analytics_plot_generators.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

# 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))
        # Define a broader range of colors or a colormap for more sentiment types
        colors_map = plt.cm.get_cmap('viridis', len(sentiment_counts))
        pie_colors = [colors_map(i) for i in range(len(sentiment_counts))]
        # Or keep your specific colors if sentiment labels are fixed:
        # colors = {'Positive': 'lightgreen', 'Negative': 'salmon', 'Neutral': 'lightskyblue', 'Mixed': 'gold'}
        # pie_colors = [colors.get(label, '#cccccc') for label in sentiment_counts.index]


        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')

# --- Existing Follower Growth Plot (can be reused or adapted) ---
def generate_total_follower_growth_plot(df, date_column='date', count_column='total_followers'): 
    """ Generates a plot for TOTAL follower growth over time. """
    # This is your existing function, ensure it's called with the correct data for overall growth.
    # For 'Follower Count Over Time (follower_gains_monthly)', we'll make a new specific one if structure differs.
    logging.info(f"Generating total follower growth plot. Date col: '{date_column}', Count col: '{count_column}'. DF rows: {len(df) if df is not None else 'None'}")
    if df is None or df.empty:
        return create_placeholder_plot(title="Total Follower Growth", message="No follower data.")
    if date_column not in df.columns or count_column not in df.columns:
        return create_placeholder_plot(title="Total Follower Growth", message=f"Missing columns: {date_column} or {count_column}.")
    try:
        df_copy = df.copy()
        df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce')
        df_copy[count_column] = pd.to_numeric(df_copy[count_column], errors='coerce')
        df_copy = df_copy.dropna(subset=[date_column, count_column]).sort_values(by=date_column)
        if df_copy.empty:
            return create_placeholder_plot(title="Total Follower Growth", message="No valid data after cleaning.")
        
        fig, ax = plt.subplots(figsize=(10,5))
        ax.plot(df_copy[date_column], df_copy[count_column], marker='o', linestyle='-', color='green')
        ax.set_title('Total Follower Growth Over Time')
        ax.set_xlabel('Date')
        ax.set_ylabel('Total Followers')
        ax.grid(True, linestyle='--', alpha=0.7)
        plt.xticks(rotation=45)
        plt.tight_layout()
        return fig
    except Exception as e:
        logging.error(f"Error in generate_total_follower_growth_plot: {e}", exc_info=True)
        return create_placeholder_plot(title="Total Follower Growth Error", message=str(e))
    finally:
        plt.close('all')

# --- New Plot Functions ---

def generate_followers_count_over_time_plot(df, date_column='date', count_column='follower_count_o', type_filter_column='follower_count_type', type_value='follower_gains_monthly'):
    """Generates a plot for specific follower counts over time (e.g., monthly gains)."""
    title = f"Followers Count Over Time ({type_value})"
    logging.info(f"Generating {title}. Date: '{date_column}', Count: '{count_column}', 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_column, count_column, 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}.")

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

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

        df_filtered[date_column] = pd.to_datetime(df_filtered[date_column], errors='coerce')
        df_filtered[count_column] = pd.to_numeric(df_filtered[count_column], errors='coerce')
        df_filtered = df_filtered.dropna(subset=[date_column, count_column]).sort_values(by=date_column)

        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[date_column], df_filtered[count_column], marker='o', linestyle='-', color='dodgerblue')
        ax.set_title(title)
        ax.set_xlabel('Date')
        ax.set_ylabel('Follower Count')
        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_column='date', count_column='follower_count_o', type_filter_column='follower_count_type', type_value='follower_gains_monthly'):
    """Calculates and plots follower growth rate over time."""
    title = f"Follower Growth Rate ({type_value})"
    logging.info(f"Generating {title}. Date: '{date_column}', Count: '{count_column}', 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_column, count_column, 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}.")
        
    try:
        df_copy = df.copy()
        df_filtered = df_copy[df_copy[type_filter_column] == type_value]

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

        df_filtered[date_column] = pd.to_datetime(df_filtered[date_column], errors='coerce')
        df_filtered[count_column] = pd.to_numeric(df_filtered[count_column], errors='coerce')
        df_filtered = df_filtered.dropna(subset=[date_column, count_column]).sort_values(by=date_column).set_index(date_column)

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

        # Calculate growth rate: (current - previous) / previous * 100
        # Ensure previous is not zero to avoid division by zero
        df_filtered['growth_rate'] = df_filtered[count_column].pct_change() * 100
        # Replace inf with NaN (e.g. if previous was 0 and current is non-zero) then drop NaNs
        df_filtered.replace([np.inf, -np.inf], np.nan, inplace=True)
        df_filtered.dropna(subset=['growth_rate'], inplace=True)


        if df_filtered.empty:
            return create_placeholder_plot(title=title, message="No valid growth rate data after calculation.")

        fig, ax = plt.subplots(figsize=(10, 5))
        ax.plot(df_filtered.index, df_filtered['growth_rate'], marker='o', linestyle='-', color='lightcoral')
        ax.set_title(title)
        ax.set_xlabel('Date')
        ax.set_ylabel('Growth Rate (%)')
        ax.yaxis.set_major_formatter(mticker.PercentFormatter())
        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', count_column='follower_count_o', type_filter_column='follower_count_type', type_value=None, plot_title="Followers by Demographics"):
    """Generates a bar chart for follower demographics (e.g., by location, industry)."""
    logging.info(f"Generating {plot_title}. Category: '{category_col}', Count: '{count_column}', 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, count_column, 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}.")
    
    if type_value is None: # Should be specified
        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]

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

        df_filtered[count_column] = pd.to_numeric(df_filtered[count_column], errors='coerce').fillna(0)
        
        # Group by the category column and sum the count column
        demographics_data = df_filtered.groupby(category_col)[count_column].sum().sort_values(ascending=False)

        if demographics_data.empty:
            return create_placeholder_plot(title=plot_title, message="No demographic data to display after filtering and aggregation.")
        
        # Limit to top N for readability if too many categories
        top_n = 10 
        if len(demographics_data) > top_n:
            demographics_data = demographics_data.head(top_n)
            plot_title += f" (Top {top_n})"


        fig, ax = plt.subplots(figsize=(10, 6) if len(demographics_data) > 5 else (8,5) )
        demographics_data.plot(kind='bar', ax=ax, color='teal')
        ax.set_title(plot_title)
        ax.set_xlabel(category_col.replace('_', ' ').title())
        ax.set_ylabel('Number of Followers')
        ax.grid(axis='y', linestyle='--', alpha=0.7)
        plt.xticks(rotation=45, ha="right")
        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')
        # Assuming 'engagement' is already a rate (e.g., 0.05 for 5%). If it's an absolute count, this logic needs change.
        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.")

        # Resample daily and calculate mean engagement rate
        engagement_over_time = df_copy.resample('D')[engagement_rate_col].mean()
        engagement_over_time = engagement_over_time.dropna() # Remove days with no data after resampling

        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')
        ax.yaxis.set_major_formatter(mticker.PercentFormatter(xmax=1.0 if engagement_over_time.max() <=1 else 100.0)) # Adjust based on rate scale
        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'): # Using clickCount as proxy for Reach
    """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:
            return create_placeholder_plot(title=title, message="No valid data after cleaning.")

        reach_over_time = df_copy.resample('D')[reach_col].sum()
        
        if reach_over_time.empty and not df_copy.empty : # if original had data but resampling resulted in empty (e.g. all NaNs for sum)
             pass # allow plot of zeros if that's the case
        elif reach_over_time.sum() == 0 and not df_copy.empty : # if all values are zero
             pass


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

        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 (merged with stats)
    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],
        '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] # Engagement Rate
    }
    sample_merged_posts_df = pd.DataFrame(posts_data)

    # Follower Stats Data
    follower_data = {
        'date': pd.to_datetime(['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15', '2023-03-01', # For time series
                                '2023-03-01', '2023-03-01', '2023-03-01', '2023-03-01', '2023-03-01', # For demographics (snapshot)
                                '2023-03-01', '2023-03-01', '2023-03-01', '2023-03-01', '2023-03-01',
                                '2023-03-01', '2023-03-01', '2023-03-01', '2023-03-01', '2023-03-01'
                                ]),
        'follower_count_type': ['follower_gains_monthly', 'follower_gains_monthly', 'follower_gains_monthly', 'follower_gains_monthly', 'follower_gains_monthly',
                                'follower_geo', 'follower_geo', 'follower_geo', # Location
                                'follower_function', 'follower_function', 'follower_function', # Role
                                'follower_industry', 'follower_industry', 'follower_industry', # Industry
                                'follower_seniority', 'follower_seniority', 'follower_seniority', # Seniority
                                'total_followers_snapshot', 'total_followers_snapshot', 'total_followers_snapshot' # For existing total growth
                                ],
        'category_name': ['Jan', 'Jan-Mid', 'Feb', 'Feb-Mid', 'Mar', # Corresponds to follower_gains_monthly
                          'USA', 'Canada', 'UK',                     # Geo
                          'Engineering', 'Sales', 'Marketing',        # Function/Role
                          'Tech', 'Finance', 'Healthcare',           # Industry
                          'Senior', 'Junior', 'Manager',             # Seniority
                          'Overall1', 'Overall2', 'Overall3'         # For total_followers_snapshot
                         ],
        'follower_count_o': [100, 105, 115, 120, 130, # Counts for monthly gains
                             500, 300, 200,          # Geo counts
                             400, 350, 250,          # Role counts
                             600, 200, 200,          # Industry counts
                             300, 400, 300,          # Seniority counts
                             1000, 1010, 1025        # For total_followers_snapshot
                            ],
        'total_followers': [None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,100,115,130] # For existing total growth plot
    }
    sample_follower_stats_df = pd.DataFrame(follower_data)
    # Ensure 'total_followers' for generate_total_follower_growth_plot is correctly populated for its specific rows
    sample_follower_stats_df.loc[sample_follower_stats_df['follower_count_type'] == 'total_followers_snapshot', 'total_followers'] = sample_follower_stats_df['follower_count_o']


    logging.info("--- Testing New Plot Generations ---")

    fig_followers_count = generate_followers_count_over_time_plot(sample_follower_stats_df.copy(), date_column='date', count_column='follower_count_o', type_value='follower_gains_monthly')
    if fig_followers_count: logging.info("Followers Count Over Time (monthly) plot generated.")

    fig_followers_rate = generate_followers_growth_rate_plot(sample_follower_stats_df.copy(), date_column='date', count_column='follower_count_o', type_value='follower_gains_monthly')
    if fig_followers_rate: logging.info("Followers Growth Rate (monthly) plot generated.")

    fig_geo = generate_followers_by_demographics_plot(sample_follower_stats_df.copy(), type_value='follower_geo', plot_title="Followers by Location")
    if fig_geo: logging.info("Followers by Location plot generated.")
    
    fig_role = generate_followers_by_demographics_plot(sample_follower_stats_df.copy(), type_value='follower_function', plot_title="Followers by Role")
    if fig_role: logging.info("Followers by Role plot generated.")

    fig_industry = generate_followers_by_demographics_plot(sample_follower_stats_df.copy(), type_value='follower_industry', plot_title="Followers by Industry")
    if fig_industry: logging.info("Followers by Industry plot generated.")

    fig_seniority = generate_followers_by_demographics_plot(sample_follower_stats_df.copy(), type_value='follower_seniority', plot_title="Followers by Seniority")
    if fig_seniority: logging.info("Followers by Seniority 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.")
    
    # Test existing total follower growth plot with appropriate data
    total_followers_df = sample_follower_stats_df[sample_follower_stats_df['follower_count_type'] == 'total_followers_snapshot'].copy()
    total_followers_df['date'] = pd.to_datetime(total_followers_df['date']) # Ensure date is datetime
    fig_total_growth = generate_total_follower_growth_plot(total_followers_df, date_column='date', count_column='total_followers')
    if fig_total_growth: logging.info("Total Follower Growth plot (existing function) generated.")


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