ui/analytics_plot_generator.py

import pandas as pd
import matplotlib.pyplot as plt
import logging
from io import BytesIO
import base64
import numpy as np
import matplotlib.ticker as mticker
import matplotlib.patches as patches # Added for rounded corners
import ast # For safely evaluating string representations of lists
from data_processing.analytics_data_processing import (
    generate_chatbot_data_summaries,
    prepare_filtered_analytics_data
)

# Configure logging for this module
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(module)s - %(message)s')

def _apply_theme_aware_styling(fig, ax):
    """
    Helper to apply theme-aware styling to a Matplotlib plot.
    It reads colors from rcParams, which Gradio should set based on the current theme.
    This makes text, backgrounds, and grids adapt to light/dark mode.
    """
    # Get theme-aware colors from Matplotlib's runtime configuration
    THEME_TEXT_COLOR = plt.rcParams.get('text.color', 'black')
    THEME_GRID_COLOR = plt.rcParams.get('grid.color', 'lightgray')
    THEME_AXES_FACE_COLOR = plt.rcParams.get('axes.facecolor', 'whitesmoke')
    THEME_AXES_EDGE_COLOR = plt.rcParams.get('axes.edgecolor', 'lightgray')

    # Make the original figure and axes backgrounds transparent to draw our own.
    fig.patch.set_alpha(0.0)
    ax.patch.set_alpha(0.0)

    # Turn off original spines to draw a new rounded background shape.
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['left'].set_visible(False)

    # Add a new rounded background for the axes area using theme colors.
    rounded_rect_bg = patches.FancyBboxPatch(
        (0, 0), 1, 1,
        boxstyle="round,pad=0,rounding_size=0.015",
        transform=ax.transAxes,
        facecolor=THEME_AXES_FACE_COLOR,
        edgecolor=THEME_AXES_EDGE_COLOR,
        linewidth=0.5,
        zorder=-1
    )
    ax.add_patch(rounded_rect_bg)

    # Apply the theme's text color to all major text elements.
    ax.xaxis.label.set_color(THEME_TEXT_COLOR)
    ax.yaxis.label.set_color(THEME_TEXT_COLOR)
    ax.title.set_color(THEME_TEXT_COLOR)

    # Apply the theme's text color to the tick labels and tick marks.
    ax.tick_params(axis='x', colors=THEME_TEXT_COLOR)
    ax.tick_params(axis='y', colors=THEME_TEXT_COLOR)

    # Set grid color and ensure it's drawn behind data
    ax.grid(True, linestyle='--', alpha=0.6, zorder=0, color=THEME_GRID_COLOR)


def create_placeholder_plot(title="No Data or Plot Error", message="Data might be empty or an error occurred."):
    """Creates a theme-aware placeholder Matplotlib plot."""
    try:
        fig, ax = plt.subplots(figsize=(8, 4))
        _apply_theme_aware_styling(fig, ax)

        # Use the theme's text color for the message
        THEME_TEXT_COLOR = plt.rcParams.get('text.color', 'black')
        ax.text(0.5, 0.5, f"{title}\n{message}", ha='center', va='center', fontsize=10, wrap=True, zorder=1, color=THEME_TEXT_COLOR)
        ax.axis('off')
        fig.subplots_adjust(top=0.90, bottom=0.10, left=0.10, right=0.90)
        return fig
    except Exception as e:
        logging.error(f"Error creating placeholder plot: {e}")
        fig_err, ax_err = plt.subplots(figsize=(8,4))
        fig_err.patch.set_alpha(0.0)
        ax_err.patch.set_alpha(0.0)
        ax_err.text(0.5, 0.5, "Fatal: Plot generation error", ha='center', va='center', zorder=1, color='red')
        ax_err.axis('off')
        return fig_err

def generate_posts_activity_plot(df, date_column='published_at'):
    """Generates a theme-aware plot for posts activity over time."""
    if df is None or df.empty or date_column not in df.columns:
        return create_placeholder_plot(title="Posts Activity Over Time", message="No data available.")

    fig = None
    try:
        df_copy = df.copy()
        df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce')
        df_copy = df_copy.dropna(subset=[date_column])
        if df_copy.empty:
            return create_placeholder_plot(title="Posts Activity Over Time", message="No valid date entries found.")

        posts_over_time = df_copy.set_index(date_column).resample('D').size()
        if posts_over_time.empty:
            return create_placeholder_plot(title="Posts Activity Over Time", message="No posts in the selected period.")

        fig, ax = plt.subplots(figsize=(10, 5))
        _apply_theme_aware_styling(fig, ax)

        posts_over_time.plot(kind='line', ax=ax, marker='o', linestyle='-', zorder=1)
        ax.set_xlabel('Date')
        ax.set_ylabel('Number of Posts')
        plt.xticks(rotation=45)
        fig.tight_layout(pad=0.5)
        fig.subplots_adjust(top=0.92, bottom=0.20, left=0.1, right=0.95)
        return fig
    except Exception as e:
        logging.error(f"Error generating posts activity plot: {e}", exc_info=True)
        if fig: plt.close(fig)
        return create_placeholder_plot(title="Posts Activity Error", message=str(e))


def generate_mentions_activity_plot(df, date_column='date'):
    """Generates a theme-aware plot for mentions activity over time."""
    if df is None or df.empty or date_column not in df.columns:
        return create_placeholder_plot(title="Mentions Activity Over Time", message="No data available.")

    fig = None
    try:
        df_copy = df.copy()
        df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce')
        df_copy = df_copy.dropna(subset=[date_column])
        if df_copy.empty:
            return create_placeholder_plot(title="Mentions Activity Over Time", message="No valid date entries found.")

        mentions_over_time = df_copy.set_index(date_column).resample('D').size()
        if mentions_over_time.empty:
            return create_placeholder_plot(title="Mentions Activity Over Time", message="No mentions in the selected period.")

        fig, ax = plt.subplots(figsize=(10, 5))
        _apply_theme_aware_styling(fig, ax)
        
        mentions_over_time.plot(kind='line', ax=ax, marker='o', linestyle='-', color='purple', zorder=1)
        ax.set_xlabel('Date')
        ax.set_ylabel('Number of Mentions')
        plt.xticks(rotation=45)
        fig.tight_layout(pad=0.5)
        fig.subplots_adjust(top=0.92, bottom=0.20, left=0.1, right=0.95)
        return fig
    except Exception as e:
        logging.error(f"Error generating mentions activity plot: {e}", exc_info=True)
        if fig: plt.close(fig)
        return create_placeholder_plot(title="Mentions Activity Error", message=str(e))

def generate_mention_sentiment_plot(df, sentiment_column='sentiment_label'):
    """Generates a theme-aware pie chart for mention sentiment distribution."""
    if df is None or df.empty or sentiment_column not in df.columns:
        return create_placeholder_plot(title="Mention Sentiment Distribution", message="No data available.")

    fig = None
    try:
        sentiment_counts = df[sentiment_column].value_counts()
        if sentiment_counts.empty:
            return create_placeholder_plot(title="Mention Sentiment Distribution", message="No sentiment data available.")

        fig, ax = plt.subplots(figsize=(8, 5))
        _apply_theme_aware_styling(fig, ax)
        
        THEME_TEXT_COLOR = plt.rcParams.get('text.color', 'black')
        pie_slice_colors = plt.cm.get_cmap('Pastel2', len(sentiment_counts))
        
        wedges, texts, autotexts = ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', startangle=90,
                                          colors=[pie_slice_colors(i) for i in range(len(sentiment_counts))])
        
        # Set text colors to be theme-aware
        for text_item in texts + autotexts:
            text_item.set_color(THEME_TEXT_COLOR)
            text_item.set_zorder(2)
        for wedge in wedges:
            wedge.set_zorder(1)

        ax.axis('equal')
        fig.subplots_adjust(top=0.95, bottom=0.05, left=0.05, right=0.95)
        return fig
    except Exception as e:
        logging.error(f"Error generating mention sentiment plot: {e}", exc_info=True)
        if fig: plt.close(fig)
        return create_placeholder_plot(title="Mention Sentiment Error", message=str(e))

def generate_followers_count_over_time_plot(df, **kwargs):
    """Generates a theme-aware plot for followers count over time."""
    type_value = kwargs.get('type_value', 'follower_gains_monthly')
    title = f"Followers Count Over Time ({type_value})"
    if df is None or df.empty:
        return create_placeholder_plot(title=title, message="No follower data available.")

    fig = None
    try:
        df_filtered = df[df['follower_count_type'] == type_value].copy()
        if df_filtered.empty:
            return create_placeholder_plot(title=title, message=f"No data for type '{type_value}'.")
        
        df_filtered['datetime_obj'] = pd.to_datetime(df_filtered['category_name'], errors='coerce')
        df_filtered['follower_count_organic'] = pd.to_numeric(df_filtered['follower_count_organic'], errors='coerce').fillna(0)
        df_filtered['follower_count_paid'] = pd.to_numeric(df_filtered['follower_count_paid'], errors='coerce').fillna(0)
        df_filtered = df_filtered.dropna(subset=['datetime_obj']).sort_values(by='datetime_obj')
        if df_filtered.empty:
            return create_placeholder_plot(title=title, message="No valid data after cleaning.")

        fig, ax = plt.subplots(figsize=(10, 5))
        _apply_theme_aware_styling(fig, ax)
        
        ax.plot(df_filtered['datetime_obj'], df_filtered['follower_count_organic'], marker='o', linestyle='-', color='dodgerblue', label='Organic Followers', zorder=1)
        ax.plot(df_filtered['datetime_obj'], df_filtered['follower_count_paid'], marker='x', linestyle='--', color='seagreen', label='Paid Followers', zorder=1)
        ax.set_xlabel('Date')
        ax.set_ylabel('Follower Count')
        
        legend = ax.legend()
        if legend:
            for text in legend.get_texts():
                text.set_color(plt.rcParams.get('text.color', 'black'))
            legend.set_zorder(2)

        plt.xticks(rotation=45)
        fig.tight_layout(pad=0.5)
        fig.subplots_adjust(top=0.92, bottom=0.20, left=0.1, right=0.95)
        return fig
    except Exception as e:
        logging.error(f"Error generating {title}: {e}", exc_info=True)
        if fig: plt.close(fig)
        return create_placeholder_plot(title=f"{title} Error", message=str(e))

def generate_followers_growth_rate_plot(df, **kwargs):
    """Generates a theme-aware plot for follower growth rate."""
    type_value = kwargs.get('type_value', 'follower_gains_monthly')
    title = f"Follower Growth Rate ({type_value})"
    if df is None or df.empty:
        return create_placeholder_plot(title=title, message="No follower data available.")

    fig = None
    try:
        df_filtered = df[df['follower_count_type'] == type_value].copy()
        if df_filtered.empty:
            return create_placeholder_plot(title=title, message=f"No data for type '{type_value}'.")
        df_filtered['datetime_obj'] = pd.to_datetime(df_filtered['category_name'], errors='coerce')
        df_filtered['follower_count_organic'] = pd.to_numeric(df_filtered['follower_count_organic'], errors='coerce')
        df_filtered['follower_count_paid'] = pd.to_numeric(df_filtered['follower_count_paid'], errors='coerce')
        df_filtered = df_filtered.dropna(subset=['datetime_obj']).sort_values(by='datetime_obj').set_index('datetime_obj')

        if len(df_filtered) < 2:
            return create_placeholder_plot(title=title, message="Not enough data points to calculate growth rate.")
            
        df_filtered['organic_growth_rate'] = df_filtered['follower_count_organic'].pct_change() * 100
        df_filtered['paid_growth_rate'] = df_filtered['follower_count_paid'].pct_change() * 100
        df_filtered.replace([np.inf, -np.inf], np.nan, inplace=True)

        fig, ax = plt.subplots(figsize=(10, 5))
        _apply_theme_aware_styling(fig, ax)
        
        plotted = False
        if not df_filtered['organic_growth_rate'].dropna().empty:
            ax.plot(df_filtered.index, df_filtered['organic_growth_rate'], marker='o', linestyle='-', color='lightcoral', label='Organic Growth Rate', zorder=1)
            plotted = True
        if not df_filtered['paid_growth_rate'].dropna().empty:
            ax.plot(df_filtered.index, df_filtered['paid_growth_rate'], marker='x', linestyle='--', color='mediumpurple', label='Paid Growth Rate', zorder=1)
            plotted = True
        
        if not plotted:
            return create_placeholder_plot(title=title, message="No growth rate data to display.")

        ax.set_xlabel('Date')
        ax.set_ylabel('Growth Rate (%)')
        ax.yaxis.set_major_formatter(mticker.PercentFormatter())
        
        legend = ax.legend()
        if legend:
            for text in legend.get_texts():
                text.set_color(plt.rcParams.get('text.color', 'black'))
            legend.set_zorder(2)

        plt.xticks(rotation=45)
        fig.tight_layout(pad=0.5)
        fig.subplots_adjust(top=0.92, bottom=0.20, left=0.1, right=0.95)
        return fig
    except Exception as e:
        logging.error(f"Error generating {title}: {e}", exc_info=True)
        if fig: plt.close(fig)
        return create_placeholder_plot(title=f"{title} Error", message=str(e))

def generate_followers_by_demographics_plot(df, **kwargs):
    """Generates a theme-aware bar plot for followers by demographics."""
    plot_title = kwargs.get('plot_title', "Followers by Demographics")
    type_value = kwargs.get('type_value')
    category_col = 'category_name'

    if df is None or df.empty or not type_value:
        return create_placeholder_plot(title=plot_title, message="No data or demographic type not specified.")

    fig = None
    try:
        df_filtered = df[df['follower_count_type'] == type_value].copy()
        if df_filtered.empty:
            return create_placeholder_plot(title=plot_title, message=f"No data for type '{type_value}'.")
        
        df_filtered['follower_count_organic'] = pd.to_numeric(df_filtered['follower_count_organic'], errors='coerce').fillna(0)
        df_filtered['follower_count_paid'] = pd.to_numeric(df_filtered['follower_count_paid'], errors='coerce').fillna(0)
        demographics_data = df_filtered.groupby(category_col)[['follower_count_organic', 'follower_count_paid']].sum()
        demographics_data['total_for_sort'] = demographics_data.sum(axis=1)
        demographics_data = demographics_data.sort_values(by='total_for_sort', ascending=False).head(10).drop(columns=['total_for_sort'])

        if demographics_data.empty:
            return create_placeholder_plot(title=plot_title, message="No demographic data to display.")
            
        fig, ax = plt.subplots(figsize=(12, 7))
        _apply_theme_aware_styling(fig, ax)
        
        demographics_data.plot(kind='bar', ax=ax, zorder=1, width=0.8, color=['dodgerblue', 'seagreen'])
        ax.set_xlabel(category_col.replace('_', ' ').title())
        ax.set_ylabel('Number of Followers')
        
        legend = ax.legend(['Organic', 'Paid'])
        if legend:
            for text in legend.get_texts():
                text.set_color(plt.rcParams.get('text.color', 'black'))
            legend.set_zorder(2)
            
        plt.xticks(rotation=45, ha="right")
        fig.tight_layout(pad=0.5)
        fig.subplots_adjust(top=0.92, bottom=0.25, left=0.1, right=0.95)
        return fig
    except Exception as e:
        logging.error(f"Error generating {plot_title}: {e}", exc_info=True)
        if fig: plt.close(fig)
        return create_placeholder_plot(title=f"{plot_title} Error", message=str(e))

def generate_generic_time_series_plot(df, date_column, value_column, title, ylabel, color='blue'):
    """Generic function to create a theme-aware time series plot."""
    if df is None or df.empty or date_column not in df.columns or value_column not in df.columns:
        return create_placeholder_plot(title=title, message="No data available.")
    
    fig = None
    try:
        df_copy = df.copy()
        df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce')
        df_copy[value_column] = pd.to_numeric(df_copy[value_column], errors='coerce')
        df_copy = df_copy.dropna(subset=[date_column, value_column]).set_index(date_column)
        if df_copy.empty:
            return create_placeholder_plot(title=title, message="No valid data.")

        data_over_time = df_copy.resample('D')[value_column].sum()
        if data_over_time.empty:
            return create_placeholder_plot(title=title, message="No data in the selected period.")

        fig, ax = plt.subplots(figsize=(10, 5))
        _apply_theme_aware_styling(fig, ax)

        ax.plot(data_over_time.index, data_over_time.values, marker='.', linestyle='-', color=color, zorder=1)
        ax.set_title(title)
        ax.set_xlabel('Date')
        ax.set_ylabel(ylabel)
        plt.xticks(rotation=45)
        fig.tight_layout(pad=0.5)
        fig.subplots_adjust(top=0.92, bottom=0.20, left=0.1, right=0.95)
        return fig
    except Exception as e:
        logging.error(f"Error generating {title}: {e}", exc_info=True)
        if fig: plt.close(fig)
        return create_placeholder_plot(title=f"{title} Error", message=str(e))

def generate_engagement_rate_over_time_plot(df, date_column='published_at', engagement_rate_col='engagement'):
    """Generates a theme-aware plot for engagement rate with special y-axis formatting."""
    title = "Engagement Rate Over Time"
    if df is None or df.empty or date_column not in df.columns or engagement_rate_col not in df.columns:
        return create_placeholder_plot(title=title, message="No data available.")
    fig = None
    try:
        df_copy = df.copy()
        df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce')
        df_copy[engagement_rate_col] = pd.to_numeric(df_copy[engagement_rate_col], errors='coerce')
        df_copy = df_copy.dropna(subset=[date_column, engagement_rate_col])

        if df_copy.empty:
            return create_placeholder_plot(title=title, message="No valid data.")

        engagement_over_time = df_copy.set_index(date_column).resample('D')[engagement_rate_col].mean().dropna()

        if engagement_over_time.empty:
            return create_placeholder_plot(title=title, message="No data to display.")

        fig, ax = plt.subplots(figsize=(10,5))
        _apply_theme_aware_styling(fig,ax)
        
        ax.plot(engagement_over_time.index, engagement_over_time.values, marker='.', linestyle='-', color='darkorange', zorder=1)
        
        # Determine the correct formatter based on the data's scale
        max_rate = engagement_over_time.max()
        formatter_xmax = 1.0 if max_rate <= 1.5 else 100.0
        ax.yaxis.set_major_formatter(mticker.PercentFormatter(xmax=formatter_xmax))
        
        ax.set_title(title)
        ax.set_xlabel('Date')
        ax.set_ylabel('Engagement Rate')
        plt.xticks(rotation=45)
        fig.tight_layout(pad=0.5)
        return fig
    except Exception as e:
        logging.error(f"Error generating {title}: {e}", exc_info=True)
        if fig: plt.close(fig)
        return create_placeholder_plot(title=f"{title} Error", message=str(e))

def generate_reach_over_time_plot(df, **kwargs):
    return generate_generic_time_series_plot(df, 'published_at', 'clickCount', 'Reach Over Time (Clicks)', 'Total Clicks', color='mediumseagreen')

def generate_impressions_over_time_plot(df, **kwargs):
    return generate_generic_time_series_plot(df, 'published_at', 'impressionCount', 'Impressions Over Time', 'Total Impressions', color='slateblue')

def generate_likes_over_time_plot(df, **kwargs):
    return generate_generic_time_series_plot(df, 'published_at', 'likeCount', 'Reactions (Likes) Over Time', 'Total Likes', color='crimson')

def generate_clicks_over_time_plot(df, **kwargs):
    return generate_generic_time_series_plot(df, 'published_at', 'clickCount', 'Clicks Over Time', 'Total Clicks', color='mediumseagreen')

def generate_shares_over_time_plot(df, **kwargs):
    return generate_generic_time_series_plot(df, 'published_at', 'shareCount', 'Shares Over Time', 'Total Shares', color='teal')

def generate_comments_over_time_plot(df, **kwargs):
    return generate_generic_time_series_plot(df, 'published_at', 'commentCount', 'Comments Over Time', 'Total Comments', color='gold')

def generate_comments_sentiment_breakdown_plot(df, sentiment_column='comment_sentiment', **kwargs):
    """Generates a theme-aware pie chart for comment sentiment."""
    title = "Breakdown of Comments by Sentiment"
    if df is None or df.empty or sentiment_column not in df.columns:
        return create_placeholder_plot(title=title, message="No data available.")
    
    fig = None
    try:
        sentiment_counts = df[sentiment_column].value_counts().dropna()
        if sentiment_counts.empty:
            return create_placeholder_plot(title=title, message="No sentiment data available.")

        fig, ax = plt.subplots(figsize=(8, 5))
        _apply_theme_aware_styling(fig, ax)
        
        THEME_TEXT_COLOR = plt.rcParams.get('text.color', 'black')
        pie_slice_colors = plt.cm.get_cmap('coolwarm', len(sentiment_counts))
        wedges, texts, autotexts = ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', startangle=90, colors=[pie_slice_colors(i) for i in range(len(sentiment_counts))])
        
        for text_item in texts + autotexts:
            text_item.set_color(THEME_TEXT_COLOR)
        
        ax.set_title(title)
        ax.axis('equal')
        fig.subplots_adjust(top=0.95, bottom=0.05, left=0.05, right=0.95)
        return fig
    except Exception as e:
        logging.error(f"Error generating {title}: {e}", exc_info=True)
        if fig: plt.close(fig)
        return create_placeholder_plot(title=f"{title} Error", message=str(e))

def generate_post_frequency_plot(df, date_column='published_at', **kwargs):
    """Generates a theme-aware plot for post frequency, using .size() for counting."""
    title = "Post Frequency Over Time"
    if df is None or df.empty or date_column not in df.columns:
        return create_placeholder_plot(title=title, message="No data available.")
    
    fig = None
    try:
        df_copy = df.copy()
        df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce')
        df_copy = df_copy.dropna(subset=[date_column]).set_index(date_column)
        if df_copy.empty:
            return create_placeholder_plot(title=title, message="No valid data.")

        data_over_time = df_copy.resample('D').size() # Use size() to count posts
        if data_over_time.empty:
            return create_placeholder_plot(title=title, message="No data in the selected period.")

        fig, ax = plt.subplots(figsize=(10, 5))
        _apply_theme_aware_styling(fig, ax)

        ax.plot(data_over_time.index, data_over_time.values, marker='.', linestyle='-', zorder=1)
        ax.set_title(title)
        ax.set_xlabel('Date')
        ax.set_ylabel('Number of Posts')
        plt.xticks(rotation=45)
        fig.tight_layout(pad=0.5)
        fig.subplots_adjust(top=0.92, bottom=0.20, left=0.1, right=0.95)
        return fig
    except Exception as e:
        logging.error(f"Error generating {title}: {e}", exc_info=True)
        if fig: plt.close(fig)
        return create_placeholder_plot(title=f"{title} Error", message=str(e))

def generate_content_format_breakdown_plot(df, format_col='media_type', **kwargs):
    """Generates a theme-aware bar chart for content format breakdown."""
    title = "Breakdown of Content by Format"
    if df is None or df.empty or format_col not in df.columns:
        return create_placeholder_plot(title=title, message="No data available.")
    
    fig = None
    try:
        format_counts = df[format_col].value_counts().dropna()
        if format_counts.empty:
            return create_placeholder_plot(title=title, message="No format data.")

        fig, ax = plt.subplots(figsize=(8,6))
        _apply_theme_aware_styling(fig,ax)
        
        format_counts.plot(kind='bar', ax=ax, zorder=1, color=plt.cm.get_cmap('viridis')(np.linspace(0, 1, len(format_counts))))
        ax.set_title(title)
        ax.set_xlabel('Media Type')
        ax.set_ylabel('Number of Posts')
        plt.xticks(rotation=45, ha="right")
        
        # Add text labels with theme color
        TEXT_COLOR = plt.rcParams.get('text.color', 'black')
        for i, v in enumerate(format_counts):
            ax.text(i, v + (0.01 * format_counts.max()), str(v), ha='center', va='bottom', zorder=2, color=TEXT_COLOR)
        
        fig.tight_layout(pad=0.5)
        fig.subplots_adjust(top=0.92, bottom=0.20, left=0.15, right=0.95)
        return fig
    except Exception as e:
        logging.error(f"Error generating {title}: {e}", exc_info=True)
        if fig: plt.close(fig)
        return create_placeholder_plot(title=f"{title} Error", message=str(e))

def _parse_eb_label(label_data):
    if isinstance(label_data, list): return label_data
    if isinstance(label_data, str):
        try:
            parsed = ast.literal_eval(label_data)
            return parsed if isinstance(parsed, list) else [str(parsed)]
        except (ValueError, SyntaxError):
            return [label_data.strip()] if label_data.strip() else []
    return [] if pd.isna(label_data) else [str(label_data)]

def generate_content_topic_breakdown_plot(df, topics_col='li_eb_labels', **kwargs):
    """Generates a theme-aware horizontal bar chart for content topics."""
    title = "Breakdown of Content by Topics (Top 15)"
    if df is None or df.empty or topics_col not in df.columns:
        return create_placeholder_plot(title=title, message="No data available.")
    
    fig = None
    try:
        topic_counts = df[topics_col].apply(_parse_eb_label).explode().dropna().value_counts()
        topic_counts = topic_counts[topic_counts.index != '']
        if topic_counts.empty:
            return create_placeholder_plot(title=title, message="No topic data found.")
        
        top_topics = topic_counts.nlargest(15).sort_values(ascending=True)

        fig, ax = plt.subplots(figsize=(10, 8))
        _apply_theme_aware_styling(fig,ax)
        
        top_topics.plot(kind='barh', ax=ax, zorder=1, color=plt.cm.get_cmap('YlGnBu')(np.linspace(0.3, 1, len(top_topics))))
        ax.set_title(title)
        ax.set_xlabel('Number of Posts')
        ax.set_ylabel('Topic')

        # Add text labels with theme color
        TEXT_COLOR = plt.rcParams.get('text.color', 'black')
        for i, (topic, count) in enumerate(top_topics.items()):
            ax.text(count + (0.01 * top_topics.max()), i, f' {count}', va='center', ha='left', zorder=2, color=TEXT_COLOR)
            
        fig.tight_layout(pad=0.5)
        fig.subplots_adjust(top=0.92, bottom=0.1, left=0.3, right=0.95)
        return fig
    except Exception as e:
        logging.error(f"Error generating {title}: {e}", exc_info=True)
        if fig: plt.close(fig)
        return create_placeholder_plot(title=f"{title} Error", message=str(e))

def update_analytics_plots_figures(token_state_value, date_filter_option, custom_start_date, custom_end_date, current_plot_configs):
    """
    Main function to generate all analytics plots based on provided data and configurations.
    Uses a dictionary-based approach for cleaner execution.
    """
    logging.info(f"Updating analytics plot figures for theme-aware plotting. Filter: {date_filter_option}")
    num_expected_plots = len(current_plot_configs)
    
    plot_data_summaries_for_chatbot = {}

    if not token_state_value or not token_state_value.get("token"):
        message = "❌ Accesso negato. Nessun token. Impossibile generare le analisi."
        logging.warning(message)
        placeholder_figs = [create_placeholder_plot(title="Accesso Negato") for _ in range(num_expected_plots)]
        summaries = {p_cfg["id"]: "Accesso negato, nessun dato per il chatbot." for p_cfg in current_plot_configs}
        return [message] + placeholder_figs + [summaries]

    try:
        (filtered_merged_posts_df, filtered_mentions_df, date_filtered_follower_stats_df,
         raw_follower_stats_df, start_dt_for_msg, end_dt_for_msg) = \
            prepare_filtered_analytics_data(token_state_value, date_filter_option, custom_start_date, custom_end_date)
        
        plot_data_summaries_for_chatbot = generate_chatbot_data_summaries(
            current_plot_configs, filtered_merged_posts_df, filtered_mentions_df,
            date_filtered_follower_stats_df, raw_follower_stats_df, token_state_value
        )
    except Exception as e:
        error_msg = f"❌ Errore durante la preparazione dei dati per le analisi: {e}"
        logging.error(error_msg, exc_info=True)
        placeholder_figs = [create_placeholder_plot(title="Errore Preparazione Dati", message=str(e)) for _ in range(num_expected_plots)]
        summaries = {p_cfg["id"]: f"Errore preparazione dati: {e}" for p_cfg in current_plot_configs}
        return [error_msg] + placeholder_figs + [summaries]

    # Map plot IDs to their respective generation functions
    plot_functions = {
        "followers_count": lambda: generate_followers_count_over_time_plot(date_filtered_follower_stats_df, type_value='follower_gains_monthly'),
        "followers_growth_rate": lambda: generate_followers_growth_rate_plot(date_filtered_follower_stats_df, type_value='follower_gains_monthly'),
        "followers_by_location": lambda: generate_followers_by_demographics_plot(raw_follower_stats_df, type_value='follower_geo', plot_title="Follower per Località"),
        "followers_by_role": lambda: generate_followers_by_demographics_plot(raw_follower_stats_df, type_value='follower_function', plot_title="Follower per Ruolo"),
        "followers_by_industry": lambda: generate_followers_by_demographics_plot(raw_follower_stats_df, type_value='follower_industry', plot_title="Follower per Settore"),
        "followers_by_seniority": lambda: generate_followers_by_demographics_plot(raw_follower_stats_df, type_value='follower_seniority', plot_title="Follower per Anzianità"),
        "engagement_rate": lambda: generate_engagement_rate_over_time_plot(filtered_merged_posts_df),
        "reach_over_time": lambda: generate_reach_over_time_plot(filtered_merged_posts_df),
        "impressions_over_time": lambda: generate_impressions_over_time_plot(filtered_merged_posts_df),
        "likes_over_time": lambda: generate_likes_over_time_plot(filtered_merged_posts_df),
        "clicks_over_time": lambda: generate_clicks_over_time_plot(filtered_merged_posts_df),
        "shares_over_time": lambda: generate_shares_over_time_plot(filtered_merged_posts_df),
        "comments_over_time": lambda: generate_comments_over_time_plot(filtered_merged_posts_df),
        "comments_sentiment": lambda: generate_comments_sentiment_breakdown_plot(filtered_merged_posts_df),
        "post_frequency_cs": lambda: generate_post_frequency_plot(filtered_merged_posts_df),
        "content_format_breakdown_cs": lambda: generate_content_format_breakdown_plot(filtered_merged_posts_df, format_col=token_state_value.get("config_media_type_col", "media_type")),
        "content_topic_breakdown_cs": lambda: generate_content_topic_breakdown_plot(filtered_merged_posts_df, topics_col=token_state_value.get("config_eb_labels_col", "li_eb_labels")),
        "mention_analysis_volume": lambda: generate_mentions_activity_plot(filtered_mentions_df, date_column=token_state_value.get("config_date_col_mentions", "date")),
        "mention_analysis_sentiment": lambda: generate_mention_sentiment_plot(filtered_mentions_df)
    }

    plot_figs = []
    for config in current_plot_configs:
        plot_id = config["id"]
        if plot_id in plot_functions:
            try:
                fig = plot_functions[plot_id]()
                plot_figs.append(fig)
            except Exception as e:
                logging.error(f"Failed to generate plot for '{plot_id}': {e}", exc_info=True)
                plot_figs.append(create_placeholder_plot(title=f"Error: {config.get('label', plot_id)}", message=str(e)))
        else:
            logging.warning(f"No plot function found for ID: '{plot_id}'")
            plot_figs.append(create_placeholder_plot(title=f"Plot Not Implemented: {config.get('label', plot_id)}"))

    message = f"📊 Analisi aggiornate per il periodo: {date_filter_option}"
    if date_filter_option == "Intervallo Personalizzato":
        s_display = start_dt_for_msg.strftime('%Y-%m-%d') if start_dt_for_msg else "N/A"
        e_display = end_dt_for_msg.strftime('%Y-%m-%d') if end_dt_for_msg else "N/A"
        message += f" (Da: {s_display} A: {e_display})"
        
    return [message] + plot_figs + [plot_data_summaries_for_chatbot]