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| 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() |