import gradio as gr from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download from src.about import ( INTRODUCTION_TEXT, BENCHMARKS_TEXT, TITLE, EVALUATION_QUEUE_TEXT ) from src.display.css_html_js import custom_css from src.leaderboard.read_evals import get_raw_eval_results, get_leaderboard_df from src.envs import API, EVAL_RESULTS_PATH, REPO_ID, RESULTS_REPO, TOKEN from utils import update_table, update_metric, update_table_long_doc, upload_file, get_default_cols, submit_results from src.benchmarks import DOMAIN_COLS_QA, LANG_COLS_QA, DOMAIN_COLS_LONG_DOC, LANG_COLS_LONG_DOC, metric_list from src.display.utils import TYPES_QA, TYPES_LONG_DOC def restart_space(): API.restart_space(repo_id=REPO_ID) try: snapshot_download( repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN ) except Exception: restart_space() raw_data = get_raw_eval_results(f"{EVAL_RESULTS_PATH}/AIR-Bench_24.04") original_df_qa = get_leaderboard_df( raw_data, task='qa', metric='ndcg_at_3') original_df_long_doc = get_leaderboard_df( raw_data, task='long-doc', metric='ndcg_at_3') print(f'raw data: {len(raw_data)}') print(f'QA data loaded: {original_df_qa.shape}') print(f'Long-Doc data loaded: {len(original_df_long_doc)}') leaderboard_df_qa = original_df_qa.copy() shown_columns_qa = get_default_cols('qa', leaderboard_df_qa.columns, add_fix_cols=True) leaderboard_df_qa = leaderboard_df_qa[shown_columns_qa] leaderboard_df_long_doc = original_df_long_doc.copy() shown_columns_long_doc = get_default_cols('long-doc', leaderboard_df_long_doc.columns, add_fix_cols=True) leaderboard_df_long_doc = leaderboard_df_long_doc[shown_columns_long_doc] def update_metric_qa( metric: str, domains: list, langs: list, reranking_model: list, query: str, ): return update_metric(raw_data, 'qa', metric, domains, langs, reranking_model, query) def update_metric_long_doc( metric: str, domains: list, langs: list, reranking_model: list, query: str, ): return update_metric(raw_data, "long-doc", metric, domains, langs, reranking_model, query) demo = gr.Blocks(css=custom_css) with demo: gr.HTML(TITLE) gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") with gr.Tabs(elem_classes="tab-buttons") as tabs: with gr.TabItem("QA", elem_id="qa-benchmark-tab-table", id=0): with gr.Row(): with gr.Column(): # search bar for model name with gr.Row(): search_bar = gr.Textbox( placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...", show_label=False, elem_id="search-bar", ) # select the metric selected_metric = gr.Dropdown( choices=metric_list, value=metric_list[1], label="Select the metric", interactive=True, elem_id="metric-select", ) with gr.Column(min_width=320): # select domain with gr.Row(): selected_domains = gr.CheckboxGroup( choices=DOMAIN_COLS_QA, value=DOMAIN_COLS_QA, label="Select the domains", elem_id="domain-column-select", interactive=True, ) # select language with gr.Row(): selected_langs = gr.Dropdown( choices=LANG_COLS_QA, value=LANG_COLS_QA, label="Select the languages", elem_id="language-column-select", multiselect=True, interactive=True ) # select reranking model reranking_models = list(frozenset([eval_result.reranking_model for eval_result in raw_data])) with gr.Row(): selected_rerankings = gr.CheckboxGroup( choices=reranking_models, value=reranking_models, label="Select the reranking models", elem_id="reranking-select", interactive=True ) leaderboard_table = gr.components.Dataframe( value=leaderboard_df_qa, datatype=TYPES_QA, elem_id="leaderboard-table", interactive=False, visible=True, ) # Dummy leaderboard for handling the case when the user uses backspace key hidden_leaderboard_table_for_search = gr.components.Dataframe( value=leaderboard_df_qa, datatype=TYPES_QA, # headers=COLS, # datatype=TYPES, visible=False, ) # Set search_bar listener search_bar.submit( update_table, [ hidden_leaderboard_table_for_search, selected_domains, selected_langs, selected_rerankings, search_bar, ], leaderboard_table, ) # Set column-wise listener for selector in [ selected_domains, selected_langs, selected_rerankings ]: selector.change( update_table, [ hidden_leaderboard_table_for_search, selected_domains, selected_langs, selected_rerankings, search_bar, ], leaderboard_table, queue=True, ) # set metric listener selected_metric.change( update_metric_qa, [ selected_metric, selected_domains, selected_langs, selected_rerankings, search_bar, ], leaderboard_table, queue=True ) with gr.TabItem("Long Doc", elem_id="long-doc-benchmark-tab-table", id=1): with gr.Row(): with gr.Column(): with gr.Row(): search_bar = gr.Textbox( placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...", show_label=False, elem_id="search-bar-long-doc", ) # select the metric selected_metric = gr.Dropdown( choices=metric_list, value=metric_list[1], label="Select the metric", interactive=True, elem_id="metric-select-long-doc", ) with gr.Column(min_width=320): # select domain with gr.Row(): selected_domains = gr.CheckboxGroup( choices=DOMAIN_COLS_LONG_DOC, value=DOMAIN_COLS_LONG_DOC, label="Select the domains", elem_id="domain-column-select-long-doc", interactive=True, ) # select language with gr.Row(): selected_langs = gr.Dropdown( choices=LANG_COLS_LONG_DOC, value=LANG_COLS_LONG_DOC, label="Select the languages", elem_id="language-column-select-long-doc", multiselect=True, interactive=True ) # select reranking model reranking_models = list(frozenset([eval_result.reranking_model for eval_result in raw_data])) with gr.Row(): selected_rerankings = gr.CheckboxGroup( choices=reranking_models, value=reranking_models, label="Select the reranking models", elem_id="reranking-select-long-doc", interactive=True ) leaderboard_table_long_doc = gr.components.Dataframe( value=leaderboard_df_long_doc, datatype=TYPES_LONG_DOC, elem_id="leaderboard-table-long-doc", interactive=False, visible=True, ) # Dummy leaderboard for handling the case when the user uses backspace key hidden_leaderboard_table_for_search = gr.components.Dataframe( value=leaderboard_df_long_doc, datatype=TYPES_LONG_DOC, visible=False, ) # Set search_bar listener search_bar.submit( update_table_long_doc, [ hidden_leaderboard_table_for_search, selected_domains, selected_langs, selected_rerankings, search_bar, ], leaderboard_table_long_doc, ) # Set column-wise listener for selector in [ selected_domains, selected_langs, selected_rerankings ]: selector.change( update_table_long_doc, [ hidden_leaderboard_table_for_search, selected_domains, selected_langs, selected_rerankings, search_bar, ], leaderboard_table_long_doc, queue=True, ) # set metric listener selected_metric.change( update_metric_long_doc, [ selected_metric, selected_domains, selected_langs, selected_rerankings, search_bar, ], leaderboard_table_long_doc, queue=True ) with gr.TabItem("🚀Submit here!", elem_id="submit-tab-table", id=2): with gr.Column(): with gr.Row(): gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") with gr.Row(): gr.Markdown("## ✉️Submit your model here!", elem_classes="markdown-text") with gr.Row(): with gr.Column(): benchmark_version = gr.Dropdown( ["AIR-Bench_24.04",], value="AIR-Bench_24.04", interactive=True, label="AIR-Bench Version") with gr.Column(): model_name = gr.Textbox(label="Model name") with gr.Column(): model_url = gr.Textbox(label="Model URL") with gr.Row(): upload_button = gr.UploadButton("Click to upload search results", file_count="single") with gr.Row(): file_output = gr.File() with gr.Row(): submit_anonymous = gr.Checkbox( label="Nope. I want to submit anonymously 🥷", value=False, info="Do you want to shown on the leaderboard by default?") with gr.Row(): submit_button = gr.Button("Submit") with gr.Row(): submission_result = gr.Markdown() upload_button.upload( upload_file, [ upload_button, ], file_output) submit_button.click( submit_results, [ file_output, model_name, model_url, benchmark_version, submit_anonymous ], submission_result, show_progress="hidden" ) with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=3): gr.Markdown(BENCHMARKS_TEXT, elem_classes="markdown-text") scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", seconds=1800) scheduler.start() demo.queue(default_concurrency_limit=40).launch()