app.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# flake8: noqa E501

import shutil

import gradio as gr
from apscheduler.schedulers.background import BackgroundScheduler
from gradio_leaderboard import ColumnFilter, Leaderboard, SelectColumns
from huggingface_hub import snapshot_download

from src.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    EVALUATION_REQUESTS_TEXT,
    EVALUATION_SCRIPT,
    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,
    AutoEvalColumn,
    ModelType,
    Precision,
    WeightType,
    fields,
)
from src.envs import (
    API,
    CACHE_PATH,
    EVAL_REQUESTS_PATH,
    EVAL_RESULTS_PATH,
    REPO_ID,
    REQUESTS_REPO,
    RESULTS_REPO,
    TOKEN,
)
from src.populate import get_evaluation_requests_df, get_leaderboard_df
from src.submission.submit import add_new_eval


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

# Space initialisation
shutil.rmtree(CACHE_PATH, ignore_errors=True)
try:
    snapshot_download(
        repo_id=REQUESTS_REPO,
        local_dir=EVAL_REQUESTS_PATH,
        repo_type="dataset",
        tqdm_class=None,
        etag_timeout=30,
        token=TOKEN,
    )
except Exception:
    restart_space()

try:
    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,
)

(
    finished_eval_requests_df,
    running_eval_requests_df,
    pending_eval_requests_df,
) = get_evaluation_requests_df(EVAL_REQUESTS_PATH, EVAL_COLS)

def init_leaderboard(dataframe):
    if dataframe is None or dataframe.empty:
        raise ValueError("Leaderboard DataFrame is empty or None.")
    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="Columns",
        ),
        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
        filter_columns=[
            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Floating-point format"),
            ColumnFilter(
                AutoEvalColumn.params.name,
                type="slider",
                min=1,
                max=500,
                label="Number of parameters (billions)",
            ),
        ],
        bool_checkboxgroup_label=' ',
        interactive=False,
    )


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("🏆 Ranking", elem_id="llm-benchmark-tab-table", id=0):
            leaderboard = init_leaderboard(LEADERBOARD_DF)

        with gr.TabItem("🧠 About", elem_id="llm-benchmark-tab-table", id=2):
            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
            with gr.Accordion(
                        "Evaluation script",
                        open=False,
                    ):
                gr.Markdown(
                    EVALUATION_SCRIPT,
                    elem_classes="markdown-text",
                )

        with gr.TabItem("🧪 Submissions", elem_id="llm-benchmark-tab-table", id=3):
            with gr.Column():
                with gr.Row():
                    gr.Markdown(EVALUATION_REQUESTS_TEXT, elem_classes="markdown-text")

                with gr.Column():
                    with gr.Accordion(
                        f"✅ Finished ({len(finished_eval_requests_df)})",
                        open=False,
                    ):
                        with gr.Row():
                            finished_eval_table = gr.components.Dataframe(
                                value=finished_eval_requests_df,
                                headers=EVAL_COLS,
                                datatype=EVAL_TYPES,
                                row_count=5,
                            )

                    with gr.Accordion(
                        f"⏳ Pending ({len(pending_eval_requests_df)})",
                        open=False,
                    ):
                        with gr.Row():
                            pending_eval_table = gr.components.Dataframe(
                                value=pending_eval_requests_df,
                                headers=EVAL_COLS,
                                datatype=EVAL_TYPES,
                                row_count=5,
                            )
            with gr.Row():
                gr.Markdown("# ✉️ Submission", 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 in [ModelType.PT, ModelType.FT]],
                        label="Model type",
                        multiselect=False,
                        value=None,
                        interactive=True,
                    )
                    # precision = gr.Dropdown(
                    #     choices=[i.value.name for i in Precision if i != Precision.Unknown],
                    #     label="Precision",
                    #     multiselect=False,
                    #     value="bfloat16",
                    #     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")
            submission_result = gr.Markdown()
            
            def submit_with_braindao_check(model_name, revision, model_type):
                if model_name.split("/")[0] == "braindao":
                    model_type = ModelType.BrainDAO.to_str(" ")
                return add_new_eval(model_name, revision, model_type)
            
            submit_button.click(
                submit_with_braindao_check,
                [
                    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=900)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch(
    server_name="0.0.0.0",
    allowed_paths=["images/solbench.svg"],
)