import json
import math
from statistics import mean
from datetime import datetime

import pandas as pd
import gradio as gr
import plotly.graph_objects as go
from gradio_leaderboard import Leaderboard

from src.utils import(
    _safe_numeric,
    calculate_cumulative_average,
    create_dataframe,
    get_aggregated_columns,
    load_data,
    load_model_metadata,
    load_raw_model_data,
    build_year_column_mapping,
)
from content import LLMLAGBENCH_INTRO, LEADERBOARD_INTRO, MODEL_COMPARISON_INTRO, AUTHORS, CIT_BTN_TEXT, CIT_BTN_LABEL, EXEMPLARY_QUESTIONS_INTRO, EXEMPLARY_QUESTIONS_DATA


### CONFIGURATION

cfg = {
    "data_path": "data/leaderboard_graph_data.json",
    "metadata_path": "data/model_metadata.json",
    "years": ["2021", "2022", "2023", "2024", "2025"],
    "months": [f"{i:02d}" for i in range(1, 13)]
}


### CALLBACKS

# updated update_dash to create interactive plot
def update_dashboard(graph_years, graph_model_filter):
    """
    graph_years: list like ["2024","2025"] for graph from graph_year_selector
    graph_model_filter: list of models for the line plot or None
    """

    # Table always shows all years and all models
    table_years = cfg.get("years")
    table_model_filter = None

    # Default: show all years if none selected for graph
    if not graph_years:
        graph_years = cfg.get("years")

    # keep some necessary metadata columns in the specified order
    metadata_cols = ["Model", "Overall Average", "1st Detected cutoff", "2nd Detected cutoff", "Provider cutoff", "Provider", "Release date", "Self-declared cutoff", "trend_changepoints", "Parameters", "Evaluation period"]
    cols = metadata_cols.copy()

    yearly_df = df.copy()
    monthly_df = df.copy()
    graph_df = df.copy()

    # TODO if >1 year - aggregate the values to be per year, not per month
    if len(table_years) > 1:
        lb_cols = ["Model", "Overall Average", "1st Detected cutoff", "2nd Detected cutoff", "Provider cutoff", "Provider", "Release date", "Self-declared cutoff", "Parameters", "Evaluation period"] + [y for y in cfg.get("aggregated_cols_year") if y in table_years]
        yearly_df = yearly_df[lb_cols]

    # Expand years into their YYYY_MM columns (for table)
    chosen_months = []
    for y in table_years:
        chosen_months.extend(year_to_columns.get(y, []))
    # Sort chronologically using the global aggregated_cols order
    # Only include months that actually exist in the dataframe
    chosen_months_with_years = table_years + [c for c in cfg.get("aggregated_cols_month") if c in chosen_months and c in monthly_df.columns]
    cols.extend(chosen_months_with_years)

    # Filter by models for table if requested
    if table_model_filter:
        yearly_df = yearly_df[yearly_df["Model"].isin(table_model_filter)]
        monthly_df = monthly_df[monthly_df["Model"].isin(table_model_filter)]

    # Sort by Overall Average in descending order
    yearly_df = yearly_df.sort_values(by="Overall Average", ascending=False)
    monthly_df = monthly_df.sort_values(by="Overall Average", ascending=False)

    # Reduce columns
    monthly_df = monthly_df[cols]

    # Filter by models for graph if requested (use separate dataframe)
    # Build graph columns based on graph_years
    graph_months = []
    for y in graph_years:
        graph_months.extend(year_to_columns.get(y, []))
    graph_months_with_years = graph_years + [c for c in cfg.get("aggregated_cols_month") if c in graph_months and c in graph_df.columns]
    graph_cols = metadata_cols + graph_months_with_years
    graph_df = graph_df[graph_cols]
    if graph_model_filter:
        graph_df = graph_df[graph_df["Model"].isin(graph_model_filter)]

    # Build tidy dataframe for gr.LinePlot with columns x, y, Model
    records = []
    # Exclude all metadata columns and yearly aggregates from x_labels - only keep monthly columns
    excluded_cols = {"Model", "Overall Average", "Parameters", "1st Detected cutoff", "2nd Detected cutoff", "Provider", "Provider cutoff", "Release date", "Self-declared cutoff", "trend_changepoints", "Evaluation period"}
    x_labels = [c for c in graph_cols if c not in excluded_cols and c not in graph_years]  # only months for the plot
    for _, row in graph_df.iterrows():
        for col in x_labels:
            y_val = _safe_numeric(row.get(col))
            records.append({"x": col, "y": y_val, "Model": row["Model"]})
    lineplot_df = pd.DataFrame(records)

    # Ensure chronological order using global sorted list - double sorting? TODO verify
    chronological_order = [c for c in cfg.get("aggregated_cols_month") if c in lineplot_df["x"].unique()]
    lineplot_df["x"] = pd.Categorical(lineplot_df["x"], categories=chronological_order, ordered=True)
    lineplot_df = lineplot_df.sort_values(by="x")

    # Build Plotly figure
    fig = go.Figure()

    for _, row in graph_df.iterrows():
        model = row["Model"]
        color = GLOBAL_MODEL_COLORS[model]
        model_data = lineplot_df[lineplot_df["Model"] == model]

        fig.add_trace(go.Scatter(
            x=model_data["x"],
            y=model_data["y"],
            mode="lines",
            name=model,
            line=dict(width=2, color=color),
            hovertemplate="Model: %{text}<br>x=%{x}<br>y=%{y}",
            text=[model] * len(model_data),
            showlegend=True,
            line_shape='spline'
        ))

        # Highlight changepoints (can be multiple)
        changepoints = row.get("trend_changepoints", [])
        if isinstance(changepoints, list):
            for idx, bp in enumerate(changepoints):
                if bp in model_data["x"].values:
                    cp_row = model_data[model_data["x"] == bp]
                    # Make first changepoint smaller if there are multiple
                    marker_size = 12 if idx == 0 else 6
                    fig.add_trace(go.Scatter(
                        x=cp_row["x"],
                        y=cp_row["y"],
                        mode="markers",
                        marker=dict(
                            size=marker_size,
                            color=color,
                            symbol="circle-open",
                            line=dict(width=3, color="white")
                        ),
                        hovertemplate=f"<b>Trend Changepoint</b><br>Model: {model}<br>x=%{{x}}<br>y=%{{y}}",
                        showlegend=False
                    ))

    # Style the figure & Lock axis order
    fig.update_layout(
        xaxis=dict(
            categoryorder="array",
            categoryarray=chronological_order,
            title="Year_Month",
            color="#e5e7eb",
            gridcolor="#374151",
            nticks=30  # Limit number of x-axis ticks displayed
        ),
        yaxis=dict(title="Average Faithfulness (0-2 scale)", color="#e5e7eb", gridcolor="#374151"),
        paper_bgcolor="#1f2937",
        plot_bgcolor="#1f2937",
        font=dict(family="IBM Plex Sans", size=12, color="#e5e7eb"),
        hoverlabel=dict(bgcolor="#374151", font=dict(color="#e5e7eb"), bordercolor="#4b5563"),
        margin=dict(l=40, r=20, t=60, b=40),
        # title=dict(text="Model Comparison with Trend Changepoints", x=0.5, font=dict(color="#e5e7eb")),
        showlegend=True,
        yaxis_range=[-0.1, 2.1],
        xaxis_tickangle=-45
    )

    if len(table_years) > 1:
        return yearly_df, fig
    else:
        return monthly_df, fig


def create_faithfulness_plot(model_name):
    """
    Create a Plotly figure showing faithfulness scores with segments and cumulative refusals.

    Args:
        model_name: Name of the model to plot

    Returns:
        Plotly Figure object or None if model not found
    """
    if not model_name:
        return go.Figure()

    # Load raw model data
    model_data = load_raw_model_data(cfg.get("data_path"), model_name)

    if not model_data:
        return go.Figure()

    # Extract data
    dates = model_data.get('dates', [])
    faithfulness = model_data.get('faithfulness', [])
    cumulative_refusals = model_data.get('cumulative_refusals', [])
    segments = model_data.get('segments', [])
    changepoint_dates = model_data.get('changepoint_dates', [])
    total_obs = model_data.get('total_observations', max(cumulative_refusals) if cumulative_refusals else 1)

    # Calculate cumulative average faithfulness
    cumulative_avg_faithfulness = calculate_cumulative_average(faithfulness) if faithfulness else []

    # Create figure with secondary y-axis
    fig = go.Figure()

    # Add faithfulness scatter points
    fig.add_trace(go.Scatter(
        x=dates,
        y=faithfulness,
        mode='markers',
        name='Faithfulness',
        marker=dict(size=4, color='steelblue', opacity=0.6),
        hovertemplate='Date: %{x}<br>Faithfulness: %{y}<extra></extra>',
        yaxis='y'
    ))

    # Add cumulative average faithfulness line (green curve)
    if cumulative_avg_faithfulness:
        fig.add_trace(go.Scatter(
            x=dates,
            y=cumulative_avg_faithfulness,
            mode='lines',
            name='Cumulative Average',
            line=dict(color='#22c55e', width=2.5),
            hovertemplate='Date: %{x}<br>Cumulative Avg: %{y:.3f}<extra></extra>',
            yaxis='y'
        ))

    # Add segment mean lines (horizontal lines for each segment)
    for seg in segments:
        fig.add_trace(go.Scatter(
            x=[seg['start_date'], seg['end_date']],
            y=[seg['mean_faithfulness'], seg['mean_faithfulness']],
            mode='lines',
            name=f"Segment Mean ({seg['mean_faithfulness']:.2f})",
            line=dict(color='red', width=2),
            hovertemplate=f"Mean: {seg['mean_faithfulness']:.2f}<br>Refusal Rate: {seg['refusal_rate_percent']:.1f}%<extra></extra>",
            yaxis='y',
            showlegend=False
        ))

    # Add changepoint vertical lines
    for cp_date in changepoint_dates:
        fig.add_vline(
            x=cp_date,
            line=dict(color='darkred', dash='dash', width=1.5),
            opacity=0.7
        )

    # Add cumulative refusals line (on secondary y-axis)
    fig.add_trace(go.Scatter(
        x=dates,
        y=cumulative_refusals,
        mode='lines',
        name='Cumulative Refusals',
        line=dict(color='darkorange', width=2),
        hovertemplate='Date: %{x}<br>Cumulative Refusals: %{y}<extra></extra>',
        yaxis='y2'
    ))

    # Add refusal rate annotations for each segment
    for seg in segments:
        # Calculate midpoint date for annotation
        start = datetime.strptime(seg['start_date'], '%Y-%m-%d')
        end = datetime.strptime(seg['end_date'], '%Y-%m-%d')
        mid_date = start + (end - start) / 2

        fig.add_annotation(
            x=mid_date.strftime('%Y-%m-%d'),
            y=1.85,
            text=f"{seg['refusal_rate_percent']:.1f}%",
            showarrow=False,
            font=dict(size=10, color='#fbbf24', family='IBM Plex Sans'),
            bgcolor='rgba(55, 65, 81, 0.9)',
            bordercolor='#fbbf24',
            borderwidth=1,
            yref='y'
        )

    # Update layout with dual y-axes
    fig.update_layout(
        title=dict(
            text=f"{model_name}: Faithfulness with PELT Changepoints",
            x=0.5,
            font=dict(color='#e5e7eb', size=14, family='IBM Plex Sans')
        ),
        xaxis=dict(
            title='Date',
            color='#e5e7eb',
            gridcolor='#374151',
            tickangle=-45
        ),
        yaxis=dict(
            title='Faithfulness Score',
            color='#e5e7eb',
            gridcolor='#374151',
            range=[-0.05, 2.05],
            side='left'
        ),
        yaxis2=dict(
            title='Cumulative Refusals',
            color='#fbbf24',
            gridcolor='#374151',
            range=[0, total_obs],
            overlaying='y',
            side='right'
        ),
        paper_bgcolor='#1f2937',
        plot_bgcolor='#1f2937',
        font=dict(family='IBM Plex Sans', size=12, color='#e5e7eb'),
        hoverlabel=dict(bgcolor='#374151', font=dict(color='#e5e7eb'), bordercolor='#4b5563'),
        margin=dict(l=60, r=60, t=80, b=80),
        showlegend=False,
        legend=dict(
            x=0.02,
            y=0.98,
            bgcolor='rgba(55, 65, 81, 0.9)',
            bordercolor='#4b5563',
            borderwidth=1
        )
    )

    return fig


def update_model_comparison(split_enabled, model1, model2):
    """
    Update model comparison plots based on split checkbox and model selections.

    Args:
        split_enabled: Boolean indicating if 2 graphs should be shown
        model1: First model name
        model2: Second model name (only used if split_enabled)

    Returns:
        Tuple of (plot1, plot2, visibility_dict)
    """
    if split_enabled:
        # Show 2 graphs side by side
        plot1 = create_faithfulness_plot(model1) if model1 else go.Figure()
        plot2 = create_faithfulness_plot(model2) if model2 else go.Figure()
        return plot1, plot2, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
    else:
        # Show only 1 graph
        plot1 = create_faithfulness_plot(model1) if model1 else go.Figure()
        return plot1, go.Figure(), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)


def initialize_model_comparison():
    """
    Initialize model comparison section with random model selections on page load.

    Returns:
        Tuple of (model1_value, model2_value, plot1, plot2, col_plot_1_visible, col_model_2_visible, col_plot_2_visible)
    """
    import random

    # Select random models for initial display
    if len(all_models) >= 2:
        random_models = random.sample(all_models, 2)
        model1 = random_models[0]
        model2 = random_models[1]
    elif len(all_models) == 1:
        model1 = all_models[0]
        model2 = None
    else:
        model1 = None
        model2 = None

    # Generate initial plot for model1 only (split_enabled=False by default)
    plot1 = create_faithfulness_plot(model1) if model1 else go.Figure()

    return (
        gr.update(value=model1),  # model_dropdown_1
        gr.update(value=model2),  # model_dropdown_2
        plot1,                     # comparison_plot_1
        go.Figure(),              # comparison_plot_2 (empty since split is disabled)
        gr.update(visible=True),  # col_plot_1
        gr.update(visible=False), # col_model_2
        gr.update(visible=False)  # col_plot_2
    )


def initialize_main_dashboard(graph_year_selector_value):
    """
    Initialize main dashboard with random model selections on page load.

    Args:
        graph_year_selector_value: Selected years from the graph year selector

    Returns:
        Tuple of (graph_model_filter_value, leaderboard, line_plot)
    """
    import random

    # Select random models for initial display (5 models for graph)
    num_models = min(5, len(all_models))
    if num_models > 0:
        random_graph_models = random.sample(all_models, num_models)
    else:
        random_graph_models = []

    # Generate dashboard with random models
    leaderboard, line_plot = update_dashboard(graph_year_selector_value, random_graph_models)

    return (
        gr.update(value=random_graph_models),  # graph_model_filter
        leaderboard,
        line_plot
    )


def initialize_all_components(graph_year_selector_value):
    """
    Initialize all components on page load: main dashboard and model comparison.
    Combining into a single load function to prevent double-rendering issues in HF Spaces.

    Args:
        graph_year_selector_value: Selected years from the graph year selector

    Returns:
        Tuple of all outputs for both dashboard and comparison sections
    """
    import random

    # Initialize main dashboard
    num_models = min(5, len(all_models))
    if num_models > 0:
        random_graph_models = random.sample(all_models, num_models)
    else:
        random_graph_models = []

    leaderboard, line_plot = update_dashboard(graph_year_selector_value, random_graph_models)

    # Initialize model comparison with 2 graphs side by side (split_enabled=True)
    if len(all_models) >= 2:
        random_models = random.sample(all_models, 2)
        model1 = random_models[0]
        model2 = random_models[1]
    elif len(all_models) == 1:
        model1 = all_models[0]
        model2 = None
    else:
        model1 = None
        model2 = None

    # Generate both plots for split view
    plot1 = create_faithfulness_plot(model1) if model1 else go.Figure()
    plot2 = create_faithfulness_plot(model2) if model2 else go.Figure()

    return (
        gr.update(value=random_graph_models),  # graph_model_filter
        leaderboard,
        line_plot,
        gr.update(value=model1),  # model_dropdown_1
        gr.update(value=model2),  # model_dropdown_2
        plot1,                     # comparison_plot_1
        plot2,                     # comparison_plot_2 (now showing model2)
        gr.update(visible=True),  # col_plot_1
        gr.update(visible=True),  # col_model_2 (now visible)
        gr.update(visible=True)   # col_plot_2 (now visible)
    )


### HELPER FUNCTIONS

def generate_distinct_colors(n):
    """
    Generate n distinct colors using HSL color space.

    Args:
        n: Number of distinct colors needed

    Returns:
        List of hex color strings
    """
    colors = []
    for i in range(n):
        hue = (i * 360 / n) % 360
        saturation = 70 + (i % 3) * 10  # Vary saturation slightly for more distinction
        lightness = 55 + (i % 2) * 10   # Vary lightness slightly

        # Convert HSL to RGB
        h = hue / 360
        s = saturation / 100
        l = lightness / 100

        if s == 0:
            r = g = b = l
        else:
            def hue_to_rgb(p, q, t):
                if t < 0: t += 1
                if t > 1: t -= 1
                if t < 1/6: return p + (q - p) * 6 * t
                if t < 1/2: return q
                if t < 2/3: return p + (q - p) * (2/3 - t) * 6
                return p

            q = l * (1 + s) if l < 0.5 else l + s - l * s
            p = 2 * l - q
            r = hue_to_rgb(p, q, h + 1/3)
            g = hue_to_rgb(p, q, h)
            b = hue_to_rgb(p, q, h - 1/3)

        # Convert to hex
        hex_color = f"#{int(r*255):02x}{int(g*255):02x}{int(b*255):02x}"
        colors.append(hex_color)

    return colors


### DATA PREP
# Load data
data = load_data(cfg.get("data_path"))

# Load model metadata
model_metadata = load_model_metadata(cfg.get("metadata_path"))

# Build year to columns mapping
year_to_columns = build_year_column_mapping(cfg.get("years"), cfg.get("months"))

# Create DataFrame (new format doesn't need models_map or metrics)
df = create_dataframe(cfg, data, model_metadata=model_metadata)

# Get aggregated column lists
aggregated_cols_year, aggregated_cols_month = get_aggregated_columns(
    cfg.get("years"), year_to_columns
)
cfg["aggregated_cols_year"] = aggregated_cols_year
cfg["aggregated_cols_month"] = aggregated_cols_month

# Generate consistent color mapping for all models (do this once globally)
all_models = sorted(df["Model"].unique().tolist())  # Sort for consistency
colors = generate_distinct_colors(len(all_models))
GLOBAL_MODEL_COLORS = {model: colors[i] for i, model in enumerate(all_models)}


### BUILD UI
theme = gr.themes.Base(
    primary_hue="green",
    secondary_hue="green",
    radius_size="lg",
    text_size="sm",
)

# Custom CSS for scrollable dropdown and table styling
custom_css = """
/* Limit the height of selected items in multiselect dropdown */
.scrollable-dropdown .wrap-inner {
    max-height: 100px !important;
    overflow-y: auto !important;
}
/* Alternative selector for the selected items container */
.scrollable-dropdown div[data-testid="block-label-inner"] ~ div {
    max-height: 100px !important;
    overflow-y: auto !important;
}
/* Style the leaderboard table background */
.gradio-container .gr-table-wrap,
.gradio-container .gr-dataframe,
.gradio-leaderboard {
    background-color: #fafafa !important;
}
.gradio-container table {
    background-color: #fafafa !important;
}
/* Header row - gray background */
.gradio-container table thead tr,
.gradio-container table thead th {
    background-color: #f3f4f6 !important;
}
/* First column (td:first-child) - gray background for all rows */
.gradio-container table tbody tr td:first-child {
    background-color: #f3f4f6 !important;
}
/* Odd rows - very light background (excluding first column) */
.gradio-container table tbody tr:nth-child(odd) td {
    background-color: #fafafa !important;
}
/* Even rows - white background (excluding first column) */
.gradio-container table tbody tr:nth-child(even) td {
    background-color: white !important;
}
/* Keep first column gray for both odd and even rows */
.gradio-container table tbody tr:nth-child(odd) td:first-child,
.gradio-container table tbody tr:nth-child(even) td:first-child {
    background-color: #f3f4f6 !important;
}
/* Hover effect for all rows */
.gradio-container table tbody tr:hover td {
    background-color: #f3f4f6 !important;
}
/* Keep first column darker gray on hover */
.gradio-container table tbody tr:hover td:first-child {
    background-color: #e5e7eb !important;
}
"""

# JavaScript to force light mode
js_func = """
function refresh() {
    const url = new URL(window.location);
    if (url.searchParams.get('__theme') !== 'light') {
        url.searchParams.set('__theme', 'light');
        window.location.href = url.href;
    }
}
"""

with gr.Blocks(theme=theme, css=custom_css, js=js_func) as demo:
    gr.Markdown(
        """
        <div style='text-align: center;'>
            <h1>📶 LLMLagBench - All LLMs lag behind</h1>
        </div>
        """
    )
    gr.Markdown(AUTHORS)
    gr.Markdown("<hr>")
    gr.Markdown(LLMLAGBENCH_INTRO)
    gr.Markdown("<hr>")

    with gr.Row():
        # Year selector for graph
        with gr.Column(scale=1):
            graph_year_selector = gr.CheckboxGroup(choices=cfg.get("years"), value=["2021", "2022", "2023", "2024", "2025"], label="Select Years for Graph")
        with gr.Column(scale=1):
            graph_model_filter = gr.Dropdown(
                choices=df["Model"].unique().tolist(),
                multiselect=True,
                filterable=True,
                value=None,  # Will be set randomly on page load
                label="Select Models for Graph",
                elem_classes="scrollable-dropdown"
            )

    gr.Markdown("## Model Comparison with Trend Changepoints")
    line_plot = gr.Plot(label="Model Trends")
    gr.Markdown('<hr>')
    gr.Markdown('<br>')

    gr.Markdown(LEADERBOARD_INTRO)

    leaderboard = Leaderboard(
        value=df,
        search_columns=["Model"],
        interactive=False,
    )

    # Wire events — graph inputs update the leaderboard + plot
    for comp in (graph_year_selector, graph_model_filter):
        comp.change(
            fn=update_dashboard,
            inputs=[graph_year_selector, graph_model_filter],
            outputs=[leaderboard, line_plot],
        )

    gr.Markdown('<hr>')
    gr.Markdown('<br>')

    # Model comparison section - wrap everything in a container to prevent duplication
    with gr.Column():
        gr.Markdown("## Model Comparison: Faithfulness to Ideal Answer with PELT Changepoints")
        gr.Markdown(MODEL_COMPARISON_INTRO)

        with gr.Row():
            split_checkbox = gr.Checkbox(
                label="Split into 2 segments",
                value=True,
                info="Enable to compare two models side by side"
            )

    with gr.Row():
        with gr.Column(scale=1):
            model_dropdown_1 = gr.Dropdown(
                choices=all_models,
                value=None,  # Will be set randomly on page load
                label="Select Model 1",
                filterable=True,
                elem_classes="scrollable-dropdown"
            )
        with gr.Column(scale=1, visible=False) as col_model_2:
            model_dropdown_2 = gr.Dropdown(
                choices=all_models,
                value=None,  # Will be set randomly on page load
                label="Select Model 2",
                filterable=True,
                elem_classes="scrollable-dropdown"
            )

    with gr.Row():
        with gr.Column(scale=1) as col_plot_1:
            comparison_plot_1 = gr.Plot(label="Model Faithfulness Analysis")
        with gr.Column(scale=1, visible=False) as col_plot_2:
            comparison_plot_2 = gr.Plot(label="Model Faithfulness Analysis")

    # Wire model comparison events
    split_checkbox.change(
        fn=update_model_comparison,
        inputs=[split_checkbox, model_dropdown_1, model_dropdown_2],
        outputs=[comparison_plot_1, comparison_plot_2, col_plot_1, col_model_2, col_plot_2]
    )

    model_dropdown_1.change(
        fn=update_model_comparison,
        inputs=[split_checkbox, model_dropdown_1, model_dropdown_2],
        outputs=[comparison_plot_1, comparison_plot_2, col_plot_1, col_model_2, col_plot_2]
    )

    model_dropdown_2.change(
        fn=update_model_comparison,
        inputs=[split_checkbox, model_dropdown_1, model_dropdown_2],
        outputs=[comparison_plot_1, comparison_plot_2, col_plot_1, col_model_2, col_plot_2]
    )

    gr.Markdown('<hr>')
    gr.Markdown('<br>')

    # Exemplary Questions section
    gr.Markdown(EXEMPLARY_QUESTIONS_INTRO)

    exemplary_questions_df = pd.DataFrame(
        EXEMPLARY_QUESTIONS_DATA,
        columns=["Date", "Question", "Gold Answer", "Possible decision"]
    )

    gr.Dataframe(
        value=exemplary_questions_df,
        interactive=False,
        wrap=True
    )

    # Citation
    gr.Markdown('<hr>')
    gr.Markdown('<br>')
    with gr.Row():
        with gr.Accordion("📙 Citation", open=False):
            citation_button = gr.Textbox(
                value=CIT_BTN_TEXT,
                label=CIT_BTN_LABEL,
                lines=20,
                elem_id="citation-button",
                show_copy_button=True,
            )

    # Initialize all components on load with a single load call (prevents double-rendering in HF Spaces)
    demo.load(
        fn=initialize_all_components,
        inputs=[graph_year_selector],
        outputs=[graph_model_filter, leaderboard, line_plot, model_dropdown_1, model_dropdown_2, comparison_plot_1, comparison_plot_2, col_plot_1, col_model_2, col_plot_2]
    )

if __name__ == "__main__":
    demo.launch()