app.py

import os
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,
    ModelTraining,
    fields,
    MalteseTraining
)
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, read_configuration


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_0_SHOT_DF = get_leaderboard_df(os.path.join(EVAL_RESULTS_PATH, "zero-shot"), EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
LEADERBOARD_1_SHOT_DF = get_leaderboard_df(os.path.join(EVAL_RESULTS_PATH, "few-shot"), 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, fewshot=True):
    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 and not (fewshot and c.hidden_in_fewshot)],
            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_training.name, type="checkboxgroup", label="Model types"),
            ColumnFilter(AutoEvalColumn.maltese_training.name, type="checkboxgroup", label="Maltese training"),
            ColumnFilter(
                AutoEvalColumn.language_count.name,
                type="slider",
                min=1,
                max=1000,
                label="Number of languages during training",
            ),
            ColumnFilter(
                AutoEvalColumn.params.name,
                type="slider",
                min=0.01,
                max=150,
                label="Select the number of parameters (B)",
            ),
            ColumnFilter(AutoEvalColumn.prompt_version.name, type="checkboxgroup", label="Prompt Version"),
            ColumnFilter(AutoEvalColumn.n_shot.name, type="slider", min=0, max=100, label="Number of Shots"),
            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)
    gr.HTML(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):
            with gr.TabItem("Zero-Shot", elem_id="zero-shot"):
                leaderboard = init_leaderboard(LEADERBOARD_0_SHOT_DF, fewshot=False)
            with gr.TabItem("Few-Shot", elem_id="few-shot"):
                leaderboard = init_leaderboard(LEADERBOARD_1_SHOT_DF, fewshot=True)

        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():
                files = gr.File(
                    label="Files (Configuration File & Prediction Outputs)",
                    file_count="directory",
                    type="filepath",
                )

            with gr.Row(equal_height=True):
                with gr.Column():
                    model_name = gr.Textbox(
                        label="Model name",
                        info="Read automatically from the results file.",
                        interactive=False,
                    )
                    version = gr.Textbox(
                        label="Prompt Version",
                        info="Read automatically from the results file.",
                        interactive=False,
                    )
                    n_shots = gr.Number(
                        label="Number of Shots",
                        info="Read automatically from the results file.",
                        interactive=False,
                    )

                with gr.Column():
                    model_training = gr.Dropdown(
                        choices=[t.to_str(": ") for t in ModelTraining if t != ModelTraining.NK],
                        label="Model Training",
                        info="How to model is trained.",
                        multiselect=False,
                        value=None,
                        interactive=True,
                    )
                    maltese_training = gr.Dropdown(
                        choices=[t.to_str(": ") for t in MalteseTraining if t != ModelTraining.NK],
                        label="Maltese Training",
                        info="The last stage of training in which Maltese was included.",
                        multiselect=False,
                        value=None,
                        interactive=True,
                    )
                    language_count = gr.Number(
                        label="Number of languages",
                        info="Include languages for all training stages. Set to 0 if unknown.",
                        minimum=0,
                        interactive=True,
                    )

            submit_button = gr.Button("Submit Eval")
            submission_result = gr.Markdown()

            configuration = gr.State()
            file_paths = gr.State()
            files.change(read_configuration,
                         files,
                         [configuration, file_paths, model_name, version, n_shots, submission_result])

            submit_button.click(
                add_new_eval,
                [
                    model_training,
                    maltese_training,
                    language_count,
                    configuration,
                    file_paths
                ],
                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()