Update analytics_plot_generator.py

analytics_plot_generator.py

@@ -7,6 +7,7 @@ import numpy as np
 import matplotlib.ticker as mticker
 import matplotlib.patches as patches # Added for rounded corners
 import ast # For safely evaluating string representations of lists
+from analytics_data_processing import generate_chatbot_data_summaries
 
 # Configure logging for this module
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(module)s - %(message)s')
@@ -839,3 +840,133 @@ def generate_content_topic_breakdown_plot(df, topics_col='li_eb_labels', top_n=1
         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))
+
+
+# --- Analytics Tab: Plot Figure Generation Function ---
+def update_analytics_plots_figures(token_state_value, date_filter_option, custom_start_date, custom_end_date, current_plot_configs):
+    logging.info(f"Updating analytics plot figures. Filter: {date_filter_option}, Custom Start: {custom_start_date}, Custom End: {custom_end_date}")
+    num_expected_plots = 19 # Ensure this matches the number of plots generated
+    
+    plot_data_summaries_for_chatbot = {} # Initialize dict for chatbot summaries
+
+    if not token_state_value or not token_state_value.get("token"):
+        message = "❌ Accesso negato. Nessun token. Impossibile generare le analisi."
+        logging.warning(message)
+        placeholder_figs = [create_placeholder_plot(title="Accesso Negato", message="Nessun token.") for _ in range(num_expected_plots)]
+        # For each plot_config, add a default "no data" summary
+        for p_cfg in current_plot_configs:
+            plot_data_summaries_for_chatbot[p_cfg["id"]] = "Accesso negato, nessun dato per il chatbot."
+        return [message] + placeholder_figs + [plot_data_summaries_for_chatbot]
+    try:
+        (filtered_merged_posts_df, 
+         filtered_mentions_df, 
+         date_filtered_follower_stats_df, # For time-based follower plots
+         raw_follower_stats_df,       # For demographic follower plots
+         start_dt_for_msg, end_dt_for_msg) = \
+            prepare_filtered_analytics_data(
+                token_state_value, date_filter_option, custom_start_date, custom_end_date
+            )
+        
+        # Generate data summaries for chatbot AFTER data preparation
+        plot_data_summaries_for_chatbot = generate_chatbot_data_summaries(
+            current_plot_configs, # Pass the plot_configs list
+            filtered_merged_posts_df,
+            filtered_mentions_df,
+            date_filtered_follower_stats_df,
+            raw_follower_stats_df,
+            token_state_value
+        )
+
+    except Exception as e:
+        error_msg = f"❌ Errore durante la preparazione dei dati per le analisi: {e}"
+        logging.error(error_msg, exc_info=True)
+        placeholder_figs = [create_placeholder_plot(title="Errore Preparazione Dati", message=str(e)) for _ in range(num_expected_plots)]
+        for p_cfg in current_plot_configs:
+            plot_data_summaries_for_chatbot[p_cfg["id"]] = f"Errore preparazione dati: {e}"
+        return [error_msg] + placeholder_figs + [plot_data_summaries_for_chatbot]
+    
+    date_column_posts = token_state_value.get("config_date_col_posts", "published_at")
+    date_column_mentions = token_state_value.get("config_date_col_mentions", "date")
+    media_type_col_name = token_state_value.get("config_media_type_col", "media_type")
+    eb_labels_col_name = token_state_value.get("config_eb_labels_col", "li_eb_label")
+    
+    plot_figs = [] # Initialize list to hold plot figures
+    
+    plot_titles_for_errors = [p_cfg["label"] for p_cfg in current_plot_configs] 
+
+    try:
+        # Dinamiche dei Follower (2 plots)
+        plot_figs.append(generate_followers_count_over_time_plot(date_filtered_follower_stats_df, type_value='follower_gains_monthly'))
+        plot_figs.append(generate_followers_growth_rate_plot(date_filtered_follower_stats_df, type_value='follower_gains_monthly')) # Assuming this uses 'follower_gains_monthly' to calculate rate
+        
+        # Demografia Follower (4 plots)
+        plot_figs.append(generate_followers_by_demographics_plot(raw_follower_stats_df, type_value='follower_geo', plot_title="Follower per Località"))
+        plot_figs.append(generate_followers_by_demographics_plot(raw_follower_stats_df, type_value='follower_function', plot_title="Follower per Ruolo"))
+        plot_figs.append(generate_followers_by_demographics_plot(raw_follower_stats_df, type_value='follower_industry', plot_title="Follower per Settore"))
+        plot_figs.append(generate_followers_by_demographics_plot(raw_follower_stats_df, type_value='follower_seniority', plot_title="Follower per Anzianità"))
+
+        # Approfondimenti Performance Post (4 plots)
+        plot_figs.append(generate_engagement_rate_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts))
+        plot_figs.append(generate_reach_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts))
+        plot_figs.append(generate_impressions_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts)) # Ensure 'impressions_sum' or equivalent is used by this func
+        plot_figs.append(generate_likes_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts))
+        
+        # Engagement Dettagliato Post nel Tempo (4 plots)
+        plot_figs.append(generate_clicks_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts))
+        plot_figs.append(generate_shares_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts))
+        plot_figs.append(generate_comments_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts))
+        plot_figs.append(generate_comments_sentiment_breakdown_plot(filtered_merged_posts_df, sentiment_column='comment_sentiment')) # Make sure 'comment_sentiment' exists
+        
+        # Analisi Strategia Contenuti (3 plots)
+        plot_figs.append(generate_post_frequency_plot(filtered_merged_posts_df, date_column=date_column_posts))
+        plot_figs.append(generate_content_format_breakdown_plot(filtered_merged_posts_df, format_col=media_type_col_name))
+        plot_figs.append(generate_content_topic_breakdown_plot(filtered_merged_posts_df, topics_col=eb_labels_col_name))
+        
+        # Analisi Menzioni (Dettaglio) (2 plots)
+        plot_figs.append(generate_mentions_activity_plot(filtered_mentions_df, date_column=date_column_mentions))
+        plot_figs.append(generate_mention_sentiment_plot(filtered_mentions_df)) # Make sure this function handles empty/malformed df
+
+        if len(plot_figs) != num_expected_plots:
+            logging.warning(f"Mismatch in generated plots. Expected {num_expected_plots}, got {len(plot_figs)}. This will cause UI update issues.")
+            while len(plot_figs) < num_expected_plots:
+                plot_figs.append(create_placeholder_plot(title="Grafico Non Generato", message="Logica di generazione incompleta."))
+        
+        message = f"📊 Analisi aggiornate per il periodo: {date_filter_option}"
+        if date_filter_option == "Intervallo Personalizzato": 
+            s_display = start_dt_for_msg.strftime('%Y-%m-%d') if start_dt_for_msg else "Qualsiasi"
+            e_display = end_dt_for_msg.strftime('%Y-%m-%d') if end_dt_for_msg else "Qualsiasi"
+            message += f" (Da: {s_display} A: {e_display})"
+        
+        final_plot_figs = []
+        for i, p_fig_candidate in enumerate(plot_figs):
+            if p_fig_candidate is not None and not isinstance(p_fig_candidate, str): # Basic check for a plot object
+                final_plot_figs.append(p_fig_candidate)
+            else:
+                err_title = plot_titles_for_errors[i] if i < len(plot_titles_for_errors) else f"Grafico {i+1}"
+                logging.warning(f"Plot {err_title} (index {i}) non è una figura valida: {p_fig_candidate}. Uso placeholder.")
+                final_plot_figs.append(create_placeholder_plot(title=f"Errore: {err_title}", message="Impossibile generare figura."))
+        
+        return [message] + final_plot_figs[:num_expected_plots] + [plot_data_summaries_for_chatbot]
+
+    except (KeyError, ValueError) as e_plot_data:
+        logging.error(f"Errore dati durante la generazione di un grafico specifico: {e_plot_data}", exc_info=True)
+        error_msg_display = f"Errore dati in un grafico: {str(e_plot_data)[:100]}"
+        
+        num_already_generated = len(plot_figs)
+        for i in range(num_already_generated, num_expected_plots):
+            err_title_fill = plot_titles_for_errors[i] if i < len(plot_titles_for_errors) else f"Grafico {i+1}"
+            plot_figs.append(create_placeholder_plot(title=f"Errore Dati: {err_title_fill}", message=f"Precedente errore: {str(e_plot_data)[:50]}"))
+        
+        for p_cfg in current_plot_configs: # Ensure summaries dict is populated on error
+            if p_cfg["id"] not in plot_data_summaries_for_chatbot:
+                 plot_data_summaries_for_chatbot[p_cfg["id"]] = f"Errore dati grafico: {e_plot_data}"
+        return [error_msg_display] + plot_figs[:num_expected_plots] + [plot_data_summaries_for_chatbot]
+
+    except Exception as e_general:
+        error_msg = f"❌ Errore generale durante la generazione dei grafici: {e_general}"
+        logging.error(error_msg, exc_info=True)
+        placeholder_figs_general = [create_placeholder_plot(title=plot_titles_for_errors[i] if i < len(plot_titles_for_errors) else f"Grafico {i+1}", message=str(e_general)) for i in range(num_expected_plots)]
+        for p_cfg in current_plot_configs: # Ensure summaries dict is populated on error
+             if p_cfg["id"] not in plot_data_summaries_for_chatbot:
+                 plot_data_summaries_for_chatbot[p_cfg["id"]] = f"Errore generale grafici: {e_general}"
+        return [error_msg] + placeholder_figs_general + [plot_data_summaries_for_chatbot]