app.py

import logging
import os
import subprocess

import gradio as gr
from apscheduler.schedulers.background import BackgroundScheduler
from gradio_leaderboard import Leaderboard, SelectColumns
from gradio_space_ci import enable_space_ci

from src.display.about import (
    INTRODUCTION_TEXT,
    TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
    AutoEvalColumn,
    fields,
)
from src.envs import API, H4_TOKEN, HF_HOME, REPO_ID, RESET_JUDGEMENT_ENV
from src.leaderboard.build_leaderboard import build_leadearboard_df, download_openbench

os.environ["GRADIO_ANALYTICS_ENABLED"] = "false"

# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")

# Start ephemeral Spaces on PRs (see config in README.md)
enable_space_ci()

download_openbench()


def restart_space():
    API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)


def build_demo():
    demo = gr.Blocks(title="Chatbot Arena Leaderboard", css=custom_css)
    leaderboard_df = build_leadearboard_df()
    with demo:
        gr.HTML(TITLE)
        gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

        with gr.Tabs(elem_classes="tab-buttons"):
            with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
                Leaderboard(
                    value=leaderboard_df,
                    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 or c.dummy],
                        label="Select Columns to Display:",
                    ),
                    search_columns=[
                        AutoEvalColumn.model.name,
                        # AutoEvalColumn.fullname.name,
                        # AutoEvalColumn.license.name
                    ],
                )

            # with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=1):
            #    gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
            # with gr.TabItem("❗FAQ", elem_id="llm-benchmark-tab-table", id=2):
            #    gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")

            with gr.TabItem("🚀 Submit ", elem_id="llm-benchmark-tab-table", id=3):
                with gr.Row():
                    gr.Markdown("# ✨ Submit your model here!", elem_classes="markdown-text")

                with gr.Column():
                    model_name_textbox = gr.Textbox(label="Model name")
                    submitter_username = gr.Textbox(label="Username")

                    def upload_file(file):
                        file_path = file.name.split("/")[-1] if "/" in file.name else file.name
                        logging.info("New submition: file saved to %s", file_path)
                        API.upload_file(
                            path_or_fileobj=file.name,
                            path_in_repo="model_answers/external/" + file_path,
                            repo_id="Vikhrmodels/openbench-eval",
                            repo_type="dataset",
                        )
                        os.environ[RESET_JUDGEMENT_ENV] = "1"
                        return file.name

                    if model_name_textbox and submitter_username:
                        file_output = gr.File()
                        upload_button = gr.UploadButton(
                            "Click to Upload & Submit Answers", file_types=["*"], file_count="single"
                        )
                        upload_button.upload(upload_file, upload_button, file_output)

        return demo


# print(os.system('cd src/gen && ../../.venv/bin/python gen_judgment.py'))
# print(os.system('cd src/gen/ && python show_result.py --output'))


def update_board():
    need_reset = os.environ.get(RESET_JUDGEMENT_ENV)
    logging.info("Updating the judgement: %s", need_reset)
    if need_reset != "1":
        return
    os.environ[RESET_JUDGEMENT_ENV] = "0"

    # gen_judgement_file = os.path.join(HF_HOME, "src/gen/gen_judgement.py")
    # subprocess.run(["python3", gen_judgement_file], check=True)

    show_result_file = os.path.join(HF_HOME, "src/gen/show_result.py")
    subprocess.run(["python3", show_result_file, "--output"], check=True)

    # update the gr item with leaderboard
    # TODO


if __name__ == "__main__":
    os.environ[RESET_JUDGEMENT_ENV] = "1"

    scheduler = BackgroundScheduler()
    scheduler.add_job(update_board, "interval", minutes=10)
    scheduler.start()

    demo_app = build_demo()
    demo_app.launch(debug=True)