app.py

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>

"""

TAB_CSS = """

/* 1. Target the real tab‐list container (old & new class names + role attr) */

#leaderboard_tabs [role="tablist"],

#leaderboard_tabs .gradio-tabs-tablist,

#leaderboard_tabs .tab-container[role="tablist"] {

  display: flex       !important;

  flex-wrap: wrap     !important;  /* allow multi‑row */

  white-space: normal !important;  /* cancel nowrap */

  overflow-x: visible!important;  /* don’t clip off */

  height: auto        !important;  /* grow as tall as needed */

  max-width: none     !important;  /* cancel any max‑width */

}



/* 2. Stop each button from flexing */

#leaderboard_tabs [role="tab"],

#leaderboard_tabs .tab-container[role="tablist"] .tab-button,

#leaderboard_tabs .gradio-tabs-tab {

  flex: none !important;

}



/* 3. Hide every possible “more/overflow” toggle */

#leaderboard_tabs .overflow-menu,

#leaderboard_tabs [class*="overflow-button"],

#leaderboard_tabs button[aria-label*="More"],

#leaderboard_tabs .gradio-tabs-overflow,

#leaderboard_tabs .gradio-tabs-overflow-button {

  display: none !important;

}

"""

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']
    model_list=[]
    task_list=[]
    benchmark_list=[]
    for task in results:
        task_list+=[task]
        for benchmark in results[task]:
            if benchmark!='category':
                benchmark_list+=[benchmark]
                model_list+=list(results[task][benchmark].keys())

    model_list=list(set(model_list))
    N_MODEL=len(model_list) 
    N_TASK=len(task_list)
    N_DATA = len(list(set(benchmark_list))) 
    DATASETS = benchmark_list

    gr.Markdown(LEADERBORAD_INTRODUCTION.format(N_DATA,N_TASK,EVAL_TIME))
    structs = [abc.abstractproperty() for _ in range(N_TASK)] #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, task in enumerate(task_list):
            with gr.TabItem(f'📊 {task} Leaderboard', elem_id=task, id=i + 2):
                if task in LEADERBOARD_MD:
                    gr.Markdown(LEADERBOARD_MD[task])

                s = structs[i]
                s.table, s.check_box = BUILD_L2_DF(results, task)
                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'{task} CheckBoxes',
                    interactive=True,
                )
                s.headers = s.check_box['essential'] + s.checkbox_group.value

                with gr.Row():
                    s.model_name = gr.Textbox(
                        value='Input the Model Name (fuzzy, case insensitive)', 
                        label='Model 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=task, label=task, visible=False)

                def filter_df_l2(dataset_name, fields, model_name):
                    s = structs[task_list.index(dataset_name)]
                    headers = s.check_box['essential'] + fields
                    df = cp.deepcopy(s.table)
                    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['Method']]
                        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)