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Update app.py
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app.py
CHANGED
@@ -14,6 +14,11 @@ model_choices = sorted(df['model_cls'].dropna().unique().tolist())
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metric_choices = ["num_params_B", "flops_G", "time_plain_s", "mem_plain_GB", "time_compile_s", "mem_compile_GB"]
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group_choices = ["scenario"]
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# Analysis function using global df
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def analyze(analysis_type, n_rows, metric, selected_model):
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columns = df.columns
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@@ -37,7 +42,7 @@ def analyze(analysis_type, n_rows, metric, selected_model):
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# prepare bars
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scenarios = plot_df['scenario']
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values = plot_df[metric]
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bars = ax.barh(scenarios, values)
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# prettify
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@@ -71,8 +76,8 @@ def analyze(analysis_type, n_rows, metric, selected_model):
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return None, fig
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scenarios = filt['scenario']
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plain = filt['time_plain_s']
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compile = filt['time_compile_s']
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x = range(len(scenarios))
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width = 0.35
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metric_choices = ["num_params_B", "flops_G", "time_plain_s", "mem_plain_GB", "time_compile_s", "mem_compile_GB"]
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group_choices = ["scenario"]
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def filter_float(value):
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if isinstance(value, str):
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return float(value.split()[0])
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return value
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# Analysis function using global df
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def analyze(analysis_type, n_rows, metric, selected_model):
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columns = df.columns
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# prepare bars
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scenarios = plot_df['scenario']
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values = plot_df[metric].map(filter_float)
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bars = ax.barh(scenarios, values)
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# prettify
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return None, fig
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scenarios = filt['scenario']
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plain = filt['time_plain_s'].map(filter_float)
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compile = filt['time_compile_s'].map(filter_float)
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x = range(len(scenarios))
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width = 0.35
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