app.py

import logging
import os
os.makedirs("tmp", exist_ok=True)
os.environ['TMP_DIR'] = "tmp"
import subprocess
import shutil
import gradio as gr
from apscheduler.schedulers.background import BackgroundScheduler
from gradio_leaderboard import Leaderboard, SelectColumns
from gradio_space_ci import enable_space_ci
import json
from io import BytesIO


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, download_dataset
import huggingface_hub
# huggingface_hub.login(token=H4_TOKEN)

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)
    download_openbench()


def build_demo():
    demo = gr.Blocks(title="Small Shlepa", 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,su,mn):
                        file_path = file.name.split("/")[-1] if "/" in file.name else file.name
                        logging.info("New submition: file saved to %s", file_path)
                        with open(file.name, "r") as f:
                            v=json.load(f)
                            new_file = v['results']
                            new_file['model'] = mn+"/"+su
                            new_file['moviesmc']=new_file['moviemc']["acc,none"]
                            new_file['musicmc']=new_file['musicmc']["acc,none"]
                            new_file['booksmc']=new_file['bookmc']["acc,none"]
                            new_file['lawmc']=new_file['lawmc']["acc,none"]
                            # name = v['config']["model_args"].split('=')[1].split(',')[0]
                            new_file['model_dtype'] = v['config']["model_dtype"]
                            new_file['ppl'] = 0
                            new_file.pop('moviemc')
                            new_file.pop('bookmc')
                        buf = BytesIO()
                        buf.write(json.dumps(new_file).encode('utf-8'))
                        API.upload_file(
                            path_or_fileobj=buf,
                            path_in_repo="model_data/external/" + su+mn + ".json",
                            repo_id="Vikhrmodels/s-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,model_name_textbox,submitter_username], 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"
    import shutil

    # `shutil.rmtree("./m_data")` is a Python command that removes a directory and all its contents
    # recursively. In this specific context, it is used to delete the directory named "m_data" along
    # with all its files and subdirectories. This command helps in cleaning up the existing data in
    # the "m_data" directory before downloading new dataset files into it.
    # shutil.rmtree("./m_data")
    # shutil.rmtree("./data")
    download_dataset("Vikhrmodels/s-openbench-eval", "m_data")
    import glob
    data_list = [{"musicmc": 0.3021276595744681, "lawmc": 0.2800829875518672, "model": "apsys/saiga_3_8b", "moviesmc": 0.3472222222222222, "booksmc": 0.2800829875518672, "model_dtype": "torch.float16", "ppl": 0}]
    for file in glob.glob("./m_data/model_data/external/*.json"):
        with open(file) as f:
            try:
                data = json.load(f)
                data_list.append(data)
            except:
                continue
    if len(data_list) >1:
        data_list.pop(0)
    with open("genned.json", "w") as f:
        json.dump(data_list, f)


    API.upload_file(
            path_or_fileobj="genned.json",
            path_in_repo="leaderboard.json",
            repo_id="Vikhrmodels/s-shlepa-metainfo",
            repo_type="dataset",
    )
    restart_space()

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



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

    scheduler = BackgroundScheduler()
    scheduler.add_job(update_board, "interval", minutes=600)
    scheduler.start()

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