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Running
| import logging | |
| import os | |
| os.makedirs("tmp", exist_ok=True) | |
| os.environ['TMP_DIR'] = "tmp" | |
| 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 | |
| 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") | |
| 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=1) | |
| scheduler.start() | |
| demo_app = build_demo() | |
| demo_app.launch(debug=True) | |