File size: 7,854 Bytes
bb8ff6c ea5e3a1 bb8ff6c ea5e3a1 bb8ff6c ea5e3a1 bb8ff6c ea5e3a1 bb8ff6c ea5e3a1 bb8ff6c ea5e3a1 bb8ff6c ea5e3a1 bb8ff6c ea5e3a1 bb8ff6c ea5e3a1 bb8ff6c ea5e3a1 bb8ff6c ea5e3a1 bb8ff6c ea5e3a1 bb8ff6c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
import abc, sys
import gradio as gr
from gen_table import *
from meta_data import *
# import pandas as pd
# pd.set_option('display.max_colwidth', 0)
head_style = """
<style>
@media (min-width: 1536px)
{
.gradio-container {
min-width: var(--size-full) !important;
}
}
</style>
"""
with gr.Blocks(title="Cybersecurity Leaderboard", head=
head_style) as demo:
struct = load_results()
timestamp = struct['time']
EVAL_TIME = format_timestamp(timestamp)
results = struct['results']
benchmark_list=list(results.keys())
N_DATA = len(benchmark_list)
DATASETS = benchmark_list
gr.Markdown(LEADERBORAD_INTRODUCTION.format(N_DATA,EVAL_TIME))
structs = [abc.abstractproperty() for _ in range(N_DATA)]
with gr.Tabs(elem_id="leaderboard_tabs", elem_classes='tab-buttons') as tabs:
# with gr.TabItem('🏅 Cybersecurity Main Leaderboard', elem_id='main', id=0):
# gr.Markdown(LEADERBOARD_MD['MAIN'].format(N_DATA,N_DATA))
# _, check_box = BUILD_L1_DF(results, DEFAULT_TASK)
# table = generate_table(results, DEFAULT_TASK)
# type_map = check_box['type_map']
# checkbox_group = gr.CheckboxGroup(
# choices=check_box['all'],
# value=check_box['required'],
# label='Aspects of Cybersecurity Work',
# interactive=True,
# )
# headers = check_box['essential'] + checkbox_group.value
# with gr.Row():
# model_name = gr.Textbox(
# value='Input the Model Name (fuzzy, case insensitive)',
# label='Model Name',
# interactive=True,
# visible=True)
# data_component = gr.components.DataFrame(
# value=table[headers],
# type='pandas',
# datatype=[type_map[x] for x in headers],
# interactive=False,
# wrap=True,
# visible=True)
# def filter_df(fields, model_name):
# headers = check_box['essential'] + fields
# df = generate_table(results, fields)
# default_val = 'Input the Model Name (fuzzy, case insensitive)'
# if model_name != default_val:
# print(model_name)
# model_name = model_name.lower()
# method_names = [x.split('</a>')[0].split('>')[-1].lower() for x in df['Model']]
# flag = [model_name in name for name in method_names]
# df['TEMP_FLAG'] = flag
# df = df[df['TEMP_FLAG'] == True]
# df.pop('TEMP_FLAG')
# comp = gr.components.DataFrame(
# value=df[headers],
# type='pandas',
# datatype=[type_map[x] for x in headers],
# interactive=False,
# wrap=True,
# visible=True)
# return comp
# for cbox in [checkbox_group]:
# cbox.change(fn=filter_df, inputs=[checkbox_group, model_name], outputs=data_component)
# model_name.submit(fn=filter_df, inputs=[checkbox_group, model_name], outputs=data_component)
with gr.TabItem('🔍 About', elem_id='about', id=1):
with open("about.md", 'r', encoding="utf-8") as file:
gr.Markdown(file.read())
for i, benchmark in enumerate(benchmark_list):
with gr.TabItem(f'📊 {benchmark} Leaderboard', elem_id=benchmark, id=i + 2):
if benchmark in LEADERBOARD_MD:
gr.Markdown(LEADERBOARD_MD[benchmark])
s = structs[i]
s.table, s.check_box = BUILD_L2_DF(results, benchmark)
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'{benchmark} CheckBoxes',
interactive=True,
)
s.headers = s.check_box['essential'] + s.checkbox_group.value
if benchmark!='SWE-bench-verified':
with gr.Row():
s.model_name = gr.Textbox(
value='Input the Model Name (fuzzy, case insensitive)',
label='Model Name',
interactive=True,
visible=True)
else:
with gr.Row():
s.model_name = gr.Textbox(
value='Input the Agent Name (fuzzy, case insensitive)',
label='Agent Name',
interactive=True,
visible=True)
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,
wrap=True,
visible=True)
s.dataset = gr.Textbox(value=benchmark, label=benchmark, visible=False)
def filter_df_l2(dataset_name, fields, model_name):
s = structs[benchmark_list.index(dataset_name)]
headers = s.check_box['essential'] + fields
df = cp.deepcopy(s.table)
if dataset_name!="SWE-bench-verified":
default_val = 'Input the Model Name (fuzzy, case insensitive)'
else:
default_val = 'Input the Agent Name (fuzzy, case insensitive)'
if model_name != default_val:
print(model_name)
model_name = model_name.lower()
if dataset_name!="SWE-bench-verified":
method_names = [x.split('</a>')[0].split('>')[-1].lower() for x in df['Model']]
else:
method_names = [x.split('</a>')[0].split('>')[-1].lower() for x in df['Agent']]
flag = [model_name in name for name in method_names]
df['TEMP_FLAG'] = flag
df = df[df['TEMP_FLAG'] == True]
df.pop('TEMP_FLAG')
comp = gr.components.DataFrame(
value=df[headers],
type='pandas',
datatype=[s.type_map[x] for x in headers],
interactive=False,
wrap=True,
visible=True)
return comp
for cbox in [s.checkbox_group]:
cbox.change(
fn=filter_df_l2,
inputs=[s.dataset, s.checkbox_group, s.model_name],
outputs=s.data_component)
s.model_name.submit(
fn=filter_df_l2,
inputs=[s.dataset, s.checkbox_group, s.model_name],
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', share=True)
|