app.py

# app.py
import gradio as gr
import pandas as pd
import os
import logging
import matplotlib
matplotlib.use('Agg') # Set backend for Matplotlib to avoid GUI conflicts with Gradio
import matplotlib.pyplot as plt
import time # For profiling if needed
from datetime import datetime, timedelta # Added timedelta
import numpy as np
from collections import OrderedDict, defaultdict # To maintain section order and for OKR processing
import asyncio # For async operations

# --- Module Imports ---
from gradio_utils import get_url_user_token
# Functions from newly created/refactored modules
from config import (
    LINKEDIN_CLIENT_ID_ENV_VAR, BUBBLE_APP_NAME_ENV_VAR,
    BUBBLE_API_KEY_PRIVATE_ENV_VAR, BUBBLE_API_ENDPOINT_ENV_VAR,
    PLOT_ID_TO_FORMULA_KEY_MAP)
from services.state_manager import process_and_store_bubble_token
from services.sync_logic import sync_all_linkedin_data_orchestrator
from ui.ui_generators import (
    display_main_dashboard,
    build_analytics_tab_plot_area, # EXPECTED TO RETURN: plot_ui_objects, section_titles_map
    BOMB_ICON, EXPLORE_ICON, FORMULA_ICON, ACTIVE_ICON
)
from ui.analytics_plot_generator import update_analytics_plots_figures, create_placeholder_plot
from formulas import PLOT_FORMULAS

# --- EXISTING CHATBOT MODULE IMPORTS ---
from chatbot_prompts import get_initial_insight_prompt_and_suggestions # MODIFIED IMPORT
from chatbot_handler import generate_llm_response
# --- END EXISTING CHATBOT MODULE IMPORTS ---

# --- NEW AGENTIC PIPELINE IMPORTS ---
try:
    from run_agentic_pipeline import run_full_analytics_orchestration
    from ui.insights_ui_generator import (
        format_report_to_markdown,
        extract_key_results_for_selection,
        format_single_okr_for_display
    )
    AGENTIC_MODULES_LOADED = True
except ImportError as e:
    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
    def format_report_to_markdown(report_string): return "Agentic modules not loaded. Report unavailable."
    def extract_key_results_for_selection(okrs_dict): return []
    def format_single_okr_for_display(okr_data, **kwargs): return "Agentic modules not loaded. OKR display unavailable."

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(module)s - %(message)s')

# 1. Set Vertex AI usage preference (if applicable)
os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "False"

# 2. Get your API key from your chosen environment variable name
user_provided_api_key = os.environ.get("GEMINI_API_KEY")

if user_provided_api_key:
    os.environ["GOOGLE_API_KEY"] = user_provided_api_key
    logging.info("GOOGLE_API_KEY environment variable has been set from GEMINI_API_KEY.")
else:
    logging.error(f"CRITICAL ERROR: The API key environment variable 'GEMINI_API_KEY' was not found. The application may not function correctly.")


# --- Gradio UI Blocks ---
with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
                title="LinkedIn Organization Dashboard") as app:
    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(), # 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",
        "config_eb_labels_col": "li_eb_label"
    })

    # States for existing analytics tab chatbot
    chat_histories_st = gr.State({})
    current_chat_plot_id_st = gr.State(None)
    plot_data_for_chatbot_st = gr.State({})

    # --- NEW STATES FOR AGENTIC PIPELINE ---
    orchestration_raw_results_st = gr.State(None)
    key_results_for_selection_st = gr.State([])
    selected_key_result_ids_st = gr.State([])


    gr.Markdown("# 🚀 LinkedIn Organization Dashboard")
    url_user_token_display = gr.Textbox(label="User Token (Nascosto)", interactive=False, visible=False)
    status_box = gr.Textbox(label="Stato Generale Token LinkedIn", interactive=False, value="Inizializzazione...")
    org_urn_display = gr.Textbox(label="URN Organizzazione (Nascosto)", interactive=False, visible=False)

    app.load(fn=get_url_user_token, inputs=None, outputs=[url_user_token_display, org_urn_display], api_name="get_url_params", show_progress=False)

    def initial_load_sequence(url_token, org_urn_val, current_state):
        status_msg, new_state, btn_update = process_and_store_bubble_token(url_token, org_urn_val, current_state)
        dashboard_content = display_main_dashboard(new_state)
        return status_msg, new_state, btn_update, dashboard_content

    with gr.Tabs() as tabs:
        with gr.TabItem("1️⃣ Dashboard & Sync", id="tab_dashboard_sync"):
            gr.Markdown("Il sistema controlla i dati esistenti da Bubble. 'Sincronizza' si attiva se sono necessari nuovi dati.")
            sync_data_btn = gr.Button("🔄 Sincronizza Dati LinkedIn", variant="primary", visible=False, interactive=False)
            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>")

        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(
                        "<p style='font-size:0.9em; text-align:center; margin-top:10px;'><em>Click the active ƒ button on the plot again to close this panel.</em></p>",
                        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)