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import abc
import gradio as gr
from gen_table import *
from meta_data import *
head_style = """
<style>
@media (min-width: 1536px)
{
.gradio-container {
min-width: var(--size-full) !important;
}
}
</style>
"""
def math_main_tab(results):
_, check_box = BUILD_L1_DF(results)
table = generate_table(results)
table['Rank'] = list(range(1, len(table) + 1))
type_map = check_box['type_map']
type_map['Rank'] = 'number'
with gr.Row():
checkbox_group = gr.CheckboxGroup(choices=check_box['all'], value=check_box['required'], label='Evaluation Dimension')
circular = gr.CheckboxGroup(choices=['CircularEval'], value=[], label='Evaluation Mode')
headers = ['Rank'] + check_box['essential'] + checkbox_group.value
with gr.Row():
model_name = gr.Textbox(value='Input the Model Name (fuzzy)', label='Model Name')
model_size = gr.CheckboxGroup(choices=MODEL_SIZE, value=MODEL_SIZE, label='Model Size')
model_type = gr.CheckboxGroup(choices=MODEL_TYPE, value=MODEL_TYPE, label='Model Type')
data_component = gr.components.DataFrame(value=table[headers], datatype=[type_map[x] for x in headers])
def filter_df(fields, model_name, model_size, model_type, circular):
results = load_results()['results']
headers = ['Rank'] + check_box['essential'] + fields
circ_flag = 'CircularEval' in circular
if circ_flag:
headers = [x for x in headers if x not in NON_MCQ_DATASETS]
df = generate_table(results, circular=circ_flag)
df['flag'] = [model_size_flag(x, model_size) for x in df['Param (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')
df['Rank'] = list(range(1, len(df) + 1))
default_val = 'Input the Model Name (fuzzy)'
if model_name != default_val:
method_names = [x.split('</a>')[0].split('>')[-1].lower() for x in df['Method']]
flag = [model_name.lower() in name for name in method_names]
df['TEMP'] = flag
df = df[df['TEMP'] == True]
df.pop('TEMP')
comp = gr.components.DataFrame(value=df[headers], datatype=[type_map[x] for x in headers])
return comp
for cbox in [checkbox_group, model_size, model_type, circular]:
cbox.change(fn=filter_df, inputs=[checkbox_group, model_name, model_size, model_type, circular], outputs=data_component)
model_name.submit(fn=filter_df, inputs=[checkbox_group, model_name, model_size, model_type, circular], outputs=data_component)
def dataset_tab(results, struct, dataset):
s = struct
s.table, s.check_box = BUILD_L2_DF(results, dataset)
s.type_map = s.check_box['type_map']
s.type_map['Rank'] = 'number'
with gr.Row():
s.checkbox_group = gr.CheckboxGroup(choices=s.check_box['all'], value=s.check_box['required'], label=f'{dataset} CheckBoxes')
s.circular = gr.CheckboxGroup(choices=['CircularEval'], value=[], label='Evaluation Mode')
s.headers = ['Rank'] + s.check_box['essential'] + s.checkbox_group.value
s.table['Rank'] = list(range(1, len(s.table) + 1))
with gr.Row():
s.model_name = gr.Textbox(value='Input the Model Name (fuzzy)', label='Model Name')
s.model_size = gr.CheckboxGroup(choices=MODEL_SIZE, value=MODEL_SIZE, label='Model Size')
s.model_type = gr.CheckboxGroup(choices=MODEL_TYPE, value=MODEL_TYPE, label='Model Type')
s.data_component = gr.components.DataFrame(value=s.table[s.headers], datatype=[s.type_map[x] for x in s.headers])
s.dataset = gr.Textbox(value=dataset, label=dataset, visible=False)
def filter_df_l2(dataset_name, fields, model_name, model_size, model_type, circular):
results = load_results()['results']
s = structs[DATASETS.index(dataset_name)]
headers = ['Rank'] + s.check_box['essential'] + fields
circ_flag = 'CircularEval' in circular
if circ_flag and dataset_name in NON_MCQ_DATASETS:
circ_flag = False
df, _ = BUILD_L2_DF(results, dataset_name, circular=circ_flag)
df['flag'] = [model_size_flag(x, model_size) for x in df['Param (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')
df['Rank'] = list(range(1, len(df) + 1))
default_val = 'Input the Model Name (fuzzy)'
if model_name != default_val:
method_names = [x.split('</a>')[0].split('>')[-1].lower() for x in df['Method']]
flag = [model_name.lower() in name for name in method_names]
df['TEMP'] = flag
df = df[df['TEMP'] == True]
df.pop('TEMP')
comp = gr.components.DataFrame(value=df[headers], datatype=[s.type_map[x] for x in headers])
return comp
for cbox in [s.checkbox_group, s.model_size, s.model_type, s.circular]:
cbox.change(
fn=filter_df_l2,
inputs=[s.dataset, s.checkbox_group, s.model_name, s.model_size, s.model_type, s.circular],
outputs=s.data_component)
s.model_name.submit(
fn=filter_df_l2,
inputs=[s.dataset, s.checkbox_group, s.model_name, s.model_size, s.model_type, s.circular],
outputs=s.data_component)
with gr.Blocks(title="Spatial Leaderboard", head=head_style) as demo:
results = load_results()['results']
N_MODEL = len(results)
DATASETS = []
for m in results:
DATASETS.extend(results[m].keys())
DATASETS = [d for d in set(DATASETS) if d != 'META' and 'circular' not in d]
N_DATA = len(DATASETS)
structs = [abc.abstractproperty() for _ in range(N_DATA)]
gr.Markdown(LEADERBORAD_INTRODUCTION)
with gr.Tabs(elem_classes='tab-buttons') as tabs:
with gr.TabItem('🏅 LMM Spatial Leaderboard', elem_id='main', id=0):
math_main_tab(results)
for i, dataset in enumerate(DATASETS):
with gr.TabItem(
f'📊 {dataset}', elem_id=dataset, id=i + 2):
dataset_tab(results, structs[i], dataset)
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')
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