app.py

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 (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    EVALUATION_QUEUE_TEXT,
    INTRODUCTION_TEXT,
    LLM_BENCHMARKS_TEXT,
    FAQ_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, DYNAMIC_INFO_REPO, DYNAMIC_INFO_FILE_PATH, DYNAMIC_INFO_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
from src.scripts.update_all_request_files import update_dynamic_files
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():
    """
    Restarts a Space instance specified by its repository ID.

    This function is used to restart a Space instance within the Hugging Face platform.
    It requires the repository ID and a valid API token for authentication.

    Parameters as env variables
    ---------------------------
    repo_id : str
        The ID of the repository associated with the Space instance to be restarted.

    token : str
        A valid API token with the necessary permissions to restart the Space.

    Returns
    -------
    None
        This function does not return any value. It simply restarts the specified Space instance.

    Example
    -------
    >>> restart_space(repo_id="example_repo_id", token="example_token")
    """
    API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)


def init_space():
    """
    Initializes the Hugging Face Space environment.

    This function initializes the Hugging Face Space environment by performing the following steps:
    1. Downloads evaluation requests, dynamic information, and evaluation results.
    2. Processes the raw data into a leaderboard DataFrame.
    3. Updates collections with the original DataFrame.
    4. Creates a plot DataFrame for visualization.
    5. Retrieves evaluation queue DataFrames.

    Returns
    -------
    tuple
        A tuple containing the following elements:
        - leaderboard_df : pandas.DataFrame
            DataFrame containing the leaderboard data.
            
        - original_df : pandas.DataFrame
            Original DataFrame obtained from the evaluation results.
            
        - plot_df : pandas.DataFrame
            DataFrame suitable for creating plots.
            
        - finished_eval_queue_df : pandas.DataFrame
            DataFrame containing finished evaluation queue data.
            
        - running_eval_queue_df : pandas.DataFrame
            DataFrame containing running evaluation queue data.
            
        - pending_eval_queue_df : pandas.DataFrame
            DataFrame containing pending evaluation queue data.

    Example
    -------
    >>> (
    ...     leaderboard_df,
    ...     original_df,
    ...     plot_df,
    ...     finished_eval_queue_df,
    ...     running_eval_queue_df,
    ...     pending_eval_queue_df,
    ... ) = init_space()
    """
    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
        )
    except Exception:
        restart_space()
    try:
        print(DYNAMIC_INFO_PATH)
        snapshot_download(
            repo_id=DYNAMIC_INFO_REPO, local_dir=DYNAMIC_INFO_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
        )
    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
        )
    except Exception:
        restart_space()


    raw_data, original_df = get_leaderboard_df(
        results_path=EVAL_RESULTS_PATH, 
        requests_path=EVAL_REQUESTS_PATH, 
        dynamic_path=DYNAMIC_INFO_FILE_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))

    (
        finished_eval_queue_df,
        running_eval_queue_df,
        pending_eval_queue_df,
    ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)

    return leaderboard_df, original_df, plot_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df

leaderboard_df, original_df, plot_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()


# Searching and filtering
def update_table(
    hidden_df: pd.DataFrame,
    columns: list,
    type_query: list,
    precision_query: str,
    size_query: list,
    hide_models: list,
    query: str,
):
    """
    Updates a table DataFrame based on specified criteria.

    This function filters the input DataFrame based on specified criteria and returns a new DataFrame with selected columns.

    Parameters
    ----------
    hidden_df : pandas.DataFrame
        The DataFrame to be filtered and updated.

    columns : list
        List of column names to be included in the updated DataFrame.

    type_query : list
        List of types to filter models.

    precision_query : str
        Precision value to filter models.

    size_query : list
        List of sizes to filter models.

    hide_models : list
        List of models to be hidden.

    query : str
        Query string to filter rows in the DataFrame.

    Returns
    -------
    updated_df : pandas.DataFrame
        A DataFrame containing filtered and updated data based on the specified criteria.

    Example
    -------
    >>> updated_df = update_table(
    ...     hidden_df=original_df,
    ...     columns=["Model", "Type", "Precision"],
    ...     type_query=["type1", "type2"],
    ...     precision_query="high",
    ...     size_query=["large"],
    ...     hide_models=["model1", "model2"],
    ...     query="column1 > 0 and column2 == 'value'",
    ... )
    """
    filtered_df = filter_models(df=hidden_df, type_query=type_query, size_query=size_query, precision_query=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
    """
    Loads a query parameter from a request object.

    It returns the query parameter value for the "search_bar" component and for a hidden component that triggers a reload only if the value has changed.

    Parameters
    ----------
    request : gr.Request
        The request object containing query parameters.

    Returns
    -------
    tuple
        A tuple containing two identical query parameter values:
        - query_search_bar : str
            The query parameter value for the "search_bar" component.
            
        - query_hidden : str
            The query parameter value for a hidden component that triggers a reload only if the value has changed.

    Example
    -------
    >>> query_search_bar, query_hidden = load_query(request)
    """
    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:
    """
    Searches a DataFrame for rows containing a specified query.

    This function filters the input DataFrame based on a specified query and returns a new DataFrame containing rows where the query matches any part of the specified column.

    Parameters
    ----------
    df : pandas.DataFrame
        The DataFrame to be searched.

    query : str
        The query string to search for within the DataFrame.

    Returns
    -------
    filtered_df : pandas.DataFrame
        A DataFrame containing rows where the query matches any part of the specified column.

    Example
    -------
    >>> filtered_df = search_table(df=original_df, query="example_query")
    """
    return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]


def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
    """
    Selects specified columns from a DataFrame.

    This function selects specified columns from the input DataFrame and returns a new DataFrame containing only those columns.

    Parameters
    ----------
    df : pandas.DataFrame
        The DataFrame from which columns are to be selected.

    columns : list
        List of column names to be selected from the DataFrame.

    Returns
    -------
    filtered_df : pandas.DataFrame
        A DataFrame containing only the specified columns.

    Example
    -------
    >>> filtered_df = select_columns(df=original_df, columns=["column1", "column2", "column3"])
    """
    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"""
    """
    Filters DataFrame rows based on specified query strings.

    This function filters the input DataFrame based on specified query strings and returns a new DataFrame containing rows that match any of the queries.

    Parameters
    ----------
    query : str
        The query string containing one or more search queries separated by semicolons (;).

    filtered_df : pandas.DataFrame
        The DataFrame to be filtered based on the queries.

    Returns
    -------
    filtered_df : pandas.DataFrame
        A DataFrame containing rows that match any of the specified queries.

    Example
    -------
    >>> filtered_df = filter_queries(
    ...     query="query1; query2; query3",
    ...     filtered_df=original_df,
    ... )
    """
    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, hide_models: list
) -> pd.DataFrame:
    """
    Filters DataFrame rows based on specified criteria.

    This function filters the input DataFrame based on specified criteria such as model type, size, precision, and models to hide.

    Parameters
    ----------
    df : pandas.DataFrame
        The DataFrame to be filtered.

    type_query : list
        List of tuples containing model types to include in the filtering. Each tuple consists of a model type abbreviation and its corresponding emoji.

    size_query : list
        List of size categories to include in the filtering.

    precision_query : list
        List of precision values to include in the filtering.

    hide_models : list
        List of model categories to hide from the DataFrame.

    Returns
    -------
    filtered_df : pandas.DataFrame
        A DataFrame containing rows that meet the specified filtering criteria.

    Example
    -------
    >>> filtered_df = filter_models(
    ...     df=original_df,
    ...     type_query=[("Type1", "🔥"), ("Type2", "⭐")],
    ...     size_query=["Large", "Medium"],
    ...     precision_query=["High", "Medium"],
    ...     hide_models=["Private or deleted", "Contains a merge/moerge", "MoE", "Flagged"],
    ... )
    """
    # 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.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

leaderboard_df = filter_models(
    df=leaderboard_df, 
    type_query=[t.to_str(" : ") for t in ModelType], 
    size_query=list(NUMERIC_INTERVALS.keys()), 
    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_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]
                    + 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_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_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_precision, filter_columns_size, hide_models]:
                selector.change(
                    update_table,
                    [
                        hidden_leaderboard_table_for_search,
                        shown_columns,
                        filter_columns_type,
                        filter_columns_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")
            gr.Markdown(FAQ_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")
                    private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC)
                    model_type = gr.Dropdown(
                        choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
                        label="Model type",
                        multiselect=False,
                        value=ModelType.FT.to_str(" : "),
                        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,
                    private,
                    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=10800) # restarted every 3h
scheduler.add_job(update_dynamic_files, "cron", minute=30) # launched every hour on the hour
scheduler.start()

demo.queue(default_concurrency_limit=40).launch()