import gradio as gr from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download from src.about import ( CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, TITLE, SUBMIT_TEXT ) from src.display.css_html_js import custom_css from src.display.utils import ( BENCHMARK_COLS, COLS, AutoEvalColumn, fields, ) from src.envs import API, EVAL_RESULTS_PATH, REPO_ID, RESULTS_REPO, TOKEN from src.populate import get_leaderboard_df def restart_space(): API.restart_space(repo_id=REPO_ID) ### Space initialisation 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() LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, COLS, BENCHMARK_COLS) def init_leaderboard(dataframe, css): if dataframe is None or dataframe.empty: raise ValueError("Leaderboard DataFrame is empty or None.") with gr.Blocks(css=css) as app: # Title gr.Markdown("# Leaderboard") # Select Columns - Full width, as CheckboxGroup select_columns = gr.CheckboxGroup( label="Select Columns to Display:", choices=[c.name for c in fields(AutoEvalColumn)], value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default], elem_id="select-columns" ) # Search Columns - Full width search_columns = gr.Textbox( label="Search", placeholder=f"Search in {', '.join([AutoEvalColumn.model_name.name])}...", lines=1, elem_id="search-columns" ) # Initialize DataFrame with only default-selected columns default_columns = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default] initial_dataframe = dataframe[default_columns].copy() # Leaderboard Component leaderboard = gr.Dataframe( value=initial_dataframe, datatype=[c.type for c in fields(AutoEvalColumn) if c.name in default_columns], headers=default_columns, wrap=True, interactive=False, max_height=800 ) # Update function def update_leaderboard(search, selected_cols): df = dataframe.copy() # Apply search if search: df = df[df[AutoEvalColumn.model_name.name].str.contains(search, case=False, na=False)] # Filter columns to display visible_cols = [col for col in selected_cols if col in df.columns] df = df[visible_cols] return df # Connect inputs to update leaderboard search_columns.change( fn=update_leaderboard, inputs=[search_columns, select_columns], outputs=leaderboard ) select_columns.change( fn=update_leaderboard, inputs=[search_columns, select_columns], outputs=leaderboard ) return app demo = gr.Blocks(fill_height=False, 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("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0): leaderboard = init_leaderboard(LEADERBOARD_DF, css=custom_css) with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2): gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3): with gr.Row(): gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text") with gr.Row(): gr.Markdown(SUBMIT_TEXT, elem_classes="markdown-text") with gr.Row(): with gr.Accordion("📙 Citation", open=True): citation_button = gr.Textbox( value=CITATION_BUTTON_TEXT, label=CITATION_BUTTON_LABEL, lines=10, elem_id="citation-button", show_copy_button=True, ) scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", seconds=1800) scheduler.start() demo.queue(default_concurrency_limit=40).launch()