#!/usr/bin/env python # -*- coding: utf-8 -*- # flake8: noqa E501 import shutil import gradio as gr from apscheduler.schedulers.background import BackgroundScheduler from gradio_leaderboard import ColumnFilter, Leaderboard, SelectColumns from huggingface_hub import snapshot_download from src.about import ( CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, EVALUATION_REQUESTS_TEXT, EVALUATION_SCRIPT, 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, CACHE_PATH, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, REPO_ID, REQUESTS_REPO, RESULTS_REPO, TOKEN, ) from src.populate import get_evaluation_requests_df, get_leaderboard_df from src.submission.submit import add_new_eval def restart_space(): API.restart_space(repo_id=REPO_ID) # Space initialisation shutil.rmtree(CACHE_PATH, ignore_errors=True) try: snapshot_download( repo_id=REQUESTS_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN, ) except Exception: restart_space() 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, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS, ) ( finished_eval_requests_df, running_eval_requests_df, pending_eval_requests_df, ) = get_evaluation_requests_df(EVAL_REQUESTS_PATH, EVAL_COLS) def init_leaderboard(dataframe): if dataframe is None or dataframe.empty: raise ValueError("Leaderboard DataFrame is empty or None.") 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], label="Columns", ), search_columns=[AutoEvalColumn.model.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='Training Type', ), ColumnFilter( AutoEvalColumn.task00.name, type='slider', default=[ LEADERBOARD_DF[AutoEvalColumn.task00.name].min(), LEADERBOARD_DF[AutoEvalColumn.task00.name].max(), ], label=AutoEvalColumn.task00.name, ), ColumnFilter( AutoEvalColumn.task01.name, type='slider', default=[ LEADERBOARD_DF[AutoEvalColumn.task01.name].min(), LEADERBOARD_DF[AutoEvalColumn.task01.name].max(), ], label=AutoEvalColumn.task01.name, ), ColumnFilter( AutoEvalColumn.task02.name, type='slider', default=[ LEADERBOARD_DF[AutoEvalColumn.task02.name].min(), LEADERBOARD_DF[AutoEvalColumn.task02.name].max(), ], label=AutoEvalColumn.task02.name, ), ], bool_checkboxgroup_label=' ', interactive=False, ) 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("๐Ÿ† Ranking", elem_id="llm-benchmark-tab-table", id=0): leaderboard = init_leaderboard(LEADERBOARD_DF) with gr.TabItem("๐Ÿง  About", elem_id="llm-benchmark-tab-table", id=2): gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") # with gr.Accordion( # "Evaluation script", # open=False, # ): # gr.Markdown( # EVALUATION_SCRIPT, # elem_classes="markdown-text", # ) with gr.TabItem("๐Ÿงช Submissions", elem_id="llm-benchmark-tab-table", id=3): with gr.Column(): with gr.Row(): gr.Markdown(EVALUATION_REQUESTS_TEXT, elem_classes="markdown-text") # with gr.Column(): # with gr.Accordion( # f"โœ… Finished ({len(finished_eval_requests_df)})", # open=False, # ): # with gr.Row(): # finished_eval_table = gr.components.Dataframe( # value=finished_eval_requests_df, # headers=EVAL_COLS, # datatype=EVAL_TYPES, # row_count=5, # ) # with gr.Accordion( # f"โณ Pending ({len(pending_eval_requests_df)})", # open=False, # ): # with gr.Row(): # pending_eval_table = gr.components.Dataframe( # value=pending_eval_requests_df, # headers=EVAL_COLS, # datatype=EVAL_TYPES, # row_count=5, # ) with gr.Row(): gr.Markdown("# โœ‰๏ธ Submission", 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 in [ModelType.PT, ModelType.FT]], label="Model type", multiselect=False, value=None, interactive=True, ) # precision = gr.Dropdown( # choices=[i.value.name for i in Precision if i != Precision.Unknown], # label="Precision", # multiselect=False, # value="bfloat16", # 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)") submit_button = gr.Button("Submit") submission_result = gr.Markdown() def submit_with_braindao_check(model_name, revision, model_type): if model_name.split("/")[0] == "braindao": model_type = ModelType.BrainDAO.to_str(" ") return add_new_eval(model_name, revision, model_type) submit_button.click( submit_with_braindao_check, [ model_name_textbox, # base_model_name_textbox, revision_name_textbox, # precision, # weight_type, model_type, ], submission_result, ) # with gr.Row(): # with gr.Accordion("๐Ÿ“™ 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", seconds=900) scheduler.start() demo.queue(default_concurrency_limit=40).launch( server_name="0.0.0.0", allowed_paths=[ "images/logo.svg", "images/social.jpg", ], )