app.py

import os
import json
import logging
import pandas as pd
import gradio as gr
import multiprocessing

from src.backend import pull_search_results
from src.envs import (
 API, START_COMMIT_ID, REPO_ID,
 HF_CACHE_DIR, SUBMIT_INFOS_DIR, SUBMIT_INFOS_FILE_NAME,
 HF_SEARCH_RESULTS_REPO_DIR, HF_EVAL_RESULTS_REPO_DIR, SUBMIT_INFOS_REPO,
 UNZIP_TARGET_DIR,
 TIME_DURATION,
 EVAL_K_VALUES,
 SUBMIT_INFOS_TABLE_COLS
)
from src.css_html_js import custom_css

logger = logging.getLogger(__name__)
logging.basicConfig(
 level=logging.WARNING,
 datefmt='%Y-%m-%d %H:%M:%S',
 format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
 force=True
)


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


def load_submit_infos_df():
 # Pull the submit infos
 API.snapshot_download(
 repo_id=SUBMIT_INFOS_REPO,
 repo_type="dataset",
 local_dir=SUBMIT_INFOS_DIR,
 etag_timeout=30
 )
 submit_infos_save_path = os.path.join(SUBMIT_INFOS_DIR, SUBMIT_INFOS_FILE_NAME)
 
 if os.path.exists(submit_infos_save_path):
 with open(submit_infos_save_path, 'r', encoding='utf-8') as f:
 submit_infos = json.load(f)
 else:
 submit_infos = []
 if submit_infos:
 submit_infos_df = pd.DataFrame(submit_infos)[SUBMIT_INFOS_TABLE_COLS]
 else:
 submit_infos_df = pd.DataFrame(columns=SUBMIT_INFOS_TABLE_COLS)
 return submit_infos_df


with gr.Blocks(css=custom_css) as demo:
 gr.Markdown("## Submission Infos Table")
 
 table = gr.components.Dataframe(
 value=load_submit_infos_df(),
 elem_id="submission-infos-table",
 interactive=False,
 datatype="markdown"
 )
 
 refresh_button = gr.Button("Refresh Submission Infos")

 refresh_button.click(
 fn=load_submit_infos_df,
 outputs=table,
 )


if __name__ == "__main__":
 process = multiprocessing.Process(
 target=pull_search_results,
 args=(
 HF_SEARCH_RESULTS_REPO_DIR,
 HF_EVAL_RESULTS_REPO_DIR,
 UNZIP_TARGET_DIR,
 SUBMIT_INFOS_DIR,
 SUBMIT_INFOS_FILE_NAME,
 EVAL_K_VALUES,
 HF_CACHE_DIR,
 TIME_DURATION,
 START_COMMIT_ID,
 ),
 )
 process.start()

 demo.queue(default_concurrency_limit=40)
 demo.launch()