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,
    SPEECH_BENCHMARK_COLS,
    COLS,
    COLS_SPEECH,
    EVAL_COLS,
    EVAL_TYPES,
    AutoEvalColumn,
    AutoEvalColumnSpeech,
    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,result_type='text'):
    if dataframe is None or dataframe.empty:
        raise ValueError("Leaderboard DataFrame is empty or None.")
    column_class = AutoEvalColumn if result_type == "text" else AutoEvalColumnSpeech

    return Leaderboard(
        value=dataframe,
        datatype=[c.type for c in fields(column_class)],
        select_columns=SelectColumns(
            default_selection=[c.name for c in fields(column_class) if c.displayed_by_default],
            cant_deselect=[c.name for c in fields(column_class) if c.never_hidden],
            label="Select Columns to Display:",
        ),
        # search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
        search_columns=[column_class.model.name],
        hide_columns=[c.name for c in fields(column_class) 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,
    )

REGION_MAP = {
    "All": "All",
    "Africa": "Africa",
    "Americas/Oceania": "Americas_Oceania",
    "Asia (S)": "Asia_S",
    "Asia (SE)": "Asia_SE",
    "Asia (W, C)": "Asia_W_C",
    "Asia (E)": "Asia_E",
    "Europe (W, N, S)": "Europe_W_N_S",
    "Europe (E)": "Europe_E",
}

REGIONS = ["All", "Africa", "Americas_Oceania", "Asia_S", "Asia_SE", "Asia_W_C", "Asia_E", "Europe_W_N_S", "Europe_E"]

leaderboard_dataframes = {
    region: get_leaderboard_df(
        EVAL_RESULTS_PATH,
        EVAL_REQUESTS_PATH,
        COLS,
        BENCHMARK_COLS,
        region if region != "All" else None,
        result_type="text"
    )
    for region in REGIONS
}

leaderboard_dataframes_speech = {
    region: get_leaderboard_df(
        EVAL_RESULTS_PATH,
        EVAL_REQUESTS_PATH,
        COLS_SPEECH,
        SPEECH_BENCHMARK_COLS,
        region if region != "All" else None,
        result_type="speech"
    )
    for region in REGIONS
}
# Preload leaderboard blocks
js_switch_code = """
(displayRegion) => {
    const regionMap = {
        "All": "All",
        "Africa": "Africa",
        "Americas/Oceania": "Americas_Oceania",
        "Asia (S)": "Asia_S",
        "Asia (SE)": "Asia_SE",
        "Asia (W, C)": "Asia_W_C",
        "Asia (E)": "Asia_E",
        "Europe (W, N, S)": "Europe_W_N_S",
        "Europe (E)": "Europe_E"
    };
    const region = regionMap[displayRegion];
    document.querySelectorAll('[id^="leaderboard-"]').forEach(el => el.classList.remove("visible"));
    const target = document.getElementById("leaderboard-" + region);
    if (target) {
        target.classList.add("visible");
        // 🧠 Trigger reflow to fix row cutoff
        void target.offsetHeight;  // Trigger reflow
        target.style.display = "none"; // Hide momentarily
        requestAnimationFrame(() => {
            target.style.display = "";
        });
    }
}
"""


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("🏅 mSTEB Text Benchmark", elem_id="llm-benchmark-tab-table", id=0):
            with gr.Row():
                region_dropdown = gr.Dropdown(
                    choices=list(REGION_MAP.keys()),
                    label="Select Region",
                    value="All",
                    interactive=True,
                )

            # Region-specific leaderboard containers
            for display_name, region_key in REGION_MAP.items():
                with gr.Column(
                        elem_id=f"leaderboard-{region_key}",
                        elem_classes=["visible"] if region_key == "All" else []
                ):
                    init_leaderboard(leaderboard_dataframes[region_key], result_type="text")

            # JS hook to toggle visible leaderboard
            region_dropdown.change(None, js=js_switch_code, inputs=[region_dropdown])

        with gr.TabItem("🗣️ mSTEB Speech Benchmark", elem_id="speech-benchmark-tab-table", id=1):
            with gr.Row():
                speech_region_dropdown = gr.Dropdown(
                    choices=list(REGION_MAP.keys()),
                    label="Select Region",
                    value="All",
                    interactive=True,
                )

            for display_name, region_key in REGION_MAP.items():
                with gr.Column(
                        elem_id=f"speech-leaderboard-{region_key}",
                        elem_classes=["visible"] if region_key == "All" else []
                ):
                    init_leaderboard(leaderboard_dataframes_speech[region_key],result_type='speech')

            speech_region_dropdown.change(
                None,
                js=js_switch_code.replace("leaderboard-", "speech-leaderboard-"),
                inputs=[speech_region_dropdown]
            )
        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()