import subprocess import gradio as gr import pandas as pd from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download from src.about import ( CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, EVALUATION_QUEUE_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, TITLE, ABOUT_TEXT, SUBMIT_CHALLENGE_TEXT, ) from src.display.css_html_js import custom_css from src.display.utils import ( BENCHMARK_COLS, COLS, COLS_PAIRED, EVAL_COLS, EVAL_TYPES, NUMERIC_INTERVALS, TYPES, AutoEvalColumn, AlgoType, fields, WeightType, Precision ) from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, DATA_REPO, REPO_ID, TOKEN, REQUESTS_REPO_PATH, RESULTS_REPO_PATH, CACHE_PATH from src.populate import get_evaluation_queue_df, get_leaderboard_df, calc_average from src.submission.submit import add_new_eval, add_new_challenge def restart_space(): API.restart_space(repo_id=REPO_ID) try: print(CACHE_PATH) snapshot_download( repo_id=DATA_REPO, local_dir=CACHE_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN ) except Exception: print("Could not download the dataset. Please check your token and network connection.") restart_space() original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS, COLS_PAIRED) leaderboard_df = original_df.copy() # Searching and filtering def update_table( hidden_df: pd.DataFrame, columns: list, ): df = select_columns(hidden_df, columns) if AutoEvalColumn.average.name in df.columns: df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) df[[AutoEvalColumn.average.name]] = df[[AutoEvalColumn.average.name]].round(decimals=4) elif AutoEvalColumn.model.name in df.columns: df = df.sort_values(by=[AutoEvalColumn.model.name], ascending=True) return df def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame: return df[(df[AutoEvalColumn.model.name].str.contains(query, case=False))] def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame: always_here_cols = [ # AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name, ] # We use COLS to maintain sorting filtered_df = df[ always_here_cols + [c for c in COLS if c in df.columns and c in columns] ] if AutoEvalColumn.average.name in filtered_df.columns: filtered_df[AutoEvalColumn.average.name] = filtered_df.apply(lambda row: calc_average(row, [col[0] for col in BENCHMARK_COLS]), axis=1) return filtered_df 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("Leaderboard", elem_id="llm-benchmark-tab-table", id=0): with gr.Row(): shown_columns = gr.CheckboxGroup( choices=[ c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden ], value=[ c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden and not c.never_hidden ], label="Select columns to show", elem_id="column-select", interactive=True, ) leaderboard_table = gr.components.Dataframe( value=leaderboard_df[ [c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value ], headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value, datatype=TYPES, elem_id="leaderboard-table", interactive=False, visible=True, ) # Dummy leaderboard for handling the case when the user uses backspace key hidden_leaderboard_table_for_search = gr.components.Dataframe( value=original_df[COLS], headers=COLS, datatype=TYPES, visible=False, ) for selector in [shown_columns]: selector.change( update_table, [ hidden_leaderboard_table_for_search, shown_columns, ], leaderboard_table, queue=True, ) with gr.TabItem("Submit Algorithm", elem_id="llm-benchmark-tab-table", id=1): with gr.Row(): gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") with gr.Row(): gr.Markdown("# Submission Form\nSubmitted files will be stored and made public. If you have any questions, please [contact](mailto:qiutianyi.qty@gmail.com) the ProgressGym team.", elem_classes="markdown-text") with gr.Row(): with gr.Column(): submission_file = gr.File(label="Evaluation result (JSON file generated by run_benchmark.py, one algorithm on all challenges)", file_types=['.json']) with gr.Column(): algo_name = gr.Textbox(label="Algorithm display name") algo_info = gr.Textbox(label="Optional: Comments & extra information") algo_link = gr.Textbox(label="Optional: One external link (e.g. GitHub repo, paper, project page)") submitter_email = gr.Textbox(label="Optional: Email address for contact (will be encrypted with RSA-2048 for privacy before storage and public archiving)") submit_button = gr.Button("Submit Algorithm") submission_result = gr.Markdown() submit_button.click( add_new_eval, [ submission_file, algo_name, algo_info, algo_link, submitter_email, ], submission_result, ) with gr.TabItem("Submit Challenge", elem_id="llm-benchmark-tab-table", id=2): with gr.Row(): gr.Markdown(SUBMIT_CHALLENGE_TEXT, elem_classes="markdown-text") with gr.Row(): gr.Markdown("# Submission Form\nSubmitted files will be stored and made public. If you have any questions, please [contact](mailto:qiutianyi.qty@gmail.com) the ProgressGym team.", elem_classes="markdown-text") with gr.Row(): with gr.Column(): challenge_submission_file = gr.File(label="Optional: Evaluation results (JSON file(s) generated by run_benchmark.py, testing all algorithms on your challenge)", file_count='multiple', file_types=['.json']) with gr.Column(): challenge_name = gr.Textbox(label="Challenge display name") challenge_info = gr.Textbox(label="Comments & extra information", lines=3) challenge_link = gr.Textbox(label="One external link (e.g. GitHub repo, paper, project page)") challenge_submitter_email = gr.Textbox(label="Email address for contact (will be encrypted with RSA-2048 for privacy before storage and public archiving)") challenge_submit_button = gr.Button("Submit Challenge") challenge_submission_result = gr.Markdown() challenge_submit_button.click( add_new_challenge, [ challenge_submission_file, challenge_name, challenge_info, challenge_link, challenge_submitter_email, ], challenge_submission_result, ) with gr.Row(): with gr.Accordion("About & Citation 📖", open=False): about_text = gr.Markdown(ABOUT_TEXT, elem_classes="markdown-text") citation_button = gr.Textbox( value=CITATION_BUTTON_TEXT, label=CITATION_BUTTON_LABEL, lines=20, elem_id="citation-button", show_copy_button=True, ) scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", seconds=1800) scheduler.start() demo.queue(default_concurrency_limit=40).launch()