import os import gradio as gr import pandas as pd from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download from src.about import ( BOTTOM_LOGO, CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, EVALUATION_QUEUE_TEXT, INTRODUCTION_TEXT, INTRODUCTION_TEXT_JP, 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, AddSpecialTokens, AutoEvalColumn, ModelType, NumFewShots, Precision, WeightType, fields, ) from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO 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 initialization 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, ) 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, ) except Exception: restart_space() # Searching and filtering def filter_models( df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, add_special_tokens_query: list, num_few_shots_query: list, show_deleted: bool, show_merges: bool, show_flagged: bool, ) -> pd.DataFrame: print(f"Initial df shape: {df.shape}") print(f"Initial df content:\n{df}") filtered_df = df # Model Type フィルタリング type_column = "T" if "T" in df.columns else "Type_" type_emoji = [t.split()[0] for t in type_query] filtered_df = df[df[type_column].isin(type_emoji)] print(f"After type filter: {filtered_df.shape}") # Precision フィルタリング filtered_df = filtered_df[filtered_df["Precision"].isin(precision_query + ["Unknown", "?"])] print(f"After precision filter: {filtered_df.shape}") # Model Size フィルタリング if "Unknown" in size_query: size_mask = filtered_df["#Params (B)"].isna() | (filtered_df["#Params (B)"] == 0) else: size_mask = filtered_df["#Params (B)"].apply( lambda x: any(x in NUMERIC_INTERVALS[s] for s in size_query if s != "Unknown") ) filtered_df = filtered_df[size_mask] print(f"After size filter: {filtered_df.shape}") # Add Special Tokens フィルタリング filtered_df = filtered_df[filtered_df["Add Special Tokens"].isin(add_special_tokens_query + ["Unknown", "?"])] print(f"After add_special_tokens filter: {filtered_df.shape}") # Num Few Shots フィルタリング filtered_df = filtered_df[ filtered_df["Few-shot"].astype(str).isin([str(x) for x in num_few_shots_query] + ["Unknown", "?"]) ] print(f"After num_few_shots filter: {filtered_df.shape}") # Show deleted models フィルタリング if not show_deleted: filtered_df = filtered_df[filtered_df["Available on the hub"]] print(f"After show_deleted filter: {filtered_df.shape}") print("Filtered dataframe head:") print(filtered_df.head()) return filtered_df def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: return df[df[AutoEvalColumn.dummy.name].str.contains(query, case=False)] def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame: """Added by Abishek""" if not query: return filtered_df final_df = [] 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 select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame: always_here_cols = [ AutoEvalColumn.model_type_symbol.name, # 'T' AutoEvalColumn.model.name, # 'Model' ] # 'always_here_cols' を 'columns' から除外して重複を避ける columns = [c for c in columns if c not in always_here_cols] new_columns = ( always_here_cols + [c for c in COLS if c in df.columns and c in columns] + [AutoEvalColumn.dummy.name] ) # 重複を排除しつつ順序を維持 seen = set() unique_columns = [] for c in new_columns: if c not in seen: unique_columns.append(c) seen.add(c) # フィルタリングされたカラムでデータフレームを作成 filtered_df = df[unique_columns] return filtered_df def update_table( hidden_df: pd.DataFrame, columns: list, type_query: list, precision_query: str, size_query: list, add_special_tokens_query: list, num_few_shots_query: list, show_deleted: bool, show_merges: bool, show_flagged: bool, query: str, ): print( f"Update table called with: type_query={type_query}, precision_query={precision_query}, size_query={size_query}" ) print(f"hidden_df shape before filtering: {hidden_df.shape}") filtered_df = filter_models( hidden_df, type_query, size_query, precision_query, add_special_tokens_query, num_few_shots_query, show_deleted, show_merges, show_flagged, ) print(f"filtered_df shape after filter_models: {filtered_df.shape}") filtered_df = filter_queries(query, filtered_df) print(f"filtered_df shape after filter_queries: {filtered_df.shape}") print( f"Filter applied: query={query}, columns={columns}, type_query={type_query}, precision_query={precision_query}" ) print("Filtered dataframe head:") print(filtered_df.head()) df = select_columns(filtered_df, columns) print(f"Final df shape: {df.shape}") print("Final dataframe head:") print(df.head()) 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 # Prepare the dataframes 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, failed_eval_queue_df, ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) leaderboard_df = filter_models( leaderboard_df, [t.to_str(" : ") for t in ModelType], list(NUMERIC_INTERVALS.keys()), [i.value.name for i in Precision], [i.value.name for i in AddSpecialTokens], [i.value.name for i in NumFewShots], False, False, False, ) leaderboard_df_filtered = filter_models( leaderboard_df, [t.to_str(" : ") for t in ModelType], list(NUMERIC_INTERVALS.keys()), [i.value.name for i in Precision], [i.value.name for i in AddSpecialTokens], [i.value.name for i in NumFewShots], False, False, False, ) # DataFrameの初期化部分のみを修正 initial_columns = ["T"] + [ c.name for c in fields(AutoEvalColumn) if (c.never_hidden or c.displayed_by_default) and c.name != "T" ] leaderboard_df_filtered = select_columns(leaderboard_df, initial_columns) # Leaderboard demo with gr.Blocks() as demo_leaderboard: 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( label="Select columns to show", 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 ], elem_id="column-select", ) with gr.Row(): deleted_models_visibility = gr.Checkbox(label="Show private/deleted models", value=False) merged_models_visibility = gr.Checkbox(label="Show merges", value=False) flagged_models_visibility = gr.Checkbox(label="Show flagged models", value=False) with gr.Column(min_width=320): filter_columns_type = gr.CheckboxGroup( label="Model types", choices=[t.to_str() for t in ModelType], value=[t.to_str() for t in ModelType], elem_id="filter-columns-type", ) filter_columns_precision = gr.CheckboxGroup( label="Precision", choices=[i.value.name for i in Precision], value=[i.value.name for i in Precision], elem_id="filter-columns-precision", ) filter_columns_size = gr.CheckboxGroup( label="Model sizes (in billions of parameters)", choices=list(NUMERIC_INTERVALS.keys()), value=list(NUMERIC_INTERVALS.keys()), elem_id="filter-columns-size", ) filter_columns_add_special_tokens = gr.CheckboxGroup( label="Add Special Tokens", choices=[i.value.name for i in AddSpecialTokens], value=[i.value.name for i in AddSpecialTokens], elem_id="filter-columns-add-special-tokens", ) filter_columns_num_few_shots = gr.CheckboxGroup( label="Num Few Shots", choices=[i.value.name for i in NumFewShots], value=[i.value.name for i in NumFewShots], elem_id="filter-columns-num-few-shots", ) # DataFrameコンポーネントの初期化 leaderboard_table = gr.Dataframe( value=leaderboard_df_filtered, headers=initial_columns, 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, ) # 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) gr.on( triggers=[ hidden_search_bar.change, shown_columns.change, filter_columns_type.change, filter_columns_precision.change, filter_columns_size.change, filter_columns_add_special_tokens.change, filter_columns_num_few_shots.change, deleted_models_visibility.change, merged_models_visibility.change, flagged_models_visibility.change, search_bar.submit, ], fn=update_table, inputs=[ hidden_leaderboard_table_for_search, shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, filter_columns_add_special_tokens, filter_columns_num_few_shots, deleted_models_visibility, merged_models_visibility, flagged_models_visibility, search_bar, ], outputs=leaderboard_table, ) # Check query parameter once at startup and update search bar + hidden component demo_leaderboard.load(fn=load_query, outputs=[search_bar, hidden_search_bar]) # Submission demo with gr.Blocks() as demo_submission: 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.Accordion( f"❎ Failed Evaluation Queue ({len(failed_eval_queue_df)})", open=False, ): with gr.Row(): failed_eval_table = gr.Dataframe( value=failed_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( label="Model type", choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown], multiselect=False, value=lambda: None, ) with gr.Column(): precision = gr.Dropdown( label="Precision", choices=[i.value.name for i in Precision if i != Precision.Unknown], multiselect=False, value="float16", ) weight_type = gr.Dropdown( label="Weights type", choices=[i.value.name for i in WeightType], multiselect=False, value="Original", ) base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)") add_special_tokens = gr.Dropdown( label="AddSpecialTokens", choices=[i.value.name for i in AddSpecialTokens if i != AddSpecialTokens.Unknown], multiselect=False, value="False", ) 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, add_special_tokens, ], submission_result, ) # Main demo def set_default_language(request: gr.Request) -> gr.Dropdown: if request.headers["Accept-Language"].split(",")[0].lower().startswith("ja"): return gr.Dropdown(value="🇯🇵 JP") else: return gr.Dropdown(value="🇺🇸 EN") def update_language(language: str) -> gr.Markdown: if language == "🇯🇵 JP": return gr.Markdown(value=INTRODUCTION_TEXT_JP) else: return gr.Markdown(value=INTRODUCTION_TEXT) with gr.Blocks(css=custom_css, css_paths="style.css", theme=gr.themes.Base()) as demo: gr.HTML(TITLE) introduction_text = gr.Markdown(INTRODUCTION_TEXT_JP, 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): demo_leaderboard.render() 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): demo_submission.render() with gr.Row(): with gr.Accordion("📙 Citation", open=False): citation_button = gr.Textbox( label=CITATION_BUTTON_LABEL, value=CITATION_BUTTON_TEXT, lines=20, elem_id="citation-button", show_copy_button=True, ) gr.HTML(BOTTOM_LOGO) language = gr.Dropdown( choices=["🇯🇵 JP", "🇺🇸 EN"], value="🇯🇵 JP", elem_classes="language-selector", show_label=False, container=False, ) demo.load(fn=set_default_language, outputs=language) language.change( fn=update_language, inputs=language, outputs=introduction_text, api_name=False, ) if __name__ == "__main__": if os.getenv("SPACE_ID"): scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", seconds=1800) scheduler.start() demo.queue(default_concurrency_limit=40).launch()