Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
[Feature] Add name filtering
Browse files
app.py
CHANGED
@@ -51,6 +51,11 @@ with gr.Blocks(title="Open VLM Leaderboard", head=head_style) as demo:
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headers = ['Rank'] + check_box['essential'] + checkbox_group.value
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with gr.Row():
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model_size = gr.CheckboxGroup(
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choices=MODEL_SIZE,
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value=MODEL_SIZE,
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@@ -71,7 +76,7 @@ with gr.Blocks(title="Open VLM Leaderboard", head=head_style) as demo:
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wrap=True,
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visible=True)
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-
def filter_df(fields, model_size, model_type):
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filter_list = ['Avg Score', 'Avg Rank', 'OpenSource']
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headers = ['Rank'] + check_box['essential'] + fields
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@@ -86,6 +91,15 @@ with gr.Blocks(title="Open VLM Leaderboard", head=head_style) as demo:
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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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comp = gr.components.DataFrame(
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value=df[headers],
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@@ -97,7 +111,8 @@ with gr.Blocks(title="Open VLM Leaderboard", head=head_style) as demo:
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return comp
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for cbox in [checkbox_group, model_size, model_type]:
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cbox.change(fn=filter_df, inputs=[checkbox_group, model_size, model_type], outputs=data_component)
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with gr.TabItem('π About', elem_id='about', id=1):
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gr.Markdown(urlopen(VLMEVALKIT_README).read().decode())
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@@ -122,6 +137,11 @@ with gr.Blocks(title="Open VLM Leaderboard", head=head_style) as demo:
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s.table['Rank'] = list(range(1, len(s.table) + 1))
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with gr.Row():
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s.model_size = gr.CheckboxGroup(
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choices=MODEL_SIZE,
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value=MODEL_SIZE,
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@@ -143,7 +163,7 @@ with gr.Blocks(title="Open VLM Leaderboard", head=head_style) as demo:
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visible=True)
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s.dataset = gr.Textbox(value=dataset, label=dataset, visible=False)
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def filter_df_l2(dataset_name, fields, model_size, model_type):
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s = structs[DATASETS.index(dataset_name)]
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headers = ['Rank'] + s.check_box['essential'] + fields
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df = cp.deepcopy(s.table)
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@@ -155,6 +175,15 @@ with gr.Blocks(title="Open VLM Leaderboard", head=head_style) as demo:
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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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comp = gr.components.DataFrame(
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value=df[headers],
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@@ -168,8 +197,12 @@ with gr.Blocks(title="Open VLM Leaderboard", head=head_style) as demo:
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for cbox in [s.checkbox_group, s.model_size, s.model_type]:
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cbox.change(
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fn=filter_df_l2,
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inputs=[s.dataset, s.checkbox_group, s.model_size, s.model_type],
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outputs=s.data_component)
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with gr.Row():
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with gr.Accordion('Citation', open=False):
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headers = ['Rank'] + check_box['essential'] + checkbox_group.value
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with gr.Row():
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model_name = gr.Textbox(
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value='Input the Model Name (fuzzy, case insensitive)',
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label='Model Name',
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interactive=True,
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visible=True)
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model_size = gr.CheckboxGroup(
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choices=MODEL_SIZE,
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value=MODEL_SIZE,
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wrap=True,
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visible=True)
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def filter_df(fields, model_name, model_size, model_type):
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filter_list = ['Avg Score', 'Avg Rank', 'OpenSource']
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headers = ['Rank'] + check_box['essential'] + fields
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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, case insensitive)'
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if model_name != default_val:
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print(model_name)
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model_name = model_name.lower()
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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 in name for name in method_names]
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df['TEMP_FLAG'] = flag
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df = df[df['TEMP_FLAG'] == True]
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df.pop('TEMP_FLAG')
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comp = gr.components.DataFrame(
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value=df[headers],
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return comp
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for cbox in [checkbox_group, model_size, model_type]:
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cbox.change(fn=filter_df, inputs=[checkbox_group, model_name, model_size, model_type], outputs=data_component)
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model_name.submit(fn=filter_df, inputs=[checkbox_group, model_name, model_size, model_type], outputs=data_component)
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with gr.TabItem('π About', elem_id='about', id=1):
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gr.Markdown(urlopen(VLMEVALKIT_README).read().decode())
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s.table['Rank'] = list(range(1, len(s.table) + 1))
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with gr.Row():
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s.model_name = gr.Textbox(
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value='Input the Model Name (fuzzy, case insensitive)',
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label='Model Name',
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interactive=True,
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visible=True)
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s.model_size = gr.CheckboxGroup(
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choices=MODEL_SIZE,
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value=MODEL_SIZE,
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visible=True)
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s.dataset = gr.Textbox(value=dataset, label=dataset, visible=False)
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def filter_df_l2(dataset_name, fields, model_name, model_size, model_type):
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s = structs[DATASETS.index(dataset_name)]
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headers = ['Rank'] + s.check_box['essential'] + fields
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df = cp.deepcopy(s.table)
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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, case insensitive)'
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if model_name != default_val:
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print(model_name)
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model_name = model_name.lower()
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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 in name for name in method_names]
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df['TEMP_FLAG'] = flag
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df = df[df['TEMP_FLAG'] == True]
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df.pop('TEMP_FLAG')
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comp = gr.components.DataFrame(
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value=df[headers],
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for cbox in [s.checkbox_group, s.model_size, s.model_type]:
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cbox.change(
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fn=filter_df_l2,
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inputs=[s.dataset, s.checkbox_group, s.model_name, s.model_size, s.model_type],
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outputs=s.data_component)
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s.model_name.submit(
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fn=filter_df_l2,
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inputs=[s.dataset, s.checkbox_group, s.model_name, s.model_size, s.model_type],
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outputs=s.data_component)
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with gr.Row():
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with gr.Accordion('Citation', open=False):
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