Stato sincronizzazione...
") dashboard_display_html = gr.HTML("Caricamento dashboard...
") 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...") 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 ) 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") def toggle_custom_date_pickers(selection): is_custom = selection == "Intervallo Personalizzato" return gr.update(visible=is_custom), gr.update(visible=is_custom) date_filter_selector.change( fn=toggle_custom_date_pickers, inputs=[date_filter_selector], outputs=[custom_start_date_picker, custom_end_date_picker] ) plot_configs = [ {"label": "Numero di Follower nel Tempo", "id": "followers_count", "section": "Dinamiche dei Follower"}, {"label": "Tasso di Crescita Follower", "id": "followers_growth_rate", "section": "Dinamiche dei Follower"}, {"label": "Follower per Località", "id": "followers_by_location", "section": "Demografia Follower"}, {"label": "Follower per Ruolo (Funzione)", "id": "followers_by_role", "section": "Demografia Follower"}, {"label": "Follower per Settore", "id": "followers_by_industry", "section": "Demografia Follower"}, {"label": "Follower per Anzianità", "id": "followers_by_seniority", "section": "Demografia Follower"}, {"label": "Tasso di Engagement nel Tempo", "id": "engagement_rate", "section": "Approfondimenti Performance Post"}, {"label": "Copertura nel Tempo", "id": "reach_over_time", "section": "Approfondimenti Performance Post"}, {"label": "Visualizzazioni nel Tempo", "id": "impressions_over_time", "section": "Approfondimenti Performance Post"}, {"label": "Reazioni (Like) nel Tempo", "id": "likes_over_time", "section": "Approfondimenti Performance Post"}, {"label": "Click nel Tempo", "id": "clicks_over_time", "section": "Engagement Dettagliato Post nel Tempo"}, {"label": "Condivisioni nel Tempo", "id": "shares_over_time", "section": "Engagement Dettagliato Post nel Tempo"}, {"label": "Commenti nel Tempo", "id": "comments_over_time", "section": "Engagement Dettagliato Post nel Tempo"}, {"label": "Ripartizione Commenti per Sentiment", "id": "comments_sentiment", "section": "Engagement Dettagliato Post nel Tempo"}, {"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)"}, # 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. ] # 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) active_panel_action_state = gr.State(None) explored_plot_id_state = gr.State(None) plot_ui_objects = {} section_titles_map = {} with gr.Row(equal_height=False): with gr.Column(scale=8) as plots_area_col: ui_elements_tuple = build_analytics_tab_plot_area(plot_configs) if isinstance(ui_elements_tuple, tuple) and len(ui_elements_tuple) == 2: plot_ui_objects, section_titles_map = ui_elements_tuple if not all(sec_name in section_titles_map for sec_name in unique_ordered_sections): logging.error("section_titles_map from build_analytics_tab_plot_area is incomplete.") for sec_name in unique_ordered_sections: if sec_name not in section_titles_map: section_titles_map[sec_name] = gr.Markdown(f"### {sec_name} (Error Placeholder)") else: logging.error("build_analytics_tab_plot_area did not return a tuple of (plot_ui_objects, section_titles_map).") plot_ui_objects = ui_elements_tuple if isinstance(ui_elements_tuple, dict) else {} for sec_name in unique_ordered_sections: section_titles_map[sec_name] = gr.Markdown(f"### {sec_name} (Error Placeholder)") with gr.Column(scale=4, visible=False) as global_actions_column_ui: gr.Markdown("### 💡 Azioni Contestuali Grafico") insights_chatbot_ui = gr.Chatbot( label="Chat Insights", type="messages", height=450, bubble_full_width=False, visible=False, show_label=False, placeholder="L'analisi AI del grafico apparirà qui. Fai domande di approfondimento!" ) insights_chat_input_ui = gr.Textbox( label="La tua domanda:", placeholder="Chiedi all'AI riguardo a questo grafico...", lines=2, visible=False, show_label=False ) with gr.Row(visible=False) as insights_suggestions_row_ui: insights_suggestion_1_btn = gr.Button(value="Suggerimento 1", size="sm", min_width=50) insights_suggestion_2_btn = gr.Button(value="Suggerimento 2", size="sm", min_width=50) insights_suggestion_3_btn = gr.Button(value="Suggerimento 3", size="sm", min_width=50) formula_display_markdown_ui = gr.Markdown( "I dettagli sulla formula/metodologia appariranno qui.", visible=False ) formula_close_hint_md = gr.Markdown( "Click the active ƒ button on the plot again to close this panel.
", visible=False ) async def handle_panel_action( plot_id_clicked: str, action_type: str, current_active_action_from_state: dict, current_chat_histories: dict, current_chat_plot_id: str, current_plot_data_for_chatbot: dict, current_explored_plot_id: str ): logging.info(f"Panel Action: '{action_type}' for plot '{plot_id_clicked}'. Active: {current_active_action_from_state}, Explored: {current_explored_plot_id}") clicked_plot_config = next((p for p in plot_configs if p["id"] == plot_id_clicked), None) if not clicked_plot_config: logging.error(f"Config not found for plot_id {plot_id_clicked}") num_plots = len(plot_configs) error_list_len = 15 + (4 * num_plots) + num_unique_sections error_list = [gr.update()] * error_list_len error_list[11] = current_active_action_from_state; error_list[12] = current_chat_plot_id error_list[13] = current_chat_histories; error_list[14] = current_explored_plot_id return error_list clicked_plot_label = clicked_plot_config["label"]; clicked_plot_section = clicked_plot_config["section"] hypothetical_new_active_state = {"plot_id": plot_id_clicked, "type": action_type} is_toggling_off = current_active_action_from_state == hypothetical_new_active_state action_col_visible_update = gr.update(visible=False) insights_chatbot_visible_update, insights_chat_input_visible_update, insights_suggestions_row_visible_update = gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) formula_display_visible_update = gr.update(visible=False); formula_close_hint_visible_update = gr.update(visible=False) chatbot_content_update, s1_upd, s2_upd, s3_upd, formula_content_update = gr.update(), gr.update(), gr.update(), gr.update(), gr.update() new_active_action_state_to_set, new_current_chat_plot_id = None, current_chat_plot_id updated_chat_histories, new_explored_plot_id_to_set = current_chat_histories, current_explored_plot_id generated_panel_vis_updates = []; generated_bomb_btn_updates = []; generated_formula_btn_updates = []; generated_explore_btn_updates = [] section_title_vis_updates = [gr.update()] * num_unique_sections if is_toggling_off: new_active_action_state_to_set = None; action_col_visible_update = gr.update(visible=False) logging.info(f"Toggling OFF panel {action_type} for {plot_id_clicked}.") for _ in plot_configs: generated_bomb_btn_updates.append(gr.update(value=BOMB_ICON)); generated_formula_btn_updates.append(gr.update(value=FORMULA_ICON)) if current_explored_plot_id: explored_cfg = next((p for p in plot_configs if p["id"] == current_explored_plot_id), None) explored_sec = explored_cfg["section"] if explored_cfg else None for i, sec_name in enumerate(unique_ordered_sections): section_title_vis_updates[i] = gr.update(visible=(sec_name == explored_sec)) for cfg in plot_configs: is_exp = (cfg["id"] == current_explored_plot_id); generated_panel_vis_updates.append(gr.update(visible=is_exp)); generated_explore_btn_updates.append(gr.update(value=ACTIVE_ICON if is_exp else EXPLORE_ICON)) else: for i in range(num_unique_sections): section_title_vis_updates[i] = gr.update(visible=True) for _ in plot_configs: generated_panel_vis_updates.append(gr.update(visible=True)); generated_explore_btn_updates.append(gr.update(value=EXPLORE_ICON)) if action_type == "insights": new_current_chat_plot_id = None else: new_active_action_state_to_set = hypothetical_new_active_state; action_col_visible_update = gr.update(visible=True) new_explored_plot_id_to_set = None logging.info(f"Toggling ON panel {action_type} for {plot_id_clicked}. Cancelling explore view if any.") for i, sec_name in enumerate(unique_ordered_sections): section_title_vis_updates[i] = gr.update(visible=(sec_name == clicked_plot_section)) for cfg in plot_configs: generated_panel_vis_updates.append(gr.update(visible=(cfg["id"] == plot_id_clicked))); generated_explore_btn_updates.append(gr.update(value=EXPLORE_ICON)) for cfg_btn in plot_configs: is_act_ins = new_active_action_state_to_set == {"plot_id": cfg_btn["id"], "type": "insights"} is_act_for = new_active_action_state_to_set == {"plot_id": cfg_btn["id"], "type": "formula"} generated_bomb_btn_updates.append(gr.update(value=ACTIVE_ICON if is_act_ins else BOMB_ICON)); generated_formula_btn_updates.append(gr.update(value=ACTIVE_ICON if is_act_for else FORMULA_ICON)) if action_type == "insights": insights_chatbot_visible_update, insights_chat_input_visible_update, insights_suggestions_row_visible_update = gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) new_current_chat_plot_id = plot_id_clicked history = current_chat_histories.get(plot_id_clicked, []) summary = current_plot_data_for_chatbot.get(plot_id_clicked, f"No summary for '{clicked_plot_label}'.") if not history: prompt, sugg = get_initial_insight_prompt_and_suggestions(plot_id_clicked, clicked_plot_label, summary) llm_hist = [{"role": "user", "content": prompt}] resp = await generate_llm_response(prompt, plot_id_clicked, clicked_plot_label, llm_hist, summary) history = [{"role": "assistant", "content": resp}]; updated_chat_histories = {**current_chat_histories, plot_id_clicked: history} else: _, sugg = get_initial_insight_prompt_and_suggestions(plot_id_clicked, clicked_plot_label, summary) chatbot_content_update = gr.update(value=history) s1_upd,s2_upd,s3_upd = gr.update(value=sugg[0] if sugg else "N/A"),gr.update(value=sugg[1] if len(sugg)>1 else "N/A"),gr.update(value=sugg[2] if len(sugg)>2 else "N/A") elif action_type == "formula": formula_display_visible_update = gr.update(visible=True); formula_close_hint_visible_update = gr.update(visible=True) f_key = PLOT_ID_TO_FORMULA_KEY_MAP.get(plot_id_clicked) f_text = f"**Formula/Methodology for: {clicked_plot_label}** (ID: `{plot_id_clicked}`)\n\n" if f_key and f_key in PLOT_FORMULAS: f_data = PLOT_FORMULAS[f_key]; f_text += f"### {f_data['title']}\n\n{f_data['description']}\n\n**Calculation:**\n" + "\n".join([f"- {s}" for s in f_data['calculation_steps']]) else: f_text += "(No detailed formula information found.)" formula_content_update = gr.update(value=f_text); new_current_chat_plot_id = None final_updates = [action_col_visible_update, insights_chatbot_visible_update, chatbot_content_update, insights_chat_input_visible_update, insights_suggestions_row_visible_update, s1_upd, s2_upd, s3_upd, formula_display_visible_update, formula_content_update, formula_close_hint_visible_update, new_active_action_state_to_set, new_current_chat_plot_id, updated_chat_histories, new_explored_plot_id_to_set] final_updates.extend(generated_panel_vis_updates); final_updates.extend(generated_bomb_btn_updates); final_updates.extend(generated_formula_btn_updates); final_updates.extend(generated_explore_btn_updates); final_updates.extend(section_title_vis_updates) logging.debug(f"handle_panel_action returning {len(final_updates)} updates. Expected {15 + 4*len(plot_configs) + num_unique_sections}.") return final_updates async def handle_chat_message_submission(user_message: str, current_plot_id: str, chat_histories: dict, current_plot_data_for_chatbot: dict ): if not current_plot_id or not user_message.strip(): current_history_for_plot = chat_histories.get(current_plot_id, []) if not isinstance(current_history_for_plot, list): current_history_for_plot = [] yield current_history_for_plot, gr.update(value=""), chat_histories; return cfg = next((p for p in plot_configs if p["id"] == current_plot_id), None) lbl = cfg["label"] if cfg else "Selected Plot" summary = current_plot_data_for_chatbot.get(current_plot_id, f"No summary for '{lbl}'.") hist_for_plot = chat_histories.get(current_plot_id, []) if not isinstance(hist_for_plot, list): hist_for_plot = [] hist = hist_for_plot.copy() + [{"role": "user", "content": user_message}] yield hist, gr.update(value=""), chat_histories resp = await generate_llm_response(user_message, current_plot_id, lbl, hist, summary) hist.append({"role": "assistant", "content": resp}) updated_chat_histories = {**chat_histories, current_plot_id: hist} yield hist, "", updated_chat_histories async def handle_suggested_question_click(suggestion_text: str, current_plot_id: str, chat_histories: dict, current_plot_data_for_chatbot: dict): if not current_plot_id or not suggestion_text.strip() or suggestion_text == "N/A": current_history_for_plot = chat_histories.get(current_plot_id, []) if not isinstance(current_history_for_plot, list): current_history_for_plot = [] yield current_history_for_plot, gr.update(value=""), chat_histories; return async for update_chunk in handle_chat_message_submission(suggestion_text, current_plot_id, chat_histories, current_plot_data_for_chatbot): yield update_chunk def handle_explore_click(plot_id_clicked, current_explored_plot_id_from_state, current_active_panel_action_state): logging.info(f"Explore Click: Plot '{plot_id_clicked}'. Current Explored: {current_explored_plot_id_from_state}. Active Panel: {current_active_panel_action_state}") num_plots = len(plot_configs) if not plot_ui_objects: logging.error("plot_ui_objects not populated for handle_explore_click.") error_list_len = 4 + (4 * num_plots) + num_unique_sections; error_list = [gr.update()] * error_list_len error_list[0] = current_explored_plot_id_from_state; error_list[2] = current_active_panel_action_state return error_list new_explored_id_to_set = None is_toggling_off_explore = (plot_id_clicked == current_explored_plot_id_from_state) action_col_upd = gr.update(); new_active_panel_state_upd = current_active_panel_action_state; formula_hint_upd = gr.update(visible=False) panel_vis_updates = []; explore_btns_updates = []; bomb_btns_updates = []; formula_btns_updates = [] section_title_vis_updates = [gr.update()] * num_unique_sections clicked_cfg = next((p for p in plot_configs if p["id"] == plot_id_clicked), None) sec_of_clicked = clicked_cfg["section"] if clicked_cfg else None if is_toggling_off_explore: new_explored_id_to_set = None logging.info(f"Stopping explore for {plot_id_clicked}. All plots/sections to be visible.") for i in range(num_unique_sections): section_title_vis_updates[i] = gr.update(visible=True) for _ in plot_configs: panel_vis_updates.append(gr.update(visible=True)); explore_btns_updates.append(gr.update(value=EXPLORE_ICON)); bomb_btns_updates.append(gr.update()); formula_btns_updates.append(gr.update()) else: new_explored_id_to_set = plot_id_clicked logging.info(f"Exploring {plot_id_clicked}. Hiding other plots/sections.") for i, sec_name in enumerate(unique_ordered_sections): section_title_vis_updates[i] = gr.update(visible=(sec_name == sec_of_clicked)) for cfg in plot_configs: is_target = (cfg["id"] == new_explored_id_to_set); panel_vis_updates.append(gr.update(visible=is_target)); explore_btns_updates.append(gr.update(value=ACTIVE_ICON if is_target else EXPLORE_ICON)) if current_active_panel_action_state: logging.info("Closing active insight/formula panel due to explore click.") action_col_upd = gr.update(visible=False); new_active_panel_state_upd = None; formula_hint_upd = gr.update(visible=False) bomb_btns_updates = [gr.update(value=BOMB_ICON) for _ in plot_configs]; formula_btns_updates = [gr.update(value=FORMULA_ICON) for _ in plot_configs] else: bomb_btns_updates = [gr.update() for _ in plot_configs]; formula_btns_updates = [gr.update() for _ in plot_configs] final_explore_updates = [new_explored_id_to_set, action_col_upd, new_active_panel_state_upd, formula_hint_upd] final_explore_updates.extend(panel_vis_updates); final_explore_updates.extend(explore_btns_updates); final_explore_updates.extend(bomb_btns_updates); final_explore_updates.extend(formula_btns_updates); final_explore_updates.extend(section_title_vis_updates) logging.debug(f"handle_explore_click returning {len(final_explore_updates)} updates. Expected {4 + 4*len(plot_configs) + num_unique_sections}.") return final_explore_updates _base_action_panel_ui_outputs = [global_actions_column_ui, insights_chatbot_ui, insights_chatbot_ui, insights_chat_input_ui, insights_suggestions_row_ui, insights_suggestion_1_btn, insights_suggestion_2_btn, insights_suggestion_3_btn, formula_display_markdown_ui, formula_display_markdown_ui, formula_close_hint_md] _action_panel_state_outputs = [active_panel_action_state, current_chat_plot_id_st, chat_histories_st, explored_plot_id_state] action_panel_outputs_list = _base_action_panel_ui_outputs + _action_panel_state_outputs action_panel_outputs_list.extend([plot_ui_objects.get(pc["id"], {}).get("panel_component", gr.update()) for pc in plot_configs]); action_panel_outputs_list.extend([plot_ui_objects.get(pc["id"], {}).get("bomb_button", gr.update()) for pc in plot_configs]); action_panel_outputs_list.extend([plot_ui_objects.get(pc["id"], {}).get("formula_button", gr.update()) for pc in plot_configs]); action_panel_outputs_list.extend([plot_ui_objects.get(pc["id"], {}).get("explore_button", gr.update()) for pc in plot_configs]); action_panel_outputs_list.extend([section_titles_map.get(s_name, gr.update()) for s_name in unique_ordered_sections]) _explore_base_outputs = [explored_plot_id_state, global_actions_column_ui, active_panel_action_state, formula_close_hint_md] explore_outputs_list = _explore_base_outputs explore_outputs_list.extend([plot_ui_objects.get(pc["id"], {}).get("panel_component", gr.update()) for pc in plot_configs]); explore_outputs_list.extend([plot_ui_objects.get(pc["id"], {}).get("explore_button", gr.update()) for pc in plot_configs]); explore_outputs_list.extend([plot_ui_objects.get(pc["id"], {}).get("bomb_button", gr.update()) for pc in plot_configs]); explore_outputs_list.extend([plot_ui_objects.get(pc["id"], {}).get("formula_button", gr.update()) for pc in plot_configs]); explore_outputs_list.extend([section_titles_map.get(s_name, gr.update()) for s_name in unique_ordered_sections]) action_click_inputs = [active_panel_action_state, chat_histories_st, current_chat_plot_id_st, plot_data_for_chatbot_st, explored_plot_id_state] explore_click_inputs = [explored_plot_id_state, active_panel_action_state] def create_panel_action_handler(p_id, action_type_str): async def _handler(curr_active_val, curr_chats_val, curr_chat_pid, curr_plot_data, curr_explored_id): return await handle_panel_action(p_id, action_type_str, curr_active_val, curr_chats_val, curr_chat_pid, curr_plot_data, curr_explored_id) return _handler for config_item in plot_configs: plot_id = config_item["id"] if plot_id in plot_ui_objects: ui_obj = plot_ui_objects[plot_id] if ui_obj.get("bomb_button"): ui_obj["bomb_button"].click(fn=create_panel_action_handler(plot_id, "insights"), inputs=action_click_inputs, outputs=action_panel_outputs_list, api_name=f"action_insights_{plot_id}") if ui_obj.get("formula_button"): ui_obj["formula_button"].click(fn=create_panel_action_handler(plot_id, "formula"), inputs=action_click_inputs, outputs=action_panel_outputs_list, api_name=f"action_formula_{plot_id}") if ui_obj.get("explore_button"): ui_obj["explore_button"].click(fn=lambda current_explored_val, current_active_panel_val, p_id=plot_id: handle_explore_click(p_id, current_explored_val, current_active_panel_val), inputs=explore_click_inputs, outputs=explore_outputs_list, api_name=f"action_explore_{plot_id}") else: logging.warning(f"UI object for plot_id '{plot_id}' not found for click handlers.") chat_submission_outputs = [insights_chatbot_ui, insights_chat_input_ui, chat_histories_st] chat_submission_inputs = [insights_chat_input_ui, current_chat_plot_id_st, chat_histories_st, plot_data_for_chatbot_st] insights_chat_input_ui.submit(fn=handle_chat_message_submission, inputs=chat_submission_inputs, outputs=chat_submission_outputs, api_name="submit_chat_message") suggestion_click_inputs_base = [current_chat_plot_id_st, chat_histories_st, plot_data_for_chatbot_st] insights_suggestion_1_btn.click(fn=handle_suggested_question_click, inputs=[insights_suggestion_1_btn] + suggestion_click_inputs_base, outputs=chat_submission_outputs, api_name="click_suggestion_1") 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") # 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)") 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("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.") with gr.Row(): with gr.Column(scale=1): gr.Markdown("### Suggested Key Results (da analisi 'Sempre')") key_results_cbg = gr.CheckboxGroup(label="Select Key Results", choices=[], value=[], interactive=True) with gr.Column(scale=3): gr.Markdown("### Detailed OKRs and Tasks for Selected Key Results") okr_detail_display_md = gr.Markdown("I dettagli OKR appariranno qui dopo l'esecuzione della pipeline AI.") def update_okr_display_on_selection(selected_kr_unique_ids: list, raw_orchestration_results: dict, all_krs_for_selection: list): if not raw_orchestration_results or not AGENTIC_MODULES_LOADED: return gr.update(value="Nessun dato dalla pipeline AI o moduli non caricati.") actionable_okrs_dict = raw_orchestration_results.get("actionable_okrs_and_tasks") if not actionable_okrs_dict or not isinstance(actionable_okrs_dict.get("okrs"), list): return gr.update(value="Nessun OKR trovato nei risultati della pipeline.") okrs_list = actionable_okrs_dict["okrs"] kr_id_to_indices = {kr_info['unique_kr_id']: (kr_info['okr_index'], kr_info['kr_index']) for kr_info in all_krs_for_selection} selected_krs_by_okr_idx = defaultdict(list) if selected_kr_unique_ids: for kr_unique_id in selected_kr_unique_ids: if kr_unique_id in kr_id_to_indices: okr_idx, kr_idx = kr_id_to_indices[kr_unique_id]; selected_krs_by_okr_idx[okr_idx].append(kr_idx) output_md_parts = [] if not okrs_list: output_md_parts.append("Nessun OKR generato.") else: for okr_idx, okr_data in enumerate(okrs_list): accepted_indices_for_this_okr = selected_krs_by_okr_idx.get(okr_idx) if selected_kr_unique_ids: if accepted_indices_for_this_okr is not None: output_md_parts.append(format_single_okr_for_display(okr_data, accepted_kr_indices=accepted_indices_for_this_okr, okr_main_index=okr_idx)) else: output_md_parts.append(format_single_okr_for_display(okr_data, accepted_kr_indices=None, okr_main_index=okr_idx)) if not output_md_parts and selected_kr_unique_ids: final_md = "Nessun OKR corrisponde alla selezione corrente o i KR selezionati non hanno task dettagliati." elif not output_md_parts and not selected_kr_unique_ids: final_md = "Nessun OKR generato." else: final_md = "\n\n---\n\n".join(output_md_parts) return gr.update(value=final_md) if AGENTIC_MODULES_LOADED: key_results_cbg.change(fn=update_okr_display_on_selection, inputs=[key_results_cbg, orchestration_raw_results_st, key_results_for_selection_st], outputs=[okr_detail_display_md]) async def refresh_analytics_graphs_ui(current_token_state_val, date_filter_val, custom_start_val, custom_end_val, current_chat_histories_val): logging.info("Refreshing analytics graph UI elements and resetting actions/chat.") start_time = time.time() plot_gen_results = update_analytics_plots_figures(current_token_state_val, date_filter_val, custom_start_val, custom_end_val, plot_configs) status_msg, gen_figs, new_summaries = plot_gen_results[0], plot_gen_results[1:-1], plot_gen_results[-1] all_updates = [status_msg] all_updates.extend(gen_figs if len(gen_figs) == len(plot_configs) else [create_placeholder_plot("Error", f"Fig missing {i}") for i in range(len(plot_configs))]) all_updates.extend([gr.update(visible=False), gr.update(value=[], visible=False), gr.update(value="", visible=False), gr.update(visible=False), gr.update(value="S1"), gr.update(value="S2"), gr.update(value="S3"), gr.update(value="Formula details here.", visible=False), gr.update(visible=False)]) all_updates.extend([None, None, {}, new_summaries]) for _ in plot_configs: all_updates.extend([gr.update(value=BOMB_ICON), gr.update(value=FORMULA_ICON), gr.update(value=EXPLORE_ICON), gr.update(visible=True)]) all_updates.append(None) all_updates.extend([gr.update(visible=True)] * num_unique_sections) end_time = time.time() logging.info(f"Analytics graph refresh took {end_time - start_time:.2f} seconds.") expected_len = 15 + 5 * len(plot_configs) + num_unique_sections 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): # 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 ( 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 logging.info("Agentic pipeline starting autonomously with 'Sempre' filter.") yield ( gr.update(value="Analisi AI (Sempre) in corso..."), gr.update(choices=[], value=[], interactive=False), gr.update(value="Dettagli OKR (Sempre) in corso di generazione..."), 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)..." ) if not AGENTIC_MODULES_LOADED: logging.warning("Agentic modules not loaded. Skipping autonomous pipeline.") yield ( gr.update(value="Moduli AI non caricati. Report non disponibile."), gr.update(choices=[], value=[], interactive=False), gr.update(value="Moduli AI non caricati. OKR non disponibili."), None, [], [], "Pipeline AI: Moduli non caricati." ) return try: date_filter_val_agentic = "Sempre"; custom_start_val_agentic = None; custom_end_val_agentic = None orchestration_output = await run_full_analytics_orchestration(current_token_state_val, date_filter_val_agentic, custom_start_val_agentic, custom_end_val_agentic) agentic_status_text = "Pipeline AI (Sempre) completata." logging.info(f"Autonomous agentic pipeline finished. Output keys: {orchestration_output.keys() if orchestration_output else 'None'}") if orchestration_output: orchestration_results_update = orchestration_output report_str = orchestration_output.get('comprehensive_analysis_report') agentic_report_md_update = gr.update(value=format_report_to_markdown(report_str)) actionable_okrs = orchestration_output.get('actionable_okrs_and_tasks') krs_for_ui_selection_list = extract_key_results_for_selection(actionable_okrs) krs_for_selection_update = krs_for_ui_selection_list kr_choices_for_cbg = [(kr['kr_description'], kr['unique_kr_id']) for kr in krs_for_ui_selection_list] key_results_cbg_update = gr.update(choices=kr_choices_for_cbg, value=[], interactive=True) all_okrs_md_parts = [] if actionable_okrs and isinstance(actionable_okrs.get("okrs"), list): for okr_idx, okr_item in enumerate(actionable_okrs["okrs"]): all_okrs_md_parts.append(format_single_okr_for_display(okr_item, accepted_kr_indices=None, okr_main_index=okr_idx)) if not all_okrs_md_parts: okr_detail_display_md_update = gr.update(value="Nessun OKR generato o trovato (Sempre).") else: okr_detail_display_md_update = gr.update(value="\n\n---\n\n".join(all_okrs_md_parts)) selected_krs_update = [] else: agentic_report_md_update = gr.update(value="Nessun report generato dalla pipeline AI (Sempre).") key_results_cbg_update = gr.update(choices=[], value=[], interactive=False) okr_detail_display_md_update = gr.update(value="Nessun OKR generato o errore nella pipeline AI (Sempre).") orchestration_results_update = None; selected_krs_update = []; krs_for_selection_update = [] yield (agentic_report_md_update, key_results_cbg_update, okr_detail_display_md_update, orchestration_results_update, selected_krs_update, krs_for_selection_update, agentic_status_text) except Exception as e: logging.error(f"Error during autonomous agentic pipeline execution: {e}", exc_info=True) agentic_status_text = f"Errore pipeline AI (Sempre): {str(e)}" yield (gr.update(value=f"Errore generazione report AI (Sempre): {str(e)}"), gr.update(choices=[], value=[], interactive=False), gr.update(value=f"Errore generazione OKR AI (Sempre): {str(e)}"), None, [], [], agentic_status_text) graph_refresh_outputs_list = [analytics_status_md] graph_refresh_outputs_list.extend([plot_ui_objects.get(pc["id"], {}).get("plot_component", gr.update()) for pc in plot_configs]) _ui_resets_for_graphs = [global_actions_column_ui, insights_chatbot_ui, insights_chat_input_ui, insights_suggestions_row_ui, insights_suggestion_1_btn, insights_suggestion_2_btn, insights_suggestion_3_btn, formula_display_markdown_ui, formula_close_hint_md] graph_refresh_outputs_list.extend(_ui_resets_for_graphs) _state_resets_for_graphs = [active_panel_action_state, current_chat_plot_id_st, chat_histories_st, plot_data_for_chatbot_st] graph_refresh_outputs_list.extend(_state_resets_for_graphs) for pc in plot_configs: pid = pc["id"]; graph_refresh_outputs_list.extend([plot_ui_objects.get(pid, {}).get("bomb_button", gr.update()), plot_ui_objects.get(pid, {}).get("formula_button", gr.update()), plot_ui_objects.get(pid, {}).get("explore_button", gr.update()), plot_ui_objects.get(pid, {}).get("panel_component", gr.update())]) graph_refresh_outputs_list.append(explored_plot_id_state) graph_refresh_outputs_list.extend([section_titles_map.get(s_name, gr.update()) for s_name in unique_ordered_sections]) 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] apply_filter_btn.click( fn=refresh_analytics_graphs_ui, inputs=graph_refresh_inputs, outputs=graph_refresh_outputs_list, show_progress="full" ) 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" ) initial_load_event.then( 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, inputs=agentic_pipeline_inputs, outputs=agentic_pipeline_outputs_list, show_progress="minimal" ) sync_event_part1 = sync_data_btn.click( fn=sync_all_linkedin_data_orchestrator, inputs=[token_state], outputs=[sync_status_html_output, token_state], show_progress="full" ) 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], show_progress=False ) 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, # token_state is the first element outputs=agentic_pipeline_outputs_list, show_progress="minimal" ) sync_event_part3 = sync_event_part2.then( fn=display_main_dashboard, inputs=[token_state], outputs=[dashboard_display_html], show_progress=False ) sync_event_graphs_after_sync = sync_event_part3.then( 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" ) 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 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)