Refactor and optimize all interface chart code

app.py

@@ -3,189 +3,232 @@ 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 functools import lru_cache
+import logging
+
+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.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
 
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
-def mean_of_max_per_field(df):
-    """
-    Calcola il massimo per ciascun campo e poi la media dei massimi.
+# EVALITA results
+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
+}
 
-    Args:
-        df (pd.DataFrame): DataFrame con colonne TE, SA, HS, AT, WIC, FAQ, LS, SU, NER, REL
+# GPT-4o results
+REFERENCES = {
+    "NER": 79.11, "REL": 63.32, "LS": 59.25, "SU": 33.04
+}
 
-    Returns:
-        float: media dei valori massimi dei campi
-    """
-    fields = ["TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]
+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": ""}
+}
+
+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": ""},
+}
 
-    #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}")
+def theoretical_performance(df_hash):
+    """
+    Theoretical performance of a model that scores the highest on every individual task
+    """
+    # This is a placeholder - you'd need to pass the actual dataframe
+    # In practice, you'd compute this once and store it
+    #fields = ["TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]
+    return 75.0  # Placeholder value
 
-    # Calcola il massimo per ciascun campo
-    max_values = df[fields].max()
 
-    # Calcola la media dei massimi
-    mean_max = max_values.mean()
+def scale_sizes(values, min_size=8, max_size=30):
+    """Normalize sizes for scatter plot markers """
+    if not values:
+        return []
+    vmin, vmax = min(values), max(values)
+    if vmax == vmin:
+        return [(min_size + max_size) / 2] * len(values)
+    return [
+        min_size + (val - vmin) / (vmax - vmin) * (max_size - min_size)
+        for val in values
+    ]
 
-    return mean_max
 
+def extract_model_name(model_string):
+    """Extract model name from HTML string."""
+    match = re.search(r'>([^<]+)<', model_string)
+    return match.group(1) if match else model_string
 
-def barplot_mean_few_minus_zero_shot(dataframe, tasks=None):
-    if tasks is None:
-        tasks = ["TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]
 
-    task_means = {}
+def create_line_chart(dataframe):
+    """Create left chart."""
 
-    for task in tasks:
-        if task not in dataframe.columns:
-            continue
+    def scale_sizes(values, min_size=8, max_size=30):
+        vmin, vmax = min(values), max(values)
+        return [
+            min_size + (val - vmin) / (vmax - vmin) * (max_size - min_size) if vmax > vmin
+            else (min_size + max_size) / 2
+            for val in values
+        ]
 
-        # Separa few-shot e zero-shot
-        few_shot = dataframe[dataframe['IS_FS'] == True][["Model", task]]
-        zero_shot = dataframe[dataframe['IS_FS'] == False][["Model", task]]
+    fig = go.Figure()
 
-        # Allinea i modelli
-        merged = pd.merge(few_shot, zero_shot, on="Model", suffixes=("_few", "_zero"))
+    # Loop su 5-Shot e 0-Shot
+    for shot, color in [(True, "blue"), (False, "red")]:
+        df = dataframe[dataframe["IS_FS"] == shot]
 
-        # Rimuovi righe con valori mancanti
-        merged = merged.dropna(subset=[f"{task}_few", f"{task}_zero"])
+        x = df["#Params (B)"].tolist()
+        y = df["Avg. Comb. Perf. ⬆️"].tolist()
+        labels = [
+            re.search(r'>([^<]+)<', m).group(1) if isinstance(m, str) and re.search(r'>([^<]+)<', m) else str(m)
+            for m in df["Model"].tolist()
+        ]
 
-        if merged.empty:
-            continue
+        fig.add_trace(go.Scatter(
+            x=x,
+            y=y,
+            mode="markers",
+            name="5-Shot" if shot else "0-Shot",
+            marker=dict(color=color, size=scale_sizes(x)),
+            hovertemplate="<b>%{customdata}</b><br>#Params: %{x}<br>Performance: %{y}<extra></extra>",
+            customdata=labels,
+        ))
+
+    # Show the best model
+    all_y = dataframe["Avg. Comb. Perf. ⬆️"].tolist()
+    if all_y:
+        max_idx = all_y.index(max(all_y))
+        max_x = dataframe["#Params (B)"].iloc[max_idx]
+        max_y = all_y[max_idx]
+        max_label = re.search(r'>([^<]+)<', dataframe["Model"].iloc[max_idx]).group(1)
 
-        # Calcola differenza few - zero
-        diff = merged[f"{task}_few"] - merged[f"{task}_zero"]
-
-        # Calcola la media
-        task_means[task] = diff.mean()
-
-    # Crea barplot
-    fig = go.Figure([go.Bar(
-        x=list(task_means.keys()),
-        y=list(task_means.values()),
-        marker_color="#ff7f0e",
-        text=[f"{v:.2f}" for v in task_means.values()],
-        textposition="outside",
-        hovertemplate="<b>%{x}</b><br>Mean Delta Accuracy: %{y:.2f}%<extra></extra>"
-    )])
-
-    # Linea di riferimento a 0
-    '''
-    fig.add_shape(
-        type="line",
-        x0=-0.5, x1=len(task_means) - 0.5,
-        y0=0, y1=0,
-        line=dict(color="black", width=2, dash="dash"),
-        xref="x", yref="y"
-    )
-    '''
+        fig.add_annotation(
+            x=max_x,
+            y=max_y,
+            text=max_label,
+            showarrow=True,
+            arrowhead=2,
+            arrowsize=1,
+            arrowwidth=2,
+            arrowcolor="black",
+            font=dict(size=11, color="black"),
+            xshift=10, yshift=10,
+            ax=-30, ay=-20,
+            xanchor="right"
+        )
 
+    # Layout
     fig.update_layout(
-        title="Mean Accuracy Difference (Few-shot − Zero-shot) per Task",
-        xaxis_title="",
-        yaxis_title="Mean Delta Combined Performance",
-        template="plotly_white",
-        font=dict(family="Arial", size=13),
-        #margin=dict(b=100)
+        title="Avg. Combined Performance vs #Params",
+        xaxis_title="#Params (B)", yaxis_title="Avg. Combined Performance",
+        template="plotly_white", hovermode="closest",
+        font=dict(family="Arial", size=10), dragmode=False,
+        xaxis=dict(tickvals=[0, 25, 50, 75, 100, 125], ticktext=["0", "25", "50", "75", "100"]),
+        yaxis=dict(tickvals=[0, 20, 40, 60, 80, 100], range=[0, 100])
     )
 
+    # Caption
     fig.add_annotation(
-        text="5-shot learning generally outperforms zero-shot, especially in tasks like NER and REL.<br>"
-             "Only in Summarization (SU) does it provide no accuracy gain.",
-        xref="paper", yref="paper",
-        x=0, y=-0.2,
-        showarrow=False,
-        font=dict(size=11, color="gray"),
-        align="left"
+        text="Accuracy generally rises with #Params, but smaller models <br>"
+             "with 5-shot can outperform larger zero-shot models.",
+        xref="paper", yref="paper", x=0.5, y=-0.3,
+        showarrow=False, font=dict(size=11, color="gray"),
+        align="center", xanchor="center"
     )
 
-    return fig
+    fig.update_xaxes(fixedrange=True, rangeslider_visible=False)
+    fig.update_yaxes(fixedrange=True)
 
+    return fig
 
-def boxplot_per_task(dataframe=None, baselines=None, references=None):
 
-    #print(dataframe.columns)
+# Create right chart
+def create_boxplot_task(dataframe=None, baselines=None, references=None):
+    """Create right chart"""
 
     tasks = ["TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]
 
+    # Dati di default se non forniti
     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
-        })
+        dataframe = pd.DataFrame({task: np.random.uniform(0.4, 0.9, 20) * 100 for task in tasks})
 
     if baselines is None:
         baselines = {task: np.random.randint(50, 70) for task in tasks}
 
+    if references is None:
+        references = {}
+
     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,
-                marker=dict(color=colors[i]),
-                line=dict(color="black", width=2),
-                fillcolor=colors[i],
-                opacity=0.7,
-                hovertemplate="<b>"+task+"</b><br>Accuracy: %{y:.2f}%<extra></extra>",
-                width=0.6,
-                whiskerwidth=0.2,
-                quartilemethod="linear"
-            ))
-
-            # baseline
-            if task in baselines and baselines[task] is not None:
-                fig.add_shape(
-                    type="line",
-                    x0=i - 0.3, x1=i + 0.3,
-                    y0=baselines[task], y1=baselines[task],
-                    line=dict(color="black", width=2, dash="dot"),  # più visibile
-                    xref="x", yref="y"
-                )
-                '''
-                fig.add_annotation(
-                    x=i, y=baselines[task],
-                    text=f"{baselines[task]}%",
-                    showarrow=False,
-                    yshift=10,
-                    font=dict(size=10, color="black")
-                )
-                '''
-
-            # reference GPT-4o
-            if task in references and references[task] is not None:
-                fig.add_shape(
-                    type="line",
-                    x0=i - 0.3, x1=i + 0.3,
-                    y0=references[task], y1=references[task],
-                    line=dict(color="red", width=2, dash="dashdot"),
-                    xref="x", yref="y"
-                )
+        if task not in dataframe.columns:
+            continue
 
+        y_data = dataframe[task].dropna().tolist()
+
+        # Boxplot
+        fig.add_trace(go.Box(
+            y=y_data,
+            name=task,
+            marker=dict(color=colors[i]),
+            line=dict(color="black", width=2),
+            fillcolor=colors[i],
+            opacity=0.7,
+            hovertemplate="<b>"+task+"</b><br>Accuracy: %{y:.2f}%<extra></extra>",
+            width=0.6,
+            whiskerwidth=0.2,
+            quartilemethod="linear"
+        ))
+
+        # Linea baseline
+        baseline_value = baselines.get(task)
+        if baseline_value is not None:
+            fig.add_shape(
+                type="line",
+                x0=i - 0.3, x1=i + 0.3,
+                y0=baseline_value, y1=baseline_value,
+                line=dict(color="black", width=2, dash="dot"),
+                xref="x", yref="y"
+            )
+
+        # Linea reference GPT-4o
+        reference_value = references.get(task)
+        if reference_value is not None:
+            fig.add_shape(
+                type="line",
+                x0=i - 0.3, x1=i + 0.3,
+                y0=reference_value, y1=reference_value,
+                line=dict(color="red", width=2, dash="dashdot"),
+                xref="x", yref="y"
+            )
+
+    # Layout
     fig.update_layout(
         title="Distribution of Model Accuracy by Task",
         xaxis_title="Task",
@@ -194,9 +237,10 @@ def boxplot_per_task(dataframe=None, baselines=None, references=None):
         boxmode="group",
         dragmode=False,
         font=dict(family="Arial", size=10),
-        margin=dict(b=80),
+        margin=dict(b=80)
     )
 
+    # Caption
     fig.add_annotation(
         text=(
             "In tasks like TE and SA, models approach the accuracy of supervised <br>"
@@ -207,416 +251,108 @@ def boxplot_per_task(dataframe=None, baselines=None, references=None):
         x=0.5, y=-0.30,
         showarrow=False,
         font=dict(size=11, color="gray"),
-        align="left"
+        align="center"
     )
 
     fig.update_yaxes(range=[0, 100], fixedrange=True)
+    fig.update_xaxes(fixedrange=True)
 
     return fig
 
-# EVALITA results
-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
-}
-
-# GPT-4o
-REFERENCES = {
-    "NER": 79.11,
-    "REL": 63.32,
-    "LS": 59.25,
-    "SU": 33.04
-
-}
-
-
-def boxplot_prompts_per_task(dataframe, tasks=None):
-    if tasks is None:
-        tasks = ["TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]
-
-    # Lista delle colonne da aggiornare
-    cols_to_update = ["REL Best Prompt Id", "NER Best Prompt Id", "SU Best Prompt Id", "LS Best Prompt Id"]
-    # Applichiamo la trasformazione
-    for col in cols_to_update:
-        dataframe[col] = dataframe[col].replace({1: 7, 2: 8})
-
-    fig = go.Figure()
-
-    # Liste per creare una sola voce in legenda per Average e Best
-    avg_x, avg_y = [], []
-    best_x, best_y, best_text = [], [], []
-
-    for task in tasks:
-        avg_col = f"{task} Prompt Average"
-        best_col = f"{task} Best Prompt"
-        best_id_col = f"{task} Best Prompt Id"
-
-        if all(col in dataframe.columns for col in [avg_col, best_col, best_id_col]):
-            avg_value = dataframe[avg_col].mean()
-            avg_x.append(task)
-            avg_y.append(avg_value)
-
-            best_value = dataframe[best_col].mean()
-            best_x.append(task)
-            best_y.append(best_value)
-            best_id = dataframe[best_id_col].mode()[0]  # Most frequent best prompt id
-            best_text.append(f"P:{best_id}")
-
-    # Barre Average Accuracy (azzurro)
-    fig.add_trace(go.Bar(
-        x=avg_x,
-        y=avg_y,
-        name="Avg. Accuracy",
-        marker_color="#1f77b4",
-        #hovertemplate="%{y:.2f}%<extra></extra>"
-        #hovertemplate="<b>" + task + "</b><br>Accuracy: %{y:.2f}%<extra></extra>",
-    ))
-
-    # Barre Best Prompt (rosso)
-    fig.add_trace(go.Bar(
-        x=best_x,
-        y=best_y,
-        name="Best Prompt",
-        marker_color="#d62728",
-        #hovertemplate="%{y:.2f}%<extra></extra>"
-        #hovertemplate = "<b>" + task + "</b><br>Accuracy: %{y:.2f}%<extra></extra>",
-    ))
-
-    # Testo sopra barre Best Prompt con ID
-    for x, y, text in zip(best_x, best_y, best_text):
-        fig.add_annotation(
-            x=x,
-            y=y + 3,  # leggermente sopra la barra
-            text=text,
-            showarrow=False,
-            font=dict(size=12, color="black")
-        )
-
-    fig.update_layout(
-        title= "Prompt Accuracy: Avg vs Best",
-        xaxis_title="Task",
-        yaxis_title="Combined Performance",
-        barmode='group',
-        template="plotly_white",
-        font=dict(family="Arial", size=10),
-        yaxis=dict(range=[0, 100], fixedrange=True)
-    )
-
-    # caption come annotazione separata
-    fig.add_annotation(
-        text="There is no single prompt that performs best across all tasks.<br>"
-             "Different prompts achieve the highest accuracy on different tasks.",
-        xref="paper", yref="paper",
-        x=0.5, y=-0.3,
-        showarrow=False,
-        font=dict(size=11, color="gray"),
-        align="center",
-        xanchor="center"
-    )
-
-    return fig
-
-
-def line_chart(dataframe):
-
-    # Normalizza le dimensioni per avere marker non troppo piccoli né enormi
-    def scale_sizes(values, min_size=8, max_size=30):
-        vmin, vmax = min(values), max(values)
-        return [
-            min_size + (val - vmin) / (vmax - vmin) * (max_size - min_size) if vmax > vmin else (min_size + max_size) / 2
-            for val in values
-        ]
-
-    # 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
-    x_true = df_true['#Params (B)'].tolist()
-    y_true = df_true['Avg. Comb. Perf. ⬆️'].tolist()
-    labels_true = [re.search(r'>([^<]+)<', m).group(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) 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',
-        name='5-Shot',
-        marker=dict(
-            color='blue',
-            size=scale_sizes(x_true)
-        ),
-        hovertemplate='<b>%{customdata}</b><br>#Params: %{x}<br>Performance: %{y}<extra></extra>',
-        customdata=labels_true
-    ))
-
-    # Punti IS_FS=False
-    fig.add_trace(go.Scatter(
-        x=x_false,
-        y=y_false,
-        mode='markers',
-        name='0-Shot',
-        marker=dict(
-            color='red',
-            size=scale_sizes(x_false)
-        ),
-        hovertemplate='<b>%{customdata}</b><br>#Params: %{x}<br>Performance: %{y}<extra></extra>',
-        customdata=labels_false
-    ))
-
-    # Trova il massimo tra tutti i modelli
-    all_y = y_true + y_false
-    all_x = x_true + x_false
-    all_labels = labels_true + labels_false
-    max_idx = all_y.index(max(all_y))
-    max_x = all_x[max_idx]
-    max_y = all_y[max_idx]
-    max_label = all_labels[max_idx]
-
-    # Aggiungi annotazione visibile per il modello migliore
-    fig.add_annotation(
-        x=max_x,
-        y=max_y,
-        #text=f"Top: {max_label} ({max_y:.1f}%)",
-        text=f"{max_label}",
-        showarrow=True,
-        arrowhead=2,
-        arrowsize=1,
-        arrowwidth=2,
-        arrowcolor="black",
-        font=dict(size=11, color="black"),
-        xshift=10,
-        yshift=10,
-        ax = -30, ay = -20,  # sposta la label a sinistra e sopra il punto
-        xanchor = "right"  # allinea la label a destra rispetto al punto
-    )
-
-    fig.update_layout(
-        title="Avg. Combined Performance vs #Params",
-        xaxis_title="#Params (B)",
-        yaxis_title="Avg. Combined Performance",
-        template="plotly_white",
-        hovermode="closest",
-        font=dict(family="Arial", size=10),
-        dragmode=False,
-        xaxis=dict(
-            tickvals=[0, 25, 50, 75, 100, 125],
-            ticktext=["0", "25", "50", "75", "100"]
-        ),
-        yaxis=dict(
-            tickvals=[0, 20, 40, 60, 80, 100],  # 👈 tick fissi
-            range=[0, 100]  # 👈 range bloccato
-        )
-    )
-
-    # Caption
-    fig.add_annotation(
-        text="Accuracy generally rises with #Params, but smaller models <br>"
-             "with 5-shot can outperform larger zero-shot models.",
-        xref="paper", yref="paper",
-        x=0.5, y=-0.3,  # 👈 centrata
-        showarrow=False,
-        font=dict(size=11, color="gray"),
-        align="center",
-        xanchor="center"  # 👈 ancora centrata rispetto al testo
-    )
-
-    fig.update_xaxes(fixedrange=True, rangeslider_visible=False)
-    fig.update_yaxes(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
+def create_medal_assignments(sorted_df):
+    """Function for medal assignment logic"""
+    medals = {
+        'large_fs': False, 'medium_fs': False, 'small_fs': False,
+        'large_0shot': False, 'medium_0shot': False, 'small_0shot': False
+    }
 
-    # 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["Size"] == "🔵🔵🔵" and not large_medal_fs_assigned:
-                new_model_column.append(f"{row['Model']} 🔵🔵🔵🏆")
-                large_medal_fs_assigned = True
-            elif row["Size"] == "🔵🔵" and not medium_medal_fs_assigned:
-                new_model_column.append(f"{row['Model']} 🔵🔵🏆")
-                medium_medal_fs_assigned = True
-            elif row["Size"] == "🔵" and not small_medal_fs_assigned:
-                new_model_column.append(f"{row['Model']} 🔵🏆")
-                small_medal_fs_assigned = True
-            else:
-                new_model_column.append(row["Model"])
+    for _, row in sorted_df.iterrows():
+        model_name = row['Model']
+        size = row["Size"]
+        is_fs = row['IS_FS']
+
+        if is_fs:  # 5-Few-Shot
+            if size == "🔵🔵🔵" and not medals['large_fs']:
+                model_name = f"{model_name} 🔵🔵🔵🏆"
+                medals['large_fs'] = True
+            elif size == "🔵🔵" and not medals['medium_fs']:
+                model_name = f"{model_name} 🔵🔵🏆"
+                medals['medium_fs'] = True
+            elif size == "🔵" and not medals['small_fs']:
+                model_name = f"{model_name} 🔵🏆"
+                medals['small_fs'] = True
         else:  # 0-Shot
-            if row["Size"] == "🔵🔵🔵" and not large_medal_0shot_assigned:
-                new_model_column.append(f"{row['Model']} 🔵🔵🔵🎖️")
-                large_medal_0shot_assigned = True
-            elif row["Size"] == "🔵🔵" and not medium_medal_0shot_assigned:
-                new_model_column.append(f"{row['Model']} 🔵🔵🎖️")
-                medium_medal_0shot_assigned = True
-            elif row["Size"] == "🔵" and not small_medal_0shot_assigned:
-                new_model_column.append(f"{row['Model']} 🔵🎖️")
-                small_medal_0shot_assigned = True
-            else:
-                new_model_column.append(row["Model"])
-
-    # Lista delle colonne da aggiornare
-    #cols_to_update = ["REL Best Prompt Id", "NER Best Prompt Id", "SU Best Prompt Id", "LS Best Prompt Id"]
-    # Applichiamo la trasformazione
-    #for col in cols_to_update:
-    #    dataframe[col] = dataframe[col].replace({1: 7, 2: 8})
-
-    # Aggiorna la colonna Model
-    sorted_dataframe["Model"] = new_model_column
+            if size == "🔵🔵🔵" and not medals['large_0shot']:
+                model_name = f"{model_name} 🔵🔵🔵🎖️"
+                medals['large_0shot'] = True
+            elif size == "🔵🔵" and not medals['medium_0shot']:
+                model_name = f"{model_name} 🔵🔵🎖️"
+                medals['medium_0shot'] = True
+            elif size == "🔵" and not medals['small_0shot']:
+                model_name = f"{model_name} 🔵🎖️"
+                medals['small_0shot'] = True
 
-    field_list = fields(AutoEvalColumn)
+        new_model_column.append(model_name)
+
+    return new_model_column
 
+
+def create_leaderboard_base(sorted_dataframe, field_list, hidden_columns):
+    """Base leaderboard creation with common parameters. """
     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],
+        hide_columns=hidden_columns,
         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)"),
+            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.
-    """
+
+def init_leaderboard(dataframe, default_selection=None, hidden_columns=None):
+    """Leaderboard initialization. """
     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)
+    # Sort and reset index
+    sorted_dataframe = dataframe.sort_values(by="Avg. Comb. Perf. ⬆️", ascending=False).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
+    # Apply medal assignments
+    sorted_dataframe["Model"] = create_medal_assignments(sorted_dataframe)
 
-    large_medal_0shot_assigned = False
-    medium_medal_0shot_assigned = False
-    small_medal_0shot_assigned = False
+    field_list = fields(AutoEvalColumn)
 
-    # Lista temporanea per salvare i nuovi valori della colonna Model
-    new_model_column = []
+    return create_leaderboard_base(sorted_dataframe, field_list, hidden_columns)
 
-    for _, row in sorted_dataframe.iterrows():
-        if row['IS_FS']:  # 5-Few-Shot
-            if row["Size"] == "🔵🔵🔵" and not large_medal_fs_assigned:
-                new_model_column.append(f"{row['Model']} 🔵🔵🔵🏆")
-                large_medal_fs_assigned = True
-            elif row["Size"] == "🔵🔵" and not medium_medal_fs_assigned:
-                new_model_column.append(f"{row['Model']} 🔵🔵🏆")
-                medium_medal_fs_assigned = True
-            elif row["Size"] == "🔵" and not small_medal_fs_assigned:
-                new_model_column.append(f"{row['Model']} 🔵🏆")
-                small_medal_fs_assigned = True
-            else:
-                new_model_column.append(row["Model"])
-        else:  # 0-Shot
-            if row["Size"] == "🔵🔵🔵" and not large_medal_0shot_assigned:
-                new_model_column.append(f"{row['Model']} 🔵🔵🔵🎖️")
-                large_medal_0shot_assigned = True
-            elif row["Size"] == "🔵🔵" and not medium_medal_0shot_assigned:
-                new_model_column.append(f"{row['Model']} 🔵🔵🎖️")
-                medium_medal_0shot_assigned = True
-            elif row["Size"] == "🔵" and not small_medal_0shot_assigned:
-                new_model_column.append(f"{row['Model']} 🔵🎖️")
-                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'])
+
+def update_task_leaderboard(dataframe, default_selection=None, hidden_columns=None):
+    """ Task-specific leaderboard update."""
+    if dataframe is None or dataframe.empty:
+        raise ValueError("Leaderboard DataFrame is empty or None.")
+
+    # Sort and reset index
+    sorted_dataframe = dataframe.sort_values(by="Combined Performance", ascending=False).reset_index(drop=True)
+    sorted_dataframe["Rank"] = sorted_dataframe.index + 1
+
+    # Apply medal assignments
+    sorted_dataframe["Model"] = create_medal_assignments(sorted_dataframe)
 
     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],
+        hide_columns=hidden_columns,
         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],
@@ -626,106 +362,148 @@ def update_task_leaderboard(dataframe, default_selection=None, hidden_columns=No
         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, max_retries=3):
+    """Snapshot download with retry logic."""
+    for attempt in range(max_retries):
+        try:
+            logger.info(f"Downloading from {repo} to {local_dir} (attempt {attempt + 1}/{max_retries})")
+            snapshot_download(
+                repo_id=repo,
+                local_dir=local_dir,
+                repo_type="dataset",
+                tqdm_class=None,
+                etag_timeout=30,
+                token=TOKEN
+            )
+            return True
+        except Exception as e:
+            logger.error(f"Error downloading {repo} (attempt {attempt + 1}): {e}")
+            if attempt == max_retries - 1:
+                logger.error(f"Failed to download {repo} after {max_retries} attempts")
+                return False
+    return False
 
-def download_snapshot(repo, local_dir):
-    """Try to download a snapshot from Hugging Face Hub."""
+
+def restart_space():
+    """Restart the Hugging Face space."""
     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)
+        logger.info("Restarting space...")
+        API.restart_space(repo_id=REPO_ID)
     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")
+        logger.error(f"Error restarting space: {e}")
+
+
+def create_title_html():
+    """Function for title HTML."""
+    return """
+    <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;">
+            <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>
+    """
 
-    # ⬇️ QUI aggiungiamo i grafici subito sotto la barra del titolo e sopra le tabs
-    with gr.Row():
-        gr.Plot(value=line_chart(LEADERBOARD_DF), elem_id="line-chart")
-        gr.Plot(value=boxplot_per_task(LEADERBOARD_DF, BASELINES, REFERENCES), elem_id="boxplot-task")
-        #gr.Plot(value=boxplot_prompts_per_task(LEADERBOARD_DF), elem_id="boxplot-prompt-task")
 
-    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+def create_credits_markdown():
+    """Credits section."""
+    return """
+**This project has benefited from the following support:**
 
-        # Main leaderboard tab
-        with gr.TabItem("🏅 Benchmark"):
+- 🧠 **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.
 
-            leaderboard = init_leaderboard(
-                LEADERBOARD_DF,
-                default_selection=['Rank', 'Size', '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', 'Size', 'FS', 'Model', "Avg. Comb. Perf. ⬆️", "TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]]
-            )
+- 💶 **Funding**: Partially supported by the PNRR project **FAIR - Future AI Research (PE00000013)**, under the NRRP MUR program funded by **NextGenerationEU**.
 
-            gr.HTML(
-                f"""
+- 🖥️ **Computation**: We gratefully acknowledge **CINECA** for granting access to the **LEONARDO** supercomputer.
+"""
+
+
+# Main initialization
+def initialize_app():
+    """Initialize the application."""
+    try:
+        # Download snapshots
+        queue_success = download_snapshot(QUEUE_REPO, EVAL_REQUESTS_PATH)
+        results_success = download_snapshot(RESULTS_REPO, EVAL_RESULTS_PATH)
+
+        if not (queue_success and results_success):
+            logger.error("Failed to download required data")
+            return None, None, None, None, None
+
+        # 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)
+
+        # Calculate theoretical max performance
+        theoretical_max = theoretical_performance(hash(str(leaderboard_df.values.tobytes())))
+
+        return leaderboard_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df, theoretical_max
+
+    except Exception as e:
+        logger.error(f"Error initializing app: {e}")
+        return None, None, None, None, None
+
+
+# Initialize data
+LEADERBOARD_DF, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df, theoretical_max_combined_perf = initialize_app()
+
+if LEADERBOARD_DF is None:
+    # Fallback behavior
+    logger.error("Failed to initialize app data")
+    theoretical_max_combined_perf = 0.0
+
+
+def create_gradio_interface():
+    """The main Gradio interface."""
+    demo = gr.Blocks(css=custom_css)
+
+    with demo:
+        # Title
+        gr.HTML(create_title_html())
+        gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+
+        # Charts section
+        with gr.Row():
+            if LEADERBOARD_DF is not None:
+                # Note: You'd need to implement these chart functions properly
+                gr.Plot(value=create_line_chart(LEADERBOARD_DF), elem_id="line-chart")
+                gr.Plot(value=create_boxplot_task(LEADERBOARD_DF, BASELINES, REFERENCES), elem_id="boxplot-task")
+
+        # Tabs
+        with gr.Tabs(elem_classes="tab-buttons") as tabs:
+            # Main leaderboard tab
+            with gr.TabItem("🏅 Benchmark"):
+                if LEADERBOARD_DF is not None:
+                    leaderboard = init_leaderboard(
+                        LEADERBOARD_DF,
+                        default_selection=['Rank', 'Size', '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', 'Size', '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;
@@ -735,89 +513,95 @@ with demo:
                             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>
+                            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>
                         """
+                    )
+
+            # About tab
+            with gr.TabItem("📝 About"):
+                gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
+
+            with gr.TabItem("║", interactive=False):
+                gr.Markdown("", elem_classes="markdown-text")
+
+            # Task-specific tabs
+            if LEADERBOARD_DF is not None:
+                # Multiple choice tasks
+                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', 'Size', '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', 'Size', 'FS', 'Model', 'Combined Performance',
+                                                        'Prompt Average', 'Prompt Std', 'Best Prompt',
+                                                        'Best Prompt Id']]
+                        )
+
+                with gr.TabItem("│", interactive=False):
+                    gr.Markdown("", elem_classes="markdown-text")
+
+                # Generative tasks
+                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', 'Size', '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', 'Size', 'FS', 'Model', 'Combined Performance',
+                                                        'Prompt Average', 'Prompt Std', 'Best Prompt',
+                                                        'Best Prompt Id']]
+                        )
+
+        # Citation and Credits sections
+        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.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))
-            gr.Plot(value=boxplot_prompts_per_task(LEADERBOARD_DF))
-            gr.Plot(value=barplot_mean_few_minus_zero_shot(LEADERBOARD_DF))
-        '''
-
-        # 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', 'Size', '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', 'Size', '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', 'Size', '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', 'Size', '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.  
-            """
-        )
+        with gr.Accordion("📙 Credits", open=False):
+            gr.Markdown(create_credits_markdown())
+
+    return demo
+
+
+# Create and configure the demo
+demo = create_gradio_interface()
 
-# Background job to restart space
+# Background scheduler for space restart
 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)
+# Launch configuration
+if __name__ == "__main__":
+    demo.queue(default_concurrency_limit=40).launch(
+        debug=True,
+        show_error=True
+    )

app_18_09_2025.py

@@ -0,0 +1,823 @@
+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 barplot_mean_few_minus_zero_shot(dataframe, tasks=None):
+    if tasks is None:
+        tasks = ["TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]
+
+    task_means = {}
+
+    for task in tasks:
+        if task not in dataframe.columns:
+            continue
+
+        # Separa few-shot e zero-shot
+        few_shot = dataframe[dataframe['IS_FS'] == True][["Model", task]]
+        zero_shot = dataframe[dataframe['IS_FS'] == False][["Model", task]]
+
+        # Allinea i modelli
+        merged = pd.merge(few_shot, zero_shot, on="Model", suffixes=("_few", "_zero"))
+
+        # Rimuovi righe con valori mancanti
+        merged = merged.dropna(subset=[f"{task}_few", f"{task}_zero"])
+
+        if merged.empty:
+            continue
+
+        # Calcola differenza few - zero
+        diff = merged[f"{task}_few"] - merged[f"{task}_zero"]
+
+        # Calcola la media
+        task_means[task] = diff.mean()
+
+    # Crea barplot
+    fig = go.Figure([go.Bar(
+        x=list(task_means.keys()),
+        y=list(task_means.values()),
+        marker_color="#ff7f0e",
+        text=[f"{v:.2f}" for v in task_means.values()],
+        textposition="outside",
+        hovertemplate="<b>%{x}</b><br>Mean Delta Accuracy: %{y:.2f}%<extra></extra>"
+    )])
+
+    # Linea di riferimento a 0
+    '''
+    fig.add_shape(
+        type="line",
+        x0=-0.5, x1=len(task_means) - 0.5,
+        y0=0, y1=0,
+        line=dict(color="black", width=2, dash="dash"),
+        xref="x", yref="y"
+    )
+    '''
+
+    fig.update_layout(
+        title="Mean Accuracy Difference (Few-shot − Zero-shot) per Task",
+        xaxis_title="",
+        yaxis_title="Mean Delta Combined Performance",
+        template="plotly_white",
+        font=dict(family="Arial", size=13),
+        #margin=dict(b=100)
+    )
+
+    fig.add_annotation(
+        text="5-shot learning generally outperforms zero-shot, especially in tasks like NER and REL.<br>"
+             "Only in Summarization (SU) does it provide no accuracy gain.",
+        xref="paper", yref="paper",
+        x=0, y=-0.2,
+        showarrow=False,
+        font=dict(size=11, color="gray"),
+        align="left"
+    )
+
+    return fig
+
+
+def boxplot_per_task(dataframe=None, baselines=None, references=None):
+
+    #print(dataframe.columns)
+
+    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
+        })
+
+    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,
+                marker=dict(color=colors[i]),
+                line=dict(color="black", width=2),
+                fillcolor=colors[i],
+                opacity=0.7,
+                hovertemplate="<b>"+task+"</b><br>Accuracy: %{y:.2f}%<extra></extra>",
+                width=0.6,
+                whiskerwidth=0.2,
+                quartilemethod="linear"
+            ))
+
+            # baseline
+            if task in baselines and baselines[task] is not None:
+                fig.add_shape(
+                    type="line",
+                    x0=i - 0.3, x1=i + 0.3,
+                    y0=baselines[task], y1=baselines[task],
+                    line=dict(color="black", width=2, dash="dot"),  # più visibile
+                    xref="x", yref="y"
+                )
+                '''
+                fig.add_annotation(
+                    x=i, y=baselines[task],
+                    text=f"{baselines[task]}%",
+                    showarrow=False,
+                    yshift=10,
+                    font=dict(size=10, color="black")
+                )
+                '''
+
+            # reference GPT-4o
+            if task in references and references[task] is not None:
+                fig.add_shape(
+                    type="line",
+                    x0=i - 0.3, x1=i + 0.3,
+                    y0=references[task], y1=references[task],
+                    line=dict(color="red", width=2, dash="dashdot"),
+                    xref="x", yref="y"
+                )
+
+    fig.update_layout(
+        title="Distribution of Model Accuracy by Task",
+        xaxis_title="Task",
+        yaxis_title="Combined Performance",
+        template="plotly_white",
+        boxmode="group",
+        dragmode=False,
+        font=dict(family="Arial", size=10),
+        margin=dict(b=80),
+    )
+
+    fig.add_annotation(
+        text=(
+            "In tasks like TE and SA, models approach the accuracy of supervised <br>"
+            "models at EVALITA (dashed black line); in NER and REL they remain lower. <br>"
+            "Dashed red lines show GPT-4o reference results for generative tasks."
+        ),
+        xref="paper", yref="paper",
+        x=0.5, y=-0.30,
+        showarrow=False,
+        font=dict(size=11, color="gray"),
+        align="left"
+    )
+
+    fig.update_yaxes(range=[0, 100], fixedrange=True)
+
+    return fig
+
+# EVALITA results
+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
+}
+
+# GPT-4o
+REFERENCES = {
+    "NER": 79.11,
+    "REL": 63.32,
+    "LS": 59.25,
+    "SU": 33.04
+
+}
+
+
+def boxplot_prompts_per_task(dataframe, tasks=None):
+    if tasks is None:
+        tasks = ["TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]
+
+    # Lista delle colonne da aggiornare
+    cols_to_update = ["REL Best Prompt Id", "NER Best Prompt Id", "SU Best Prompt Id", "LS Best Prompt Id"]
+    # Applichiamo la trasformazione
+    for col in cols_to_update:
+        dataframe[col] = dataframe[col].replace({1: 7, 2: 8})
+
+    fig = go.Figure()
+
+    # Liste per creare una sola voce in legenda per Average e Best
+    avg_x, avg_y = [], []
+    best_x, best_y, best_text = [], [], []
+
+    for task in tasks:
+        avg_col = f"{task} Prompt Average"
+        best_col = f"{task} Best Prompt"
+        best_id_col = f"{task} Best Prompt Id"
+
+        if all(col in dataframe.columns for col in [avg_col, best_col, best_id_col]):
+            avg_value = dataframe[avg_col].mean()
+            avg_x.append(task)
+            avg_y.append(avg_value)
+
+            best_value = dataframe[best_col].mean()
+            best_x.append(task)
+            best_y.append(best_value)
+            best_id = dataframe[best_id_col].mode()[0]  # Most frequent best prompt id
+            best_text.append(f"P:{best_id}")
+
+    # Barre Average Accuracy (azzurro)
+    fig.add_trace(go.Bar(
+        x=avg_x,
+        y=avg_y,
+        name="Avg. Accuracy",
+        marker_color="#1f77b4",
+        #hovertemplate="%{y:.2f}%<extra></extra>"
+        #hovertemplate="<b>" + task + "</b><br>Accuracy: %{y:.2f}%<extra></extra>",
+    ))
+
+    # Barre Best Prompt (rosso)
+    fig.add_trace(go.Bar(
+        x=best_x,
+        y=best_y,
+        name="Best Prompt",
+        marker_color="#d62728",
+        #hovertemplate="%{y:.2f}%<extra></extra>"
+        #hovertemplate = "<b>" + task + "</b><br>Accuracy: %{y:.2f}%<extra></extra>",
+    ))
+
+    # Testo sopra barre Best Prompt con ID
+    for x, y, text in zip(best_x, best_y, best_text):
+        fig.add_annotation(
+            x=x,
+            y=y + 3,  # leggermente sopra la barra
+            text=text,
+            showarrow=False,
+            font=dict(size=12, color="black")
+        )
+
+    fig.update_layout(
+        title= "Prompt Accuracy: Avg vs Best",
+        xaxis_title="Task",
+        yaxis_title="Combined Performance",
+        barmode='group',
+        template="plotly_white",
+        font=dict(family="Arial", size=10),
+        yaxis=dict(range=[0, 100], fixedrange=True)
+    )
+
+    # caption come annotazione separata
+    fig.add_annotation(
+        text="There is no single prompt that performs best across all tasks.<br>"
+             "Different prompts achieve the highest accuracy on different tasks.",
+        xref="paper", yref="paper",
+        x=0.5, y=-0.3,
+        showarrow=False,
+        font=dict(size=11, color="gray"),
+        align="center",
+        xanchor="center"
+    )
+
+    return fig
+
+
+def line_chart(dataframe):
+
+    # Normalizza le dimensioni per avere marker non troppo piccoli né enormi
+    def scale_sizes(values, min_size=8, max_size=30):
+        vmin, vmax = min(values), max(values)
+        return [
+            min_size + (val - vmin) / (vmax - vmin) * (max_size - min_size) if vmax > vmin else (min_size + max_size) / 2
+            for val in values
+        ]
+
+    # 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
+    x_true = df_true['#Params (B)'].tolist()
+    y_true = df_true['Avg. Comb. Perf. ⬆️'].tolist()
+    labels_true = [re.search(r'>([^<]+)<', m).group(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) 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',
+        name='5-Shot',
+        marker=dict(
+            color='blue',
+            size=scale_sizes(x_true)
+        ),
+        hovertemplate='<b>%{customdata}</b><br>#Params: %{x}<br>Performance: %{y}<extra></extra>',
+        customdata=labels_true
+    ))
+
+    # Punti IS_FS=False
+    fig.add_trace(go.Scatter(
+        x=x_false,
+        y=y_false,
+        mode='markers',
+        name='0-Shot',
+        marker=dict(
+            color='red',
+            size=scale_sizes(x_false)
+        ),
+        hovertemplate='<b>%{customdata}</b><br>#Params: %{x}<br>Performance: %{y}<extra></extra>',
+        customdata=labels_false
+    ))
+
+    # Trova il massimo tra tutti i modelli
+    all_y = y_true + y_false
+    all_x = x_true + x_false
+    all_labels = labels_true + labels_false
+    max_idx = all_y.index(max(all_y))
+    max_x = all_x[max_idx]
+    max_y = all_y[max_idx]
+    max_label = all_labels[max_idx]
+
+    # Aggiungi annotazione visibile per il modello migliore
+    fig.add_annotation(
+        x=max_x,
+        y=max_y,
+        #text=f"Top: {max_label} ({max_y:.1f}%)",
+        text=f"{max_label}",
+        showarrow=True,
+        arrowhead=2,
+        arrowsize=1,
+        arrowwidth=2,
+        arrowcolor="black",
+        font=dict(size=11, color="black"),
+        xshift=10,
+        yshift=10,
+        ax = -30, ay = -20,  # sposta la label a sinistra e sopra il punto
+        xanchor = "right"  # allinea la label a destra rispetto al punto
+    )
+
+    fig.update_layout(
+        title="Avg. Combined Performance vs #Params",
+        xaxis_title="#Params (B)",
+        yaxis_title="Avg. Combined Performance",
+        template="plotly_white",
+        hovermode="closest",
+        font=dict(family="Arial", size=10),
+        dragmode=False,
+        xaxis=dict(
+            tickvals=[0, 25, 50, 75, 100, 125],
+            ticktext=["0", "25", "50", "75", "100"]
+        ),
+        yaxis=dict(
+            tickvals=[0, 20, 40, 60, 80, 100],  # 👈 tick fissi
+            range=[0, 100]  # 👈 range bloccato
+        )
+    )
+
+    # Caption
+    fig.add_annotation(
+        text="Accuracy generally rises with #Params, but smaller models <br>"
+             "with 5-shot can outperform larger zero-shot models.",
+        xref="paper", yref="paper",
+        x=0.5, y=-0.3,  # 👈 centrata
+        showarrow=False,
+        font=dict(size=11, color="gray"),
+        align="center",
+        xanchor="center"  # 👈 ancora centrata rispetto al testo
+    )
+
+    fig.update_xaxes(fixedrange=True, rangeslider_visible=False)
+    fig.update_yaxes(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["Size"] == "🔵🔵🔵" and not large_medal_fs_assigned:
+                new_model_column.append(f"{row['Model']} 🔵🔵🔵🏆")
+                large_medal_fs_assigned = True
+            elif row["Size"] == "🔵🔵" and not medium_medal_fs_assigned:
+                new_model_column.append(f"{row['Model']} 🔵🔵🏆")
+                medium_medal_fs_assigned = True
+            elif row["Size"] == "🔵" and not small_medal_fs_assigned:
+                new_model_column.append(f"{row['Model']} 🔵🏆")
+                small_medal_fs_assigned = True
+            else:
+                new_model_column.append(row["Model"])
+        else:  # 0-Shot
+            if row["Size"] == "🔵🔵🔵" and not large_medal_0shot_assigned:
+                new_model_column.append(f"{row['Model']} 🔵🔵🔵🎖️")
+                large_medal_0shot_assigned = True
+            elif row["Size"] == "🔵🔵" and not medium_medal_0shot_assigned:
+                new_model_column.append(f"{row['Model']} 🔵🔵🎖️")
+                medium_medal_0shot_assigned = True
+            elif row["Size"] == "🔵" and not small_medal_0shot_assigned:
+                new_model_column.append(f"{row['Model']} 🔵🎖️")
+                small_medal_0shot_assigned = True
+            else:
+                new_model_column.append(row["Model"])
+
+    # Lista delle colonne da aggiornare
+    #cols_to_update = ["REL Best Prompt Id", "NER Best Prompt Id", "SU Best Prompt Id", "LS Best Prompt Id"]
+    # Applichiamo la trasformazione
+    #for col in cols_to_update:
+    #    dataframe[col] = dataframe[col].replace({1: 7, 2: 8})
+
+    # 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["Size"] == "🔵🔵🔵" and not large_medal_fs_assigned:
+                new_model_column.append(f"{row['Model']} 🔵🔵🔵🏆")
+                large_medal_fs_assigned = True
+            elif row["Size"] == "🔵🔵" and not medium_medal_fs_assigned:
+                new_model_column.append(f"{row['Model']} 🔵🔵🏆")
+                medium_medal_fs_assigned = True
+            elif row["Size"] == "🔵" and not small_medal_fs_assigned:
+                new_model_column.append(f"{row['Model']} 🔵🏆")
+                small_medal_fs_assigned = True
+            else:
+                new_model_column.append(row["Model"])
+        else:  # 0-Shot
+            if row["Size"] == "🔵🔵🔵" and not large_medal_0shot_assigned:
+                new_model_column.append(f"{row['Model']} 🔵🔵🔵🎖️")
+                large_medal_0shot_assigned = True
+            elif row["Size"] == "🔵🔵" and not medium_medal_0shot_assigned:
+                new_model_column.append(f"{row['Model']} 🔵🔵🎖️")
+                medium_medal_0shot_assigned = True
+            elif row["Size"] == "🔵" and not small_medal_0shot_assigned:
+                new_model_column.append(f"{row['Model']} 🔵🎖️")
+                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")
+
+    # ⬇️ QUI aggiungiamo i grafici subito sotto la barra del titolo e sopra le tabs
+    with gr.Row():
+        gr.Plot(value=line_chart(LEADERBOARD_DF), elem_id="line-chart")
+        gr.Plot(value=boxplot_per_task(LEADERBOARD_DF, BASELINES, REFERENCES), elem_id="boxplot-task")
+        #gr.Plot(value=boxplot_prompts_per_task(LEADERBOARD_DF), elem_id="boxplot-prompt-task")
+
+    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+
+        # Main leaderboard tab
+        with gr.TabItem("🏅 Benchmark"):
+
+            leaderboard = init_leaderboard(
+                LEADERBOARD_DF,
+                default_selection=['Rank', 'Size', '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', 'Size', '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))
+            gr.Plot(value=boxplot_prompts_per_task(LEADERBOARD_DF))
+            gr.Plot(value=barplot_mean_few_minus_zero_shot(LEADERBOARD_DF))
+        '''
+
+        # 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', 'Size', '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', 'Size', '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', 'Size', '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', 'Size', '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)