Update app.py

app.py

@@ -1,184 +1,184 @@
-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)
+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="Frontier AI Cybersecurity Observatory", 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)