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 ast # For safely evaluating string representations of lists # 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') fig.tight_layout() # MODIFIED # Add spacing for consistency, though it might be less critical for placeholders fig.subplots_adjust(top=0.90) 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') fig_err.tight_layout() # MODIFIED fig_err.subplots_adjust(top=0.90) 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.") fig = None # Initialize fig to None 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', y=1.03) # Matplotlib title ax.set_xlabel('Date') ax.set_ylabel('Number of Posts') ax.grid(True, linestyle='--', alpha=0.7) plt.xticks(rotation=45) fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label 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) if fig: plt.close(fig) # Close if fig was created before error return create_placeholder_plot(title="Posts Activity Error", message=str(e)) finally: pass def generate_engagement_type_plot(df, likes_col='likeCount', comments_col='commentCount', shares_col='shareCount'): """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) fig = None 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', y=1.03) # Matplotlib title 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') fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label 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) if fig: plt.close(fig) return create_placeholder_plot(title="Engagement Type Error", message=str(e)) finally: pass 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.") fig = None 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', y=1.03) # Matplotlib title ax.set_xlabel('Date') ax.set_ylabel('Number of Mentions') ax.grid(True, linestyle='--', alpha=0.7) plt.xticks(rotation=45) fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label 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) if fig: plt.close(fig) return create_placeholder_plot(title="Mentions Activity Error", message=str(e)) finally: pass 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) fig = None 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('Pastel1', 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', y=1.03) # Matplotlib title ax.axis('equal') fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label 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) if fig: plt.close(fig) return create_placeholder_plot(title="Mention Sentiment Error", message=str(e)) finally: pass # --- 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'): 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()}") fig = None 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').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, y=1.03) # Matplotlib title ax.set_xlabel('Date') ax.set_ylabel('Follower Count') ax.legend() ax.grid(True, linestyle='--', alpha=0.7) plt.xticks(rotation=45) fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label 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)) finally: pass 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'): 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()}") fig = None 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: 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 df_filtered.replace([np.inf, -np.inf], np.nan, inplace=True) 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, y=1.03) # Matplotlib 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) fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label 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)) finally: pass 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"): 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.") fig = None 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() 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, y=1.03) # Matplotlib title 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) for bar_group in [bars1, bars2]: for bar_item in bar_group: # Renamed 'bar' to 'bar_item' to avoid conflict yval = bar_item.get_height() if yval > 0: ax.text(bar_item.get_x() + bar_item.get_width()/2.0, yval + (0.01 * ax.get_ylim()[1]), str(int(yval)), ha='center', va='bottom', fontsize=8) fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.85) # Adjust top for more space, especially with rotated labels 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)) finally: pass def generate_engagement_rate_over_time_plot(df, date_column='published_at', engagement_rate_col='engagement'): 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()}") 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]).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, y=1.03) # Matplotlib title ax.set_xlabel('Date') ax.set_ylabel('Engagement Rate') max_rate_val = engagement_over_time.max() if not engagement_over_time.empty else 0 formatter_xmax = 1.0 if 0 <= max_rate_val <= 1.5 else 100.0 if max_rate_val > 1.5 and formatter_xmax == 1.0: formatter_xmax = 100.0 elif max_rate_val > 100 and formatter_xmax == 1.0: formatter_xmax = max_rate_val ax.yaxis.set_major_formatter(mticker.PercentFormatter(xmax=formatter_xmax)) ax.grid(True, linestyle='--', alpha=0.7) plt.xticks(rotation=45) fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label 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)) finally: pass def generate_reach_over_time_plot(df, date_column='published_at', reach_col='clickCount'): title = "Reach Over Time (Clicks)" # Title matches the config in app.py 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()}") fig = None 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 for reach plot.") reach_over_time = df_copy.resample('D')[reach_col].sum() 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, y=1.03) # Matplotlib title ax.set_xlabel('Date') ax.set_ylabel('Total Clicks') # Label consistent with reach_col='clickCount' ax.grid(True, linestyle='--', alpha=0.7) plt.xticks(rotation=45) fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label 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)) finally: pass def generate_impressions_over_time_plot(df, date_column='published_at', impressions_col='impressionCount'): 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()}") fig = None 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 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, y=1.03) # Matplotlib title ax.set_xlabel('Date') ax.set_ylabel('Total Impressions') ax.grid(True, linestyle='--', alpha=0.7) plt.xticks(rotation=45) fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label 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)) finally: pass def generate_likes_over_time_plot(df, date_column='published_at', likes_col='likeCount'): title = "Reactions (Likes) Over Time" logging.info(f"Generating {title}. Date: '{date_column}', Likes Col: '{likes_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 likes.") required_cols = [date_column, likes_col] if any(col not in df.columns for col in required_cols): return create_placeholder_plot(title=title, message=f"Missing one of required columns: {required_cols}. Available: {df.columns.tolist()}") fig = None try: df_copy = df.copy() df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce') df_copy[likes_col] = pd.to_numeric(df_copy[likes_col], errors='coerce') df_copy = df_copy.dropna(subset=[date_column, likes_col]).set_index(date_column) if df_copy.empty: return create_placeholder_plot(title=title, message="No valid data after cleaning.") data_over_time = df_copy.resample('D')[likes_col].sum() fig, ax = plt.subplots(figsize=(10, 5)) ax.plot(data_over_time.index, data_over_time.values, marker='.', linestyle='-', color='crimson') ax.set_title(title, y=1.03) # Matplotlib title ax.set_xlabel('Date') ax.set_ylabel('Total Likes') ax.grid(True, linestyle='--', alpha=0.7) plt.xticks(rotation=45) fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label 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)) finally: pass def generate_clicks_over_time_plot(df, date_column='published_at', clicks_col='clickCount'): title = "Clicks Over Time" logging.info(f"Generating {title}. Date: '{date_column}', Clicks Col: '{clicks_col}'. DF rows: {len(df) if df is not None else 'None'}") # This function essentially calls generate_reach_over_time_plot with specific params # The fig.tight_layout() and fig.subplots_adjust will be handled within that function. return generate_reach_over_time_plot(df, date_column, clicks_col) def generate_shares_over_time_plot(df, date_column='published_at', shares_col='shareCount'): title = "Shares Over Time" logging.info(f"Generating {title}. Date: '{date_column}', Shares Col: '{shares_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 shares.") required_cols = [date_column, shares_col] if any(col not in df.columns for col in required_cols): return create_placeholder_plot(title=title, message=f"Missing one of required columns: {required_cols}. Available: {df.columns.tolist()}") fig = None try: df_copy = df.copy() df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce') df_copy[shares_col] = pd.to_numeric(df_copy[shares_col], errors='coerce') df_copy = df_copy.dropna(subset=[date_column, shares_col]).set_index(date_column) if df_copy.empty: return create_placeholder_plot(title=title, message="No valid data after cleaning.") data_over_time = df_copy.resample('D')[shares_col].sum() fig, ax = plt.subplots(figsize=(10, 5)) ax.plot(data_over_time.index, data_over_time.values, marker='.', linestyle='-', color='teal') ax.set_title(title, y=1.03) # Matplotlib title ax.set_xlabel('Date') ax.set_ylabel('Total Shares') ax.grid(True, linestyle='--', alpha=0.7) plt.xticks(rotation=45) fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label 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)) finally: pass def generate_comments_over_time_plot(df, date_column='published_at', comments_col='commentCount'): title = "Comments Over Time" logging.info(f"Generating {title}. Date: '{date_column}', Comments Col: '{comments_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 comments.") required_cols = [date_column, comments_col] if any(col not in df.columns for col in required_cols): return create_placeholder_plot(title=title, message=f"Missing one of required columns: {required_cols}. Available: {df.columns.tolist()}") fig = None try: df_copy = df.copy() df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce') df_copy[comments_col] = pd.to_numeric(df_copy[comments_col], errors='coerce') df_copy = df_copy.dropna(subset=[date_column, comments_col]).set_index(date_column) if df_copy.empty: return create_placeholder_plot(title=title, message="No valid data after cleaning.") data_over_time = df_copy.resample('D')[comments_col].sum() fig, ax = plt.subplots(figsize=(10, 5)) ax.plot(data_over_time.index, data_over_time.values, marker='.', linestyle='-', color='gold') ax.set_title(title, y=1.03) # Matplotlib title ax.set_xlabel('Date') ax.set_ylabel('Total Comments') ax.grid(True, linestyle='--', alpha=0.7) plt.xticks(rotation=45) fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label 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)) finally: pass def generate_comments_sentiment_breakdown_plot(df, sentiment_column='comment_sentiment', date_column=None): title = "Breakdown of Comments by Sentiment" logging.info(f"Generating {title}. Sentiment Col: '{sentiment_column}'. 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 data for comment sentiment.") if sentiment_column not in df.columns: if 'sentiment' in df.columns and sentiment_column != 'sentiment': logging.warning(f"Sentiment column '{sentiment_column}' not found, attempting to use 'sentiment' column as fallback for comment sentiment plot.") sentiment_column = 'sentiment' else: return create_placeholder_plot(title=title, message=f"Sentiment column '{sentiment_column}' (and fallback 'sentiment') not found. Available: {df.columns.tolist()}") if df[sentiment_column].isnull().all(): return create_placeholder_plot(title=title, message=f"Sentiment column '{sentiment_column}' contains no valid data.") fig = None try: df_copy = df.copy() df_copy[sentiment_column] = df_copy[sentiment_column].astype(str) sentiment_counts = df_copy[sentiment_column].value_counts().dropna() if sentiment_counts.empty or sentiment_counts.sum() == 0: return create_placeholder_plot(title=title, message="No comment sentiment data to display after processing.") fig, ax = plt.subplots(figsize=(8, 5)) colors_map = plt.cm.get_cmap('coolwarm', 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(title, y=1.03) # Matplotlib title ax.axis('equal') fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label 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)) finally: pass def generate_post_frequency_plot(df, date_column='published_at', resample_period='D'): title = f"Post Frequency Over Time ({resample_period})" logging.info(f"Generating {title}. Date column: '{date_column}'. Input 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 data available.") if date_column not in df.columns: return create_placeholder_plot(title=title, message=f"Date column '{date_column}' not found.") fig = None 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: return create_placeholder_plot(title=title, message="No valid date entries found.") post_frequency = df_copy.set_index(date_column).resample(resample_period).size() if post_frequency.empty: return create_placeholder_plot(title=title, message=f"No posts found for the period after resampling by '{resample_period}'.") fig, ax = plt.subplots(figsize=(10, 5)) post_frequency.plot(kind='bar' if resample_period in ['M', 'W'] else 'line', ax=ax, marker='o' if resample_period=='D' else None) ax.set_title(title, y=1.03) # Matplotlib title ax.set_xlabel('Date' if resample_period == 'D' else 'Period') ax.set_ylabel('Number of Posts') ax.grid(True, linestyle='--', alpha=0.7) plt.xticks(rotation=45) fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label logging.info(f"Successfully generated {title} plot.") 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)) finally: pass def generate_content_format_breakdown_plot(df, format_col='media_type'): title = "Breakdown of Content by Format" logging.info(f"Generating {title}. Format column: '{format_col}'. Input 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 data available.") if format_col not in df.columns: return create_placeholder_plot(title=title, message=f"Format column '{format_col}' not found. Available: {df.columns.tolist()}") fig = None try: df_copy = df.copy() format_counts = df_copy[format_col].value_counts().dropna() if format_counts.empty: return create_placeholder_plot(title=title, message="No content format data available.") fig, ax = plt.subplots(figsize=(8, 6)) format_counts.plot(kind='bar', ax=ax, color='skyblue') ax.set_title(title, y=1.03) # Matplotlib title ax.set_xlabel('Media Type') ax.set_ylabel('Number of Posts') ax.grid(axis='y', linestyle='--', alpha=0.7) plt.xticks(rotation=45, ha="right") for i, v in enumerate(format_counts): ax.text(i, v + (0.01 * format_counts.max()), str(v), ha='center', va='bottom') fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label logging.info(f"Successfully generated {title} plot.") 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)) finally: pass 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) if isinstance(parsed, list): return parsed return [str(parsed)] except (ValueError, SyntaxError): return [label_data] if label_data.strip() else [] if pd.isna(label_data): return [] return [] def generate_content_topic_breakdown_plot(df, topics_col='eb_labels', top_n=15): title = f"Breakdown of Content by Topics (Top {top_n})" logging.info(f"Generating {title}. Topics column: '{topics_col}'. Input 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 data available.") if topics_col not in df.columns: return create_placeholder_plot(title=title, message=f"Topics column '{topics_col}' not found. Available: {df.columns.tolist()}") fig = None try: df_copy = df.copy() parsed_labels = df_copy[topics_col].apply(_parse_eb_label) exploded_labels = parsed_labels.explode().dropna() if exploded_labels.empty: return create_placeholder_plot(title=title, message="No topic data found after processing labels.") topic_counts = exploded_labels.value_counts() if topic_counts.empty: return create_placeholder_plot(title=title, message="No topics to display after counting.") top_topics = topic_counts.nlargest(top_n).sort_values(ascending=True) fig, ax = plt.subplots(figsize=(10, 8 if len(top_topics) > 5 else 6)) top_topics.plot(kind='barh', ax=ax, color='mediumseagreen') ax.set_title(title, y=1.03) # Matplotlib title ax.set_xlabel('Number of Posts') ax.set_ylabel('Topic') for i, (topic, count) in enumerate(top_topics.items()): ax.text(count + (0.01 * top_topics.max()), i, str(count), va='center') fig.tight_layout() # MODIFIED fig.subplots_adjust(top=0.88) # Add space for Gradio label logging.info(f"Successfully generated {title} plot.") 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)) finally: pass if __name__ == '__main__': # Create dummy data for testing posts_data = { 'id': [f'post{i}' for i in range(1, 8)], '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], 'media_type': ['TEXT', 'IMAGE', 'TEXT', 'VIDEO', 'IMAGE', 'TEXT', 'IMAGE'], 'eb_labels': [ "['AI', 'Tech']", ['Innovation'], 'General', None, ['Tech', 'Future'], "['AI', 'Development']", ['Tech'] ], 'comment_sentiment': ['Positive', 'Neutral', 'Positive', 'Negative', 'Positive', 'Neutral', 'Positive'] } sample_merged_posts_df = pd.DataFrame(posts_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': [ '2024-01-01', '2024-02-01', '2024-03-01', 'USA', 'Canada', 'UK', 'Engineering', 'Sales', 'Tech', 'Finance', 'Senior', 'Junior' ], 'follower_count_organic': [ 100, 110, 125, 500, 300, 150, 400, 200, 250, 180, 300, 220 ], 'follower_count_paid': [ 20, 30, 25, 50, 40, 60, 30, 20, 45, 35, 60, 40 ] } sample_follower_stats_df = pd.DataFrame(follower_data) logging.info("--- Testing Existing Plot Generations ---") 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.") 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_followers_count = generate_followers_count_over_time_plot(sample_follower_stats_df.copy(), type_value='follower_gains_monthly') if fig_followers_count: logging.info("Followers Count Over Time plot generated.") fig_followers_rate = generate_followers_growth_rate_plot(sample_follower_stats_df.copy(), type_value='follower_gains_monthly') if fig_followers_rate: logging.info("Followers Growth Rate 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_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.") fig_likes_time = generate_likes_over_time_plot(sample_merged_posts_df.copy()) if fig_likes_time: logging.info("Likes Over Time plot generated.") fig_clicks_time = generate_clicks_over_time_plot(sample_merged_posts_df.copy()) if fig_clicks_time: logging.info("Clicks Over Time plot generated.") fig_shares_time = generate_shares_over_time_plot(sample_merged_posts_df.copy()) if fig_shares_time: logging.info("Shares Over Time plot generated.") fig_comments_time = generate_comments_over_time_plot(sample_merged_posts_df.copy()) if fig_comments_time: logging.info("Comments Over Time plot generated.") fig_comments_sentiment = generate_comments_sentiment_breakdown_plot(sample_merged_posts_df.copy(), sentiment_column='comment_sentiment') if fig_comments_sentiment: logging.info("Comments Sentiment Breakdown plot generated.") logging.info("--- Testing NEW Plot Generations for Content Strategy ---") fig_post_freq = generate_post_frequency_plot(sample_merged_posts_df.copy(), date_column='published_at', resample_period='D') if fig_post_freq: logging.info("Post Frequency (Daily) plot generated.") fig_post_freq_w = generate_post_frequency_plot(sample_merged_posts_df.copy(), date_column='published_at', resample_period='W') if fig_post_freq_w: logging.info("Post Frequency (Weekly) plot generated.") fig_content_format = generate_content_format_breakdown_plot(sample_merged_posts_df.copy(), format_col='media_type') if fig_content_format: logging.info("Content Format Breakdown plot generated.") fig_content_topics = generate_content_topic_breakdown_plot(sample_merged_posts_df.copy(), topics_col='eb_labels', top_n=5) if fig_content_topics: logging.info("Content Topic Breakdown plot generated.") logging.info("--- Testing NEW Plot Generations with Edge Cases ---") empty_df = pd.DataFrame() fig_post_freq_empty = generate_post_frequency_plot(empty_df.copy()) if fig_post_freq_empty: logging.info("Post Frequency (empty df) placeholder generated.") fig_content_format_missing_col = generate_content_format_breakdown_plot(sample_merged_posts_df.copy(), format_col='non_existent_col') if fig_content_format_missing_col: logging.info("Content Format (missing col) placeholder generated.") fig_content_topics_no_labels = generate_content_topic_breakdown_plot(sample_merged_posts_df[['id', 'published_at']].copy(), topics_col='eb_labels') if fig_content_topics_no_labels: logging.info("Content Topic (missing col) placeholder generated.") df_no_topics_data = sample_merged_posts_df.copy() df_no_topics_data['eb_labels'] = None fig_content_topics_all_none = generate_content_topic_breakdown_plot(df_no_topics_data, topics_col='eb_labels') if fig_content_topics_all_none: logging.info("Content Topic (all None labels) placeholder generated.") logging.info("Test script finished. Review plots if displayed locally or saved.")