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| import gradio as gr | |
| from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns | |
| 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, | |
| TASK_TEXT, | |
| LLM_BENCHMARKS_TEXT, | |
| TITLE, | |
| ) | |
| from src.display.css_html_js import custom_css | |
| from src.display.utils import ( | |
| BENCHMARK_COLS, | |
| COLS, | |
| EVAL_COLS, | |
| EVAL_TYPES, | |
| AutoEvalColumn, | |
| ModelType, | |
| fields, | |
| WeightType, | |
| Precision | |
| ) | |
| from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN | |
| from src.populate import get_evaluation_queue_df, get_leaderboard_df | |
| from src.submission.submit import add_new_eval | |
| import pdb | |
| def restart_space(): | |
| API.restart_space(repo_id=REPO_ID) | |
| ### Space initialisation | |
| # try: | |
| # print(EVAL_REQUESTS_PATH) | |
| # snapshot_download( | |
| # repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN | |
| # ) | |
| # except Exception: | |
| # restart_space() | |
| # try: | |
| # print(EVAL_RESULTS_PATH) | |
| # snapshot_download( | |
| # repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN | |
| # ) | |
| # except Exception: | |
| # restart_space() | |
| task = ['Overall', 'Acrostic', 'Crossword', 'Cryptogram', 'Logic_Puzzle', 'Sudoku', 'Drop_Quote'] | |
| leaderboard_dict = {} | |
| for t in task: | |
| leaderboard_dict[t] = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS, task=t) | |
| ( | |
| finished_eval_queue_df, | |
| running_eval_queue_df, | |
| pending_eval_queue_df, | |
| ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) | |
| def init_leaderboard(dataframe): | |
| if dataframe is None or dataframe.empty: | |
| raise ValueError("Leaderboard DataFrame is empty or None.") | |
| # pdb.set_trace() | |
| def highlight_max_bold(s): | |
| return ['font-weight: bold' if v == s.max() and v != s.min() else '' for v in s] | |
| num_cols = dataframe.select_dtypes(include=['float']).columns | |
| styler = dataframe.style.format({col: "{:.1f}" for col in num_cols}) | |
| styler = styler.apply(highlight_max_bold, subset=num_cols) | |
| return gr.components.Dataframe( | |
| value=styler, | |
| headers=[c.name for c in fields(AutoEvalColumn)], | |
| datatype=[c.type for c in fields(AutoEvalColumn)], | |
| row_count=10, | |
| interactive=False, | |
| column_widths=[180, 60, 80, 80, 80, 80, 60], | |
| ) | |
| # return Leaderboard( | |
| # value=dataframe, | |
| # 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], | |
| # label="Select Columns to Display:", | |
| # ), | |
| # # search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name], | |
| # # hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden], | |
| # # filter_columns=[ | |
| # # ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"), | |
| # # ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"), | |
| # # ColumnFilter( | |
| # # AutoEvalColumn.params.name, | |
| # # type="slider", | |
| # # min=0.01, | |
| # # max=150, | |
| # # label="Select the number of parameters (B)", | |
| # # ), | |
| # # ColumnFilter( | |
| # # AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True | |
| # # ), | |
| # # ], | |
| # # bool_checkboxgroup_label="Hide models", | |
| # interactive=False, | |
| # ) | |
| demo = gr.Blocks(css=custom_css) | |
| with demo: | |
| gr.HTML(TITLE) | |
| gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") | |
| with gr.Tabs(elem_id="main-tabs", elem_classes="tab-buttons") as tabs: | |
| with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0): | |
| # leaderboard = init_leaderboard(LEADERBOARD_DF) | |
| with gr.Tabs(): | |
| for i, t in enumerate(task): | |
| with gr.TabItem(t.replace("_", " "), elem_id=f"llm-benchmark-tab-table-{t}", id=i): | |
| if TASK_TEXT.get(t, None): | |
| gr.Markdown(TASK_TEXT[t], elem_classes="markdown-text") | |
| leaderboard = init_leaderboard(leaderboard_dict[t]) | |
| # with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2): | |
| # gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") | |
| with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3): | |
| with gr.Column(): | |
| with gr.Row(): | |
| gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") | |
| with gr.Column(): | |
| with gr.Accordion( | |
| f"✅ Finished Evaluations ({len(finished_eval_queue_df)})", | |
| open=False, | |
| ): | |
| with gr.Row(): | |
| finished_eval_table = gr.components.Dataframe( | |
| value=finished_eval_queue_df, | |
| headers=EVAL_COLS, | |
| datatype=EVAL_TYPES, | |
| row_count=5, | |
| ) | |
| with gr.Accordion( | |
| f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})", | |
| open=False, | |
| ): | |
| with gr.Row(): | |
| running_eval_table = gr.components.Dataframe( | |
| value=running_eval_queue_df, | |
| headers=EVAL_COLS, | |
| datatype=EVAL_TYPES, | |
| row_count=5, | |
| ) | |
| with gr.Accordion( | |
| f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})", | |
| open=False, | |
| ): | |
| with gr.Row(): | |
| pending_eval_table = gr.components.Dataframe( | |
| value=pending_eval_queue_df, | |
| headers=EVAL_COLS, | |
| datatype=EVAL_TYPES, | |
| row_count=5, | |
| ) | |
| with gr.Row(): | |
| gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text") | |
| with gr.Row(): | |
| with gr.Column(): | |
| model_name_textbox = gr.Textbox(label="Model name") | |
| revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main") | |
| model_type = gr.Dropdown( | |
| choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown], | |
| label="Model type", | |
| multiselect=False, | |
| value=None, | |
| interactive=True, | |
| ) | |
| with gr.Column(): | |
| precision = gr.Dropdown( | |
| choices=[i.value.name for i in Precision if i != Precision.Unknown], | |
| label="Precision", | |
| multiselect=False, | |
| value="float16", | |
| interactive=True, | |
| ) | |
| weight_type = gr.Dropdown( | |
| choices=[i.value.name for i in WeightType], | |
| label="Weights type", | |
| multiselect=False, | |
| value="Original", | |
| interactive=True, | |
| ) | |
| base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)") | |
| submit_button = gr.Button("Submit Eval") | |
| submission_result = gr.Markdown() | |
| submit_button.click( | |
| add_new_eval, | |
| [ | |
| model_name_textbox, | |
| base_model_name_textbox, | |
| revision_name_textbox, | |
| precision, | |
| weight_type, | |
| model_type, | |
| ], | |
| submission_result, | |
| ) | |
| with gr.Row(): | |
| # gr.Markdown() | |
| citation_button = gr.Textbox( | |
| value=CITATION_BUTTON_TEXT, | |
| label=CITATION_BUTTON_LABEL, | |
| 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() |