import gradio as gr import pandas as pd 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 ( # EVAL_TYPES,; WeightType,; BENCHMARK_COLS,; EVAL_COLS,; NUMERIC_INTERVALS,; ModelType,; Precision, COLS, TYPES, AutoEvalColumn, fields, ) # from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN from src.envs import CRM_RESULTS_PATH from src.populate import get_leaderboard_df_crm original_df = get_leaderboard_df_crm(CRM_RESULTS_PATH, COLS) # raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) leaderboard_df = original_df.copy() # leaderboard_df = leaderboard_df.style.format({"accuracy_metric_average": "{0:.2f}"}) # Searching and filtering def update_table( hidden_df: pd.DataFrame, columns: list, accuracy_method_query: str, # type_query: list, # precision_query: str, # size_query: list, # show_deleted: bool, # query: str, ): # filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted) # filtered_df = filter_queries(query, filtered_df) filtered_df = filter_accuracy_method_func(hidden_df, accuracy_method_query) df = select_columns(filtered_df, columns) return df def filter_accuracy_method_func(df: pd.DataFrame, accuracy_method_query: str) -> pd.DataFrame: return df[df["Accuracy Method"] == accuracy_method_query] # 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.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]] 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 # filtered_df = df # # if show_deleted: # # filtered_df = df # # else: # Show only still on the hub models # # filtered_df = df[df[AutoEvalColumn.still_on_hub.name] is True] # 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.precision.name].isin(precision_query + ["None"])] # 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 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], 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(): # deleted_models_visibility = gr.Checkbox( # value=False, 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=[t.to_str() for t in ModelType], # value=[t.to_str() for t in ModelType], # interactive=True, # 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], # interactive=True, # 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()), # interactive=True, # elem_id="filter-columns-size", # ) with gr.Row(): with gr.Column(): filter_use_case_type = gr.CheckboxGroup( choices=["Summary", "Generation"], value=["Summary", "Generation"], label="Use Case Type", info="", interactive=True, ) with gr.Column(): filter_use_case = gr.Dropdown( choices=list(original_df["Use Case Name"].unique()), # value=list(original_df["Use Case Name"].unique()), label="Use Case", info="", multiselect=True, interactive=True, ) with gr.Column(): filter_metric_area = gr.CheckboxGroup( choices=["Accuracy", "Speed (Latency)", "Trust & Safety", "Cost"], value=["Accuracy", "Speed (Latency)", "Trust & Safety", "Cost"], label="Metric Area", info="", interactive=True, ) with gr.Column(): filter_accuracy_method = gr.Radio( choices=["Manual", "Auto"], value="Manual", label="Accuracy Method", info="accuracy method", interactive=True, ) with gr.Column(): filter_accuracy_threshold = gr.Number( value="3", label="Accuracy Threshold", info="", interactive=True, ) with gr.Column(): filter_llm = gr.CheckboxGroup( choices=list(original_df["Model Name"].unique()), value=list(original_df["Model Name"].unique()), label="Model Name", info="", interactive=True, ) with gr.Column(): filter_llm_provider = gr.CheckboxGroup( choices=list(original_df["LLM Provider"].unique()), value=list(original_df["LLM Provider"].unique()), label="LLM Provider", info="", interactive=True, ) leaderboard_table = gr.components.Dataframe( value=leaderboard_df[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value], 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, ) # 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_precision, # filter_columns_size, # deleted_models_visibility, # search_bar, # ], # leaderboard_table, # ) for selector in [ shown_columns, filter_accuracy_method, # filter_columns_type, # filter_columns_precision, # filter_columns_size, # deleted_models_visibility, ]: selector.change( update_table, [ hidden_leaderboard_table_for_search, shown_columns, filter_accuracy_method, # filter_columns_type, # filter_columns_precision, # filter_columns_size, # deleted_models_visibility, # search_bar, ], 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.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()