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 def handle_file_upload(file): file_path = file.name.split("/")[-1] if "/" in file.name else file.name logging.info("File uploaded: %s", file_path) with open(file.name, "r") as f: v = json.load(f) return v, file_path def submit_file(v, file_path, su, mn): new_file = v['results'] new_file['model'] = su + "/" + mn 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"] 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 "Success!" from src.display.about import ( INTRODUCTION_TEXT, TITLE, LLM_BENCHMARKS_TEXT ) 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(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") with gr.Row(): gr.Markdown("# ✨ Submit your model here!", elem_classes="markdown-text") with gr.Column(): # 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 model_name_textbox = gr.Textbox(label="Model name") submitter_username = gr.Textbox(label="Username") # def toggle_upload_button(model_name, username): # return bool(model_name) and bool(username) file_output = gr.File(label="Drag and drop JSON file judgment here", type="filepath") # upload_button = gr.Button("Click to Upload & Submit Answers", elem_id="upload_button",variant='primary') uploaded_file = gr.State() file_path = gr.State() out = gr.Textbox("Статус отправки") submit_button = gr.Button("Submit File", elem_id="submit_button", variant='primary') file_output.upload( handle_file_upload, file_output, [uploaded_file, file_path] ) submit_button.click( submit_file, [uploaded_file, file_path, submitter_username, model_name_textbox], [out] ) 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 pass 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: data = json.load(f) data_list.append(data) 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() # update_board() scheduler.add_job(update_board, "interval", minutes=600) scheduler.start() demo_app = build_demo() demo_app.launch(debug=True)