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| import subprocess | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| from huggingface_hub import snapshot_download | |
| from pandas.io.formats.style import Styler | |
| from src.about import ( | |
| CITATION_BUTTON_LABEL, | |
| CITATION_BUTTON_TEXT, | |
| EVALUATION_QUEUE_TEXT, | |
| INTRODUCTION_TEXT, | |
| LLM_BENCHMARKS_TEXT, | |
| TITLE, Tasks, | |
| ) | |
| 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, | |
| NShotType, | |
| ) | |
| from src.envs import API, DEVICE, 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 | |
| # subprocess.run(["python", "scripts/fix_harness_import.py"]) | |
| def restart_space(): | |
| API.restart_space(repo_id=REPO_ID) | |
| def launch_backend(): | |
| _ = subprocess.run(["python", "main_backend.py"]) | |
| 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() | |
| leaderboard_df = leaderboard_df[leaderboard_df[AutoEvalColumn.still_on_hub.name] == True] | |
| # leaderboard_df = leaderboard_df[('speakleash' not in leaderboard_df['model_name_for_query']) | ('Bielik' in leaderboard_df['model_name_for_query'])] | |
| original_df.to_csv("output.csv") | |
| ( | |
| finished_eval_queue_df, | |
| running_eval_queue_df, | |
| pending_eval_queue_df, | |
| ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) | |
| def style_df(df: pd.DataFrame) -> Styler: | |
| # new_df = df.copy(deep=True) | |
| # new_df['polish_poleval2018_task3_test_10k'] = -new_df['polish_poleval2018_task3_test_10k'] | |
| # new_df = new_df.to_frame() | |
| leaderboard_df_styled = df.style.background_gradient(cmap="RdYlGn") | |
| inverted_colors_columns = ['rank'] | |
| # if "poleval2018_task3_test_10k" in df.columns: | |
| # inverted_colors_columns.append("poleval2018_task3_test_10k") | |
| if '#Params (B)' in df.columns: | |
| inverted_colors_columns.append("#Params (B)") | |
| leaderboard_df_styled = leaderboard_df_styled.background_gradient(cmap="RdYlGn_r", subset=inverted_colors_columns) | |
| rounding = {'#Params (B)': "{:.1f}", 'rank': "{:.0f}"} | |
| for task in Tasks: | |
| rounding[task.value.col_name] = "{:.2f}" | |
| for column_name in ["Average ⬆️"]: | |
| rounding[column_name] = "{:.2f}" | |
| leaderboard_df_styled = leaderboard_df_styled.format(rounding) | |
| return leaderboard_df_styled | |
| # Searching and filtering | |
| def update_table( | |
| hidden_df: pd.DataFrame, | |
| columns: list, | |
| type_query: list, | |
| precision_query: str, | |
| size_query: list, | |
| nshot_query: list, | |
| show_deleted: bool, | |
| query: str, | |
| ): | |
| filtered_df = filter_models(hidden_df, type_query, size_query, nshot_query, precision_query, show_deleted) | |
| filtered_df = filter_queries(query, filtered_df) | |
| df = select_columns(filtered_df, columns) | |
| df = df.replace({'': np.nan}) | |
| return style_df(df) | |
| def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: | |
| return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))] | |
| def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame: | |
| always_here_cols = [ | |
| AutoEvalColumn.rank.name, | |
| 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] + [AutoEvalColumn.dummy.name] | |
| ] | |
| 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.n_shot.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name] | |
| ) | |
| return filtered_df | |
| def filter_models( | |
| df: pd.DataFrame, type_query: list, size_query: list, nshot_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"])] | |
| # print(df[AutoEvalColumn.n_shot.name]) | |
| # print(nshot_query) | |
| filtered_df = filtered_df.loc[df[AutoEvalColumn.n_shot.name].isin(nshot_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.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", | |
| visible=True, | |
| ) | |
| 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", | |
| visible=False, | |
| ) | |
| 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", | |
| visible=True, | |
| ) | |
| filter_columns_nshot = gr.CheckboxGroup( | |
| label="N-shot", | |
| choices=[i.value.name for i in NShotType], | |
| value=[i.value.name for i in NShotType], | |
| interactive=True, | |
| elem_id="filter-columns-nshot", | |
| ) | |
| with gr.Row(): | |
| deleted_models_visibility = gr.Checkbox( | |
| value=False, label="Show private/deleted models", interactive=True | |
| ) | |
| with gr.Column(min_width=320): | |
| with gr.Row(): | |
| shown_columns = gr.CheckboxGroup( | |
| 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 | |
| ], | |
| label="Select columns to show", | |
| elem_id="column-select", | |
| interactive=True, | |
| ) | |
| leaderboard_table_value=leaderboard_df[ | |
| [c.name for c in fields(AutoEvalColumn) if c.never_hidden] | |
| + shown_columns.value | |
| + [AutoEvalColumn.dummy.name] | |
| ] | |
| leaderboard_df_styled=style_df(leaderboard_table_value) | |
| leaderboard_df_styled.precision = 2 | |
| leaderboard_table = gr.components.Dataframe( | |
| value=leaderboard_df_styled, | |
| 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, | |
| # column_widths=["2%", "33%"] | |
| height=800 | |
| ) | |
| # 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, | |
| filter_columns_nshot, | |
| deleted_models_visibility, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| ) | |
| for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, filter_columns_nshot, deleted_models_visibility]: | |
| selector.change( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| filter_columns_type, | |
| filter_columns_precision, | |
| filter_columns_size, | |
| filter_columns_nshot, | |
| 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.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" if DEVICE != "cpu" else "float32", | |
| # 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.Column(): | |
| 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, | |
| ) | |
| csv = gr.File(interactive=False, value="output.csv", visible=False) | |
| json = gr.File(interactive=False, value="all_data.json", visible=False) | |
| def update_visibility(radio): | |
| return gr.File(interactive=False, value="output.csv", visible=True) | |
| def update_visibility_json(radio): | |
| return gr.File(interactive=False, value="all_data.json", visible=True) | |
| deleted_models_visibility.change(update_visibility, deleted_models_visibility, csv) | |
| deleted_models_visibility.change(update_visibility_json, deleted_models_visibility, json) | |
| scheduler = BackgroundScheduler() | |
| scheduler.add_job(restart_space, "interval", seconds=1800) | |
| # scheduler.add_job(launch_backend, "interval", seconds=100) # will only allow one job to be run at the same time | |
| scheduler.start() | |
| demo.queue(default_concurrency_limit=40).launch() |