import gradio as gr from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns, SearchColumns 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, COMING_SOON_TEXT ) 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, get_model_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() 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.") return Leaderboard( value=dataframe, datatype=[c.type for c in fields(AutoEvalColumn)], select_columns=None, # 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=None, # search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name], search_columns=SearchColumns(primary_column=AutoEvalColumn.model.name, secondary_columns=[], placeholder="Search by the model name", label="Searching"), hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden], filter_columns=None, # [ # 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, ) # model_result_path = "./src/results/models_2024-10-07-14:50:12.666068.jsonl" # model_result_path = "./src/results/models_2024-10-08-03:10:26.811832.jsonl" model_result_path = "./src/results/models_2024-10-08-03:25:44.801310.jsonl" # model_leaderboard_df = get_model_leaderboard_df(model_result_path) def overall_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=None, search_columns=SearchColumns(primary_column=AutoEvalColumn.model.name, secondary_columns=[], placeholder="Search by the model name", label="Searching"), hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden], filter_columns=None, interactive=False, ) def overview_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=None, search_columns=SearchColumns(primary_column=AutoEvalColumn.model.name, secondary_columns=[], placeholder="Search by the model name", label="Searching"), hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden], filter_columns=None, 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("🏅 Overview", elem_id="llm-benchmark-tab-table", id=0): leaderboard = init_leaderboard(LEADERBOARD_DF) # leaderboard = overview_leaderboard(model_leaderboard_df) with gr.TabItem("🎯 Overall", elem_id="llm-benchmark-tab-table", id=1): leaderboard = overall_leaderboard( get_model_leaderboard_df( model_result_path, benchmark_cols=[ AutoEvalColumn.rank_overall.name, AutoEvalColumn.model.name, AutoEvalColumn.score_overall.name, AutoEvalColumn.sd_overall.name, AutoEvalColumn.license.name, AutoEvalColumn.organization.name, AutoEvalColumn.knowledge_cutoff.name, ], rank_col=[AutoEvalColumn.rank_overall.name], )) with gr.TabItem("🔢 Math", elem_id="math-tab-table", id=2): # leaderboard = init_leaderboard(LEADERBOARD_DF) with gr.TabItem("🧮 Algebra", elem_id="algebra_subtab", id=0, elem_classes="subtab"): leaderboard = overall_leaderboard( get_model_leaderboard_df( model_result_path, benchmark_cols=[ AutoEvalColumn.rank_math_algebra.name, AutoEvalColumn.model.name, AutoEvalColumn.score_math_algebra.name, AutoEvalColumn.sd_math_algebra.name, AutoEvalColumn.license.name, AutoEvalColumn.organization.name, AutoEvalColumn.knowledge_cutoff.name, ], rank_col=[AutoEvalColumn.rank_math_algebra.name], ) ) with gr.TabItem("📐 Geometry", elem_id="geometry_subtab", id=1, elem_classes="subtab"): leaderboard = overall_leaderboard( get_model_leaderboard_df( model_result_path, benchmark_cols=[ AutoEvalColumn.rank_math_geometry.name, AutoEvalColumn.model.name, AutoEvalColumn.score_math_geometry.name, AutoEvalColumn.sd_math_geometry.name, AutoEvalColumn.license.name, AutoEvalColumn.organization.name, AutoEvalColumn.knowledge_cutoff.name, ], rank_col=[AutoEvalColumn.rank_math_geometry.name], ) ) with gr.TabItem("📊 Probability", elem_id="prob_subtab", id=2, elem_classes="subtab"): leaderboard = overall_leaderboard( get_model_leaderboard_df( model_result_path, benchmark_cols=[ AutoEvalColumn.rank_math_probability.name, AutoEvalColumn.model.name, AutoEvalColumn.score_math_probability.name, AutoEvalColumn.sd_math_probability.name, AutoEvalColumn.license.name, AutoEvalColumn.organization.name, AutoEvalColumn.knowledge_cutoff.name, ], rank_col=[AutoEvalColumn.rank_math_probability.name], ) ) with gr.TabItem("🧠 Reasoning", elem_id="reasonong-tab-table", id=3): with gr.TabItem("🧩 Logical", elem_id="logical_subtab", id=0, elem_classes="subtab"): leaderboard = overall_leaderboard( get_model_leaderboard_df( model_result_path, benchmark_cols=[ AutoEvalColumn.rank_reason_logical.name, AutoEvalColumn.model.name, AutoEvalColumn.score_reason_logical.name, AutoEvalColumn.sd_reason_logical.name, AutoEvalColumn.license.name, AutoEvalColumn.organization.name, AutoEvalColumn.knowledge_cutoff.name, ], rank_col=[AutoEvalColumn.rank_reason_logical.name], ) ) with gr.TabItem("🗣️ Social", elem_id="social_subtab", id=1, elem_classes="subtab"): leaderboard = overall_leaderboard( get_model_leaderboard_df( model_result_path, benchmark_cols=[ AutoEvalColumn.rank_reason_social.name, AutoEvalColumn.model.name, AutoEvalColumn.score_reason_social.name, AutoEvalColumn.sd_reason_social.name, AutoEvalColumn.license.name, AutoEvalColumn.organization.name, AutoEvalColumn.knowledge_cutoff.name, ], rank_col=[AutoEvalColumn.rank_reason_social.name], ) ) with gr.TabItem(" Coding", elem_id="coding-tab-table", id=4): gr.Markdown(COMING_SOON_TEXT, elem_classes="markdown-text") with gr.TabItem("🔬 Science", elem_id="science-table", id=5): gr.Markdown(COMING_SOON_TEXT, elem_classes="markdown-text") with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=6): 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)") 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()