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,
    TITLE_IMAGE,
)
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_RESULTS_PATH, REPO_ID, TOKEN
from src.populate import get_leaderboard_df


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, 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],
        # 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_IMAGE)
    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()