Update app.py
Browse files
app.py
CHANGED
@@ -74,18 +74,53 @@ def make_results_tab(model_info, results):
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return df
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def show_results_tab(df):
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with gr.Row():
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model_name = gr.Textbox(value='Input the Model Name (fuzzy)', label='Model Name')
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with gr.Column():
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table = gr.DataFrame(
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value=df,
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interactive=False,
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wrap=False,
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)
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-
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def create_interface():
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model_info, results = findfile()
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@@ -98,7 +133,7 @@ def create_interface():
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df = make_results_tab(model_info, results)
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show_results_tab(df)
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with gr.TabItem('Predictions', elem_id='notmain', id=
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# dataset_tab(results, structs[i], dataset)
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pass
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@@ -112,4 +147,3 @@ if __name__ == '__main__':
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demo.queue()
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demo.launch(server_name='0.0.0.0')
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-
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return df
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def calculate_column_widths(df):
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column_widths = []
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for column in df.columns:
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header_length = len(str(column))
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max_content_length = df[column].astype(str).map(len).max()
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width = max(header_length * 10, max_content_length * 8) + 20
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width = max(160, min(400, width))
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column_widths.append(width)
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return column_widths
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def show_results_tab(df):
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with gr.Row():
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model_name = gr.Textbox(value='Input the Model Name (fuzzy)', label='Model Name')
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def filter_df(model_name):
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df = generate_table(results)
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df['flag'] = [model_size_flag(x, model_size) for x in df['Param (B)']]
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df = df[df['flag']]
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df.pop('flag')
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if len(df):
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df['flag'] = [model_type_flag(df.iloc[i], model_type) for i in range(len(df))]
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df = df[df['flag']]
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df.pop('flag')
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df['Rank'] = list(range(1, len(df) + 1))
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default_val = 'Input the Model Name (fuzzy)'
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if model_name != default_val:
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method_names = [x.split('</a>')[0].split('>')[-1].lower() for x in df['Method']]
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flag = [model_name.lower() in name for name in method_names]
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df['TEMP'] = flag
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df = df[df['TEMP'] == True]
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df.pop('TEMP')
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comp = gr.components.DataFrame(value=df[headers], datatype=[type_map[x] for x in headers])
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return comp
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with gr.Column():
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table = gr.DataFrame(
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value=df,
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interactive=False,
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wrap=False,
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column_widths=calculate_column_widths(df),
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)
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def create_interface():
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model_info, results = findfile()
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df = make_results_tab(model_info, results)
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show_results_tab(df)
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with gr.TabItem('Predictions', elem_id='notmain', id=1):
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# dataset_tab(results, structs[i], dataset)
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pass
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demo.queue()
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demo.launch(server_name='0.0.0.0')
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