import os import pandas as pd import logging import time import gradio as gr from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns from gradio_space_ci import enable_space_ci from src.display.about import ( CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, EVALUATION_QUEUE_TEXT, FAQ_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, TITLE, ) from src.display.css_html_js import custom_css from src.display.utils import ( BENCHMARK_COLS, COLS, EVAL_COLS, EVAL_TYPES, NUMERIC_INTERVALS, TYPES, AutoEvalColumn, ModelType, Precision, WeightType, fields, ) from src.envs import ( API, DYNAMIC_INFO_FILE_PATH, DYNAMIC_INFO_PATH, DYNAMIC_INFO_REPO, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO, ) from src.populate import get_evaluation_queue_df, get_leaderboard_df from src.scripts.update_all_request_files import update_dynamic_files from src.submission.submit import add_new_eval from src.tools.collections import update_collections from src.tools.plots import create_metric_plot_obj, create_plot_df, create_scores_df # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Start ephemeral Spaces on PRs (see config in README.md) enable_space_ci() def restart_space(): API.restart_space(repo_id=REPO_ID, token=H4_TOKEN) def time_diff_wrapper(func): def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() diff = end_time - start_time logging.info(f"Time taken for {func.__name__}: {diff} seconds") return result return wrapper @time_diff_wrapper def download_dataset(repo_id, local_dir, repo_type="dataset", max_attempts=3, backoff_factor=1.5): """Download dataset with exponential backoff retries.""" attempt = 0 while attempt < max_attempts: try: logging.info(f"Downloading {repo_id} to {local_dir}") snapshot_download( repo_id=repo_id, local_dir=local_dir, repo_type=repo_type, tqdm_class=None, etag_timeout=30, max_workers=8, ) logging.info("Download successful") return except Exception as e: wait_time = backoff_factor ** attempt logging.error(f"Error downloading {repo_id}: {e}, retrying in {wait_time}s") time.sleep(wait_time) attempt += 1 raise Exception(f"Failed to download {repo_id} after {max_attempts} attempts") def init_space(full_init: bool = True): """Initializes the application space, loading only necessary data.""" if full_init: # These downloads only occur on full initialization try: download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH) download_dataset(DYNAMIC_INFO_REPO, DYNAMIC_INFO_PATH) download_dataset(RESULTS_REPO, EVAL_RESULTS_PATH) except Exception: restart_space() # Always retrieve the leaderboard DataFrame raw_data, original_df = get_leaderboard_df( results_path=EVAL_RESULTS_PATH, requests_path=EVAL_REQUESTS_PATH, dynamic_path=DYNAMIC_INFO_FILE_PATH, cols=COLS, benchmark_cols=BENCHMARK_COLS, ) if full_init: # Collection update only happens on full initialization update_collections(original_df) leaderboard_df = original_df.copy() # Evaluation queue DataFrame retrieval is independent of initialization detail level eval_queue_dfs = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) return leaderboard_df, raw_data, original_df, eval_queue_dfs # Convert the environment variable "LEADERBOARD_FULL_INIT" to a boolean value, defaulting to True if the variable is not set. # This controls whether a full initialization should be performed. do_full_init = os.getenv("LEADERBOARD_FULL_INIT", "True") == "True" # Calls the init_space function with the `full_init` parameter determined by the `do_full_init` variable. # This initializes various DataFrames used throughout the application, with the level of initialization detail controlled by the `do_full_init` flag. leaderboard_df, raw_data, original_df, eval_queue_dfs = init_space(full_init=do_full_init) finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = eval_queue_dfs # Data processing for plots now only on demand in the respective Gradio tab def load_and_create_plots(): plot_df = create_plot_df(create_scores_df(raw_data)) return plot_df 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("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0): leaderboard = Leaderboard( value=leaderboard_df, datatype=[c.type for c in fields(AutoEvalColumn)], select_columns=SelectColumns( default_selection=[ c.name for c in fields(AutoEvalColumn) if c.displayed_by_default ], cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden or c.dummy], label="Select Columns to Display:", ), search_columns=[ AutoEvalColumn.model.name, AutoEvalColumn.fullname.name, AutoEvalColumn.license.name ], hide_columns=[ c.name for c in fields(AutoEvalColumn) if c.hidden ], filter_columns=[ ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"), ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"), ColumnFilter(AutoEvalColumn.params.name, type="slider", min=0, max=150, label="Select the number of parameters (B)"), ColumnFilter(AutoEvalColumn.still_on_hub.name, type="boolean", label="Private or deleted", default=True), ColumnFilter(AutoEvalColumn.merged.name, type="boolean", label="Contains a merge/moerge", default=True), ColumnFilter(AutoEvalColumn.moe.name, type="boolean", label="MoE", default=False), ColumnFilter(AutoEvalColumn.not_flagged.name, type="boolean", label="Flagged", default=True), ], bool_checkboxgroup_label="Hide models" ) with gr.TabItem("📈 Metrics through time", elem_id="llm-benchmark-tab-table", id=2): with gr.Row(): with gr.Column(): plot_df = load_and_create_plots() chart = create_metric_plot_obj( plot_df, [AutoEvalColumn.average.name], title="Average of Top Scores and Human Baseline Over Time (from last update)", ) gr.Plot(value=chart, min_width=500) with gr.Column(): plot_df = load_and_create_plots() chart = create_metric_plot_obj( plot_df, BENCHMARK_COLS, title="Top Scores and Human Baseline Over Time (from last update)", ) gr.Plot(value=chart, min_width=500) with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=3): gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") with gr.TabItem("❗FAQ", elem_id="llm-benchmark-tab-table", id=4): gr.Markdown(FAQ_TEXT, elem_classes="markdown-text") with gr.TabItem("🚀 Submit ", elem_id="llm-benchmark-tab-table", id=5): 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(): model_name_textbox = gr.Textbox(label="Model name") revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main") private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC) model_type = gr.Dropdown( choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown], label="Model type", multiselect=False, value=ModelType.FT.to_str(" : "), interactive=True, ) with gr.Column(): precision = gr.Dropdown( choices=[i.value.name for i in Precision if i != Precision.Unknown], label="Precision", multiselect=False, value="float16", interactive=True, ) weight_type = gr.Dropdown( choices=[i.value.name for i in WeightType], label="Weights type", multiselect=False, value="Original", interactive=True, ) base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)") with gr.Column(): with gr.Accordion( f"✅ Finished Evaluations ({len(finished_eval_queue_df)})", open=False, ): with gr.Row(): finished_eval_table = gr.components.Dataframe( value=finished_eval_queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5, ) with gr.Accordion( f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})", open=False, ): with gr.Row(): running_eval_table = gr.components.Dataframe( value=running_eval_queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5, ) with gr.Accordion( f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})", open=False, ): with gr.Row(): pending_eval_table = gr.components.Dataframe( value=pending_eval_queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5, ) submit_button = gr.Button("Submit Eval") submission_result = gr.Markdown() submit_button.click( add_new_eval, [ model_name_textbox, base_model_name_textbox, revision_name_textbox, precision, private, weight_type, model_type, ], submission_result, ) 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, ) scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", hours=3) # restarted every 3h scheduler.add_job(update_dynamic_files, "interval", hours=2) # launched every 2 hour scheduler.start() demo.queue(default_concurrency_limit=40).launch()