import gradio as gr import pandas as pd import os from huggingface_hub import snapshot_download, login from apscheduler.schedulers.background import BackgroundScheduler from src.display.about import ( CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, CONTACT_TEXT, EVALUATION_QUEUE_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, TITLE, SUB_TITLE, ) from src.display.css_html_js import custom_css from src.envs import API from src.leaderboard.load_results import load_data def restart_space(): API.restart_space(repo_id="Auto-Arena/Leaderboard") csv_path = f"./src/results/auto-arena-llms-results-20240531.csv" csv_path_chinese = f"./src/results/auto-arena-llms-results-chinese-20240531.csv" df_results = load_data(csv_path) df_results_chinese = load_data(csv_path_chinese) all_columns = ['Rank', 'Model', 'From', 'Open?', 'Params(B)', 'Cost', 'Score'] show_columns = ['Rank', 'Model', 'From', 'Open?', 'Params(B)', 'Cost', 'Score'] TYPES = ['number', 'markdown', 'str', 'str', 'str', 'str', 'number'] df_results_init = df_results.copy()[show_columns] df_results_chinese_init = df_results_chinese.copy()[show_columns] def update_table( hidden_df: pd.DataFrame, # columns: list, #type_query: list, open_query: list, # precision_query: str, # size_query: list, # show_deleted: bool, query: str, ): # filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted) # filtered_df = filter_queries(query, filtered_df) # df = select_columns(filtered_df, columns) filtered_df = hidden_df.copy() # filtered_df = filtered_df[filtered_df['type'].isin(type_query)] map_open = {'open': 'Yes', 'closed': 'No'} filtered_df = filtered_df[filtered_df['Open?'].isin([map_open[o] for o in open_query])] filtered_df = filter_queries(query, filtered_df) # filtered_df = filtered_df[[map_columns[k] for k in columns]] # deduplication # df = df.drop_duplicates(subset=["Model"]) df = filtered_df.drop_duplicates() df = df[show_columns] return df def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: return df[(df['Model'].str.contains(query, case=False))] def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame: final_df = [] if query != "": queries = [q.strip() for q in query.split(";")] for _q in queries: _q = _q.strip() if _q != "": temp_filtered_df = search_table(filtered_df, _q) if len(temp_filtered_df) > 0: final_df.append(temp_filtered_df) if len(final_df) > 0: filtered_df = pd.concat(final_df) return filtered_df demo = gr.Blocks(css=custom_css) with demo: gr.HTML(TITLE) gr.HTML(SUB_TITLE) gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") with gr.Tabs(elem_classes="tab-buttons") as tabs: # the first tab with gr.TabItem("English", elem_id="llm-benchmark-Sum", id=0): # meta-info with gr.Row(): with gr.Column(): search_bar = gr.Textbox( placeholder=" 🔍 Search for models you are interested in (separate multiple models with `;`) and press ENTER...", show_label=False, elem_id="search-bar", ) # with gr.Row(): # with gr.Column(): # type_query = gr.CheckboxGroup( # choices=["🟢 base", "🔶 chat"], # value=["🔶 chat" ], # label="model types to show", # elem_id="type-select", # interactive=True, # ) with gr.Column(): open_query = gr.CheckboxGroup( choices=["open", "closed"], value=["open", "closed"], label="open-source OR closed-source models?", elem_id="open-select", interactive=True, ) leaderboard_table = gr.components.Dataframe( value = df_results, datatype = TYPES, elem_id = "leaderboard-table", interactive = False, visible=True, # column_widths=["20%", "6%", "8%", "6%", "8%", "8%", "6%", "6%", "6%", "6%", "6%"], ) gr.Markdown("The \"Cost\" column is calculated as USD / Million tokens of output.") hidden_leaderboard_table_for_search = gr.components.Dataframe( value=df_results_init, # elem_id="leaderboard-table", interactive=False, visible=False, ) search_bar.submit( update_table, [ # df_avg, hidden_leaderboard_table_for_search, # shown_columns, #type_query, open_query, # filter_columns_type, # filter_columns_precision, # filter_columns_size, # deleted_models_visibility, search_bar, ], leaderboard_table, ) #for selector in [type_query, open_query]: for selector in [open_query]: selector.change( update_table, [ # df_avg, hidden_leaderboard_table_for_search, # shown_columns, #type_query, open_query, # filter_columns_type, # filter_columns_precision, # filter_columns_size, # deleted_models_visibility, search_bar, ], leaderboard_table, ) with gr.TabItem("Chinese", elem_id="llm-benchmark-Sum", id=1): # meta-info with gr.Row(): with gr.Column(): search_bar = gr.Textbox( placeholder=" 🔍 Search for models you are interested in (separate multiple models with `;`) and press ENTER...", show_label=False, elem_id="search-bar", ) # with gr.Row(): # with gr.Column(): # type_query = gr.CheckboxGroup( # choices=["🟢 base", "🔶 chat"], # value=["🔶 chat" ], # label="model types to show", # elem_id="type-select", # interactive=True, # ) with gr.Column(): open_query = gr.CheckboxGroup( choices=["open", "closed"], value=["open", "closed"], label="open-source OR closed-source models?", elem_id="open-select", interactive=True, ) leaderboard_table = gr.components.Dataframe( value = df_results_chinese, datatype = TYPES, elem_id = "leaderboard-table", interactive = False, visible=True, # column_widths=["20%", "6%", "8%", "6%", "8%", "8%", "6%", "6%", "6%", "6%", "6%"], ) gr.Markdown("The \"Cost\" column is calculated as USD / Million tokens of output.") hidden_leaderboard_table_for_search = gr.components.Dataframe( value=df_results_chinese_init, # elem_id="leaderboard-table", interactive=False, visible=False, ) search_bar.submit( update_table, [ # df_avg, hidden_leaderboard_table_for_search, # shown_columns, #type_query, open_query, # filter_columns_type, # filter_columns_precision, # filter_columns_size, # deleted_models_visibility, search_bar, ], leaderboard_table, ) #for selector in [type_query, open_query]: for selector in [open_query]: selector.change( update_table, [ # df_avg, hidden_leaderboard_table_for_search, # shown_columns, #type_query, open_query, # filter_columns_type, # filter_columns_precision, # filter_columns_size, # deleted_models_visibility, search_bar, ], leaderboard_table, ) # with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=1): # gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") # with gr.Row(): # with gr.Accordion("📙 Citation", open=False): # citation_button = gr.Textbox( # value=CITATION_BUTTON_TEXT, # label=CITATION_BUTTON_LABEL, # lines=20, # elem_id="citation-button", # show_copy_button=True, # ) gr.Markdown(CONTACT_TEXT, elem_classes="markdown-text") demo.launch() scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", seconds=1800) scheduler.start() demo.queue(default_concurrency_limit=40).launch(share=True)