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import gradio as gr |
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from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns |
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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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COLS, |
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ST_BENCHMARK_COLS, |
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AGENTIC_BENCHMARK_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, TASK_NAME_INVERSE_MAP |
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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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ST_LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, ST_BENCHMARK_COLS) |
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AGENTIC_LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, AGENTIC_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, benchmark_type): |
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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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AutoEvalColumnSubset = [c for c in fields(AutoEvalColumn) if ((c.name=="Model") or (TASK_NAME_INVERSE_MAP.get(c.name, dict()).get("type", "")==benchmark_type))] |
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return gr.components.Dataframe( |
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value=dataframe, |
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datatype=[c.type for c in AutoEvalColumnSubset], |
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column_widths=["150px" if c.name != "Model" else "250px" for c in AutoEvalColumnSubset], |
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wrap=False, |
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) |
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demo = gr.Blocks(css=custom_css, theme=gr.themes.Default(primary_hue=gr.themes.colors.pink)) |
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with demo: |
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gr.HTML(f""" |
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<div id="page-header" style="text-align:center; margin-bottom:1rem;"> |
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<div id="left-container"> |
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<img src="/gradio_api/file=src/assets/logo-icon.png"> |
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</div> |
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<div id="centre-container"> |
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<h1 style="margin-bottom: 0.25rem;">{TITLE}</h1> |
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<p style="color:#eb088a; margin:0; font-size:1.2rem;">Performance Insights & Comparison</p> |
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</div> |
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<div id="right-container"></div> |
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</div> |
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""") |
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with gr.Group(elem_classes="intro-block"): |
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gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") |
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with gr.Tabs(elem_classes=["leaderboard-table", "tab-buttons"]) as tabs: |
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with gr.TabItem("Base Benchmark", elem_id="llm-benchmark-tab-table", id=0): |
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leaderboard = init_leaderboard(ST_LEADERBOARD_DF, "base") |
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with gr.TabItem("Agentic Benchmark", elem_id="llm-benchmark-tab-table", id=1): |
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leaderboard = init_leaderboard(AGENTIC_LEADERBOARD_DF, "agentic") |
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with gr.TabItem("About", elem_id="llm-benchmark-tab-table", id=2): |
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gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") |
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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(allowed_paths=["src/assets/logo-icon.png"]) |
