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 (
    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
        )
    except Exception as e:
        print(e)
        restart_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=10800)  # restarted every 3h
scheduler.start()

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