import gradio as gr from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download from src.about import ( INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, TITLE, ) from src.display.css_html_js import custom_css from src.display.utils import ( QA_BENCHMARK_COLS, LONG_DOC_BENCHMARK_COLS, COLS_QA, COLS_LONG_DOC, TYPES, AutoEvalColumnQA, fields ) from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN from src.populate import get_leaderboard_df from utils import update_table, update_metric, update_table_long_doc from src.benchmarks import DOMAIN_COLS_QA, LANG_COLS_QA, DOMAIN_COLS_LONG_DOC, LANG_COLS_LONG_DOC, metric_list def restart_space(): API.restart_space(repo_id=REPO_ID) # try: # print(EVAL_REQUESTS_PATH) # snapshot_download( # repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, # token=TOKEN # ) # except Exception: # restart_space() # try: # print(EVAL_RESULTS_PATH) # 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() from src.leaderboard.read_evals import get_raw_eval_results raw_data_qa = get_raw_eval_results(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH) original_df_qa = get_leaderboard_df(raw_data_qa, COLS_QA, QA_BENCHMARK_COLS, task='qa', metric='ndcg_at_3') original_df_long_doc = get_leaderboard_df(raw_data_qa, COLS_LONG_DOC, LONG_DOC_BENCHMARK_COLS, task='long_doc', metric='ndcg_at_3') print(f'raw data: {len(raw_data_qa)}') print(f'QA data loaded: {original_df_qa.shape}') print(f'Long-Doc data loaded: {len(original_df_long_doc)}') leaderboard_df = original_df_qa.copy() leaderboard_df_long_doc = original_df_long_doc.copy() print(leaderboard_df_long_doc.head()) def update_metric_qa( metric: str, domains: list, langs: list, reranking_model: list, query: str, ): return update_metric(raw_data_qa, '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_qa, 'long_doc', metric, domains, langs, reranking_model, query) # ( # finished_eval_queue_df, # running_eval_queue_df, # pending_eval_queue_df, # ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) 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.CheckboxGroup( choices=LANG_COLS_QA, value=LANG_COLS_QA, label="Select the languages", elem_id="language-column-select", interactive=True ) # select reranking model reranking_models = list(frozenset([eval_result.reranking_model for eval_result in raw_data_qa])) 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, # headers=shown_columns, # datatype=TYPES, 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, # 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.CheckboxGroup( choices=LANG_COLS_LONG_DOC, value=LANG_COLS_LONG_DOC, label="Select the languages", elem_id="language-column-select-long-doc", interactive=True ) # select reranking model reranking_models = list(frozenset([eval_result.reranking_model for eval_result in raw_data_qa])) 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, # headers=shown_columns, # datatype=TYPES, 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, # headers=COLS, # datatype=TYPES, 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("📝 About", elem_id="llm-benchmark-tab-table", id=2): gr.Markdown(LLM_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()