app.py

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,
    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


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()


LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)

(
    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.")
    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=False
        #     ),
        # ],
        filter_columns=[],
        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_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.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():
    #     with gr.Accordion("📙 Citation", open=False):
    #         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()