import json import logging import os import subprocess import time import gradio as gr import pandas as pd from apscheduler.schedulers.background import BackgroundScheduler from gradio_space_ci import enable_space_ci from huggingface_hub import snapshot_download from src.display.about import ( FAQ_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, TITLE, ) from src.display.css_html_js import custom_css from src.envs import ( API, EVAL_RESULTS_PATH, H4_TOKEN, REPO_ID, RESET_JUDGEMENT_ENV, ) 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() def restart_space(): API.restart_space(repo_id=REPO_ID, token=H4_TOKEN) def time_diff_wrapper(func): def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() diff = end_time - start_time logging.info(f"Time taken for {func.__name__}: {diff} seconds") return result return wrapper @time_diff_wrapper def download_dataset(repo_id, local_dir, repo_type="dataset", max_attempts=3, backoff_factor=1.5): """Download dataset with exponential backoff retries.""" attempt = 0 while attempt < max_attempts: try: logging.info(f"Downloading {repo_id} to {local_dir}") snapshot_download( repo_id=repo_id, local_dir=local_dir, repo_type=repo_type, tqdm_class=None, etag_timeout=30, max_workers=8, ) logging.info("Download successful") return except Exception as e: wait_time = backoff_factor ** attempt logging.error(f"Error downloading {repo_id}: {e}, retrying in {wait_time}s") time.sleep(wait_time) attempt += 1 raise Exception(f"Failed to download {repo_id} after {max_attempts} attempts") def init_space(full_init: bool = True): """Initializes the application space, loading only necessary data.""" if full_init: # These downloads only occur on full initialization # try: # download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH) # download_dataset(DYNAMIC_INFO_REPO, DYNAMIC_INFO_PATH) download_dataset("Vikhrmodels/openbench-eval", EVAL_RESULTS_PATH) # print(subprocess.Popen('ls src')) subprocess.run(['rsync', '-avzP', '--ignore-existing', f'{EVAL_RESULTS_PATH[2:]}/external/*', 'src/gen/data/arena-hard-v0.1/model_answer/'], check=False) subprocess.run(['rsync', '-avzP', '--ignore-existing', f'{EVAL_RESULTS_PATH[2:]}/model_judgment/*', 'src/gen/data/arena-hard-v0.1/model_judgement/'], check=False) # except Exception: # restart_space() # Always retrieve the leaderboard DataFrame original_df = pd.DataFrame.from_records(json.load(open('eval-results/evals/upd.json','r'))) leaderboard_df = original_df.copy() return leaderboard_df # Convert the environment variable "LEADERBOARD_FULL_INIT" to a boolean value, defaulting to True if the variable is not set. # This controls whether a full initialization should be performed. do_full_init = os.getenv("LEADERBOARD_FULL_INIT", "True") == "True" # Calls the init_space function with the `full_init` parameter determined by the `do_full_init` variable. # This initializes various DataFrames used throughout the application, with the level of initialization detail controlled by the `do_full_init` flag. # leaderboard_df = init_space(full_init=do_full_init) 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: with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0): pass """ leaderboard = 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=3): gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") with gr.TabItem("❗FAQ", elem_id="llm-benchmark-tab-table", id=4): gr.Markdown(FAQ_TEXT, elem_classes="markdown-text") with gr.TabItem("🚀 Submit ", elem_id="llm-benchmark-tab-table", id=5): with gr.Row(): gr.Markdown("# ✨ Submit your model here!", elem_classes="markdown-text") with gr.Column(): model_name_textbox = gr.Textbox(label="Model name") 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='./external/'+file_path,repo_id='Vikhrmodels/openbench-eval',repo_type='dataset') os.environ[RESET_JUDGEMENT_ENV] = '1' return file.name if model_name_textbox: 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) # 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) if need_reset != '1': return os.environ[RESET_JUDGEMENT_ENV] = '0' subprocess.run(['python','../gen/gen_judgement.py'], check = False) subprocess.Popen('python3 ../gen/show_result.py --output') if __name__ == "__main__": os.environ[RESET_JUDGEMENT_ENV] = '1' scheduler = BackgroundScheduler() scheduler.add_job(update_board, "interval", minutes=10) scheduler.start() demo.queue(default_concurrency_limit=40).launch(debug=True)