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.tasks import TASK_DESCRIPTIONS, MEASURE_DESCRIPTION
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
from src.submission.submit import add_new_eval
import random
import matplotlib.pyplot as plt
import re
import plotly.express as px
import plotly.graph_objects as go
import numpy as np


def mean_of_max_per_field(df):
    """
    Calcola il massimo per ciascun campo e poi la media dei massimi.

    Args:
        df (pd.DataFrame): DataFrame con colonne TE, SA, HS, AT, WIC, FAQ, LS, SU, NER, REL

    Returns:
        float: media dei valori massimi dei campi
    """
    fields = ["TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]

    #print(df.columns)

    # Controlla che tutte le colonne esistano nel DataFrame
    missing = [f for f in fields if f not in df.columns]
    if missing:
        raise ValueError(f"Le seguenti colonne mancano nel DataFrame: {missing}")

    # Calcola il massimo per ciascun campo
    max_values = df[fields].max()

    # Calcola la media dei massimi
    mean_max = max_values.mean()

    return mean_max


def boxplot_per_task(dataframe=None, baselines=None):
    tasks = ["TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]

    if dataframe is None:
        np.random.seed(42)
        dataframe = pd.DataFrame({
            task: np.random.uniform(0.4, 0.9, 20) * 100
            for task in tasks
        })

    # baseline per ciascun task (se non viene passata, metto random tra 50 e 70)
    if baselines is None:
        baselines = {task: np.random.randint(50, 70) for task in tasks}

    colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd",
              "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf"]

    fig = go.Figure()

    for i, task in enumerate(tasks):
        if task in dataframe.columns:
            y_data = dataframe[task].dropna().tolist()

            # boxplot
            fig.add_trace(go.Box(
                y=y_data,
                name=task,
                boxmean="sd",
                marker=dict(color=colors[i], line=dict(width=1)),
                line=dict(color=colors[i]),
                fillcolor=colors[i],
                opacity=0.7,
                hovertemplate=f"<b>{task}</b><br>Accuracy: "+"%{y:.2f}%"+"<extra></extra>",
                width=0.6
            ))

            # baseline per task (se disponibile)
            if task in baselines and baselines[task] is not None:
                # baseline come linea orizzontale
                fig.add_shape(
                    type="line",
                    x0=i-0.3, x1=i+0.3,   # larghezza in corrispondenza del box
                    y0=baselines[task], y1=baselines[task],
                    line=dict(color="black", width=2, dash="dash"),
                    xref="x", yref="y"
                )

                # label con valore baseline
                fig.add_annotation(
                    x=i, y=baselines[task],
                    text=f"{baselines[task]}%",
                    showarrow=False,
                    yshift=10,
                    font=dict(size=10, color="black")
                )

    fig.update_layout(
        title="Distribution of Model Accuracy by Task.",
        xaxis_title="Task",
        yaxis_title="Accuracy (%)",
        template="plotly_white",
        boxmode="group",
        dragmode=False,
        font=dict(family="Arial", size=13),
        margin=dict(b=80),
        annotations = [
            dict(
                text=(
                    "Boxplots show LLM accuracy in zero/few-shot settings. <br>"
                    "Black dashed lines indicate the best-performing supervised models evaluated during EVALITA."
                ),
                xref="paper", yref="paper",
                x=0.5, y=-0.33,
                showarrow=False,
                font=dict(size=12, color="gray")
            )
        ]
    )
    #fig.update_yaxes(fixedrange=True)
    fig.update_yaxes(range=[0, 100], fixedrange=True)

    return fig


# 🔹 Esempio d’uso
BASELINES = {
    "TE":71.00, "SA": 66.38, "HS": 80.88, "AT": 82.40, "WIC": 85.00,
    "LS": 38.82, "SU": 38.91, "NER":88.00, "REL": 62.99
}




def line_chart(dataframe):
    # Separiamo i dati in base a IS_FS
    df_true = dataframe[dataframe['IS_FS'] == True]
    df_false = dataframe[dataframe['IS_FS'] == False]

    # Estrai valori x, y e labels per True e False
    x_true = df_true['#Params (B)'].tolist()
    y_true = df_true['Avg. Comb. Perf. ⬆️'].tolist()
    labels_true = [
        re.search(r'>([^<>/]+/[^<>]+)<', m).group(1).split('/')[-1]
        for m in df_true['Model'].tolist()
    ]

    x_false = df_false['#Params (B)'].tolist()
    y_false = df_false['Avg. Comb. Perf. ⬆️'].tolist()
    labels_false = [
        re.search(r'>([^<>/]+/[^<>]+)<', m).group(1).split('/')[-1]
        for m in df_false['Model'].tolist()
    ]

    fig = go.Figure()

    # Punti IS_FS=True
    fig.add_trace(go.Scatter(
        x=x_true,
        y=y_true,
        mode='markers',  # solo marker, niente testo
        name='5-Shot',
        marker=dict(color='red', size=10),
        hovertemplate='<b>%{customdata}</b><br>#Params: %{x}<br>Performance: %{y}<extra></extra>',
        customdata=labels_true  # tutte le informazioni sul hover
    ))

    # Punti IS_FS=False
    fig.add_trace(go.Scatter(
        x=x_false,
        y=y_false,
        mode='markers',
        name='0-Shot',
        marker=dict(color='blue', size=10),
        hovertemplate='<b>%{customdata}</b><br>#Params: %{x}<br>Performance: %{y}<extra></extra>',
        customdata=labels_false
    ))

    fig.update_layout(
        title="Avg. Combined Performance vs #Params",
        xaxis_title="#Params (B)",
        yaxis_title="Avg. Combined Performance ⬆️",
        template="plotly_white",
        hovermode="closest",
        dragmode=False
    )

    # Disabilita lo zoom e altri controlli
    fig.update_xaxes(fixedrange=True, rangeslider_visible=False)
    fig.update_yaxes(fixedrange=True)
    #fig.update_yaxes(range=[0, 100], fixedrange=True)

    return fig





# Define task metadata (icons, names, descriptions)
TASK_METADATA_MULTIPLECHOICE = {
    "TE": {"icon": "📊", "name": "Textual Entailment", "tooltip": ""},
    "SA": {"icon": "😃", "name": "Sentiment Analysis", "tooltip": ""},
    "HS": {"icon": "⚠️", "name": "Hate Speech", "tooltip": ""},
    "AT": {"icon": "🏥", "name": "Admission Test", "tooltip": ""},
    "WIC": {"icon": "🔤", "name": "Word in Context", "tooltip": ""},
    "FAQ": {"icon": "❓", "name": "Frequently Asked Questions", "tooltip": ""}
}

# Define task metadata (icons, names, descriptions)
TASK_METADATA_GENERATIVE = {
    "LS": {"icon": "🔄", "name": "Lexical Substitution", "tooltip": ""},
    "SU": {"icon": "📝", "name": "Summarization", "tooltip": ""},
    "NER": {"icon": "🏷️", "name": "Named Entity Recognition", "tooltip": ""},
    "REL": {"icon": "🔗", "name": "Relation Extraction", "tooltip": ""},
}

def restart_space():
    """Restart the Hugging Face space."""
    API.restart_space(repo_id=REPO_ID)


def init_leaderboard(dataframe, default_selection=None, hidden_columns=None):
    """
    Initialize and return the leaderboard when it is first loaded or when 'benchmark' is selected.
    The table is sorted based on the "Avg. Combined Performance" field.
    """
    if dataframe is None or dataframe.empty:
        raise ValueError("Leaderboard DataFrame is empty or None.")

    #print("????????????????????????????????", mean_of_max_per_field(dataframe))

    sorted_dataframe = dataframe.sort_values(by="Avg. Comb. Perf. ⬆️", ascending=False)

    sorted_dataframe = sorted_dataframe.reset_index(drop=True)
    sorted_dataframe["rank"] = sorted_dataframe.index + 1

    # Flag per sapere se la medaglia è già stata assegnata per categoria e tipo
    large_medal_fs_assigned = False
    medium_medal_fs_assigned = False
    small_medal_fs_assigned = False

    large_medal_0shot_assigned = False
    medium_medal_0shot_assigned = False
    small_medal_0shot_assigned = False

    # Lista temporanea per salvare i nuovi valori della colonna Model
    new_model_column = []

    for _, row in sorted_dataframe.iterrows():
        if row['IS_FS']:  # 5-Few-Shot
            if row["#Params (B)"] > 30 and not large_medal_fs_assigned:
                new_model_column.append(f"{row['Model']} 7️⃣0️⃣🅱️🏆")
                large_medal_fs_assigned = True
            elif 10 < row["#Params (B)"] <= 30 and not medium_medal_fs_assigned:
                new_model_column.append(f"{row['Model']} 3️⃣0️⃣🅱️🏆")
                medium_medal_fs_assigned = True
            elif row["#Params (B)"] <= 10 and not small_medal_fs_assigned:
                new_model_column.append(f"{row['Model']} 1️⃣0️⃣🅱️🏆")
                small_medal_fs_assigned = True
            else:
                new_model_column.append(row["Model"])
        else:  # 0-Shot
            if row["#Params (B)"] > 30 and not large_medal_0shot_assigned:
                new_model_column.append(f"{row['Model']} 7️⃣0️⃣🅱️🎖️")
                large_medal_0shot_assigned = True
            elif 10 < row["#Params (B)"] <= 30 and not medium_medal_0shot_assigned:
                new_model_column.append(f"{row['Model']} 3️⃣0️⃣🅱️🎖️")
                medium_medal_0shot_assigned = True
            elif row["#Params (B)"] <= 10 and not small_medal_0shot_assigned:
                new_model_column.append(f"{row['Model']} 1️⃣0️⃣🅱️🎖️")
                small_medal_0shot_assigned = True
            else:
                new_model_column.append(row["Model"])

    # Aggiorna la colonna Model
    sorted_dataframe["Model"] = new_model_column

    field_list = fields(AutoEvalColumn)

    return Leaderboard(
        value=sorted_dataframe,
        datatype=[c.type for c in field_list],
        #select_columns=SelectColumns(
        #    default_selection=default_selection or [c.name for c in field_list if c.displayed_by_default],
        #    cant_deselect=[c.name for c in field_list if c.never_hidden],
        #    label="Select Columns to Display:",
        #),
        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
        hide_columns=hidden_columns or [c.name for c in field_list if c.hidden],
        filter_columns=[
            ColumnFilter(AutoEvalColumn.fewshot_symbol.name, type="checkboxgroup", label="N-Shot Learning (FS)"),
            #ColumnFilter(AutoEvalColumn.fewshot_symbol.name, type="checkboxgroup", label="N-Few-Shot Learning (FS)",
            #             default=[["0️⃣", "0️⃣"]]),
            ColumnFilter(AutoEvalColumn.params.name, type="slider", min=0, max = 100, default = [0,100], label="Select the number of parameters (B)"),
        ],
        #filter_columns=[
        #    ColumnFilter("IS_FS", type="checkbox", default=False, label="5-Few-Shot")
        #    #ColumnFilter("FS", type="dropdown", label="5-Few-Shot")
        #],
        bool_checkboxgroup_label="Evaluation Mode",
        interactive=False,
    )

def update_task_leaderboard(dataframe, default_selection=None, hidden_columns=None):
    """
    Update and return the leaderboard when a specific task is selected.
    The table is sorted based on the "Combined Performance" field.
    """
    if dataframe is None or dataframe.empty:
        raise ValueError("Leaderboard DataFrame is empty or None.")

    sorted_dataframe = dataframe.sort_values(by="Combined Performance", ascending=False)

    # aggiungo la colonna rank in base alla posizione
    sorted_dataframe = sorted_dataframe.reset_index(drop=True)
    sorted_dataframe["rank"] = sorted_dataframe.index + 1

    # Flag per sapere se la medaglia è già stata assegnata per categoria e tipo
    large_medal_fs_assigned = False
    medium_medal_fs_assigned = False
    small_medal_fs_assigned = False

    large_medal_0shot_assigned = False
    medium_medal_0shot_assigned = False
    small_medal_0shot_assigned = False

    # Lista temporanea per salvare i nuovi valori della colonna Model
    new_model_column = []

    for _, row in sorted_dataframe.iterrows():
        if row['IS_FS']:  # 5-Few-Shot
            if row["#Params (B)"] > 30 and not large_medal_fs_assigned:
                new_model_column.append(f"{row['Model']} 7️⃣0️⃣🅱️🏆")
                large_medal_fs_assigned = True
            elif 10 < row["#Params (B)"] <= 30 and not medium_medal_fs_assigned:
                new_model_column.append(f"{row['Model']} 3️⃣0️⃣🅱️🏆")
                medium_medal_fs_assigned = True
            elif row["#Params (B)"] <= 10 and not small_medal_fs_assigned:
                new_model_column.append(f"{row['Model']} 1️⃣0️⃣🅱️🏆")
                small_medal_fs_assigned = True
            else:
                new_model_column.append(row["Model"])
        else:  # 0-Shot
            if row["#Params (B)"] > 30 and not large_medal_0shot_assigned:
                new_model_column.append(f"{row['Model']} 7️⃣0️⃣🅱️🎖️")
                large_medal_0shot_assigned = True
            elif 10 < row["#Params (B)"] <= 30 and not medium_medal_0shot_assigned:
                new_model_column.append(f"{row['Model']} 3️⃣0️⃣🅱️🎖️")
                medium_medal_0shot_assigned = True
            elif row["#Params (B)"] <= 10 and not small_medal_0shot_assigned:
                new_model_column.append(f"{row['Model']} 1️⃣0️⃣🅱️🎖️")
                small_medal_0shot_assigned = True
            else:
                new_model_column.append(row["Model"])

    # Aggiorna la colonna Model
    sorted_dataframe["Model"] = new_model_column

    pd.set_option('display.max_colwidth', None)
    #print("========================", dataframe['Model'])

    #print(sorted_dataframe['Combined Performance'])

    field_list = fields(AutoEvalColumn)

    return Leaderboard(
        value=sorted_dataframe,
        #datatype=[c.type for c in field_list],
        datatype=[c.type for c in field_list] + [int],
        #select_columns=SelectColumns(
        #    default_selection=default_selection or [c.name for c in field_list if c.displayed_by_default],
        #    cant_deselect=[c.name for c in field_list if c.never_hidden],
        #    label="Select Columns to Display:",
        #),
        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
        hide_columns=hidden_columns or [c.name for c in field_list if c.hidden],
        filter_columns=[
            ColumnFilter(AutoEvalColumn.fewshot_symbol.name, type="checkboxgroup", label="N-Shot Learning (FS)"),
            ColumnFilter(AutoEvalColumn.params.name, type="slider", min=0, max=100, default=[0, 100],
                         label="Select the number of parameters (B)"),
        ],
        bool_checkboxgroup_label="Evaluation Mode",
        interactive=False
    )

'''
# Helper function for leaderboard initialization
def init_leaderboard(dataframe, default_selection=None, hidden_columns=None):
    """Initialize and return a leaderboard."""
    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=default_selection or [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, AutoEvalColumn.license.name],
        hide_columns=hidden_columns or [c.name for c in fields(AutoEvalColumn) if c.hidden],
        filter_columns=[
            ColumnFilter(AutoEvalColumn.fewshot_type.name, type="checkboxgroup", label="N-Few-Shot Learning (FS)"),
            ColumnFilter(AutoEvalColumn.params.name, type="slider", min=0, max=150, label="Select the number of parameters (B)"),
        ],
        bool_checkboxgroup_label="Hide models",
        interactive=False,
    )
'''

def download_snapshot(repo, local_dir):
    """Try to download a snapshot from Hugging Face Hub."""
    try:
        print(f"Downloading from {repo} to {local_dir}...")
        snapshot_download(repo_id=repo, local_dir=local_dir, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN)
    except Exception as e:
        print(f"Error downloading {repo}: {e}")
        restart_space()


# Initialize the app by downloading snapshots
download_snapshot(QUEUE_REPO, EVAL_REQUESTS_PATH)
download_snapshot(RESULTS_REPO, EVAL_RESULTS_PATH)

# Load leaderboard data
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)
#print(LEADERBOARD_DF.columns.tolist())

theoretical_max_combined_perf = mean_of_max_per_field(LEADERBOARD_DF)

# Prepare the main interface
demo = gr.Blocks(css=custom_css)
with demo:
    #gr.HTML(TITLE)
    gr.HTML(
        """
        <div style="display: flex; align-items: center; position: relative; width: 100%; height: 60px; padding: 10px 0;">
            <h1 style="
                margin: 0 auto; 
                font-weight: 900; 
                font-size: 2.5em; 
                letter-spacing: 2px; 
                text-transform: uppercase; 
                background: linear-gradient(90deg, #1f77b4, #00c6ff); 
                -webkit-background-clip: text; 
                -webkit-text-fill-color: transparent; 
                text-shadow: 2px 2px 8px rgba(0,0,0,0.2);
            ">
                EVALITA-LLM Leaderboard
            </h1>
            <a href="https://huggingface.co/spaces/mii-llm/open_ita_llm_leaderboard" target="_blank" 
               style="position: absolute; right: 0; display: inline-flex; align-items: center; gap: 6px; text-decoration: none; color: #1f77b4; font-weight: 600;">
                <!-- Icona stilizzata -->
                <svg xmlns="http://www.w3.org/2000/svg" width="22" height="22" fill="#1f77b4" viewBox="0 0 24 24">
                    <path d="M3.9 12a5 5 0 0 1 7.07-7.07l1.41 1.41-1.41 1.41-1.42-1.42a3 3 0 1 0 4.24 4.24l3.54-3.54a5 5 0 0 1-7.07 7.07l-1.41-1.41 1.41-1.41 1.42 1.42z"/>
                    <path d="M20.1 12a5 5 0 0 1-7.07 7.07l-1.41-1.41 1.41-1.41 1.42 1.42a3 3 0 1 0-4.24-4.24l-3.54 3.54a5 5 0 0 1 7.07-7.07l1.41 1.41-1.41 1.41-1.42-1.42z"/>
                </svg>
                Open Italian LLM Leaderboard
            </a>
        </div>
        """
    )
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:

        # Main leaderboard tab
        with gr.TabItem("🏅 Benchmark"):

            leaderboard = init_leaderboard(
                LEADERBOARD_DF,
                default_selection=['rank', 'FS', 'Model', "Avg. Comb. Perf. ⬆️", "TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"],
                hidden_columns=[col for col in LEADERBOARD_DF.columns if col not in ['rank', 'FS', 'Model', "Avg. Comb. Perf. ⬆️", "TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]]
            )

            gr.HTML(
                f"""
                        <div style="
                            border: 2px solid #1f77b4;
                            border-radius: 10px;
                            padding: 10px;
                            background-color: #f0f8ff;
                            font-weight: bold;
                            font-size: 14px;
                            display: inline-block;
                        ">
                            Theoretical performance of a model that scores the highest on every individual task: <span style="color:#d62728; font-size:18px;">{theoretical_max_combined_perf:.2f}</span>
                        </div>
                        """
            )

        with gr.TabItem("📈 Charts"):
            #gr.Plot(value=line_chart(LEADERBOARD_DF), label="Andamento di esempio")
            #gr.Plot(value=line_chart_interactive_test(), label="Andamento interattivo")
            gr.Plot(value=line_chart(LEADERBOARD_DF))
            gr.Plot(value=boxplot_per_task(LEADERBOARD_DF, BASELINES))

        # About tab
        with gr.TabItem("📝 About"):
            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

        # About tab
        with gr.TabItem("║", interactive=False):
            gr.Markdown("", elem_classes="markdown-text")


        # Task-specific leaderboards
        for task, metadata in TASK_METADATA_MULTIPLECHOICE.items():

            with gr.TabItem(f"{metadata['icon']}{task}"):

                task_description = TASK_DESCRIPTIONS.get(task, "Description not available.")
                gr.Markdown(task_description, elem_classes="markdown-text")

                leaderboard = update_task_leaderboard(
                    LEADERBOARD_DF.rename(columns={f"{task} Prompt Average": "Prompt Average", f"{task} Prompt Std": "Prompt Std", f"{task} Best Prompt": "Best Prompt", f"{task} Best Prompt Id": "Best Prompt Id", task: "Combined Performance"}),
                    default_selection=['rank', 'FS', 'Model', 'Combined Performance', 'Prompt Average', 'Prompt Std', 'Best Prompt', 'Best Prompt Id'],
                    hidden_columns=[col for col in LEADERBOARD_DF.columns if col not in ['rank', 'FS', 'Model', 'Combined Performance', 'Prompt Average', 'Prompt Std', 'Best Prompt', 'Best Prompt Id']]
                )

        # About tab
        with gr.TabItem("│", interactive=False):
            gr.Markdown("", elem_classes="markdown-text")

        # Task-specific leaderboards
        for task, metadata in TASK_METADATA_GENERATIVE.items():
            with gr.TabItem(f"{metadata['icon']}{task}"):
                task_description = TASK_DESCRIPTIONS.get(task, "Description not available.")
                gr.Markdown(task_description, elem_classes="markdown-text")

                leaderboard = update_task_leaderboard(
                    LEADERBOARD_DF.rename(columns={f"{task} Prompt Average": "Prompt Average",
                                                   f"{task} Prompt Std": "Prompt Std",
                                                   f"{task} Best Prompt": "Best Prompt",
                                                   f"{task} Best Prompt Id": "Best Prompt Id",
                                                   task: "Combined Performance"}),
                    default_selection=['rank', 'FS', 'Model', 'Combined Performance', 'Prompt Average', 'Prompt Std', 'Best Prompt',
                                       'Best Prompt Id'],
                    hidden_columns=[col for col in LEADERBOARD_DF.columns if
                                    col not in ['rank', 'FS', 'Model', 'Combined Performance', 'Prompt Average', 'Prompt Std',
                                                'Best Prompt', 'Best Prompt Id']]
                )

    # Citation section
    with gr.Accordion("📙 Citation", open=False):
        gr.Textbox(value=CITATION_BUTTON_TEXT, label=CITATION_BUTTON_LABEL, lines=20, elem_id="citation-button", show_copy_button=True)

    with gr.Accordion("📙 Credits", open=False):
        gr.Markdown(
            """
    **This project has benefited from the following support:**

    - 🧠 **Codebase**: Based on and extended from the Open Italian LLM Leaderboard, developed by **Alessandro Ercolani** and **Samuele Colombo**. We warmly thank them for their invaluable support and guidance in implementing this leaderboard.

    - 💶 **Funding**: Partially supported by the PNRR project **FAIR - Future AI Research (PE00000013)**, under the NRRP MUR program funded by **NextGenerationEU**.

    - 🖥️ **Computation**: We gratefully acknowledge **CINECA** for granting access to the **LEONARDO** supercomputer.  
            """
        )

# Background job to restart space
scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()

# Launch the app with concurrent queueing
demo.queue(default_concurrency_limit=40).launch(debug=True,  # Enable Gradio debug mode
        show_error=True)