import subprocess import gradio as gr import pandas as pd from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download import os 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, NUMERIC_INTERVALS, 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 def restart_space(): API.restart_space(repo_id=REPO_ID) 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() raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) leaderboard_df = original_df.copy() ( finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df, ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) # Searching and filtering def update_table( hidden_df: pd.DataFrame, columns_info: list, columns_IE: list, columns_TA: list, columns_QA: list, columns_TG: list, columns_RM: list, columns_FO: list, columns_DM: list, columns_spanish: list, columns_other: list, type_query: list, precision_query: list, size_query: list, show_deleted: bool, query: str, ): # Combine all column selections selected_columns = ( columns_info + columns_IE + columns_TA + columns_QA + columns_TG + columns_RM + columns_FO + columns_DM + columns_spanish + columns_other ) # Filter models based on queries filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted) filtered_df = filter_queries(query, filtered_df) df = select_columns(filtered_df, selected_columns) return df def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: return df[(df[AutoEvalColumn.model.name].str.contains(query, case=False))] def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame: always_here_cols = [ AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name, ] # Ensure no duplicates and add the new average columns unique_columns = set(always_here_cols + columns) # We use COLS to maintain sorting filtered_df = df[[c for c in COLS if c in df.columns and c in unique_columns]] # Debugging print to see if the new columns are included print(f"Columns included in DataFrame: {filtered_df.columns.tolist()}") return filtered_df def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame: 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.precision.name, AutoEvalColumn.revision.name] ) return filtered_df def filter_models( df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool ) -> pd.DataFrame: # Show all models if show_deleted: filtered_df = df else: filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True] if "All" not in type_query: if "?" in type_query: filtered_df = filtered_df.loc[~df[AutoEvalColumn.model_type_symbol.name].isin([t for t in ModelType if t != "?"])] else: type_emoji = [t[0] for t in type_query] filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] if "All" not in precision_query: if "?" in precision_query: filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isna()] else: filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])] if "All" not in size_query: if "?" in size_query: filtered_df = filtered_df.loc[df[AutoEvalColumn.params.name].isna()] else: 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 def uncheck_all(): return [], [], [], [], [], [], [], [], [], [] # Get a list of all logo files in the directory logos_dir = "logos" logo_files = [f for f in os.listdir(logos_dir) if f.endswith(('.png', '.jpg', '.jpeg'))] 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(): with gr.Accordion("Select columns to show"): with gr.Tab("Model Information"): shown_columns_info = gr.CheckboxGroup( choices=[c.name for c in fields(AutoEvalColumn) if c.category == "Model Information"], value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.category == "Model Information"], label="Model Information", interactive=True, ) with gr.Tab("Information Extraction (IE)"): shown_columns_IE = gr.CheckboxGroup( choices=[c.name for c in fields(AutoEvalColumn) if c.category == "Information Extraction (IE)"], value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.category == "Information Extraction (IE)"], label="Information Extraction (IE)", interactive=True, ) with gr.Tab("Textual Analysis (TA)"): shown_columns_TA = gr.CheckboxGroup( choices=[c.name for c in fields(AutoEvalColumn) if c.category == "Textual Analysis (TA)"], value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.category == "Textual Analysis (TA)"], label="Textual Analysis (TA)", interactive=True, ) with gr.Tab("Question Answering (QA)"): shown_columns_QA = gr.CheckboxGroup( choices=[c.name for c in fields(AutoEvalColumn) if c.category == "Question Answering (QA)"], value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.category == "Question Answering (QA)"], label="Question Answering (QA)", interactive=True, ) with gr.Tab("Text Generation (TG)"): shown_columns_TG = gr.CheckboxGroup( choices=[c.name for c in fields(AutoEvalColumn) if c.category == "Text Generation (TG)"], value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.category == "Text Generation (TG)"], label="Text Generation (TG)", interactive=True, ) with gr.Tab("Risk Management (RM)"): shown_columns_RM = gr.CheckboxGroup( choices=[c.name for c in fields(AutoEvalColumn) if c.category == "Risk Management (RM)"], value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.category == "Risk Management (RM)"], label="Risk Management (RM)", interactive=True, ) with gr.Tab("Forecasting (FO)"): shown_columns_FO = gr.CheckboxGroup( choices=[c.name for c in fields(AutoEvalColumn) if c.category == "Forecasting (FO)"], value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.category == "Forecasting (FO)"], label="Forecasting (FO)", interactive=True, ) with gr.Tab("Decision-Making (DM)"): shown_columns_DM = gr.CheckboxGroup( choices=[c.name for c in fields(AutoEvalColumn) if c.category == "Decision-Making (DM)"], value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.category == "Decision-Making (DM)"], label="Decision-Making (DM)", interactive=True, ) with gr.Tab("Spanish"): shown_columns_spanish = gr.CheckboxGroup( choices=[c.name for c in fields(AutoEvalColumn) if c.category == "Spanish"], value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.category == "Spanish"], label="Spanish", interactive=True, ) with gr.Tab("Other"): shown_columns_other = gr.CheckboxGroup( choices=[c.name for c in fields(AutoEvalColumn) if c.category == "Other"], value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.category == "Other"], label="Other", interactive=True, ) with gr.Row(): uncheck_all_button = gr.Button("Uncheck All") uncheck_all_button.click( uncheck_all, inputs=[], outputs=[ shown_columns_info, shown_columns_IE, shown_columns_TA, shown_columns_QA, shown_columns_TG, shown_columns_RM, shown_columns_FO, shown_columns_DM, shown_columns_spanish, shown_columns_other, ], ) with gr.Row(): deleted_models_visibility = gr.Checkbox( value=True, label="Show gated/private/deleted models", interactive=True ) with gr.Column(min_width=320): #with gr.Box(elem_id="box-filter"): filter_columns_type = gr.CheckboxGroup( label="Model types", choices=["All"] + [t.to_str() for t in ModelType], value=["All"], interactive=True, elem_id="filter-columns-type", ) filter_columns_precision = gr.CheckboxGroup( label="Precision", choices=["All"] + [i.value.name for i in Precision], value=["All"], interactive=True, elem_id="filter-columns-precision", ) filter_columns_size = gr.CheckboxGroup( label="Model sizes (in billions of parameters)", choices=["All"] + list(NUMERIC_INTERVALS.keys()) + ["?"], value=["All"], interactive=True, elem_id="filter-columns-size", ) leaderboard_table = gr.Dataframe( value=leaderboard_df[ [c.name for c in fields(AutoEvalColumn) if c.never_hidden] + [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.never_hidden] ], headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.never_hidden], datatype=TYPES, elem_id="leaderboard-table", interactive=False, visible=True, ) # Dummy leaderboard for handling the case when the user uses backspace key hidden_leaderboard_table_for_search = gr.Dataframe( value=original_df[COLS], headers=COLS, datatype=TYPES, visible=False, ) search_bar.submit( update_table, inputs=[ hidden_leaderboard_table_for_search, shown_columns_info, shown_columns_IE, shown_columns_TA, shown_columns_QA, shown_columns_TG, shown_columns_RM, shown_columns_FO, shown_columns_DM, shown_columns_spanish, shown_columns_other, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility, search_bar, ], outputs=leaderboard_table, ) for selector in [ shown_columns_info, shown_columns_IE, shown_columns_TA, shown_columns_QA, shown_columns_TG, shown_columns_RM, shown_columns_FO, shown_columns_DM, shown_columns_spanish, shown_columns_other, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility ]: selector.change( update_table, inputs=[ hidden_leaderboard_table_for_search, shown_columns_info, shown_columns_IE, shown_columns_TA, shown_columns_QA, shown_columns_TG, shown_columns_RM, shown_columns_FO, shown_columns_DM, shown_columns_spanish, shown_columns_other, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility, search_bar, ], outputs=leaderboard_table, queue=True, ) 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.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.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.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, ) # Footer with logos with gr.Row(elem_id="footer"): num_columns = min(5, len(logo_files)) for i in range(0, len(logo_files), num_columns): with gr.Row(): for logo in logo_files[i:i + num_columns]: logo_path = os.path.join(logos_dir, logo) gr.Image(logo_path, show_label=False, elem_id="logo-image", width=100, height=100) 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()