Update app.py

app.py

@@ -23,8 +23,8 @@ from state_manager import process_and_store_bubble_token
 from sync_logic import sync_all_linkedin_data_orchestrator
 from ui_generators import (
     display_main_dashboard,
-    run_mentions_tab_display,
-    run_follower_stats_tab_display,
+    # run_mentions_tab_display, # Removed
+    # run_follower_stats_tab_display, # Removed
     build_analytics_tab_plot_area, # EXPECTED TO RETURN: plot_ui_objects, section_titles_map
     BOMB_ICON, EXPLORE_ICON, FORMULA_ICON, ACTIVE_ICON
 )
@@ -46,7 +46,7 @@ try:
     )
     AGENTIC_MODULES_LOADED = True
 except ImportError as e:
-    logging.error(f"Could not import agentic pipeline modules: {e}. Tabs 5 and 6 will be disabled.")
+    logging.error(f"Could not import agentic pipeline modules: {e}. Tabs 3 and 4 (formerly 5 and 6) will be disabled.")
     AGENTIC_MODULES_LOADED = False
     # Define placeholder functions if modules are not loaded to avoid NameErrors
     async def run_full_analytics_orchestration(*args, **kwargs): return None
@@ -76,7 +76,8 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
     token_state = gr.State(value={
         "token": None, "client_id": None, "org_urn": None,
         "bubble_posts_df": pd.DataFrame(), "bubble_post_stats_df": pd.DataFrame(),
-        "bubble_mentions_df": pd.DataFrame(), "bubble_follower_stats_df": pd.DataFrame(),
+        "bubble_mentions_df": pd.DataFrame(), # Data still in state, but not used by UI
+        "bubble_follower_stats_df": pd.DataFrame(), # Data still in state, but not used by UI
         "fetch_count_for_api": 0, "url_user_token_temp_storage": None,
         "config_date_col_posts": "published_at", "config_date_col_mentions": "date",
         "config_date_col_followers": "date", "config_media_type_col": "media_type",
@@ -113,25 +114,21 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
             sync_status_html_output = gr.HTML("<p style='text-align:center;'>Stato sincronizzazione...</p>")
             dashboard_display_html = gr.HTML("<p style='text-align:center;'>Caricamento dashboard...</p>")
 
-            # Initial load sequence will be defined later with .then() for agentic pipeline
-            # org_urn_display.change(...)
-
-        with gr.TabItem("2️⃣ Analisi", id="tab_analytics"):
+        with gr.TabItem("2️⃣ Analisi Grafici", id="tab_analytics"): # Renamed for clarity
             gr.Markdown("## 📈 Analisi Performance LinkedIn")
             gr.Markdown("Seleziona un intervallo di date per i grafici. Clicca i pulsanti (💣 Insights, ƒ Formula, 🧭 Esplora) su un grafico per azioni.")
             analytics_status_md = gr.Markdown("Stato analisi grafici...")
-            # Agentic pipeline status will be moved to Tab 5
 
             with gr.Row():
                 date_filter_selector = gr.Radio(
                     ["Sempre", "Ultimi 7 Giorni", "Ultimi 30 Giorni", "Intervallo Personalizzato"],
-                    label="Seleziona Intervallo Date per Grafici", value="Sempre", scale=3 # Clarified label
+                    label="Seleziona Intervallo Date per Grafici", value="Sempre", scale=3
                 )
                 with gr.Column(scale=2):
                     custom_start_date_picker = gr.DateTime(label="Data Inizio", visible=False, include_time=False, type="datetime")
                     custom_end_date_picker = gr.DateTime(label="Data Fine", visible=False, include_time=False, type="datetime")
 
-            apply_filter_btn = gr.Button("🔍 Applica Filtro & Aggiorna Grafici", variant="primary") # Clarified button text
+            apply_filter_btn = gr.Button("🔍 Applica Filtro & Aggiorna Grafici", variant="primary")
 
             def toggle_custom_date_pickers(selection):
                 is_custom = selection == "Intervallo Personalizzato"
@@ -161,10 +158,15 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
                 {"label": "Frequenza Post", "id": "post_frequency_cs", "section": "Analisi Strategia Contenuti"},
                 {"label": "Ripartizione Contenuti per Formato", "id": "content_format_breakdown_cs", "section": "Analisi Strategia Contenuti"},
                 {"label": "Ripartizione Contenuti per Argomenti", "id": "content_topic_breakdown_cs", "section": "Analisi Strategia Contenuti"},
-                {"label": "Volume Menzioni nel Tempo (Dettaglio)", "id": "mention_analysis_volume", "section": "Analisi Menzioni (Dettaglio)"},
-                {"label": "Ripartizione Menzioni per Sentiment (Dettaglio)", "id": "mention_analysis_sentiment", "section": "Analisi Menzioni (Dettaglio)"}
+                {"label": "Volume Menzioni nel Tempo (Dettaglio)", "id": "mention_analysis_volume", "section": "Analisi Menzioni (Dettaglio)"}, # This plot might need data from the removed mentions tab. Consider if this plot should also be removed or if its data source is independent.
+                {"label": "Ripartizione Menzioni per Sentiment (Dettaglio)", "id": "mention_analysis_sentiment", "section": "Analisi Menzioni (Dettaglio)"} # Same as above.
             ]
-            assert len(plot_configs) == 19, "Mancata corrispondenza in plot_configs e grafici attesi."
+            # IMPORTANT: Review if 'mention_analysis_volume' and 'mention_analysis_sentiment' plots
+            # can still be generated without the dedicated mentions data processing.
+            # If not, they should also be removed from plot_configs.
+            # For now, I am assuming they might draw from a general data pool in token_state.
+
+            assert len(plot_configs) == 19, "Mancata corrispondenza in plot_configs e grafici attesi. (If mentions plots were removed, adjust this number)"
             
             unique_ordered_sections = list(OrderedDict.fromkeys(pc["section"] for pc in plot_configs))
             num_unique_sections = len(unique_ordered_sections)
@@ -378,28 +380,16 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
             insights_suggestion_2_btn.click(fn=handle_suggested_question_click, inputs=[insights_suggestion_2_btn] + suggestion_click_inputs_base, outputs=chat_submission_outputs, api_name="click_suggestion_2")
             insights_suggestion_3_btn.click(fn=handle_suggested_question_click, inputs=[insights_suggestion_3_btn] + suggestion_click_inputs_base, outputs=chat_submission_outputs, api_name="click_suggestion_3")
 
-        with gr.TabItem("3️⃣ Menzioni", id="tab_mentions"):
-            refresh_mentions_display_btn = gr.Button("🔄 Aggiorna Visualizzazione Menzioni", variant="secondary")
-            mentions_html = gr.HTML("Dati menzioni...")
-            mentions_sentiment_dist_plot = gr.Plot(label="Distribuzione Sentiment Menzioni")
-            refresh_mentions_display_btn.click(fn=run_mentions_tab_display, inputs=[token_state], outputs=[mentions_html, mentions_sentiment_dist_plot], show_progress="full")
-
-        with gr.TabItem("4️⃣ Statistiche Follower", id="tab_follower_stats"):
-            refresh_follower_stats_btn = gr.Button("🔄 Aggiorna Visualizzazione Statistiche Follower", variant="secondary")
-            follower_stats_html = gr.HTML("Statistiche follower...")
-            with gr.Row(): fs_plot_monthly_gains = gr.Plot(label="Guadagni Mensili Follower")
-            with gr.Row(): fs_plot_seniority = gr.Plot(label="Follower per Anzianità (Top 10 Organici)"); fs_plot_industry = gr.Plot(label="Follower per Settore (Top 10 Organici)")
-            refresh_follower_stats_btn.click(fn=run_follower_stats_tab_display, inputs=[token_state], outputs=[follower_stats_html, fs_plot_monthly_gains, fs_plot_seniority, fs_plot_industry], show_progress="full")
-        
-        with gr.TabItem("5️⃣ Agentic Analysis Report", id="tab_agentic_report", visible=AGENTIC_MODULES_LOADED):
+        # Tab 3 (Menzioni) and Tab 4 (Statistiche Follower) are removed.
+
+        with gr.TabItem("3️⃣ Agentic Analysis Report", id="tab_agentic_report", visible=AGENTIC_MODULES_LOADED): # Renumbered from 5
             gr.Markdown("## 🤖 Comprehensive Analysis Report (AI Generated)")
-            # Moved agentic_pipeline_status_md here
             agentic_pipeline_status_md = gr.Markdown("Stato Pipeline AI (filtro 'Sempre'): In attesa...", visible=True)
             gr.Markdown("Questo report è generato da un agente AI con filtro 'Sempre' sui dati disponibili. Rivedi criticamente.")
             agentic_report_display_md = gr.Markdown("La pipeline AI si avvierà automaticamente dopo il caricamento iniziale dei dati o dopo una sincronizzazione.")
             if not AGENTIC_MODULES_LOADED: gr.Markdown("🔴 **Error:** Agentic pipeline modules could not be loaded. This tab is disabled.")
 
-        with gr.TabItem("6️⃣ Agentic OKRs & Tasks", id="tab_agentic_okrs", visible=AGENTIC_MODULES_LOADED):
+        with gr.TabItem("4️⃣ Agentic OKRs & Tasks", id="tab_agentic_okrs", visible=AGENTIC_MODULES_LOADED): # Renumbered from 6
             gr.Markdown("## 🎯 AI Generated OKRs and Actionable Tasks (filtro 'Sempre')")
             gr.Markdown("Basato sull'analisi AI (filtro 'Sempre'), l'agente ha proposto i seguenti OKR e task. Seleziona i Key Results per dettagli.")
             if not AGENTIC_MODULES_LOADED: gr.Markdown("🔴 **Error:** Agentic pipeline modules could not be loaded. This tab is disabled.")
@@ -453,24 +443,16 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
         logging.info(f"Prepared {len(all_updates)} updates for graph refresh. Expected {expected_len}.")
         return tuple(all_updates)
 
-    async def run_agentic_pipeline_autonomously(current_token_state_val, request: gr.Request):
-        # Added request: gr.Request to check if it's the initial call or subsequent.
-        # This is a simple way to prevent multiple rapid fires if not needed,
-        # though ideally Gradio's event system handles this.
-        # For more robust control, a separate state could track if the pipeline has run for current data.
-        client_ip = request.client.host if request else "unknown_client"
-        logging.info(f"Agentic pipeline check triggered for token_state update from {client_ip}. Current token: {'Set' if current_token_state_val.get('token') else 'Not Set'}")
+    async def run_agentic_pipeline_autonomously(current_token_state_val): # Removed request: gr.Request for simplicity
+        logging.info(f"Agentic pipeline check triggered for token_state update. Current token: {'Set' if current_token_state_val.get('token') else 'Not Set'}")
 
         if not current_token_state_val or not current_token_state_val.get("token"):
             logging.info("Agentic pipeline: Token not available in token_state. Skipping.")
-            yield ( # Yield initial "waiting" state if token not ready
-                gr.update(value="Pipeline AI: In attesa dei dati necessari..."), # agentic_report_display_md
-                gr.update(choices=[], value=[], interactive=False), # key_results_cbg
-                gr.update(value="Pipeline AI: In attesa dei dati necessari..."), # okr_detail_display_md
-                None, # orchestration_raw_results_st
-                [],   # selected_key_result_ids_st
-                [],   # key_results_for_selection_st
-                "Pipeline AI: In attesa dei dati..." # agentic_pipeline_status_md
+            yield (
+                gr.update(value="Pipeline AI: In attesa dei dati necessari..."),
+                gr.update(choices=[], value=[], interactive=False),
+                gr.update(value="Pipeline AI: In attesa dei dati necessari..."),
+                None, [], [], "Pipeline AI: In attesa dei dati..."
             )
             return
 
@@ -479,9 +461,9 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
             gr.update(value="Analisi AI (Sempre) in corso..."),
             gr.update(choices=[], value=[], interactive=False),
             gr.update(value="Dettagli OKR (Sempre) in corso di generazione..."),
-            None, # orchestration_raw_results_st (don't overwrite with None if it already has results)
-            [],   # selected_key_result_ids_st
-            [],   # key_results_for_selection_st
+            orchestration_raw_results_st.value, # Preserve existing results if any during processing
+            selected_key_result_ids_st.value,
+            key_results_for_selection_st.value,
             "Esecuzione pipeline AI (Sempre)..."
         )
 
@@ -539,9 +521,8 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
     agentic_pipeline_outputs_list = [agentic_report_display_md, key_results_cbg, okr_detail_display_md, orchestration_raw_results_st, selected_key_result_ids_st, key_results_for_selection_st, agentic_pipeline_status_md]
     
     graph_refresh_inputs = [token_state, date_filter_selector, custom_start_date_picker, custom_end_date_picker, chat_histories_st]
-    agentic_pipeline_inputs = [token_state] # Removed request for now, can be added back if needed for advanced logic
+    agentic_pipeline_inputs = [token_state]
 
-    # Event for Apply Filter Button (only refreshes graphs)
     apply_filter_btn.click(
         fn=refresh_analytics_graphs_ui,
         inputs=graph_refresh_inputs,
@@ -549,28 +530,24 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
         show_progress="full"
     )
 
-    # --- Event Chains for Initial Load and Sync ---
-    # Initial Load
     initial_load_event = org_urn_display.change(
         fn=initial_load_sequence,
         inputs=[url_user_token_display, org_urn_display, token_state],
         outputs=[status_box, token_state, sync_data_btn, dashboard_display_html],
         show_progress="full"
     )
-    # After initial load, refresh graphs AND run agentic pipeline
     initial_load_event.then(
-        fn=refresh_analytics_graphs_ui, # Refresh graphs with default "Sempre" filter
-        inputs=[token_state, date_filter_selector, custom_start_date_picker, custom_end_date_picker, chat_histories_st], # date_filter_selector will provide "Sempre"
+        fn=refresh_analytics_graphs_ui,
+        inputs=[token_state, date_filter_selector, custom_start_date_picker, custom_end_date_picker, chat_histories_st],
         outputs=graph_refresh_outputs_list,
         show_progress="full"
     ).then(
-        fn=run_agentic_pipeline_autonomously, # Run agentic pipeline
-        inputs=agentic_pipeline_inputs, # Uses token_state
+        fn=run_agentic_pipeline_autonomously,
+        inputs=agentic_pipeline_inputs,
         outputs=agentic_pipeline_outputs_list,
         show_progress="minimal"
     )
 
-    # Sync Process
     sync_event_part1 = sync_data_btn.click(
         fn=sync_all_linkedin_data_orchestrator,
         inputs=[token_state],
@@ -580,17 +557,15 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
     sync_event_part2 = sync_event_part1.then(
         fn=process_and_store_bubble_token,
         inputs=[url_user_token_display, org_urn_display, token_state],
-        outputs=[status_box, token_state, sync_data_btn], # token_state is updated here
+        outputs=[status_box, token_state, sync_data_btn],
         show_progress=False
     )
-    # After token_state is updated by sync, run agentic pipeline
-    sync_event_part2.then(
+    sync_event_part2.then( # This will now use the updated token_state from process_and_store_bubble_token
         fn=run_agentic_pipeline_autonomously,
-        inputs=agentic_pipeline_inputs, # Uses updated token_state
+        inputs=agentic_pipeline_inputs, # token_state is the first element
         outputs=agentic_pipeline_outputs_list,
         show_progress="minimal"
     )
-    # Separately, update dashboard and graphs after sync
     sync_event_part3 = sync_event_part2.then(
         fn=display_main_dashboard,
         inputs=[token_state],
@@ -609,8 +584,8 @@ if __name__ == "__main__":
     if not os.environ.get(LINKEDIN_CLIENT_ID_ENV_VAR): logging.warning(f"ATTENZIONE: '{LINKEDIN_CLIENT_ID_ENV_VAR}' non impostata.")
     if not all(os.environ.get(var) for var in [BUBBLE_APP_NAME_ENV_VAR, BUBBLE_API_KEY_PRIVATE_ENV_VAR, BUBBLE_API_ENDPOINT_ENV_VAR]):
         logging.warning("ATTENZIONE: Variabili Bubble non impostate.")
-    if not AGENTIC_MODULES_LOADED: logging.warning("CRITICAL: Agentic pipeline modules failed to load. Tabs 5 and 6 will be non-functional.")
-    if not os.environ.get("GEMINI_API_KEY") and AGENTIC_MODULES_LOADED: logging.warning("ATTENZIONE: 'GEMINI_API_KEY' non impostata. La pipeline AI per le tab 5 e 6 potrebbe non funzionare.")
+    if not AGENTIC_MODULES_LOADED: logging.warning("CRITICAL: Agentic pipeline modules failed to load. Tabs 3 and 4 (formerly 5 and 6) will be non-functional.")
+    if not os.environ.get("GEMINI_API_KEY") and AGENTIC_MODULES_LOADED: logging.warning("ATTENZIONE: 'GEMINI_API_KEY' non impostata. La pipeline AI per le tab 3 e 4 potrebbe non funzionare.")
     try: logging.info(f"Matplotlib: {matplotlib.__version__}, Backend: {matplotlib.get_backend()}")
     except ImportError: logging.warning("Matplotlib non trovato.")
-    app.launch(server_name="0.0.0.0", server_port=7860, debug=True)
+    app.launch(server_name="0.0.0.0", server_port=7860, debug=True)