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| 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() | |