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import gradio as gr |
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from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns, SearchColumns |
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import pandas as pd |
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from apscheduler.schedulers.background import BackgroundScheduler |
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from huggingface_hub import snapshot_download |
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from src.about import ( |
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CITATION_BUTTON_LABEL, |
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CITATION_BUTTON_TEXT, |
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EVALUATION_QUEUE_TEXT, |
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INTRODUCTION_TEXT, |
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LLM_BENCHMARKS_TEXT, |
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TITLE, |
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) |
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from src.display.css_html_js import custom_css |
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from src.display.utils import ( |
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BENCHMARK_COLS, |
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COLS, |
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EVAL_COLS, |
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EVAL_TYPES, |
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AutoEvalColumn, |
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ModelType, |
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fields, |
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WeightType, |
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Precision |
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) |
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from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN |
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from src.populate import get_evaluation_queue_df, get_leaderboard_df, get_model_leaderboard_df |
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from src.submission.submit import add_new_eval |
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def restart_space(): |
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API.restart_space(repo_id=REPO_ID) |
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try: |
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print(EVAL_REQUESTS_PATH) |
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snapshot_download( |
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repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN |
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) |
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except Exception: |
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restart_space() |
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try: |
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print(EVAL_RESULTS_PATH) |
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snapshot_download( |
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repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN |
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) |
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except Exception: |
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restart_space() |
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LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) |
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( |
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finished_eval_queue_df, |
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running_eval_queue_df, |
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pending_eval_queue_df, |
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) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) |
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def init_leaderboard(dataframe): |
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if dataframe is None or dataframe.empty: |
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raise ValueError("Leaderboard DataFrame is empty or None.") |
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return Leaderboard( |
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value=dataframe, |
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datatype=[c.type for c in fields(AutoEvalColumn)], |
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select_columns=None, |
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search_columns=SearchColumns(primary_column=AutoEvalColumn.model.name, secondary_columns=[], |
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placeholder="Search by the model name", |
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label="Searching"), |
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hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden], |
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filter_columns=None, |
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interactive=False, |
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) |
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model_leaderboard_df = get_model_leaderboard_df() |
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def overall_leaderboard(dataframe): |
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if dataframe is None or dataframe.empty: |
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raise ValueError("Leaderboard DataFrame is empty or None.") |
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return Leaderboard( |
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value=dataframe, |
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datatype=[c.type for c in fields(AutoEvalColumn)], |
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select_columns=None, |
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search_columns=SearchColumns(primary_column=AutoEvalColumn.model.name, secondary_columns=[], |
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placeholder="Search by the model name", |
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label="Searching"), |
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hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden], |
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filter_columns=None, |
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interactive=False, |
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) |
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demo = gr.Blocks(css=custom_css) |
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with demo: |
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gr.HTML(TITLE) |
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gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") |
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with gr.Tabs(elem_classes="tab-buttons") as tabs: |
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with gr.TabItem("๐
Overview", elem_id="llm-benchmark-tab-table", id=0): |
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leaderboard = init_leaderboard(LEADERBOARD_DF) |
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with gr.TabItem("๐ฏ Overall", elem_id="llm-benchmark-tab-table", id=1): |
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leaderboard = overall_leaderboard(LEADERBOARD_DF) |
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with gr.TabItem("๐ข Math", elem_id="math-tab-table", id=2): |
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with gr.TabItem("๐งฎ Algebra", elem_id="algebra_subtab", id=0, elem_classes="subtab"): |
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leaderboard = init_leaderboard(LEADERBOARD_DF) |
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with gr.TabItem("๐ Geometry", elem_id="geometry_subtab", id=1, elem_classes="subtab"): |
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leaderboard = init_leaderboard(LEADERBOARD_DF) |
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with gr.TabItem("๐ Probability", elem_id="prob_subtab", id=2, elem_classes="subtab"): |
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leaderboard = init_leaderboard(LEADERBOARD_DF) |
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with gr.TabItem("๐ง Reasoning", elem_id="reasonong-tab-table", id=3): |
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with gr.TabItem("๐งฉ Logical", elem_id="logical_subtab", id=0, elem_classes="subtab"): |
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leaderboard = init_leaderboard(LEADERBOARD_DF) |
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with gr.TabItem("๐ฃ๏ธ Social", elem_id="social_subtab", id=1, elem_classes="subtab"): |
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leaderboard = init_leaderboard(LEADERBOARD_DF) |
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with gr.TabItem("</> Coding", elem_id="coding-tab-table", id=4): |
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leaderboard = init_leaderboard(LEADERBOARD_DF) |
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with gr.TabItem("๐ฌ Science", elem_id="science-table", id=5): |
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leaderboard = init_leaderboard(LEADERBOARD_DF) |
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with gr.TabItem("๐ About", elem_id="llm-benchmark-tab-table", id=6): |
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gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") |
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''' |
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with gr.TabItem("๐ Submit here! ", elem_id="llm-benchmark-tab-table", id=3): |
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with gr.Column(): |
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with gr.Row(): |
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gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") |
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with gr.Column(): |
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with gr.Accordion( |
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f"โ
Finished Evaluations ({len(finished_eval_queue_df)})", |
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open=False, |
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): |
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with gr.Row(): |
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finished_eval_table = gr.components.Dataframe( |
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value=finished_eval_queue_df, |
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headers=EVAL_COLS, |
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datatype=EVAL_TYPES, |
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row_count=5, |
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) |
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with gr.Accordion( |
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f"๐ Running Evaluation Queue ({len(running_eval_queue_df)})", |
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open=False, |
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): |
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with gr.Row(): |
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running_eval_table = gr.components.Dataframe( |
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value=running_eval_queue_df, |
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headers=EVAL_COLS, |
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datatype=EVAL_TYPES, |
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row_count=5, |
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) |
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with gr.Accordion( |
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f"โณ Pending Evaluation Queue ({len(pending_eval_queue_df)})", |
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open=False, |
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): |
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with gr.Row(): |
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pending_eval_table = gr.components.Dataframe( |
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value=pending_eval_queue_df, |
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headers=EVAL_COLS, |
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datatype=EVAL_TYPES, |
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row_count=5, |
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) |
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with gr.Row(): |
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gr.Markdown("# โ๏ธโจ Submit your model here!", elem_classes="markdown-text") |
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with gr.Row(): |
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with gr.Column(): |
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model_name_textbox = gr.Textbox(label="Model name") |
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revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main") |
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model_type = gr.Dropdown( |
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choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown], |
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label="Model type", |
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multiselect=False, |
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value=None, |
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interactive=True, |
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) |
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with gr.Column(): |
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precision = gr.Dropdown( |
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choices=[i.value.name for i in Precision if i != Precision.Unknown], |
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label="Precision", |
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multiselect=False, |
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value="float16", |
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interactive=True, |
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) |
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weight_type = gr.Dropdown( |
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choices=[i.value.name for i in WeightType], |
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label="Weights type", |
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multiselect=False, |
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value="Original", |
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interactive=True, |
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) |
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base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)") |
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submit_button = gr.Button("Submit Eval") |
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submission_result = gr.Markdown() |
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submit_button.click( |
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add_new_eval, |
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[ |
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model_name_textbox, |
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base_model_name_textbox, |
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revision_name_textbox, |
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precision, |
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weight_type, |
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model_type, |
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], |
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submission_result, |
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) |
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''' |
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with gr.Row(): |
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with gr.Accordion("๐ Citation", open=False): |
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citation_button = gr.Textbox( |
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value=CITATION_BUTTON_TEXT, |
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label=CITATION_BUTTON_LABEL, |
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lines=20, |
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elem_id="citation-button", |
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show_copy_button=True, |
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) |
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scheduler = BackgroundScheduler() |
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scheduler.add_job(restart_space, "interval", seconds=1800) |
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scheduler.start() |
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demo.queue(default_concurrency_limit=40).launch() |