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Runtime error
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| from time import sleep | |
| import gradio as gr | |
| import pandas as pd | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| from huggingface_hub import snapshot_download | |
| from gradio_space_ci import enable_space_ci | |
| from src.display.about import ( | |
| INTRODUCTION_TEXT, | |
| LLM_BENCHMARKS_TEXT, | |
| CITATION_BUTTON_LABEL, | |
| CITATION_BUTTON_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_RESULTS_PATH, RESULTS_REPO, REPO_ID, HF_TOKEN | |
| from src.populate import get_leaderboard_df | |
| # from src.tools.collections import update_collections | |
| from src.tools.plots import ( | |
| create_metric_plot_obj, | |
| create_plot_df, | |
| create_scores_df, | |
| ) | |
| # Start ephemeral Spaces on PRs (see config in README.md) | |
| # enable_space_ci() | |
| def restart_space(): | |
| API.restart_space(repo_id=REPO_ID, token=HF_TOKEN) | |
| def init_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, | |
| resume_download=True, | |
| ) | |
| except Exception as e: | |
| print(e) | |
| sleep(180) # sleep 3 min | |
| return init_space() | |
| raw_data, original_df = get_leaderboard_df( | |
| results_path=EVAL_RESULTS_PATH, cols=COLS, benchmark_cols=BENCHMARK_COLS | |
| ) | |
| # update_collections(original_df.copy()) | |
| leaderboard_df = original_df.copy() | |
| plot_df = create_plot_df(create_scores_df(raw_data)) | |
| return leaderboard_df, original_df, plot_df | |
| leaderboard_df, original_df, plot_df = init_space() | |
| # Searching and filtering | |
| def update_table( | |
| hidden_df: pd.DataFrame, | |
| columns: list, | |
| type_query: list, | |
| weight_precision_query: str, | |
| activation_precision_query: str, | |
| size_query: list, | |
| hide_models: list, | |
| query: str, | |
| ): | |
| filtered_df = filter_models( | |
| df=hidden_df, | |
| type_query=type_query, | |
| size_query=size_query, | |
| weight_precision_query=weight_precision_query, | |
| activation_precision_query=activation_precision_query, | |
| hide_models=hide_models, | |
| ) | |
| filtered_df = filter_queries(query, filtered_df) | |
| df = select_columns(filtered_df, columns) | |
| 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 | |
| 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 = [c.name for c in fields(AutoEvalColumn) if c.never_hidden] | |
| dummy_col = [AutoEvalColumn.dummy.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] + dummy_col] | |
| return filtered_df | |
| def filter_queries(query: str, filtered_df: pd.DataFrame): | |
| """Added by Abishek""" | |
| 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.weight_precision.name, | |
| AutoEvalColumn.activation_precision.name, | |
| AutoEvalColumn.revision.name, | |
| ] | |
| ) | |
| return filtered_df | |
| def filter_models( | |
| df: pd.DataFrame, | |
| type_query: list, | |
| size_query: list, | |
| weight_precision_query: list, | |
| activation_precision_query: list, | |
| hide_models: list, | |
| ) -> pd.DataFrame: | |
| # Show all models | |
| if "Private or deleted" in hide_models: | |
| filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True] | |
| else: | |
| filtered_df = df | |
| if "Contains a merge/moerge" in hide_models: | |
| filtered_df = filtered_df[filtered_df[AutoEvalColumn.merged.name] == False] | |
| if "MoE" in hide_models: | |
| filtered_df = filtered_df[filtered_df[AutoEvalColumn.moe.name] == False] | |
| if "Flagged" in hide_models: | |
| filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False] | |
| 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.weight_precision.name].isin(weight_precision_query + ["None"])] | |
| filtered_df = filtered_df.loc[ | |
| df[AutoEvalColumn.activation_precision.name].isin(activation_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 | |
| leaderboard_df = filter_models( | |
| df=leaderboard_df, | |
| type_query=[t.to_str(" : ") for t in ModelType], | |
| size_query=list(NUMERIC_INTERVALS.keys()), | |
| weight_precision_query=[i.value.name for i in Precision], | |
| activation_precision_query=[i.value.name for i in Precision], | |
| hide_models=["Private or deleted", "Contains a merge/moerge", "Flagged"], # Deleted, merges, flagged, MoEs | |
| ) | |
| 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 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, | |
| ) | |
| with gr.Row(): | |
| hide_models = gr.CheckboxGroup( | |
| label="Hide models", | |
| choices=["Private or deleted", "Contains a merge/moerge", "Flagged", "MoE"], | |
| value=["Private or deleted", "Contains a merge/moerge", "Flagged"], | |
| 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_weight_precision = gr.CheckboxGroup( | |
| label="Weight Precision", | |
| choices=[i.value.name for i in Precision], | |
| value=[i.value.name for i in Precision], | |
| interactive=True, | |
| elem_id="filter-columns-weight-precision", | |
| ) | |
| filter_columns_activation_precision = gr.CheckboxGroup( | |
| label="Activation Precision", | |
| choices=[i.value.name for i in Precision], | |
| value=[i.value.name for i in Precision], | |
| interactive=True, | |
| elem_id="filter-columns-activation-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] | |
| + shown_columns.value | |
| + [AutoEvalColumn.dummy.name] | |
| ], | |
| 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%"] | |
| ) | |
| # 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_weight_precision, | |
| filter_columns_activation_precision, | |
| filter_columns_size, | |
| hide_models, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| ) | |
| # 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) | |
| hidden_search_bar.change( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| filter_columns_type, | |
| filter_columns_weight_precision, | |
| filter_columns_activation_precision, | |
| filter_columns_size, | |
| hide_models, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| ) | |
| # Check query parameter once at startup and update search bar + hidden component | |
| demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar]) | |
| for selector in [ | |
| shown_columns, | |
| filter_columns_type, | |
| filter_columns_weight_precision, | |
| filter_columns_activation_precision, | |
| filter_columns_size, | |
| hide_models, | |
| ]: | |
| selector.change( | |
| update_table, | |
| [ | |
| hidden_leaderboard_table_for_search, | |
| shown_columns, | |
| filter_columns_type, | |
| filter_columns_weight_precision, | |
| filter_columns_activation_precision, | |
| filter_columns_size, | |
| hide_models, | |
| search_bar, | |
| ], | |
| leaderboard_table, | |
| queue=True, | |
| ) | |
| with gr.TabItem("📈 Metrics through time", elem_id="llm-benchmark-tab-table", id=4): | |
| with gr.Row(): | |
| with gr.Column(): | |
| chart = create_metric_plot_obj( | |
| plot_df, | |
| [AutoEvalColumn.average.name], | |
| title="Average of Top Scores and Human Baseline Over Time (from last update)", | |
| ) | |
| gr.Plot(value=chart, min_width=500) | |
| with gr.Column(): | |
| chart = create_metric_plot_obj( | |
| plot_df, | |
| BENCHMARK_COLS, | |
| title="Top Scores and Human Baseline Over Time (from last update)", | |
| ) | |
| gr.Plot(value=chart, min_width=500) | |
| 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) # restarted every 3h | |
| scheduler.start() | |
| demo.queue(default_concurrency_limit=40).launch() | |