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_QUEUE_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, TITLE, ) from src.display.css_html_js import custom_css from src.display.utils import ( RGB_BENCHMARK_COLS, PGB_BENCHMARK_COLS, GUE_BENCHMARK_COLS, GB_BENCHMARK_COLS, RGB_COLS, PGB_COLS, GUE_COLS, GB_COLS, EVAL_COLS, EVAL_TYPES, AutoEvalColumnRGB, AutoEvalColumnPGB, AutoEvalColumnGUE, AutoEvalColumnGB, ModelType, Precision, WeightType, fields, ) from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN from src.populate import get_evaluation_queue_df, get_leaderboard_df from src.submission.submit import add_new_eval def restart_space(): API.restart_space(repo_id=REPO_ID) ### Space initialisation # 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() RGB_LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH+"/RGB/", EVAL_REQUESTS_PATH+"/RGB/", RGB_COLS, RGB_BENCHMARK_COLS) PGB_LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH+"/PGB/", EVAL_REQUESTS_PATH+"/PGB/", PGB_COLS, PGB_BENCHMARK_COLS) GUE_LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH+"/GUE/", EVAL_REQUESTS_PATH+"/GUE/", GUE_COLS, GUE_BENCHMARK_COLS) GB_LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH+"/GB/", EVAL_REQUESTS_PATH+"/GB/", GB_COLS, GB_BENCHMARK_COLS) ( finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df, ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) def init_leaderboard(dataframe, AutoEvalColumn): 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="Select Columns to Display:", ), 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="Model types"), ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"), ColumnFilter( AutoEvalColumn.params.name, type="slider", min=0, max=2000, label="Select the number of parameters (M)", ), # ColumnFilter( # AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True # ), ], # bool_checkboxgroup_label="Hide models", # 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("RGB", elem_id="rgb-benchmark-tab-table", id=0): leaderboard = init_leaderboard(RGB_LEADERBOARD_DF, AutoEvalColumnRGB) with gr.TabItem("PGB", elem_id="pgb-benchmark-tab-table", id=1): leaderboard2 = init_leaderboard(PGB_LEADERBOARD_DF, AutoEvalColumnPGB) with gr.TabItem("GUE", elem_id="gue-benchmark-tab-table", id=2): leaderboard3 = init_leaderboard(GUE_LEADERBOARD_DF, AutoEvalColumnGUE) with gr.TabItem("GB", elem_id="gb-benchmark-tab-table", id=3): leaderboard4 = init_leaderboard(GB_LEADERBOARD_DF, AutoEvalColumnGB) with gr.TabItem("📝 About", elem_id="rgb-benchmark-tab-table", id=4): gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") with gr.TabItem("🚀 Submit here! ", elem_id="rgb-benchmark-tab-table", id=5): with gr.Column(): with gr.Row(): gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") 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, ) 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=None, 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)") 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, ) scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", seconds=1800) scheduler.start() demo.queue(default_concurrency_limit=40).launch()