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, AUTO_RESULTS_PATH, HUMAN_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(AUTO_RESULTS_PATH) print(HUMAN_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() AUTO_LEADERBOARD_DF = get_leaderboard_df(AUTO_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) HUMAN_LEADERBOARD_DF = get_leaderboard_df(HUMAN_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.") 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.never_displayed], label="Select Columns to Display:", ), search_columns=[AutoEvalColumn.generative_model_link.name, AutoEvalColumn.retrieval_model_link.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.retrieval_model.name, type="checkboxgroup", label="Retrieval models"), ColumnFilter(AutoEvalColumn.generative_model.name, type="checkboxgroup", label="Generative models"), # 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") with gr.Tabs(elem_classes="tab-buttons") as tabs: with gr.TabItem("🏆OmniEval-Human", elem_id="llm-benchmark-tab-table", id=0): leaderboard = init_leaderboard(HUMAN_LEADERBOARD_DF) with gr.TabItem("🤖OmniEval-Auto", elem_id="llm-benchmark-tab-table", id=1): leaderboard = init_leaderboard(AUTO_LEADERBOARD_DF) with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2): gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") # with gr.TabItem("🚀 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"✅ 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)") # eval_result=gr.Textbox(label="Eval Result") # # submit_button = gr.Button("Submit Eval") # submission_result = gr.Markdown() # submit_button.click( # add_new_eval, # [ # model_name_textbox, # revision_name_textbox, # 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()