app.py

import os
import gradio as gr

# from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download

try:
    from commutil import dbg
except:
    dbg = print

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, TYPES, AutoEvalColumn, ModelType, fields, WeightType, Precision
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN, EVAL_REQUESTS_PATH_BACKEND, EVAL_RESULTS_PATH_BACKEND
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval


def restart_space():
    API.restart_space(repo_id=REPO_ID)


def uncheck_all():
    return [], [], [], [], [], [], []


# Searching and filtering
def update_table(
    hidden_df: pd.DataFrame,
    # en_shown_columns_TAD: list,
    # en_shown_columns_ACM: list,
    # en_shown_columns_KUT: list,
    # en_shown_columns_NLP: list,
    # en_shown_columns_FQA: list,
    # en_shown_columns_CDP: list,
    # shown_columns2: list,
    shown_columns_cyberkut: list,
    shown_columns_cybernlp: list,
    shown_columns_cyberdsa: list,
    shown_columns: list,
    filter_columns_type: list,
    filter_columns_precision: list,
    filter_columns_size: list,
    show_deleted: bool,
    search_bar: str,
):
    # dbg(shown_columns)
    # return hidden_df
    # Combine all column selections
    # selected_columns = shown_columns
    # selected_columns = (en_shown_columns_TAD + en_shown_columns_ACM + en_shown_columns_KUT + en_shown_columns_NLP + en_shown_columns_FQA + en_shown_columns_CDP + shown_columns2)
    selected_columns = shown_columns_cyberkut + shown_columns_cybernlp + shown_columns_cyberdsa + shown_columns

    # Filter models based on queries
    filtered_df = filter_models(hidden_df, filter_columns_type, filter_columns_size, filter_columns_precision, show_deleted)
    filtered_df = filter_queries(search_bar, filtered_df)
    # print(filtered_df)
    df = select_columns(filtered_df, selected_columns)

    # dbg(df)
    return 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:
    # return df
    # Show all models
    if show_deleted:
        filtered_df = df
    else:
        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]

    if "All" not in type_query:
        if "?" in type_query:
            filtered_df = filtered_df.loc[~df[AutoEvalColumn.model_type_symbol.name].isin([t for t in ModelType if t != "?"])]
        else:
            type_emoji = [t[0] for t in type_query]
            filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]

    if "All" not in precision_query:
        if "?" in precision_query:
            filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isna()]
        else:
            filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]

    if "All" not in size_query:
        filtered_df = df
        # if "?" in size_query:
        # filtered_df = filtered_df.loc[df[AutoEvalColumn.params.name].isna()]
        # else:
        # 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


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:
    # return df
    always_here_cols = [
        AutoEvalColumn.model_type_symbol.name,
        AutoEvalColumn.model.name,
    ]

    # Ensure no duplicates and add the new average columns
    unique_columns = set(always_here_cols + columns)

    # We use COLS to maintain sorting
    filtered_df = df[[c for c in COLS if c in df.columns and c in unique_columns]]

    # Debugging print to see if the new columns are included
    # print(f"Columns included in DataFrame: {filtered_df.columns.tolist()}")

    # dbg(filtered_df)

    return filtered_df


## Space initialisation

if not os.getenv("ENVIRONMENT"):
    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()


# LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)

# LEADERBOARD_DF.rename(columns={
#     '4、malicious_URL_acc': 'mali',
# })

LEADERBOARD_DF_CP = LEADERBOARD_DF.copy()


# dbg(LEADERBOARD_DF)

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


def init_leaderboard():
    pass
    # if dataframe is None or dataframe.empty:
    #     raise ValueError("Leaderboard DataFrame is empty or None.")
    # print(dataframe)
    # return Leaderboard(
    #     value=dataframe,
    #     datatype=[c.type for c in fields(AutoEvalColumn)],
    #     select_columns=SelectColumns(
    #         default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
    #         cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
    #         label="Select Columns to Display:",
    #     ),
    #     search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
    #     hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
    #     filter_columns=[
    #         ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
    #         ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
    #         ColumnFilter(
    #             AutoEvalColumn.params.name,
    #             type="slider",
    #             min=0.01,
    #             max=150,
    #             label="Select the number of parameters (B)",
    #         ),
    #         # ColumnFilter(
    #         #     AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
    #         # ),
    #     ],
    #     bool_checkboxgroup_label="Hide models",
    #     interactive=False,
    # )
    with gr.Column():
        # with gr.Row():
        # with gr.Column():
        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.Column(min_width=320):
        # with gr.Box(elem_id="box-filter"):
        with gr.Row():
            filter_columns_type = gr.CheckboxGroup(
                label="Model types",
                choices=["All"] + [t.to_str() for t in ModelType],
                value=["All"],
                interactive=True,
                elem_id="filter-columns-type",
            )
            filter_columns_precision = gr.CheckboxGroup(
                label="Precision",
                choices=["All"] + [i.value.name for i in Precision],
                value=["All"],
                interactive=True,
                elem_id="filter-columns-precision",
            )

            # with gr.Row():
            #     deleted_models_visibility = gr.Checkbox(
            #         value=True, label="Show gated/private/deleted models", interactive=True
            #     )

        # with gr.Row():
        # with gr.Accordion("Select columns to show"):
        # with gr.Tab("Other"):

        with gr.Accordion("Select columns to show"):
            choice_list = ["Overall", "KUT", "NLP", "DSA", "CLS", "GEN", "REA"]
            shown_columns = gr.CheckboxGroup(
                choices=[c.name for c in fields(AutoEvalColumn) if c.name not in ["Model", "T"] and any(x in c.name for x in choice_list)],
                value=[c.name for c in fields(AutoEvalColumn) if c.name not in ["Model", "T"] and any(x in c.name for x in choice_list)],
                # choices=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.never_hidden],
                # value=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.never_hidden],
                label="Select Columns to Display:",
                interactive=True,
            )
            # with gr.Tab("CyberTAD"):
            #     datasets = ["4", "6"]
            #     en_shown_columns_TAD = gr.CheckboxGroup(
            #         choices=[c.name for c in fields(AutoEvalColumn) if c.name not in ["Model", "T"] and any(x in c.name for x in datasets)],
            #         value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.name not in ["Model", "T"] and any(x in c.name for x in datasets)],
            #         interactive=True
            #     )
            # with gr.Tab("CyberACM"):
            #     datasets = ["1", "2"]
            #     en_shown_columns_ACM = gr.CheckboxGroup(
            #         choices=[c.name for c in fields(AutoEvalColumn) if c.name not in ["Model", "T"] and any(x in c.name for x in datasets)],
            #         value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.name not in ["Model", "T"] and any(x in c.name for x in datasets)],
            #         interactive=True
            #     )
            # with gr.Tab("CyberKUT"):
            #     datasets = ["8"]
            #     en_shown_columns_KUT = gr.CheckboxGroup(
            #         choices=[c.name for c in fields(AutoEvalColumn) if c.name not in ["Model", "T"] and any(x in c.name for x in datasets)],
            #         value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.name not in ["Model", "T"] and any(x in c.name for x in datasets)],
            #         interactive=True
            #     )
            # with gr.Tab("CyberNLP"):
            #     datasets = ["9", "3"]
            #     en_shown_columns_NLP = gr.CheckboxGroup(
            #         choices=[c.name for c in fields(AutoEvalColumn) if c.name not in ["Model", "T"] and any(x in c.name for x in datasets)],
            #         value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.name not in ["Model", "T"] and any(x in c.name for x in datasets)],
            #         interactive=True
            #     )
            # with gr.Tab("CyberFQA"):
            #     datasets = ["11", "13"]
            #     en_shown_columns_FQA = gr.CheckboxGroup(
            #         choices=[c.name for c in fields(AutoEvalColumn) if c.name not in ["Model", "T"] and any(x in c.name for x in datasets)],
            #         value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.name not in ["Model", "T"] and any(x in c.name for x in datasets)],
            #         interactive=True
            #     )
            # with gr.Tab("CyberCDP"):
            #     datasets = ["10"]
            #     en_shown_columns_CDP = gr.CheckboxGroup(
            #         choices=[c.name for c in fields(AutoEvalColumn) if c.name not in ["Model", "T"] and any(x in c.name for x in datasets)],
            #         value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.name not in ["Model", "T"] and any(x in c.name for x in datasets)],
            #         interactive=True
            #     )

            # dbg(fields(AutoEvalColumn))
            # dbg([c.name for c in fields(AutoEvalColumn)])

            # dbg([c.name for c in fields(AutoEvalColumn) if any(x in c.name for x in ["Averge"])])

            with gr.Tab("CyberKUT"):
                datasets = ["NetQA", "Embed", "Metric", "CodeQA"]
                shown_columns_cyberkut = gr.CheckboxGroup(choices=[c.name for c in fields(AutoEvalColumn) if c.name not in ["Model", "T"] and any(x in c.name for x in datasets)], value=[], interactive=True)

            with gr.Tab("CyberNLP"):
                datasets = ["Corpus", "CDTier", "NER", "HackerNews"]
                shown_columns_cybernlp = gr.CheckboxGroup(choices=[c.name for c in fields(AutoEvalColumn) if c.name not in ["Model", "T"] and any(x in c.name for x in datasets)], value=[], interactive=True)

            with gr.Tab("CyberDSA"):
                datasets = ["MaliURLs", "CSIC2010", "BETH", "MITRE"]
                shown_columns_cyberdsa = gr.CheckboxGroup(choices=[c.name for c in fields(AutoEvalColumn) if c.name not in ["Model", "T"] and any(x in c.name for x in datasets)], value=[], interactive=True)

        # with gr.Accordion("[zh] Select columns to show"):
        #     shown_columns2 = gr.CheckboxGroup(
        #         choices=[c.name for c in fields(AutoEvalColumn) if c.name not in ["Model", "T"]],
        #         value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and c.name not in ["Model", "T"]],

        #         # choices=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.never_hidden],
        #         # value=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.never_hidden],
        #         label="Select Columns to Display:",
        #         interactive=True,
        #     )

        # shown_columns = [en_shown_columns_TAD, en_shown_columns_ACM, en_shown_columns_KUT, en_shown_columns_NLP, en_shown_columns_FQA, en_shown_columns_CDP, shown_columns2]
        shown_columns = [shown_columns_cyberkut, shown_columns_cybernlp, shown_columns_cyberdsa, shown_columns]

        # dbg(shown_columns, "before")

        # with gr.Row():
        uncheck_all_button = gr.Button("Uncheck All")
        uncheck_all_button.click(
            uncheck_all,
            inputs=[],
            outputs=[
                *shown_columns,
            ],
        )
        # with gr.Row():
        deleted_models_visibility = gr.Checkbox(value=True, label="Show gated/private/deleted models", interactive=True, visible=False)

        filter_columns_size = gr.CheckboxGroup(
            label="Model sizes (in billions of parameters)",
            # choices=["All"] + list(NUMERIC_INTERVALS.keys()) + ["?"],
            choices=["All"] + ["?"],
            value=["All"],
            interactive=True,
            elem_id="filter-columns-size",
            visible=False,
        )

    # # TODO 添加新列的顺序
    # leaderboard_table = gr.Dataframe(
    #     value=LEADERBOARD_DF_CP[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + ["Averge KUT⬆️", "Averge NLP⬆️", "Averge DSA⬆️"] + [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.never_hidden]],
    #     headers=[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + ["Averge KUT⬆️", "Averge NLP⬆️", "Averge DSA⬆️"] + [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.never_hidden]],
    #     datatype=TYPES,
    #     elem_id="leaderboard-table",
    #     interactive=False,
    #     visible=True,
    # )
    

    # dbg([(c.name, c.never_hidden) for c in fields(AutoEvalColumn)]) 
    # dbg([c.name for c in fields(AutoEvalColumn) if c.never_hidden]  + [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.never_hidden])
    
    leaderboard_table = gr.Dataframe(
        value=LEADERBOARD_DF_CP[[c.name for c in fields(AutoEvalColumn) if c.never_hidden]  + [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.never_hidden]],
        headers=[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.never_hidden]],
        datatype=TYPES,
        elem_id="leaderboard-table",
        interactive=False,
        visible=True,
    )

    # dbg(len(LEADERBOARD_DF))

    # print(f"[DEBUG]---------- COLS :", COLS)
    # print(f"[DEBUG]---------- TYPES :", TYPES)

    # Dummy leaderboard for handling the case when the user uses backspace key
    hidden_leaderboard_table_for_search = gr.Dataframe(
        value=LEADERBOARD_DF[COLS],
        headers=COLS,
        datatype=TYPES,
        visible=False,
    )

    search_bar.submit(
        update_table,
        inputs=[
            hidden_leaderboard_table_for_search,
            *shown_columns,
            filter_columns_type,
            filter_columns_precision,
            filter_columns_size,
            deleted_models_visibility,
            search_bar,
        ],
        outputs=leaderboard_table,
        queue=True,
    )
    for selector in [*shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility, filter_columns_size]:
        selector.change(
            update_table,
            inputs=[
                hidden_leaderboard_table_for_search,
                *shown_columns,
                filter_columns_type,
                filter_columns_precision,
                filter_columns_size,
                deleted_models_visibility,
                search_bar,
            ],
            outputs=leaderboard_table,
            queue=True,
        )


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):
            init_leaderboard()

        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=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()