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 (
    COLUMNS,
    COLS,
    BENCHMARK_COLS,
    EVAL_COLS,
    EVAL_TYPES,
    ModelType,
    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 initialization
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()

# Load the leaderboard DataFrame
LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
print("LEADERBOARD_DF Shape:", LEADERBOARD_DF.shape)  # Debug
print("LEADERBOARD_DF Columns:", LEADERBOARD_DF.columns.tolist())  # Debug

# Load the evaluation queue DataFrames
finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)

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):
            if LEADERBOARD_DF.empty:
                gr.Markdown("No evaluations have been performed yet. The leaderboard is currently empty.")
            else:
                default_selection = [col.name for col in COLUMNS if col.displayed_by_default]
                print("Default Selection before ensuring 'model_name':", default_selection)  # Debug

                # Ensure "model_name" is included
                if "model_name" not in default_selection:
                    default_selection.insert(0, "model_name")
                    print("Default Selection after ensuring 'model_name':", default_selection)  # Debug

                leaderboard = Leaderboard(
                    value=LEADERBOARD_DF,
                    datatype=[col.type for col in COLUMNS],
                    select_columns=SelectColumns(
                        default_selection=default_selection,
                        cant_deselect=[col.name for col in COLUMNS if col.never_hidden],
                        label="Select Columns to Display:",
                    ),
                    search_columns=[col.name for col in COLUMNS if col.name in ["model_name", "license"]],  # Updated to 'model_name'
                    hide_columns=[col.name for col in COLUMNS if col.hidden],
                    filter_columns=[
                        ColumnFilter("model_type", type="checkboxgroup", label="Model types"),
                        ColumnFilter("precision", type="checkboxgroup", label="Precision"),
                        ColumnFilter(
                            "still_on_hub", type="boolean", label="Deleted/incomplete", default=True
                        ),
                    ],
                    bool_checkboxgroup_label="Hide models",
                    interactive=False,
                )
                # No need to call leaderboard.render() since it's created within the Gradio context

        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")

                # Since the evaluation queues are empty, display a message
                with gr.Column():
                    gr.Markdown("Evaluations are performed immediately upon submission. There are no pending or running evaluations.")

            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 for i in Precision if i != Precision.Unknown],
                        label="Precision",
                        multiselect=False,
                        value="float16",
                        interactive=True,
                    )
                    weight_type = gr.Dropdown(
                        choices=[i.value 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()