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import abc
import gradio as gr
from lb_info import *
with gr.Blocks() as demo:
struct = load_results()
timestamp = struct['time']
EVAL_TIME = format_timestamp(timestamp)
results = struct['results']
N_MODEL = len(results)
N_DATA = len(results['Video-LLaVA-7B']) - 1
DATASETS = list(results['Video-LLaVA-7B'])
DATASETS.remove('META')
print(DATASETS)
gr.Markdown(LEADERBORAD_INTRODUCTION.format(N_MODEL, N_DATA, EVAL_TIME))
structs = [abc.abstractproperty() for _ in range(N_DATA)]
with gr.Tabs(elem_classes='tab-buttons') as tabs:
with gr.TabItem('🏅 OpenVLM Video Leaderboard', elem_id='main', id=0):
gr.Markdown(LEADERBOARD_MD['MAIN'])
table, check_box = BUILD_L1_DF(results, MAIN_FIELDS)
type_map = check_box['type_map']
checkbox_group = gr.CheckboxGroup(
choices=check_box['all'],
value=check_box['required'],
label="Evaluation Dimension",
interactive=True,
)
headers = check_box['essential'] + checkbox_group.value
with gr.Row():
model_size = gr.CheckboxGroup(
choices=MODEL_SIZE,
value=MODEL_SIZE,
label='Model Size',
interactive=True
)
model_type = gr.CheckboxGroup(
choices=MODEL_TYPE,
value=MODEL_TYPE,
label='Model Type',
interactive=True
)
data_component = gr.components.DataFrame(
value=table[headers],
type="pandas",
datatype=[type_map[x] for x in headers],
interactive=False,
visible=True)
def filter_df(fields, model_size, model_type):
headers = check_box['essential'] + fields
df = cp.deepcopy(table)
df['flag'] = [model_size_flag(x, model_size) for x in df['Parameters (B)']]
df = df[df['flag']]
df.pop('flag')
if len(df):
print(model_type)
df['flag'] = [model_type_flag(df.iloc[i], model_type) for i in range(len(df))]
df = df[df['flag']]
df.pop('flag')
comp = gr.components.DataFrame(
value=df[headers],
type="pandas",
datatype=[type_map[x] for x in headers],
interactive=False,
visible=True)
return comp
for cbox in [checkbox_group, model_size, model_type]:
cbox.change(fn=filter_df, inputs=[checkbox_group, model_size, model_type], outputs=data_component)
with gr.TabItem('🔍 About', elem_id='about', id=1):
gr.Markdown(urlopen(VLMEVALKIT_README).read().decode())
for i, dataset in enumerate(DATASETS):
with gr.TabItem(f'📊 {dataset} Leaderboard', elem_id=dataset, id=i + 2):
if dataset in LEADERBOARD_MD:
gr.Markdown(LEADERBOARD_MD[dataset])
s = structs[i]
s.table, s.check_box = BUILD_L2_DF(results, dataset)
s.type_map = s.check_box['type_map']
s.checkbox_group = gr.CheckboxGroup(
choices=s.check_box['all'],
value=s.check_box['required'],
label=f"{dataset} CheckBoxes",
interactive=True,
)
s.headers = s.check_box['essential'] + s.checkbox_group.value
with gr.Row():
s.model_size = gr.CheckboxGroup(
choices=MODEL_SIZE,
value=MODEL_SIZE,
label='Model Size',
interactive=True
)
s.model_type = gr.CheckboxGroup(
choices=MODEL_TYPE,
value=MODEL_TYPE,
label='Model Type',
interactive=True
)
# Adjust column width if dataset is MMBench-Video
if dataset == "MMBench-Video":
column_widths = {col: 200 for col in headers} # Default width
column_widths['Method'] = 400 # Adjust Method column width
else:
column_widths = None
s.data_component = gr.components.DataFrame(
value=s.table[s.headers],
type="pandas",
datatype=[s.type_map[x] for x in s.headers],
interactive=False,
visible=True,
column_widths=column_widths
)
s.dataset = gr.Textbox(value=dataset, label=dataset, visible=False)
def filter_df_l2(dataset_name, fields, model_size, model_type):
s = structs[DATASETS.index(dataset_name)]
headers = s.check_box['essential'] + fields
df = cp.deepcopy(s.table)
df['flag'] = [model_size_flag(x, model_size) for x in df['Parameters (B)']]
df = df[df['flag']]
df.pop('flag')
if len(df):
df['flag'] = [model_type_flag(df.iloc[i], model_type) for i in range(len(df))]
df = df[df['flag']]
df.pop('flag')
# Adjust column width if dataset is MMBench-Video
if dataset_name == "MMBench-Video":
column_widths = {col: 200 for col in headers} # Default width
column_widths['Method'] = 400 # Adjust Method column width
else:
column_widths = None
comp = gr.components.DataFrame(
value=df[headers],
type="pandas",
datatype=[s.type_map[x] for x in headers],
interactive=False,
visible=True,
column_widths=column_widths
)
return comp
for cbox in [s.checkbox_group, s.model_size, s.model_type]:
cbox.change(fn=filter_df_l2, inputs=[s.dataset, s.checkbox_group, s.model_size, s.model_type], outputs=s.data_component)
with gr.Row():
with gr.Accordion("Citation", open=False):
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
elem_id='citation-button')
if __name__ == '__main__':
demo.launch(server_name='0.0.0.0') |