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FormulaOne-Leaderboard

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File size: 9,490 Bytes

from functools import partial

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

from src.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    EVALUATION_QUEUE_TEXT,
    INTRODUCTION_TEXT,
    LLM_BENCHMARKS_TEXT,
    TITLE,
)
from src.datamodel.data import F1Data

from src.display.css_html_js import custom_css

from src.display.utils import (
    # BENCHMARK_COLS,
    COLS,
    EVAL_COLS,
    EVAL_TYPES,
    AutoEvalColumn,
    ModelType,
    fields,
    WeightType,
    Precision,
)
from src.envs import API, REPO_ID, TOKEN, CODE_PROBLEMS_REPO, SUBMISSIONS_REPO, RESULTS_REPO
from src.logger import get_logger

from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_solutions

logger = get_logger(__name__)

SPLIT = "warmup"  # TODO temp
SKIP_VALIDATION = True  # TODO temp


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


lbdb = F1Data(cp_ds_name=CODE_PROBLEMS_REPO, sub_ds_name=SUBMISSIONS_REPO, res_ds_name=RESULTS_REPO, split=SPLIT)
leaderboard_df = get_leaderboard_df(RESULTS_REPO)


logger.info("Initialized LBDB")

# (
#     finished_eval_queue_df,
#     running_eval_queue_df,
#     pending_eval_queue_df,
# ) = get_evaluation_queue_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="Select Columns to Display:",
        ),
        search_columns=[AutoEvalColumn.system.name, AutoEvalColumn.system_type.name],
        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
        filter_columns=[
            ColumnFilter(AutoEvalColumn.system_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,
    )


# Display image using Markdown
# banner = "![Leaderboard Banner](file/assets/banner.png)"

demo = gr.Blocks(css=custom_css)
with demo:
    gr.Image(
        "assets/banner.png",
        interactive=False,
        show_label=False,
        show_download_button=False,
        container=False,
    )

    # gr.Markdown(banner)
    gr.HTML(
        """
        <style>
            body {
                background-color: #121212;
                color: white;
                margin: 0;  /* Reset browser default */
            }

            /* Outer container margin & spacing */
            .gradio-container {
                max-width: 1100px;
                margin: 2rem auto;        /* top/bottom spacing + horizontal centering */
                padding: 2rem;            /* inner spacing */
                background-color: rgba(0, 0, 0, 0.6);  /* optional: semi-transparent panel */
                border-radius: 12px;      /* rounded corners */
            }
        </style>
        """
    )

    gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 FormulaOne Leaderboard", elem_id="formulaone-leaderboar-tab-table", id=0):
            leaderboard = init_leaderboard(leaderboard_df)

        # with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=1):
        #     logger.info("Tab about")
        #     gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=2):
            logger.info("Tab submission")
            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 solutions here!", elem_classes="markdown-text")

            with gr.Row():
                with gr.Column():
                    system_name_textbox = gr.Textbox(label=AutoEvalColumn.system.name)
                    org_textbox = gr.Textbox(label=AutoEvalColumn.organization.name)
                    # revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
                    sys_type_dropdown = gr.Dropdown(
                        choices=[t.to_str(" ") for t in ModelType],
                        label=AutoEvalColumn.system_type.name,
                        multiselect=False,
                        value=ModelType.LLM.to_str(" "),
                        interactive=True,
                    )

                    # with gr.Column():
                    submission_file = gr.File(label="JSONL solutions file", file_types=[".jsonl"])
                    # 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)")

            logger.info("Submit button")
            submit_button = gr.Button("Submit")
            submission_result = gr.Markdown()

            def add_solution_cbk(system_name, org, sys_type, submission_path):
                return add_new_solutions(
                    lbdb, system_name, org, sys_type, submission_path, skip_validation=SKIP_VALIDATION
                )

            submit_button.click(
                add_solution_cbk,
                [
                    system_name_textbox,
                    org_textbox,
                    sys_type_dropdown,
                    submission_file,
                ],
                submission_result,
            )

    with gr.Row():
        logger.info("Citation")
        with gr.Accordion(CITATION_BUTTON_LABEL, open=False):
            gr.Code(
                value=CITATION_BUTTON_TEXT.strip(),
                elem_id="citation-block",
            )
            # citation_button = gr.Textbox(
            #     value=CITATION_BUTTON_TEXT,
            #     # label=CITATION_BUTTON_LABEL,
            #     lines=20,
            #     elem_id="citation-button",
            #     show_copy_button=True,
            # )

logger.info("Scheduler")
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
logger.info("Launch")
demo.queue(default_concurrency_limit=40).launch()
logger.info("Done")