import abc import gradio as gr import os from gen_table import * from meta_data import * with gr.Blocks(title="Open Agent Leaderboard") as demo: struct = load_results(OVERALL_MATH_SCORE_FILE) timestamp = struct['time'] EVAL_TIME = format_timestamp(timestamp) results = struct['results'] N_MODEL = len(results) N_DATA = len(results['IO']) DATASETS = list(results['IO']) DATASETS.remove('META') print(DATASETS) # 确保在定义llm_options之前生成overall_table check_box = BUILD_L1_DF(results, DEFAULT_MATH_BENCH) overall_table = generate_table(results, DEFAULT_MATH_BENCH) # 保存完整的overall_table为CSV文件 csv_path_overall = os.path.join(os.getcwd(), 'src/overall_results.csv') overall_table.to_csv(csv_path_overall, index=False) print(f"Overall results saved to {csv_path_overall}") # 从overall_table中提取所有可能的LLM选项 llm_options = list(set(row.LLM for row in overall_table.itertuples() if hasattr(row, 'LLM'))) gr.Markdown(LEADERBORAD_INTRODUCTION.format(EVAL_TIME)) with gr.Tabs(elem_classes='tab-buttons') as tabs: with gr.Tab(label='🏅 Open Agent Overall Math Leaderboard'): gr.Markdown(LEADERBOARD_MD['MATH_MAIN']) # 移动check_box和overall_table的定义到这里 # check_box = BUILD_L1_DF(results, DEFAULT_MATH_BENCH) # overall_table = generate_table(results, DEFAULT_MATH_BENCH) type_map = check_box['type_map'] type_map['Rank'] = 'number' checkbox_group = gr.CheckboxGroup( choices=check_box['all'], value=check_box['required'], label='Evaluation Dimension', interactive=True, ) # 新增的CheckboxGroup组件用于选择Algorithm和LLM algo_name = gr.CheckboxGroup( choices=ALGORITHMS, value=ALGORITHMS, label='Algorithm', interactive=True ) llm_name = gr.CheckboxGroup( choices=llm_options, # 使用提取的llm_options value=llm_options, label='LLM', interactive=True ) initial_headers = ['Rank'] + check_box['essential'] + checkbox_group.value available_headers = [h for h in initial_headers if h in overall_table.columns] data_component = gr.components.DataFrame( value=overall_table[available_headers], type='pandas', datatype=[type_map[x] for x in available_headers], interactive=False, wrap=True, visible=True) def filter_df(fields, algos, llms, *args): headers = ['Rank'] + check_box['essential'] + fields df = overall_table.copy() # 添加过滤逻辑 df['flag'] = df.apply(lambda row: ( row['Algorithm'] in algos and row['LLM'] in llms ), axis=1) df = df[df['flag']].copy() df.pop('flag') # Ensure all requested columns exist available_headers = [h for h in headers if h in df.columns] original_columns = df.columns.tolist() available_headers = sorted(available_headers, key=lambda x: original_columns.index(x)) # If no columns are available, return an empty DataFrame with basic columns if not available_headers: available_headers = ['Rank'] + check_box['essential'] comp = gr.components.DataFrame( value=df[available_headers], type='pandas', datatype=[type_map[x] for x in available_headers], interactive=False, wrap=True, visible=True) return comp # 更新change事件以包含新的过滤条件 checkbox_group.change( fn=filter_df, inputs=[checkbox_group, algo_name, llm_name], outputs=data_component ) algo_name.change( fn=filter_df, inputs=[checkbox_group, algo_name, llm_name], outputs=data_component ) llm_name.change( fn=filter_df, inputs=[checkbox_group, algo_name, llm_name], outputs=data_component ) with gr.Tab(label='🏅 Open Agent Detail Math Leaderboard'): gr.Markdown(LEADERBOARD_MD['MATH_DETAIL']) struct_detail = load_results(DETAIL_MATH_SCORE_FILE) timestamp = struct_detail['time'] EVAL_TIME = format_timestamp(timestamp) results_detail = struct_detail['results'] table, check_box = BUILD_L2_DF(results_detail, DEFAULT_MATH_BENCH) # 保存完整的table为CSV文件 csv_path_detail = os.path.join(os.getcwd(), 'src/detail_results.csv') table.to_csv(csv_path_detail, index=False) print(f"Detail results saved to {csv_path_detail}") type_map = check_box['type_map'] type_map['Rank'] = 'number' checkbox_group = gr.CheckboxGroup( choices=check_box['all'], value=check_box['required'], label='Evaluation Dimension', interactive=True, ) headers = ['Rank'] + checkbox_group.value with gr.Row(): algo_name = gr.CheckboxGroup( choices=ALGORITHMS, value=ALGORITHMS, label='Algorithm', interactive=True ) dataset_name = gr.CheckboxGroup( choices=DATASETS, value=DATASETS, label='Datasets', interactive=True ) llm_name = gr.CheckboxGroup( choices=check_box['LLM_options'], value=check_box['LLM_options'], label='LLM', interactive=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_df2(fields, algos, datasets, llms): headers = ['Rank'] + fields df = table.copy() # Filter data df['flag'] = df.apply(lambda row: ( row['Algorithm'] in algos and row['Dataset'] in datasets and row['LLM'] in llms ), axis=1) df = df[df['flag']].copy() df.pop('flag') # Group by dataset and calculate ranking within each group based on Score if 'Score' in df.columns: # Create a temporary ranking column df['Rank'] = df.groupby('Dataset')['Score'].rank(method='first', ascending=False) # Ensure ranking is integer df['Rank'] = df['Rank'].astype(int) original_columns = df.columns.tolist() headers = sorted(headers, key=lambda x: original_columns.index(x)) 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 # 为所有复选框组添加change事件 checkbox_group.change( fn=filter_df2, inputs=[checkbox_group, algo_name, dataset_name, llm_name], outputs=data_component ) algo_name.change( fn=filter_df2, inputs=[checkbox_group, algo_name, dataset_name, llm_name], outputs=data_component ) dataset_name.change( fn=filter_df2, inputs=[checkbox_group, algo_name, dataset_name, llm_name], outputs=data_component ) llm_name.change( fn=filter_df2, inputs=[checkbox_group, algo_name, dataset_name, llm_name], outputs=data_component ) with gr.Row(): with gr.Accordion("📙 Citation", open=False): gr.Textbox( value=CITATION_BUTTON_TEXT, lines=7, label="Copy the BibTeX snippet to cite this source", elem_id="citation-button", show_copy_button=True, ) if __name__ == '__main__': demo.launch(server_name='0.0.0.0')