Add performance metrics labels with average, std dev, and best model info.

app.py

@@ -490,69 +490,117 @@ if LEADERBOARD_DF is None:
     theoretical_max_combined_perf = 0.0
 
 
+# Main Gradio interface
 def create_gradio_interface():
     """The main Gradio interface."""
     demo = gr.Blocks(css=custom_css)
 
     with demo:
-        # Title
+        # Titolo
         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
+        # Tabs principali
         with gr.Tabs(elem_classes="tab-buttons") as tabs:
-            # Main leaderboard tab
+            # 🏅 Benchmark
             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"]]
-                    )
+                    # Labels dei campi affiancate
 
-                    gr.HTML(
-                        f"""
+                    with gr.Row():
+                        # Labels dei campi affiancate
+                        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;
+                            display: flex; justify-content: flex-start; width: 100%; gap: 5px;
                         ">
-                            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 style="
+                                border: 2px solid #1f77b4; border-radius: 8px; padding: 4px 8px;
+                                background-color: #f0f8ff; font-weight: bold; font-size: 12px;
+                                text-align: center;
+                                display: flex; align-items: center; justify-content: center;
+                                min-height: 28px; white-space: nowrap;
+                            " title="Total number of configurations (zero-shot and 5-few-shot) of the models evaluated in the leaderboard.">
+                                Models tested: {len(LEADERBOARD_DF)}
+                            </div>
+                            <div style="
+                                border: 2px solid #1f77b4; border-radius: 8px; padding: 4px 8px;
+                                background-color: #f0f8ff; font-weight: bold; font-size: 12px;
+                                text-align: center;
+                                display: flex; align-items: center; justify-content: center;
+                                min-height: 28px; white-space: nowrap;
+                            " title="Average accuracy of the evaluated model">
+                                Avg combined perf.: {LEADERBOARD_DF['Avg. Comb. Perf. ⬆️'].mean():.2f}
+                            </div>
+                            <div style="
+                                border: 2px solid #1f77b4; border-radius: 8px; padding: 4px 8px;
+                                background-color: #f0f8ff; font-weight: bold; font-size: 12px;
+                                text-align: center;
+                                display: flex; align-items: center; justify-content: center;
+                                min-height: 28px; white-space: nowrap;
+                            " title="Standard deviation of the evaluated models' performance.">
+                                Std. Dev. {LEADERBOARD_DF['Avg. Comb. Perf. ⬆️'].std():.2f}
+                            </div>
+                            <div style="
+                                border: 2px solid #1f77b4; border-radius: 8px; padding: 4px 8px;
+                                background-color: #f0f8ff; font-weight: bold; font-size: 12px;
+                                text-align: center;
+                                display: flex; align-items: center; justify-content: center;
+                                min-height: 28px; white-space: nowrap;
+                            " title="Best evaluated model.">
+                                Best model: {LEADERBOARD_DF.loc[LEADERBOARD_DF['Avg. Comb. Perf. ⬆️'].idxmax(), 'Model']}
+                            </div>
+                            <div style="
+                                border: 2px solid #1f77b4; border-radius: 8px; padding: 4px 8px;
+                                background-color: #f0f8ff; font-weight: bold; font-size: 12px;
+                                text-align: center;
+                                display: flex; align-items: center; justify-content: center;
+                                min-height: 28px; white-space: nowrap;
+                            " title="Accuracy of the best evaluated model.">
+                                Best model accuracy: {LEADERBOARD_DF.loc[LEADERBOARD_DF['Avg. Comb. Perf. ⬆️'].idxmax(), 'Avg. Comb. Perf. ⬆️']:.2f}
+                            </div>
+                            <div style="
+                                border: 2px solid #1f77b4; border-radius: 8px; padding: 4px 8px;
+                                background-color: #f0f8ff; font-weight: bold; font-size: 12px;
+                                text-align: center;
+                                display: flex; align-items: center; justify-content: center;
+                                min-height: 28px; white-space: nowrap;
+                            " title="Maximum achievable accuracy based on the highest performance for each task by any model in the leaderboard.">
+                                Theoretical max: {theoretical_max_combined_perf:.2f}
+                            </div>
                         </div>
-                        """
+                        """)
+
+                    # Grafici affiancati
+                    with gr.Row():
+                        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")
+
+                    # 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"]]
                     )
 
-            # About tab
+            # 📝 About
             with gr.TabItem("📝 About"):
                 gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
 
+            # Separatori
             with gr.TabItem("║", interactive=False):
                 gr.Markdown("", elem_classes="markdown-text")
 
-            # Task-specific tabs
+            # Task-specific tabs (Multiple Choice)
             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_task = update_task_leaderboard(
                             LEADERBOARD_DF.rename(columns={
                                 f"{task} Prompt Average": "Prompt Average",
                                 f"{task} Prompt Std": "Prompt Std",
@@ -560,24 +608,26 @@ def create_gradio_interface():
                                 f"{task} Best Prompt Id": "Best Prompt Id",
                                 task: "Comb. Perf. ⬆️"
                             }),
-                            default_selection=['Rank', 'Size', 'FS', 'Model', 'Comb. Perf. ⬆️', 'Prompt Average',
-                                               'Prompt Std', 'Best Prompt', 'Best Prompt Id'],
+                            default_selection=['Rank', 'Size', 'FS', 'Model', 'Comb. Perf. ⬆️',
+                                               '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', 'Comb. Perf. ⬆️',
                                                         'Prompt Average', 'Prompt Std', 'Best Prompt',
                                                         'Best Prompt Id']]
                         )
 
-                with gr.TabItem("│", interactive=False):
-                    gr.Markdown("", elem_classes="markdown-text")
+            # Separatori
+            with gr.TabItem("│", interactive=False):
+                gr.Markdown("", elem_classes="markdown-text")
 
-                # Generative tasks
+            # Task-specific tabs (Generative)
+            if LEADERBOARD_DF is not None:
                 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_task = update_task_leaderboard(
                             LEADERBOARD_DF.rename(columns={
                                 f"{task} Prompt Average": "Prompt Average",
                                 f"{task} Prompt Std": "Prompt Std",
@@ -585,15 +635,15 @@ def create_gradio_interface():
                                 f"{task} Best Prompt Id": "Best Prompt Id",
                                 task: "Comb. Perf. ⬆️"
                             }),
-                            default_selection=['Rank', 'Size', 'FS', 'Model', 'Comb. Perf. ⬆️', 'Prompt Average',
-                                               'Prompt Std', 'Best Prompt', 'Best Prompt Id'],
+                            default_selection=['Rank', 'Size', 'FS', 'Model', 'Comb. Perf. ⬆️',
+                                               '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', 'Comb. Perf. ⬆️',
                                                         'Prompt Average', 'Prompt Std', 'Best Prompt',
                                                         'Best Prompt Id']]
                         )
 
-        # Citation and Credits sections
+        # Citation e Credits
         with gr.Accordion("📙 Citation", open=False):
             gr.Textbox(
                 value=CITATION_BUTTON_TEXT,
@@ -622,4 +672,4 @@ if __name__ == "__main__":
     demo.queue(default_concurrency_limit=40).launch(
         debug=True,
         show_error=True
-    )
+    )

app_22_09_2025.py

@@ -0,0 +1,625 @@
+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 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.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 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__)
+
+# 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 results
+REFERENCES = {
+    "NER": 79.11, "REL": 63.32, "LS": 59.25, "SU": 33.04
+}
+
+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": ""},
+}
+
+def highlight_best_per_task(df):
+    """Add 🟡 symbol next to the maximum value in each task column"""
+
+    task_columns = ["TE", "SA", "HS", "AT", "WIC", "FAQ", "LS", "SU", "NER", "REL"]
+
+    df = df.copy()
+    for col in task_columns:
+        if col in df.columns:
+            max_val = df[col].max()
+            df[col] = df[col].apply(
+                lambda x: f"{x:.1f}🔺" if x == max_val else f"{x:.1f}"
+            )
+    return df
+
+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
+
+
+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
+    ]
+
+
+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 create_line_chart(dataframe):
+    """Create left chart."""
+
+    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
+        ]
+
+    fig = go.Figure()
+
+    # Loop su 5-Shot e 0-Shot
+    for shot, color in [(True, "blue"), (False, "red")]:
+        df = dataframe[dataframe["IS_FS"] == shot]
+
+        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()
+        ]
+
+        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)
+
+        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="Average Combined Performance vs #Params",
+        xaxis_title="#Params (B)", yaxis_title="Average 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="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"
+    )
+
+    fig.update_xaxes(fixedrange=True, rangeslider_visible=False)
+    fig.update_yaxes(fixedrange=True)
+
+    return fig
+
+
+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})
+
+    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 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>",
+            hoverlabel=dict(bgcolor=colors[i], font_color="white"),
+            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",
+        yaxis_title="Combined Performance",
+        template="plotly_white",
+        boxmode="group",
+        dragmode=False,
+        font=dict(family="Arial", size=10),
+        margin=dict(b=80),
+    )
+
+    # Caption
+    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="center"
+    )
+
+    fig.update_yaxes(range=[0, 100], fixedrange=True)
+    fig.update_xaxes(fixedrange=True)
+
+    return fig
+
+
+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
+    }
+
+    new_model_column = []
+
+    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 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
+
+        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],
+        search_columns=[AutoEvalColumn.model.name],
+        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],
+                         label="Select the number of parameters (B)"),
+        ],
+        bool_checkboxgroup_label="Evaluation Mode",
+        interactive=False,
+    )
+
+
+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.")
+
+    # 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
+
+    # Apply medal assignments
+    sorted_dataframe["Model"] = create_medal_assignments(sorted_dataframe)
+
+    # Show the best values for tasks
+    sorted_dataframe = highlight_best_per_task(sorted_dataframe)
+
+    field_list = fields(AutoEvalColumn)
+
+    return create_leaderboard_base(sorted_dataframe, field_list, hidden_columns)
+
+
+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="Comb. Perf. ⬆️", 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] + [int],
+        search_columns=[AutoEvalColumn.model.name],
+        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],
+                         label="Select the number of parameters (B)"),
+        ],
+        bool_checkboxgroup_label="Evaluation Mode",
+        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 restart_space():
+    """Restart the Hugging Face space."""
+    try:
+        logger.info("Restarting space... ")
+        API.restart_space(repo_id=REPO_ID)
+    except Exception as e:
+        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>
+    """
+
+
+def create_credits_markdown():
+    """Credits section."""
+    return """
+**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.
+"""
+
+
+# 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;
+                            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>
+                        """
+                    )
+
+            # 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: "Comb. Perf. ⬆️"
+                            }),
+                            default_selection=['Rank', 'Size', 'FS', 'Model', 'Comb. Perf. ⬆️', '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', 'Comb. Perf. ⬆️',
+                                                        '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: "Comb. Perf. ⬆️"
+                            }),
+                            default_selection=['Rank', 'Size', 'FS', 'Model', 'Comb. Perf. ⬆️', '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', 'Comb. Perf. ⬆️',
+                                                        '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.Accordion("📙 Credits", open=False):
+            gr.Markdown(create_credits_markdown())
+
+    return demo
+
+
+# Create and configure the demo
+demo = create_gradio_interface()
+
+# Background scheduler for space restart
+scheduler = BackgroundScheduler()
+scheduler.add_job(restart_space, "interval", seconds=1800)
+scheduler.start()
+
+# Launch configuration
+if __name__ == "__main__":
+    demo.queue(default_concurrency_limit=40).launch(
+        debug=True,
+        show_error=True
+    )