import gradio as gr from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns import pandas as pd from apscheduler.schedulers.background import BackgroundScheduler 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 ( BENCHMARK_COLS, COLS, EVAL_COLS, EVAL_TYPES, AutoEvalColumn, ModelType, fields, WeightType, Precision ) 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 loggedin = False def check_login(profile: gr.OAuthProfile | None) -> bool: if profile is None: return False return True 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() LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, 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): # if dataframe is None or dataframe.empty: # raise ValueError("Leaderboard DataFrame is empty or None.") # print(dataframe.columns) 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], 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="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") login_button = gr.LoginButton(elem_id="oauth-button") with gr.Tabs(elem_classes="tab-buttons") as tabs: with gr.TabItem("πŸ… LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0): leaderboard = init_leaderboard(LEADERBOARD_DF) with gr.TabItem("πŸ“ About", elem_id="llm-benchmark-tab-table", id=2): gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") with gr.TabItem("πŸš€ Submit here! ", elem_id="llm-benchmark-tab-table", id=3): # with gr.Column(): # with gr.Row(): # gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") # with gr.Column(): # with gr.Accordion( # f"βœ… 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"πŸ”„ 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("# βœ‰οΈβœ¨ 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="ΠžΡ€Π³Π°Π½ΠΈΠ·Π°Ρ†ΠΈΡ") ans_file = gr.File(label="Arena Hard Answer File", file_types=["json","jsonl"]) loggedin = login_button.click(check_login) 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, ans_file, ], submission_result, ) with gr.Row(): with gr.Accordion("πŸ“™ 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()