from time import sleep import gradio as gr import pandas as pd from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download # from gradio_space_ci import enable_space_ci from src.display.about import ( INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, TITLE, ) from src.display.css_html_js import custom_css from src.display.utils import ( BENCHMARK_COLS, COLS, EVAL_COLS, EVAL_TYPES, NUMERIC_INTERVALS, TYPES, AutoEvalColumn, ModelType, fields, WeightType, Precision, Format ) from src.envs import API, EVAL_RESULTS_PATH, RESULTS_REPO, REPO_ID, HF_TOKEN from src.populate import get_leaderboard_df # from src.tools.collections import update_collections from src.tools.plots import ( create_metric_plot_obj, create_plot_df, create_scores_df, ) # Start ephemeral Spaces on PRs (see config in README.md) # enable_space_ci() def restart_space(): API.restart_space(repo_id=REPO_ID, token=HF_TOKEN) def init_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, resume_download=True, ) except Exception as e: print(e) sleep(180) # sleep 3 min return init_space() raw_data, original_df = get_leaderboard_df( results_path=EVAL_RESULTS_PATH, cols=COLS, benchmark_cols=BENCHMARK_COLS ) # update_collections(original_df.copy()) leaderboard_df = original_df.copy() plot_df = create_plot_df(create_scores_df(raw_data)) return leaderboard_df, original_df, plot_df leaderboard_df, original_df, plot_df = init_space() # Searching and filtering def update_table( hidden_df: pd.DataFrame, columns: list, # type_query: list, weight_precision_query: str, activation_precision_query: str, size_query: list, hide_models: list, format_query: list, query: str, ): filtered_df = filter_models( df=hidden_df, # type_query=type_query, size_query=size_query, weight_precision_query=weight_precision_query, activation_precision_query=activation_precision_query, hide_models=hide_models, format_query=format_query, ) filtered_df = filter_queries(query, filtered_df) df = select_columns(filtered_df, columns) return df def load_query(request: gr.Request): # triggered only once at startup => read query parameter if it exists query = request.query_params.get("query") or "" return ( query, query, ) # return one for the "search_bar", one for a hidden component that triggers a reload only if value has changed def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))] def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame: always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden] dummy_col = [AutoEvalColumn.dummy.name] # AutoEvalColumn.model_type_symbol.name, # AutoEvalColumn.model.name, # We use COLS to maintain sorting filtered_df = df[always_here_cols + [c for c in COLS if c in df.columns and c in columns] + dummy_col] return filtered_df def filter_queries(query: str, filtered_df: pd.DataFrame): """Added by Abishek""" final_df = [] if query != "": queries = [q.strip() for q in query.split(";")] for _q in queries: _q = _q.strip() if _q != "": temp_filtered_df = search_table(filtered_df, _q) if len(temp_filtered_df) > 0: final_df.append(temp_filtered_df) if len(final_df) > 0: filtered_df = pd.concat(final_df) filtered_df = filtered_df.drop_duplicates( subset=[ AutoEvalColumn.model.name, AutoEvalColumn.weight_precision.name, AutoEvalColumn.activation_precision.name, AutoEvalColumn.revision.name, ] ) return filtered_df def filter_models( df: pd.DataFrame, # type_query: list, size_query: list, weight_precision_query: list, activation_precision_query: list, hide_models: list, format_query: list, ) -> pd.DataFrame: # Show all models if "Private or deleted" in hide_models: filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True] else: filtered_df = df if "Contains a merge/moerge" in hide_models: filtered_df = filtered_df[filtered_df[AutoEvalColumn.merged.name] == False] if "MoE" in hide_models: filtered_df = filtered_df[filtered_df[AutoEvalColumn.moe.name] == False] if "Flagged" in hide_models: filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False] # type_emoji = [t[0] for t in type_query] # filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] filtered_df = filtered_df.loc[df[AutoEvalColumn.weight_precision.name].isin(weight_precision_query + ["None"])] filtered_df = filtered_df.loc[ df[AutoEvalColumn.activation_precision.name].isin(activation_precision_query + ["None"]) ] filtered_df = filtered_df.loc[df[AutoEvalColumn.format.name].isin(format_query)] numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query])) params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce") mask = params_column.apply(lambda x: any(numeric_interval.contains(x))) filtered_df = filtered_df.loc[mask] return filtered_df leaderboard_df = filter_models( df=leaderboard_df, # type_query=[t.to_str(" : ") for t in ModelType], size_query=list(NUMERIC_INTERVALS.keys()), weight_precision_query=[i.value.name for i in Precision], activation_precision_query=[i.value.name for i in Precision], hide_models=["Private or deleted", "Contains a merge/moerge", "Flagged"], # Deleted, merges, flagged, MoEs format_query=[i.value.name for i in Format], ) 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("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0): with gr.Row(): with gr.Column(): with gr.Row(): search_bar = gr.Textbox( placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...", show_label=False, elem_id="search-bar", ) with gr.Row(): shown_columns = gr.CheckboxGroup( choices=[ c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden and not c.dummy ], value=[ c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden and not c.never_hidden ], label="Select columns to show", elem_id="column-select", interactive=True, ) with gr.Row(): hide_models = gr.CheckboxGroup( label="Hide models", choices=["Private or deleted", "Contains a merge/moerge", "Flagged"], #, "MoE"], value=["Private or deleted", "Contains a merge/moerge", "Flagged"], interactive=True, ) with gr.Column(min_width=320): # with gr.Box(elem_id="box-filter"): # filter_columns_type = gr.CheckboxGroup( # label="Model types", # choices=[t.to_str() for t in ModelType], # value=[t.to_str() for t in ModelType], # interactive=True, # elem_id="filter-columns-type", # ) filter_columns_weight_precision = gr.CheckboxGroup( label="Weight Precision", choices=[i.value.name for i in Precision], value=[i.value.name for i in Precision], interactive=True, elem_id="filter-columns-weight-precision", ) filter_columns_activation_precision = gr.CheckboxGroup( label="Activation Precision", choices=[i.value.name for i in Precision], value=[i.value.name for i in Precision], interactive=True, elem_id="filter-columns-activation-precision", ) filter_columns_size = gr.CheckboxGroup( label="Model sizes (in billions of parameters)", choices=list(NUMERIC_INTERVALS.keys()), value=list(NUMERIC_INTERVALS.keys()), interactive=True, elem_id="filter-columns-size", ) filter_format = gr.CheckboxGroup( label="Format", choices=[i.value.name for i in Format], value=[i.value.name for i in Format], interactive=True, elem_id="filter-format", ) leaderboard_table = gr.components.Dataframe( value=leaderboard_df[ [c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value + [AutoEvalColumn.dummy.name] ], headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value, datatype=TYPES, elem_id="leaderboard-table", interactive=False, visible=True, # column_widths=["2%", "33%"] ) # Dummy leaderboard for handling the case when the user uses backspace key hidden_leaderboard_table_for_search = gr.components.Dataframe( value=original_df[COLS], headers=COLS, datatype=TYPES, visible=False, ) search_bar.submit( update_table, [ hidden_leaderboard_table_for_search, shown_columns, # filter_columns_type, filter_columns_weight_precision, filter_columns_activation_precision, filter_columns_size, hide_models, filter_format, search_bar, ], leaderboard_table, ) # Define a hidden component that will trigger a reload only if a query parameter has been set hidden_search_bar = gr.Textbox(value="", visible=False) hidden_search_bar.change( update_table, [ hidden_leaderboard_table_for_search, shown_columns, # filter_columns_type, filter_columns_weight_precision, filter_columns_activation_precision, filter_columns_size, hide_models, filter_format, search_bar, ], leaderboard_table, ) # Check query parameter once at startup and update search bar + hidden component demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar]) for selector in [ shown_columns, # filter_columns_type, filter_columns_weight_precision, filter_columns_activation_precision, filter_columns_size, hide_models, filter_format, ]: selector.change( update_table, [ hidden_leaderboard_table_for_search, shown_columns, # filter_columns_type, filter_columns_weight_precision, filter_columns_activation_precision, filter_columns_size, hide_models, filter_format, search_bar, ], leaderboard_table, queue=True, ) with gr.TabItem("📈 Metrics through time", elem_id="llm-benchmark-tab-table", id=4): with gr.Row(): with gr.Column(): chart = create_metric_plot_obj( plot_df, [AutoEvalColumn.average.name], title="Average of Top Scores and Human Baseline Over Time (from last update)", ) gr.Plot(value=chart, min_width=500) with gr.Column(): chart = create_metric_plot_obj( plot_df, BENCHMARK_COLS, title="Top Scores and Human Baseline Over Time (from last update)", ) gr.Plot(value=chart, min_width=500) with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2): gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") 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) # restarted every 3h scheduler.start() demo.queue(default_concurrency_limit=40).launch()