app.py

import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns, SearchColumns
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, get_model_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=None,
        # 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=None,
        # search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
        search_columns=SearchColumns(primary_column=AutoEvalColumn.model.name, secondary_columns=[],
                                     placeholder="Search by the model name",
                                     label="Searching"),
        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
        filter_columns=None,
        # [
        #     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,
    )


model_leaderboard_df = get_model_leaderboard_df()

def overall_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=None,
        search_columns=SearchColumns(primary_column=AutoEvalColumn.model.name, secondary_columns=[],
                                     placeholder="Search by the model name",
                                     label="Searching"),
        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
        filter_columns=None,
        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("🏅 Overview", elem_id="llm-benchmark-tab-table", id=0):
            leaderboard = init_leaderboard(LEADERBOARD_DF)
            

        with gr.TabItem("🎯 Overall", elem_id="llm-benchmark-tab-table", id=1):
            leaderboard = overall_leaderboard(LEADERBOARD_DF)
            
        with gr.TabItem("🔢 Math", elem_id="math-tab-table", id=2):
            
            # leaderboard = init_leaderboard(LEADERBOARD_DF)
            with gr.TabItem("🧮 Algebra", elem_id="algebra_subtab", id=0, elem_classes="subtab"): 
                leaderboard = init_leaderboard(LEADERBOARD_DF)
                
            with gr.TabItem("📐 Geometry", elem_id="geometry_subtab", id=1, elem_classes="subtab"): 
                leaderboard = init_leaderboard(LEADERBOARD_DF)

            with gr.TabItem("📊 Probability", elem_id="prob_subtab", id=2, elem_classes="subtab"):
                leaderboard = init_leaderboard(LEADERBOARD_DF)
                

        with gr.TabItem("🧠 Reasoning", elem_id="reasonong-tab-table", id=3):

            with gr.TabItem("🧩 Logical", elem_id="logical_subtab", id=0, elem_classes="subtab"):         
                leaderboard = init_leaderboard(LEADERBOARD_DF)

            with gr.TabItem("🗣️ Social", elem_id="social_subtab", id=1, elem_classes="subtab"):         
                leaderboard = init_leaderboard(LEADERBOARD_DF)


        with gr.TabItem("</> Coding", elem_id="coding-tab-table", id=4):
            leaderboard = init_leaderboard(LEADERBOARD_DF)


        with gr.TabItem("🔬 Science", elem_id="science-table", id=5):
            leaderboard = init_leaderboard(LEADERBOARD_DF)


        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=6):
            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()