app.py

import subprocess
import gradio as gr
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download

from src.pages.about import show_about_page
from src.pages.submit import show_submit_page
from src.pages.result_table import show_result_page
from src.about import (
 CITATION_BUTTON_LABEL,
 CITATION_BUTTON_TEXT,
 INTRODUCTION_TEXT,
 TITLE,
)
from src.display.css_html_js import custom_css
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
from watch_leaderboard import watch_submit_queue

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


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()


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:
 show_result_page(root_path="MC", title="📝 Exam", index=0)
 show_result_page(
 root_path="LLM",
 title="🤖 LLM-as-a-Judge",
 index=1,
 extra=lambda: gr.Markdown(
 "* Gemini result is only reported in MT-Bench because the other benchmark was mistakenly classified as unsafe"
 ),
 )
 show_result_page(root_path="NLU", title="🕵️ NLU", index=2)
 show_result_page(root_path="NLG", title="🖊️ NLG", index=3)
 show_about_page(index=4)
 show_submit_page(index=5)

 with gr.Column():
 with gr.Accordion("📙 Citation", open=False):
 citation_button = gr.Textbox(
 value=CITATION_BUTTON_TEXT,
 label=CITATION_BUTTON_LABEL,
 lines=8,
 elem_id="citation-button",
 show_copy_button=True,
 )


scheduler = BackgroundScheduler()
scheduler.add_job(watch_submit_queue, "interval", seconds=3500)
scheduler.add_job(restart_space, "interval", seconds=3600)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()