import os import logging import time import datetime import gradio as gr import datasets from huggingface_hub import snapshot_download, WebhooksServer, WebhookPayload, RepoCard from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns 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, AutoEvalColumn, ModelType, Precision, WeightType, fields, ) from src.envs import ( API, EVAL_REQUESTS_PATH, AGGREGATED_REPO, HF_TOKEN, QUEUE_REPO, REPO_ID, HF_HOME, ) from src.populate import get_evaluation_queue_df, get_leaderboard_df from src.submission.submit import add_new_eval 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") # 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" LAST_UPDATE_LEADERBOARD = datetime.datetime.now() def restart_space(): API.restart_space(repo_id=REPO_ID, token=HF_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 get_latest_data_leaderboard(leaderboard_initial_df = None): current_time = datetime.datetime.now() global LAST_UPDATE_LEADERBOARD if current_time - LAST_UPDATE_LEADERBOARD < datetime.timedelta(minutes=10) and leaderboard_initial_df is not None: return leaderboard_initial_df LAST_UPDATE_LEADERBOARD = current_time leaderboard_dataset = datasets.load_dataset( AGGREGATED_REPO, "default", split="train", cache_dir=HF_HOME, download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, # Uses the cached dataset verification_mode="no_checks" ) leaderboard_df = get_leaderboard_df( leaderboard_dataset=leaderboard_dataset, cols=COLS, benchmark_cols=BENCHMARK_COLS, ) return leaderboard_df def get_latest_data_queue(): eval_queue_dfs = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) return eval_queue_dfs def init_space(): """Initializes the application space, loading only necessary data.""" if DO_FULL_INIT: # These downloads only occur on full initialization try: download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH) except Exception: restart_space() # Always redownload the leaderboard DataFrame leaderboard_df = get_latest_data_leaderboard() # Evaluation queue DataFrame retrieval is independent of initialization detail level eval_queue_dfs = get_latest_data_queue() return leaderboard_df, eval_queue_dfs # 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, eval_queue_dfs = init_space() 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(leaderboard_df)) return plot_df def init_leaderboard(dataframe): return Leaderboard( value = dataframe, 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=0.01, 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", ) 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 = init_leaderboard(leaderboard_df) 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") 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, 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, ) demo.load(fn=get_latest_data_leaderboard, inputs=[leaderboard], outputs=[leaderboard]) leaderboard.change(fn=get_latest_data_queue, inputs=None, outputs=[finished_eval_table, running_eval_table, pending_eval_table]) demo.queue(default_concurrency_limit=40) # Start ephemeral Spaces on PRs (see config in README.md) from gradio_space_ci.webhook import IS_EPHEMERAL_SPACE, SPACE_ID, configure_space_ci def enable_space_ci_and_return_server(ui: gr.Blocks) -> WebhooksServer: # Taken from https://huggingface.co/spaces/Wauplin/gradio-space-ci/blob/075119aee75ab5e7150bf0814eec91c83482e790/src/gradio_space_ci/webhook.py#L61 # Compared to original, this one do not monkeypatch Gradio which allows us to define more webhooks. # ht to Lucain! if SPACE_ID is None: print("Not in a Space: Space CI disabled.") return WebhooksServer(ui=demo) if IS_EPHEMERAL_SPACE: print("In an ephemeral Space: Space CI disabled.") return WebhooksServer(ui=demo) card = RepoCard.load(repo_id_or_path=SPACE_ID, repo_type="space") config = card.data.get("space_ci", {}) print(f"Enabling Space CI with config from README: {config}") return configure_space_ci( blocks=ui, trusted_authors=config.get("trusted_authors"), private=config.get("private", "auto"), variables=config.get("variables", "auto"), secrets=config.get("secrets"), hardware=config.get("hardware"), storage=config.get("storage"), ) # Create webhooks server (with CI url if in Space and not ephemeral) webhooks_server = enable_space_ci_and_return_server(ui=demo) # Add webhooks @webhooks_server.add_webhook def update_leaderboard(payload: WebhookPayload) -> None: """Redownloads the leaderboard dataset each time it updates""" if payload.repo.type == "dataset" and payload.event.action == "update": datasets.load_dataset( AGGREGATED_REPO, "default", split="train", cache_dir=HF_HOME, download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD, verification_mode="no_checks" ) # The below code is not used at the moment, as we can manage the queue file locally LAST_UPDATE_QUEUE = datetime.datetime.now() @webhooks_server.add_webhook def update_queue(payload: WebhookPayload) -> None: """Redownloads the queue dataset each time it updates""" if payload.repo.type == "dataset" and payload.event.action == "update": current_time = datetime.datetime.now() global LAST_UPDATE_QUEUE if current_time - LAST_UPDATE_QUEUE > datetime.timedelta(minutes=10): print("Would have updated the queue") # We only redownload is last update was more than 10 minutes ago, as the queue is # updated regularly and heavy to download #download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH) LAST_UPDATE_QUEUE = datetime.datetime.now() webhooks_server.launch()