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

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

def create_placeholder_plot(title="No Data or Plot Error", message="Data might be empty or an error occurred."):
    """Creates a placeholder Matplotlib plot indicating no data or an error."""
    try:
        fig, ax = plt.subplots(figsize=(8, 4)) 
        ax.text(0.5, 0.5, f"{title}\n{message}", ha='center', va='center', fontsize=10, wrap=True)
        ax.axis('off') 
        plt.tight_layout()
        return fig
    except Exception as e:
        logging.error(f"Error creating placeholder plot: {e}")
        # Fallback placeholder if the above fails
        fig_err, ax_err = plt.subplots()
        ax_err.text(0.5, 0.5, "Fatal: Plot generation error", ha='center', va='center')
        ax_err.axis('off')
        return fig_err
    # No plt.close(fig) here as Gradio handles the figure object.

def generate_posts_activity_plot(df, date_column='published_at'):
    """Generates a plot for posts activity over time."""
    logging.info(f"Generating posts activity plot. Date column: '{date_column}'. Input df rows: {len(df) if df is not None else 'None'}")
    if df is None or df.empty:
        logging.warning(f"Posts activity: DataFrame is empty.")
        return create_placeholder_plot(title="Posts Activity Over Time", message="No data available for the selected period.")
    if date_column not in df.columns:
        logging.warning(f"Posts activity: Date column '{date_column}' is missing. Cols: {df.columns.tolist()}.")
        return create_placeholder_plot(title="Posts Activity Over Time", message=f"Date column '{date_column}' not found.")

    try:
        df_copy = df.copy() 
        if not pd.api.types.is_datetime64_any_dtype(df_copy[date_column]):
            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:
            logging.info("Posts activity: DataFrame empty after NaNs dropped from date column.")
            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:
            logging.info("Posts activity: No posts after resampling by day.")
            return create_placeholder_plot(title="Posts Activity Over Time", message="No posts in the selected period.")

        fig, ax = plt.subplots(figsize=(10, 5))
        posts_over_time.plot(kind='line', ax=ax, marker='o', linestyle='-')
        ax.set_title('Posts Activity Over Time')
        ax.set_xlabel('Date')
        ax.set_ylabel('Number of Posts')
        ax.grid(True, linestyle='--', alpha=0.7)
        plt.xticks(rotation=45)
        plt.tight_layout()
        logging.info("Successfully generated posts activity plot.")
        return fig
    except Exception as e:
        logging.error(f"Error generating posts activity plot: {e}", exc_info=True)
        return create_placeholder_plot(title="Posts Activity Error", message=str(e))
    finally:
        plt.close('all') 

def generate_engagement_type_plot(df, likes_col='likeCount', comments_col='commentCount', shares_col='shareCount'): # Updated col names
    """Generates a bar plot for total engagement types (likes, comments, shares)."""
    logging.info(f"Generating engagement type plot. Input df rows: {len(df) if df is not None else 'None'}")
    
    required_cols = [likes_col, comments_col, shares_col]
    if df is None or df.empty:
        logging.warning("Engagement type: DataFrame is empty.")
        return create_placeholder_plot(title="Post Engagement Types", message="No data available for the selected period.")
    
    missing_cols = [col for col in required_cols if col not in df.columns]
    if missing_cols:
        msg = f"Engagement type: Columns missing: {missing_cols}. Available: {df.columns.tolist()}"
        logging.warning(msg)
        return create_placeholder_plot(title="Post Engagement Types", message=msg)

    try:
        df_copy = df.copy() 
        for col in required_cols: 
            df_copy[col] = pd.to_numeric(df_copy[col], errors='coerce').fillna(0)

        total_likes = df_copy[likes_col].sum()
        total_comments = df_copy[comments_col].sum()
        total_shares = df_copy[shares_col].sum()

        if total_likes == 0 and total_comments == 0 and total_shares == 0:
            logging.info("Engagement type: All engagement counts are zero.")
            return create_placeholder_plot(title="Post Engagement Types", message="No engagement data (likes, comments, shares) in the selected period.")

        engagement_data = {
            'Likes': total_likes,
            'Comments': total_comments,
            'Shares': total_shares
        }
        
        fig, ax = plt.subplots(figsize=(8, 5))
        bars = ax.bar(engagement_data.keys(), engagement_data.values(), color=['skyblue', 'lightgreen', 'salmon'])
        ax.set_title('Total Post Engagement Types')
        ax.set_xlabel('Engagement Type')
        ax.set_ylabel('Total Count')
        ax.grid(axis='y', linestyle='--', alpha=0.7)
        
        for bar in bars:
            yval = bar.get_height()
            ax.text(bar.get_x() + bar.get_width()/2.0, yval + (0.01 * max(engagement_data.values(), default=10)), str(int(yval)), ha='center', va='bottom')
            
        plt.tight_layout()
        logging.info("Successfully generated engagement type plot.")
        return fig
    except Exception as e:
        logging.error(f"Error generating engagement type plot: {e}", exc_info=True)
        return create_placeholder_plot(title="Engagement Type Error", message=str(e))
    finally:
        plt.close('all')

def generate_mentions_activity_plot(df, date_column='date'):
    """Generates a plot for mentions activity over time."""
    logging.info(f"Generating mentions activity plot. Date column: '{date_column}'. Input df rows: {len(df) if df is not None else 'None'}")
    if df is None or df.empty:
        logging.warning(f"Mentions activity: DataFrame is empty.")
        return create_placeholder_plot(title="Mentions Activity Over Time", message="No data available for the selected period.")
    if date_column not in df.columns:
        logging.warning(f"Mentions activity: Date column '{date_column}' is missing. Cols: {df.columns.tolist()}.")
        return create_placeholder_plot(title="Mentions Activity Over Time", message=f"Date column '{date_column}' not found.")
            
    try:
        df_copy = df.copy()
        if not pd.api.types.is_datetime64_any_dtype(df_copy[date_column]):
            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:
            logging.info("Mentions activity: DataFrame empty after NaNs dropped from date column.")
            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:
            logging.info("Mentions activity: No mentions after resampling by day.")
            return create_placeholder_plot(title="Mentions Activity Over Time", message="No mentions in the selected period.")

        fig, ax = plt.subplots(figsize=(10, 5))
        mentions_over_time.plot(kind='line', ax=ax, marker='o', linestyle='-', color='purple')
        ax.set_title('Mentions Activity Over Time')
        ax.set_xlabel('Date')
        ax.set_ylabel('Number of Mentions')
        ax.grid(True, linestyle='--', alpha=0.7)
        plt.xticks(rotation=45)
        plt.tight_layout()
        logging.info("Successfully generated mentions activity plot.")
        return fig
    except Exception as e:
        logging.error(f"Error generating mentions activity plot: {e}", exc_info=True)
        return create_placeholder_plot(title="Mentions Activity Error", message=str(e))
    finally:
        plt.close('all')

def generate_mention_sentiment_plot(df, sentiment_column='sentiment_label'): 
    """Generates a pie chart for mention sentiment distribution."""
    logging.info(f"Generating mention sentiment plot. Sentiment column: '{sentiment_column}'. Input df rows: {len(df) if df is not None else 'None'}")
    
    if df is None or df.empty:
        logging.warning("Mention sentiment: DataFrame is empty.")
        return create_placeholder_plot(title="Mention Sentiment Distribution", message="No data available for the selected period.")
    if sentiment_column not in df.columns:
        msg = f"Mention sentiment: Column '{sentiment_column}' is missing. Available: {df.columns.tolist()}"
        logging.warning(msg)
        return create_placeholder_plot(title="Mention Sentiment Distribution", message=msg)

    try:
        df_copy = df.copy()
        sentiment_counts = df_copy[sentiment_column].value_counts()
        if sentiment_counts.empty:
            logging.info("Mention sentiment: No sentiment data after value_counts.")
            return create_placeholder_plot(title="Mention Sentiment Distribution", message="No sentiment data available.")

        fig, ax = plt.subplots(figsize=(8, 5))
        colors_map = plt.cm.get_cmap('viridis', len(sentiment_counts))
        pie_colors = [colors_map(i) for i in range(len(sentiment_counts))]
        ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', startangle=90, colors=pie_colors)
        ax.set_title('Mention Sentiment Distribution')
        ax.axis('equal') 
        plt.tight_layout()
        logging.info("Successfully generated mention sentiment plot.")
        return fig
    except Exception as e:
        logging.error(f"Error generating mention sentiment plot: {e}", exc_info=True)
        return create_placeholder_plot(title="Mention Sentiment Error", message=str(e))
    finally:
        plt.close('all')

# --- Updated Follower Plot Functions ---

def generate_followers_count_over_time_plot(df, date_info_column='category_name', 
                                            organic_count_col='follower_count_organic', 
                                            paid_count_col='follower_count_paid',
                                            type_filter_column='follower_count_type', 
                                            type_value='follower_gains_monthly'):
    """
    Generates a plot for specific follower counts (organic and paid) over time.
    Date information is expected in 'date_info_column' as strings (e.g., "2024-08-01").
    """
    title = f"Followers Count Over Time ({type_value})"
    logging.info(f"Generating {title}. Date Info: '{date_info_column}', Organic: '{organic_count_col}', Paid: '{paid_count_col}', Type Filter: '{type_filter_column}=={type_value}'. DF rows: {len(df) if df is not None else 'None'}")

    if df is None or df.empty:
        return create_placeholder_plot(title=title, message="No follower data available.")
    
    required_cols = [date_info_column, organic_count_col, paid_count_col, type_filter_column]
    missing_cols = [col for col in required_cols if col not in df.columns]
    if missing_cols:
        return create_placeholder_plot(title=title, message=f"Missing columns: {missing_cols}. Available: {df.columns.tolist()}")

    try:
        df_copy = df.copy()
        df_filtered = df_copy[df_copy[type_filter_column] == type_value].copy() # Use .copy() to avoid SettingWithCopyWarning

        if df_filtered.empty:
            return create_placeholder_plot(title=title, message=f"No data for type '{type_value}'.")

        # Convert date_info_column to datetime
        df_filtered['datetime_obj'] = pd.to_datetime(df_filtered[date_info_column], errors='coerce')
        
        df_filtered[organic_count_col] = pd.to_numeric(df_filtered[organic_count_col], errors='coerce').fillna(0)
        df_filtered[paid_count_col] = pd.to_numeric(df_filtered[paid_count_col], errors='coerce').fillna(0)
        
        df_filtered = df_filtered.dropna(subset=['datetime_obj', organic_count_col, paid_count_col]).sort_values(by='datetime_obj')

        if df_filtered.empty:
            return create_placeholder_plot(title=title, message="No valid data after cleaning and filtering.")

        fig, ax = plt.subplots(figsize=(10, 5))
        ax.plot(df_filtered['datetime_obj'], df_filtered[organic_count_col], marker='o', linestyle='-', color='dodgerblue', label='Organic Followers')
        ax.plot(df_filtered['datetime_obj'], df_filtered[paid_count_col], marker='x', linestyle='--', color='seagreen', label='Paid Followers')
        
        ax.set_title(title)
        ax.set_xlabel('Date')
        ax.set_ylabel('Follower Count')
        ax.legend()
        ax.grid(True, linestyle='--', alpha=0.7)
        plt.xticks(rotation=45)
        plt.tight_layout()
        return fig
    except Exception as e:
        logging.error(f"Error generating {title}: {e}", exc_info=True)
        return create_placeholder_plot(title=f"{title} Error", message=str(e))
    finally:
        plt.close('all')

def generate_followers_growth_rate_plot(df, date_info_column='category_name', 
                                        organic_count_col='follower_count_organic', 
                                        paid_count_col='follower_count_paid',
                                        type_filter_column='follower_count_type', 
                                        type_value='follower_gains_monthly'):
    """
    Calculates and plots follower growth rate (organic and paid) over time.
    Date information is expected in 'date_info_column' as strings (e.g., "2024-08-01").
    """
    title = f"Follower Growth Rate ({type_value})"
    logging.info(f"Generating {title}. Date Info: '{date_info_column}', Organic: '{organic_count_col}', Paid: '{paid_count_col}', Type Filter: '{type_filter_column}=={type_value}'. DF rows: {len(df) if df is not None else 'None'}")

    if df is None or df.empty:
        return create_placeholder_plot(title=title, message="No follower data available.")

    required_cols = [date_info_column, organic_count_col, paid_count_col, type_filter_column]
    missing_cols = [col for col in required_cols if col not in df.columns]
    if missing_cols:
        return create_placeholder_plot(title=title, message=f"Missing columns: {missing_cols}. Available: {df.columns.tolist()}")
        
    try:
        df_copy = df.copy()
        df_filtered = df_copy[df_copy[type_filter_column] == 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[date_info_column], errors='coerce')
        df_filtered[organic_count_col] = pd.to_numeric(df_filtered[organic_count_col], errors='coerce')
        df_filtered[paid_count_col] = pd.to_numeric(df_filtered[paid_count_col], errors='coerce')
        
        df_filtered = df_filtered.dropna(subset=['datetime_obj']).sort_values(by='datetime_obj').set_index('datetime_obj')

        if df_filtered.empty or len(df_filtered) < 2: # Need at least 2 points for pct_change
            return create_placeholder_plot(title=title, message="Not enough data points to calculate growth rate.")

        df_filtered['organic_growth_rate'] = df_filtered[organic_count_col].pct_change() * 100
        df_filtered['paid_growth_rate'] = df_filtered[paid_count_col].pct_change() * 100
        
        # Replace inf with NaN then drop NaNs for growth rates
        df_filtered.replace([np.inf, -np.inf], np.nan, inplace=True)
        # df_filtered.dropna(subset=['organic_growth_rate', 'paid_growth_rate'], how='all', inplace=True) # Keep row if at least one rate is valid

        fig, ax = plt.subplots(figsize=(10, 5))
        
        plotted_organic = False
        if 'organic_growth_rate' in df_filtered.columns and 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')
            plotted_organic = True
            
        plotted_paid = False
        if 'paid_growth_rate' in df_filtered.columns and 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')
            plotted_paid = True

        if not plotted_organic and not plotted_paid:
            return create_placeholder_plot(title=title, message="No valid growth rate data to display after calculation.")

        ax.set_title(title)
        ax.set_xlabel('Date')
        ax.set_ylabel('Growth Rate (%)')
        ax.yaxis.set_major_formatter(mticker.PercentFormatter())
        ax.legend()
        ax.grid(True, linestyle='--', alpha=0.7)
        plt.xticks(rotation=45)
        plt.tight_layout()
        return fig
    except Exception as e:
        logging.error(f"Error generating {title}: {e}", exc_info=True)
        return create_placeholder_plot(title=f"{title} Error", message=str(e))
    finally:
        plt.close('all')

def generate_followers_by_demographics_plot(df, category_col='category_name', 
                                            organic_count_col='follower_count_organic', 
                                            paid_count_col='follower_count_paid',
                                            type_filter_column='follower_count_type', 
                                            type_value=None, plot_title="Followers by Demographics"):
    """
    Generates a grouped bar chart for follower demographics (organic and paid).
    'category_col' here is the demographic attribute (e.g., Location, Industry).
    """
    logging.info(f"Generating {plot_title}. Category: '{category_col}', Organic: '{organic_count_col}', Paid: '{paid_count_col}', Type Filter: '{type_filter_column}=={type_value}'. DF rows: {len(df) if df is not None else 'None'}")

    if df is None or df.empty:
        return create_placeholder_plot(title=plot_title, message="No follower data available.")
    
    required_cols = [category_col, organic_count_col, paid_count_col, type_filter_column]
    missing_cols = [col for col in required_cols if col not in df.columns]
    if missing_cols:
        return create_placeholder_plot(title=plot_title, message=f"Missing columns: {missing_cols}. Available: {df.columns.tolist()}")
    
    if type_value is None: 
        return create_placeholder_plot(title=plot_title, message="Demographic type (type_value) not specified.")

    try:
        df_copy = df.copy()
        df_filtered = df_copy[df_copy[type_filter_column] == type_value].copy()

        if df_filtered.empty:
            return create_placeholder_plot(title=plot_title, message=f"No data for demographic type '{type_value}'.")

        df_filtered[organic_count_col] = pd.to_numeric(df_filtered[organic_count_col], errors='coerce').fillna(0)
        df_filtered[paid_count_col] = pd.to_numeric(df_filtered[paid_count_col], errors='coerce').fillna(0)
        
        demographics_data = df_filtered.groupby(category_col)[[organic_count_col, paid_count_col]].sum()
        # Sort by total followers (organic + paid) for better visualization
        demographics_data['total_for_sort'] = demographics_data[organic_count_col] + demographics_data[paid_count_col]
        demographics_data = demographics_data.sort_values(by='total_for_sort', ascending=False).drop(columns=['total_for_sort'])


        if demographics_data.empty:
            return create_placeholder_plot(title=plot_title, message="No demographic data to display after filtering and aggregation.")
        
        top_n = 10 
        if len(demographics_data) > top_n:
            demographics_data = demographics_data.head(top_n)
            plot_title_updated = f"{plot_title} (Top {top_n})"
        else:
            plot_title_updated = plot_title

        fig, ax = plt.subplots(figsize=(12, 7) if len(demographics_data) > 5 else (10,6) )
        
        bar_width = 0.35
        index = np.arange(len(demographics_data.index))

        bars1 = ax.bar(index - bar_width/2, demographics_data[organic_count_col], bar_width, label='Organic', color='skyblue')
        bars2 = ax.bar(index + bar_width/2, demographics_data[paid_count_col], bar_width, label='Paid', color='lightcoral')

        ax.set_title(plot_title_updated)
        ax.set_xlabel(category_col.replace('_', ' ').title())
        ax.set_ylabel('Number of Followers')
        ax.set_xticks(index)
        ax.set_xticklabels(demographics_data.index, rotation=45, ha="right")
        ax.legend()
        ax.grid(axis='y', linestyle='--', alpha=0.7)
        
        # Add labels on top of bars
        for bar_group in [bars1, bars2]:
            for bar in bar_group:
                yval = bar.get_height()
                if yval > 0: # Only add label if value is not zero
                    ax.text(bar.get_x() + bar.get_width()/2.0, yval + (0.01 * ax.get_ylim()[1]), 
                            str(int(yval)), ha='center', va='bottom', fontsize=8)

        plt.tight_layout()
        return fig
    except Exception as e:
        logging.error(f"Error generating {plot_title}: {e}", exc_info=True)
        return create_placeholder_plot(title=f"{plot_title} Error", message=str(e))
    finally:
        plt.close('all')


def generate_engagement_rate_over_time_plot(df, date_column='published_at', engagement_rate_col='engagement'):
    """Generates a plot for engagement rate over time."""
    title = "Engagement Rate Over Time"
    logging.info(f"Generating {title}. Date: '{date_column}', Rate Col: '{engagement_rate_col}'. DF rows: {len(df) if df is not None else 'None'}")

    if df is None or df.empty:
        return create_placeholder_plot(title=title, message="No post data for engagement rate.")
    
    required_cols = [date_column, engagement_rate_col]
    missing_cols = [col for col in required_cols if col not in df.columns]
    if missing_cols:
        return create_placeholder_plot(title=title, message=f"Missing columns: {missing_cols}. Available: {df.columns.tolist()}")

    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]).set_index(date_column)

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

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

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

        fig, ax = plt.subplots(figsize=(10, 5))
        ax.plot(engagement_over_time.index, engagement_over_time.values, marker='.', linestyle='-', color='darkorange')
        ax.set_title(title)
        ax.set_xlabel('Date')
        ax.set_ylabel('Engagement Rate')
        # Adjust xmax for PercentFormatter based on whether rate is 0-1 or 0-100
        max_rate_val = engagement_over_time.max()
        formatter_xmax = 1.0 if max_rate_val <= 1.5 else 100.0 # Heuristic: if max is small, assume 0-1 scale
        if max_rate_val > 100 and formatter_xmax == 1.0: # If data is clearly > 100 but we assumed 0-1
             formatter_xmax = max_rate_val # Or some other sensible upper bound for formatting
        ax.yaxis.set_major_formatter(mticker.PercentFormatter(xmax=formatter_xmax)) 
        ax.grid(True, linestyle='--', alpha=0.7)
        plt.xticks(rotation=45)
        plt.tight_layout()
        return fig
    except Exception as e:
        logging.error(f"Error generating {title}: {e}", exc_info=True)
        return create_placeholder_plot(title=f"{title} Error", message=str(e))
    finally:
        plt.close('all')

def generate_reach_over_time_plot(df, date_column='published_at', reach_col='clickCount'): 
    """Generates a plot for reach (clicks) over time."""
    title = "Reach Over Time (Clicks)"
    logging.info(f"Generating {title}. Date: '{date_column}', Reach Col: '{reach_col}'. DF rows: {len(df) if df is not None else 'None'}")

    if df is None or df.empty:
        return create_placeholder_plot(title=title, message="No post data for reach.")

    required_cols = [date_column, reach_col]
    missing_cols = [col for col in required_cols if col not in df.columns]
    if missing_cols:
        return create_placeholder_plot(title=title, message=f"Missing columns: {missing_cols}. Available: {df.columns.tolist()}")

    try:
        df_copy = df.copy()
        df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce')
        df_copy[reach_col] = pd.to_numeric(df_copy[reach_col], errors='coerce')
        df_copy = df_copy.dropna(subset=[date_column, reach_col]).set_index(date_column)

        if df_copy.empty: # After dropping NaNs for essential columns
            return create_placeholder_plot(title=title, message="No valid data after cleaning for reach plot.")

        reach_over_time = df_copy.resample('D')[reach_col].sum()
        # No need to check if reach_over_time is empty if df_copy wasn't, sum of NaNs is 0.
        # Plot will show 0 if all sums are 0.

        fig, ax = plt.subplots(figsize=(10, 5))
        ax.plot(reach_over_time.index, reach_over_time.values, marker='.', linestyle='-', color='mediumseagreen')
        ax.set_title(title)
        ax.set_xlabel('Date')
        ax.set_ylabel('Total Clicks')
        ax.grid(True, linestyle='--', alpha=0.7)
        plt.xticks(rotation=45)
        plt.tight_layout()
        return fig
    except Exception as e:
        logging.error(f"Error generating {title}: {e}", exc_info=True)
        return create_placeholder_plot(title=f"{title} Error", message=str(e))
    finally:
        plt.close('all')

def generate_impressions_over_time_plot(df, date_column='published_at', impressions_col='impressionCount'):
    """Generates a plot for impressions over time."""
    title = "Impressions Over Time"
    logging.info(f"Generating {title}. Date: '{date_column}', Impressions Col: '{impressions_col}'. DF rows: {len(df) if df is not None else 'None'}")

    if df is None or df.empty:
        return create_placeholder_plot(title=title, message="No post data for impressions.")

    required_cols = [date_column, impressions_col]
    missing_cols = [col for col in required_cols if col not in df.columns]
    if missing_cols:
        return create_placeholder_plot(title=title, message=f"Missing columns: {missing_cols}. Available: {df.columns.tolist()}")

    try:
        df_copy = df.copy()
        df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce')
        df_copy[impressions_col] = pd.to_numeric(df_copy[impressions_col], errors='coerce')
        df_copy = df_copy.dropna(subset=[date_column, impressions_col]).set_index(date_column)

        if df_copy.empty: # After dropping NaNs for essential columns
             return create_placeholder_plot(title=title, message="No valid data after cleaning for impressions plot.")

        impressions_over_time = df_copy.resample('D')[impressions_col].sum()

        fig, ax = plt.subplots(figsize=(10, 5))
        ax.plot(impressions_over_time.index, impressions_over_time.values, marker='.', linestyle='-', color='slateblue')
        ax.set_title(title)
        ax.set_xlabel('Date')
        ax.set_ylabel('Total Impressions')
        ax.grid(True, linestyle='--', alpha=0.7)
        plt.xticks(rotation=45)
        plt.tight_layout()
        return fig
    except Exception as e:
        logging.error(f"Error generating {title}: {e}", exc_info=True)
        return create_placeholder_plot(title=f"{title} Error", message=str(e))
    finally:
        plt.close('all')


if __name__ == '__main__':
    # Create dummy data for testing
    posts_data = {
        'id': [f'post{i}' for i in range(1, 7)],
        'published_at': pd.to_datetime(['2023-01-01', '2023-01-01', '2023-01-02', '2023-01-03', '2023-01-03', '2023-01-03', '2023-01-04']),
        'likeCount': [10, 5, 12, 8, 15, 3, 20],
        'commentCount': [2, 1, 3, 1, 4, 0, 5],
        'shareCount': [1, 0, 1, 1, 2, 0, 1], # Assuming this is the correct column name from your data
        'clickCount': [20, 15, 30, 22, 40, 10, 50],
        'impressionCount': [200, 150, 300, 220, 400, 100, 500],
        'engagement': [0.05, 0.04, 0.06, 0.055, 0.07, 0.03, 0.08] 
    }
    sample_merged_posts_df = pd.DataFrame(posts_data)

    # Updated Follower Stats Data
    follower_data = {
        'follower_count_type': [
            'follower_gains_monthly', 'follower_gains_monthly', 'follower_gains_monthly', 
            'follower_geo', 'follower_geo', 'follower_geo',
            'follower_function', 'follower_function',
            'follower_industry', 'follower_industry',
            'follower_seniority', 'follower_seniority'
        ],
        # 'category_name' now holds dates for time-series, and actual categories for demographics
        'category_name': [
            '2024-01-01', '2024-02-01', '2024-03-01', # Dates for monthly gains
            'USA', 'Canada', 'UK',                     # Geo
            'Engineering', 'Sales',                   # Function/Role
            'Tech', 'Finance',                        # Industry
            'Senior', 'Junior'                        # Seniority
        ],
        'follower_count_organic': [
            100, 110, 125,      # Organic monthly gains
            500, 300, 150,      # Organic Geo counts
            400, 200,           # Organic Role counts
            250, 180,           # Organic Industry counts
            300, 220            # Organic Seniority counts
        ],
        'follower_count_paid': [
            20, 30, 25,         # Paid monthly gains
            50, 40, 60,         # Paid Geo counts
            30, 20,             # Paid Role counts
            45, 35,             # Paid Industry counts
            60, 40              # Paid Seniority counts
        ]
    }
    sample_follower_stats_df = pd.DataFrame(follower_data)

    logging.info("--- Testing Updated Follower Plot Generations ---")

    fig_followers_count = generate_followers_count_over_time_plot(
        sample_follower_stats_df.copy(), 
        type_value='follower_gains_monthly' # date_info_column defaults to 'category_name'
    )
    if fig_followers_count: logging.info("Followers Count Over Time (monthly, organic/paid) plot generated.")

    fig_followers_rate = generate_followers_growth_rate_plot(
        sample_follower_stats_df.copy(),
        type_value='follower_gains_monthly' # date_info_column defaults to 'category_name'
    )
    if fig_followers_rate: logging.info("Followers Growth Rate (monthly, organic/paid) plot generated.")

    fig_geo = generate_followers_by_demographics_plot(
        sample_follower_stats_df.copy(), 
        type_value='follower_geo', # category_col defaults to 'category_name'
        plot_title="Followers by Location (Organic/Paid)"
    )
    if fig_geo: logging.info("Followers by Location (grouped organic/paid) plot generated.")
    
    fig_role = generate_followers_by_demographics_plot(
        sample_follower_stats_df.copy(), 
        type_value='follower_function', 
        plot_title="Followers by Role (Organic/Paid)"
    )
    if fig_role: logging.info("Followers by Role (grouped organic/paid) plot generated.")

    fig_industry = generate_followers_by_demographics_plot(
        sample_follower_stats_df.copy(), 
        type_value='follower_industry', 
        plot_title="Followers by Industry (Organic/Paid)"
    )
    if fig_industry: logging.info("Followers by Industry (grouped organic/paid) plot generated.")

    fig_seniority = generate_followers_by_demographics_plot(
        sample_follower_stats_df.copy(), 
        type_value='follower_seniority', 
        plot_title="Followers by Seniority (Organic/Paid)"
    )
    if fig_seniority: logging.info("Followers by Seniority (grouped organic/paid) plot generated.")

    logging.info("--- Testing Other Plot Generations (No Changes to these) ---")
    fig_posts_activity = generate_posts_activity_plot(sample_merged_posts_df.copy())
    if fig_posts_activity: logging.info("Posts activity plot generated.")
    
    fig_engagement_type = generate_engagement_type_plot(sample_merged_posts_df.copy())
    if fig_engagement_type: logging.info("Engagement type plot generated.")

    # Dummy mentions for testing
    mentions_data = {
        'date': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-02', '2023-01-03']),
        'sentiment_label': ['Positive', 'Negative', 'Positive', 'Neutral']
    }
    sample_mentions_df = pd.DataFrame(mentions_data)
    fig_mentions_activity = generate_mentions_activity_plot(sample_mentions_df.copy())
    if fig_mentions_activity: logging.info("Mentions activity plot generated.")
    fig_mention_sentiment = generate_mention_sentiment_plot(sample_mentions_df.copy())
    if fig_mention_sentiment: logging.info("Mention sentiment plot generated.")

    fig_eng_rate = generate_engagement_rate_over_time_plot(sample_merged_posts_df.copy())
    if fig_eng_rate: logging.info("Engagement Rate Over Time plot generated.")

    fig_reach = generate_reach_over_time_plot(sample_merged_posts_df.copy())
    if fig_reach: logging.info("Reach Over Time (Clicks) plot generated.")

    fig_impressions = generate_impressions_over_time_plot(sample_merged_posts_df.copy())
    if fig_impressions: logging.info("Impressions Over Time plot generated.")

    logging.info("Test script finished. Review plots if displayed locally or saved.")