| import subprocess | |
| import gradio as gr | |
| import pandas as pd | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| from huggingface_hub import snapshot_download | |
| 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_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("Saving results locally at:", 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: list, | |
| 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) | |
| df = select_columns(filtered_df, 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, | |
| ] | |
| # 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 | |
| if show_deleted: | |
| filtered_df = df | |
| else: # Show only still on the hub models | |
| filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == 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 | |
| shown_columns = [ | |
| c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden and not c.never_hidden | |
| ] | |
| 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", | |
| # ) | |
| leaderboard_table = gr.components.Dataframe( | |
| value=leaderboard_df[ | |
| [c.name for c in fields(AutoEvalColumn) if c.never_hidden or c.displayed_by_default] | |
| ].applymap( | |
| lambda x: x if isinstance(x, str) or isinstance(x, float) else round(x["value"], 2) | |
| ), # ,# ] + shown_columns], | |
| headers=[ | |
| c.name for c in fields(AutoEvalColumn) if c.never_hidden or c.displayed_by_default | |
| ], ##, if c.never_hidden] + shown_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.components.Dataframe( | |
| value=original_df[COLS], | |
| headers=COLS, | |
| datatype=TYPES, | |
| visible=False, | |
| ) | |
| # search_bar.submit( | |
| # update_table, | |
| # [ | |
| # hidden_leaderboard_table_for_search, | |
| # # None, | |
| # filter_columns_type, | |
| # # filter_columns_precision, | |
| # filter_columns_size, | |
| # # None, | |
| # search_bar, | |
| # ], | |
| # leaderboard_table, | |
| # ) | |
| # for selector in [ | |
| # # shown_columns, | |
| # filter_columns_type, | |
| # # filter_columns_precision, | |
| # filter_columns_size, | |
| # # deleted_models_visibility, | |
| # ]: | |
| # selector.change( | |
| # update_table, | |
| # [ | |
| # hidden_leaderboard_table_for_search, | |
| # # None, | |
| # filter_columns_type, | |
| # # filter_columns_precision, | |
| # filter_columns_size, | |
| # # None, | |
| # 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.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.components.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.components.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.components.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, | |
| # ) | |
| 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=3600) | |
| scheduler.start() | |
| demo.queue(default_concurrency_limit=40).launch() | |