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

# --- 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
)
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,
    build_analytics_tab_plot_area, # Import the updated UI builder
    BOMB_ICON, EXPLORE_ICON, FORMULA_ICON, ACTIVE_ICON # Import icons
)
# Corrected import for analytics_data_processing
from analytics_data_processing import prepare_filtered_analytics_data
from analytics_plot_generator import (
    generate_posts_activity_plot, # Make sure this is available if posts_activity is in map
    generate_mentions_activity_plot, generate_mention_sentiment_plot,
    generate_followers_count_over_time_plot,
    generate_followers_growth_rate_plot,
    generate_followers_by_demographics_plot,
    generate_engagement_rate_over_time_plot,
    generate_reach_over_time_plot,
    generate_impressions_over_time_plot,
    create_placeholder_plot,
    generate_likes_over_time_plot,
    generate_clicks_over_time_plot,
    generate_shares_over_time_plot,
    generate_comments_over_time_plot,
    generate_comments_sentiment_breakdown_plot,
    generate_post_frequency_plot,
    generate_content_format_breakdown_plot,
    generate_content_topic_breakdown_plot
)
from formulas import PLOT_FORMULAS # Import the formula descriptions

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

# Mapping from plot_configs IDs to PLOT_FORMULAS keys
PLOT_ID_TO_FORMULA_KEY_MAP = {
    "posts_activity": "posts_activity", # Example, if you add it back to plot_configs
    "mentions_activity": "mentions_activity", # Example, if you add it back
    "mention_sentiment": "mention_sentiment", # Example, if you add it back
    "followers_count": "followers_count_over_time",
    "followers_growth_rate": "followers_growth_rate",
    "followers_by_location": "followers_by_demographics",
    "followers_by_role": "followers_by_demographics",
    "followers_by_industry": "followers_by_demographics",
    "followers_by_seniority": "followers_by_demographics",
    "engagement_rate": "engagement_rate_over_time",
    "reach_over_time": "reach_over_time",
    "impressions_over_time": "impressions_over_time",
    "likes_over_time": "likes_over_time",
    "clicks_over_time": "clicks_over_time",
    "shares_over_time": "shares_over_time",
    "comments_over_time": "comments_over_time",
    "comments_sentiment": "comments_sentiment_breakdown",
    "post_frequency_cs": "post_frequency",
    "content_format_breakdown_cs": "content_format_breakdown",
    "content_topic_breakdown_cs": "content_topic_breakdown",
    "mention_analysis_volume": "mentions_activity",
    "mention_analysis_sentiment": "mention_sentiment"
}


# --- Analytics Tab: Plot Figure Generation Function ---
def update_analytics_plots_figures(token_state_value, date_filter_option, custom_start_date, custom_end_date):
    logging.info(f"Updating analytics plot figures. Filter: {date_filter_option}, Custom Start: {custom_start_date}, Custom End: {custom_end_date}")
    # This num_expected_plots should align with the number of gr.Plot components expected as output.
    # The plot_configs list has 19 items.
    # If generate_posts_activity_plot, generate_mentions_activity_plot, generate_mention_sentiment_plot
    # are added back to the main list of plots directly, this number might change.
    # For now, it seems the detailed mention plots are re-using figures.
    num_expected_plots = 19 # Adjusted to match plot_configs length for clarity in this function's direct output list construction

    if not token_state_value or not token_state_value.get("token"):
        message = "❌ Access denied. No token. Cannot generate analytics."
        logging.warning(message)
        placeholder_figs = [create_placeholder_plot(title="Access Denied", message="No token.") for _ in range(num_expected_plots)]
        return [message] + placeholder_figs

    try:
        (filtered_merged_posts_df,
         filtered_mentions_df,
         date_filtered_follower_stats_df,
         raw_follower_stats_df,
         start_dt_for_msg, end_dt_for_msg) = \
            prepare_filtered_analytics_data(
                token_state_value, date_filter_option, custom_start_date, custom_end_date
            )
    except Exception as e:
        error_msg = f"❌ Error preparing analytics data: {e}"
        logging.error(error_msg, exc_info=True)
        placeholder_figs = [create_placeholder_plot(title="Data Preparation Error", message=str(e)) for _ in range(num_expected_plots)]
        return [error_msg] + placeholder_figs

    date_column_posts = token_state_value.get("config_date_col_posts", "published_at")
    date_column_mentions = token_state_value.get("config_date_col_mentions", "date")
    media_type_col_name = token_state_value.get("config_media_type_col", "media_type")
    eb_labels_col_name = token_state_value.get("config_eb_labels_col", "li_eb_label") # Corrected from li_eb_label to li_eb_labels if that's the actual key in formulas

    plot_figs = []
    try:
        # These plots are generated once and potentially re-used if their IDs match later plot_configs
        # However, the current plot_configs doesn't list these explicitly at the start,
        # but "mention_analysis_volume" and "mention_analysis_sentiment" will use them.
        fig_mentions_activity_shared = generate_mentions_activity_plot(filtered_mentions_df, date_column=date_column_mentions)
        fig_mention_sentiment_shared = generate_mention_sentiment_plot(filtered_mentions_df)

        # Order matters here, must match plot_configs
        plot_figs.append(generate_followers_count_over_time_plot(date_filtered_follower_stats_df, type_value='follower_gains_monthly'))
        plot_figs.append(generate_followers_growth_rate_plot(date_filtered_follower_stats_df, type_value='follower_gains_monthly'))
        plot_figs.append(generate_followers_by_demographics_plot(raw_follower_stats_df, type_value='follower_geo', plot_title="Followers by Location"))
        plot_figs.append(generate_followers_by_demographics_plot(raw_follower_stats_df, type_value='follower_function', plot_title="Followers by Role"))
        plot_figs.append(generate_followers_by_demographics_plot(raw_follower_stats_df, type_value='follower_industry', plot_title="Followers by Industry"))
        plot_figs.append(generate_followers_by_demographics_plot(raw_follower_stats_df, type_value='follower_seniority', plot_title="Followers by Seniority"))
        plot_figs.append(generate_engagement_rate_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts))
        plot_figs.append(generate_reach_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts))
        plot_figs.append(generate_impressions_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts))
        plot_figs.append(generate_likes_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts))
        plot_figs.append(generate_clicks_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts))
        plot_figs.append(generate_shares_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts))
        plot_figs.append(generate_comments_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts))
        plot_figs.append(generate_comments_sentiment_breakdown_plot(filtered_merged_posts_df, sentiment_column='comment_sentiment'))
        plot_figs.append(generate_post_frequency_plot(filtered_merged_posts_df, date_column=date_column_posts))
        plot_figs.append(generate_content_format_breakdown_plot(filtered_merged_posts_df, format_col=media_type_col_name))
        plot_figs.append(generate_content_topic_breakdown_plot(filtered_merged_posts_df, topics_col=eb_labels_col_name)) # Ensure eb_labels_col_name matches key in formulas if different from 'li_eb_labels'
        plot_figs.append(fig_mentions_activity_shared) # For "mention_analysis_volume"
        plot_figs.append(fig_mention_sentiment_shared) # For "mention_analysis_sentiment"

        message = f"📊 Analytics updated for period: {date_filter_option}"
        if date_filter_option == "Custom Range":
            s_display = start_dt_for_msg.strftime('%Y-%m-%d') if start_dt_for_msg else "Any"
            e_display = end_dt_for_msg.strftime('%Y-%m-%d') if end_dt_for_msg else "Any"
            message += f" (From: {s_display} To: {e_display})"

        final_plot_figs = []
        for i, p_fig in enumerate(plot_figs):
            if p_fig is not None and not isinstance(p_fig, str):
                final_plot_figs.append(p_fig)
            else:
                logging.warning(f"Plot figure generation failed or returned unexpected type for slot {i}, using placeholder. Figure: {p_fig}")
                final_plot_figs.append(create_placeholder_plot(title="Plot Error", message="Failed to generate this plot figure."))
        
        # Ensure the list has exactly num_expected_plots items.
        # This padding should ideally not be needed if plot_figs is constructed correctly to match num_expected_plots.
        while len(final_plot_figs) < num_expected_plots:
            logging.warning(f"Padding missing plot figure. Expected {num_expected_plots}, got {len(final_plot_figs)}.")
            final_plot_figs.append(create_placeholder_plot(title="Missing Plot", message="Plot figure could not be generated."))
        
        return [message] + final_plot_figs[:num_expected_plots] # Ensure correct number of plot figures are returned

    except Exception as e:
        error_msg = f"❌ Error generating analytics plot figures: {e}"
        logging.error(error_msg, exc_info=True)
        placeholder_figs = [create_placeholder_plot(title="Plot Generation Error", message=str(e)) for _ in range(num_expected_plots)]
        return [error_msg] + placeholder_figs


# --- 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(), "bubble_follower_stats_df": pd.DataFrame(),
        "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_labels" # Ensure this matches the column name used in plot generator and formulas.py
    })

    gr.Markdown("# 🚀 LinkedIn Organization Dashboard")
    url_user_token_display = gr.Textbox(label="User Token (Hidden)", interactive=False, visible=False)
    status_box = gr.Textbox(label="Overall LinkedIn Token Status", interactive=False, value="Initializing...")
    org_urn_display = gr.Textbox(label="Organization URN (Hidden)", 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("System checks for existing data from Bubble. 'Sync' activates if new data is needed.")
            sync_data_btn = gr.Button("🔄 Sync LinkedIn Data", variant="primary", visible=False, interactive=False)
            sync_status_html_output = gr.HTML("<p style='text-align:center;'>Sync status...</p>")
            dashboard_display_html = gr.HTML("<p style='text-align:center;'>Dashboard loading...</p>")

            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"
            )

        with gr.TabItem("2️⃣ Analytics", id="tab_analytics"):
            gr.Markdown("## 📈 LinkedIn Performance Analytics")
            gr.Markdown("Select a date range. Click buttons for actions.")

            analytics_status_md = gr.Markdown("Analytics status...")

            with gr.Row():
                date_filter_selector = gr.Radio(
                    ["All Time", "Last 7 Days", "Last 30 Days", "Custom Range"],
                    label="Select Date Range", value="All Time", scale=3
                )
                with gr.Column(scale=2):
                    custom_start_date_picker = gr.DateTime(label="Start Date", visible=False, include_time=False, type="datetime")
                    custom_end_date_picker = gr.DateTime(label="End Date", visible=False, include_time=False, type="datetime")

            apply_filter_btn = gr.Button("🔍 Apply Filter & Refresh Analytics", variant="primary")

            def toggle_custom_date_pickers(selection):
                is_custom = selection == "Custom Range"
                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": "Followers Count Over Time", "id": "followers_count", "section": "Follower Dynamics"},
                {"label": "Followers Growth Rate", "id": "followers_growth_rate", "section": "Follower Dynamics"},
                {"label": "Followers by Location", "id": "followers_by_location", "section": "Follower Demographics"},
                {"label": "Followers by Role (Function)", "id": "followers_by_role", "section": "Follower Demographics"},
                {"label": "Followers by Industry", "id": "followers_by_industry", "section": "Follower Demographics"},
                {"label": "Followers by Seniority", "id": "followers_by_seniority", "section": "Follower Demographics"},
                {"label": "Engagement Rate Over Time", "id": "engagement_rate", "section": "Post Performance Insights"},
                {"label": "Reach Over Time", "id": "reach_over_time", "section": "Post Performance Insights"},
                {"label": "Impressions Over Time", "id": "impressions_over_time", "section": "Post Performance Insights"},
                {"label": "Reactions (Likes) Over Time", "id": "likes_over_time", "section": "Post Performance Insights"},
                {"label": "Clicks Over Time", "id": "clicks_over_time", "section": "Detailed Post Engagement Over Time"},
                {"label": "Shares Over Time", "id": "shares_over_time", "section": "Detailed Post Engagement Over Time"},
                {"label": "Comments Over Time", "id": "comments_over_time", "section": "Detailed Post Engagement Over Time"},
                {"label": "Breakdown of Comments by Sentiment", "id": "comments_sentiment", "section": "Detailed Post Engagement Over Time"},
                {"label": "Post Frequency", "id": "post_frequency_cs", "section": "Content Strategy Analysis"},
                {"label": "Breakdown of Content by Format", "id": "content_format_breakdown_cs", "section": "Content Strategy Analysis"},
                {"label": "Breakdown of Content by Topics", "id": "content_topic_breakdown_cs", "section": "Content Strategy Analysis"},
                {"label": "Mentions Volume Over Time (Detailed)", "id": "mention_analysis_volume", "section": "Mention Analysis (Detailed)"},
                {"label": "Breakdown of Mentions by Sentiment (Detailed)", "id": "mention_analysis_sentiment", "section": "Mention Analysis (Detailed)"}
            ]
            assert len(plot_configs) == 19, "Mismatch in plot_configs and expected plots."

            active_panel_action_state = gr.State(None)
            explored_plot_id_state = gr.State(None)

            plot_ui_objects = {}

            with gr.Row(equal_height=False):
                with gr.Column(scale=8) as plots_area_col:
                    plot_ui_objects = build_analytics_tab_plot_area(plot_configs)

                with gr.Column(scale=4, visible=False) as global_actions_column_ui:
                    gr.Markdown("### 💡 Generated Content")
                    global_actions_markdown_ui = gr.Markdown("Click a button (💣, ƒ) on a plot to see content here.")

            # --- Event Handler for Insights and Formula Buttons ---
            def handle_panel_action(plot_id_clicked, action_type, current_active_action_from_state, current_token_state_val):
                logging.info(f"Action '{action_type}' for plot: {plot_id_clicked}. Current active from state: {current_active_action_from_state}")

                if not plot_ui_objects or plot_id_clicked not in plot_ui_objects:
                    logging.error(f"plot_ui_objects not populated or plot_id {plot_id_clicked} not found during handle_panel_action.")
                    error_updates = [gr.update(visible=False), "Error: UI components not ready.", None] + [gr.update() for _ in range(2 * len(plot_configs))]
                    return error_updates

                clicked_plot_label = plot_ui_objects.get(plot_id_clicked, {}).get("label", "Selected Plot")
                
                hypothetical_new_active_state = {"plot_id": plot_id_clicked, "type": action_type}
                is_toggling_off = current_active_action_from_state == hypothetical_new_active_state
                
                new_active_action_state_to_set = None
                content_text = ""
                action_col_visible = False

                if is_toggling_off:
                    new_active_action_state_to_set = None
                    content_text = f"{action_type.capitalize()} panel for '{clicked_plot_label}' closed."
                    action_col_visible = False
                    logging.info(f"Closing {action_type} panel for {plot_id_clicked}")
                else:
                    new_active_action_state_to_set = hypothetical_new_active_state
                    action_col_visible = True
                    if action_type == "insights":
                        # TODO: Implement actual insight generation
                        content_text = f"**Insights for: {clicked_plot_label}**\n\nPlot ID: `{plot_id_clicked}`.\n(AI insights generation placeholder)"
                    elif action_type == "formula":
                        formula_key = PLOT_ID_TO_FORMULA_KEY_MAP.get(plot_id_clicked)
                        if formula_key and formula_key in PLOT_FORMULAS:
                            formula_data = PLOT_FORMULAS[formula_key]
                            content_text = f"### {formula_data['title']}\n\n"
                            content_text += f"**Description:**\n{formula_data['description']}\n\n"
                            content_text += "**Calculation Steps:**\n"
                            for step in formula_data['calculation_steps']:
                                content_text += f"- {step}\n"
                        else:
                            content_text = f"**Formula/Methodology for: {clicked_plot_label}**\n\nPlot ID: `{plot_id_clicked}`.\n(No detailed formula information found for this plot ID in `formulas.py`)"
                        logging.info(f"Displaying formula for {plot_id_clicked} (mapped to {formula_key})")
                    logging.info(f"Opening/switching to {action_type} panel for {plot_id_clicked}")

                all_button_updates = []
                for cfg_item in plot_configs:
                    p_id_iter = cfg_item["id"]
                    if p_id_iter in plot_ui_objects:
                        if new_active_action_state_to_set == {"plot_id": p_id_iter, "type": "insights"}:
                            all_button_updates.append(gr.update(value=ACTIVE_ICON))
                        else:
                            all_button_updates.append(gr.update(value=BOMB_ICON))

                        if new_active_action_state_to_set == {"plot_id": p_id_iter, "type": "formula"}:
                            all_button_updates.append(gr.update(value=ACTIVE_ICON))
                        else:
                            all_button_updates.append(gr.update(value=FORMULA_ICON))
                    else:
                        all_button_updates.extend([gr.update(), gr.update()])
                
                final_updates = [
                    gr.update(visible=action_col_visible),
                    gr.update(value=content_text),
                    new_active_action_state_to_set
                ] + all_button_updates
                
                return final_updates

            # --- Event Handler for Explore Button ---
            def handle_explore_click(plot_id_clicked, current_explored_plot_id_from_state):
                logging.info(f"Explore clicked for: {plot_id_clicked}. Currently explored from state: {current_explored_plot_id_from_state}")

                if not plot_ui_objects:
                    logging.error("plot_ui_objects not populated during handle_explore_click.")
                    return [current_explored_plot_id_from_state] + [gr.update() for _ in range(2 * len(plot_configs))] # 2 updates per plot_config (panel, explore_button)

                new_explored_id_to_set = None
                is_toggling_off = (plot_id_clicked == current_explored_plot_id_from_state)

                if is_toggling_off:
                    new_explored_id_to_set = None
                    logging.info(f"Un-exploring plot: {plot_id_clicked}")
                else:
                    new_explored_id_to_set = plot_id_clicked
                    logging.info(f"Exploring plot: {plot_id_clicked}")
                
                panel_and_button_updates = []
                for cfg in plot_configs:
                    p_id = cfg["id"]
                    if p_id in plot_ui_objects:
                        panel_visible = not new_explored_id_to_set or (p_id == new_explored_id_to_set)
                        panel_and_button_updates.append(gr.update(visible=panel_visible))
                        
                        if p_id == new_explored_id_to_set:
                            panel_and_button_updates.append(gr.update(value=ACTIVE_ICON))
                        else:
                            panel_and_button_updates.append(gr.update(value=EXPLORE_ICON))
                    else:
                        panel_and_button_updates.extend([gr.update(), gr.update()])
                
                final_updates = [new_explored_id_to_set] + panel_and_button_updates
                return final_updates

            action_buttons_outputs_list = [
                global_actions_column_ui,
                global_actions_markdown_ui,
                active_panel_action_state
            ]
            for cfg_item_action in plot_configs:
                pid_action = cfg_item_action["id"]
                if pid_action in plot_ui_objects:
                    action_buttons_outputs_list.append(plot_ui_objects[pid_action]["bomb_button"])
                    action_buttons_outputs_list.append(plot_ui_objects[pid_action]["formula_button"])
                else:
                    action_buttons_outputs_list.extend([None, None])

            explore_buttons_outputs_list = [explored_plot_id_state]
            for cfg_item_explore in plot_configs:
                pid_explore = cfg_item_explore["id"]
                if pid_explore in plot_ui_objects:
                    explore_buttons_outputs_list.append(plot_ui_objects[pid_explore]["panel_component"])
                    explore_buttons_outputs_list.append(plot_ui_objects[pid_explore]["explore_button"])
                else:
                    explore_buttons_outputs_list.extend([None, None])

            action_click_inputs = [active_panel_action_state, token_state]
            explore_click_inputs = [explored_plot_id_state]

            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]

                    ui_obj["bomb_button"].click(
                        fn=lambda current_active_val, current_token_val, p_id=plot_id: handle_panel_action(p_id, "insights", current_active_val, current_token_val),
                        inputs=action_click_inputs,
                        outputs=action_buttons_outputs_list,
                        api_name=f"action_insights_{plot_id}"
                    )
                    ui_obj["formula_button"].click(
                        fn=lambda current_active_val, current_token_val, p_id=plot_id: handle_panel_action(p_id, "formula", current_active_val, current_token_val),
                        inputs=action_click_inputs,
                        outputs=action_buttons_outputs_list,
                        api_name=f"action_formula_{plot_id}"
                    )
                    ui_obj["explore_button"].click(
                        fn=lambda current_explored_val, p_id=plot_id: handle_explore_click(p_id, current_explored_val),
                        inputs=explore_click_inputs,
                        outputs=explore_buttons_outputs_list,
                        api_name=f"action_explore_{plot_id}"
                    )
                else:
                    logging.warning(f"UI object for plot_id '{plot_id}' not found when trying to attach click handlers.")

            def refresh_all_analytics_ui_elements(current_token_state, date_filter_val, custom_start_val, custom_end_val):
                logging.info("Refreshing all analytics UI elements and resetting actions.")
                # The number of plots expected by update_analytics_plots_figures should match plot_configs length
                # For this example, it's 19.
                plot_generation_results = update_analytics_plots_figures(
                    current_token_state, date_filter_val, custom_start_val, custom_end_val
                )

                status_message_update = plot_generation_results[0]
                generated_plot_figures = plot_generation_results[1:] # Should be list of 19 figures

                all_updates = [status_message_update]

                # Updates for plot components (19 of them)
                for i in range(len(plot_configs)): # Iterate 19 times
                    if i < len(generated_plot_figures):
                        all_updates.append(generated_plot_figures[i])
                    else:
                        all_updates.append(create_placeholder_plot("Figure Error", f"Missing figure for plot {plot_configs[i]['id']}"))
                
                all_updates.append(gr.update(visible=False))
                all_updates.append(gr.update(value="Click a button (💣, ƒ) on a plot..."))
                all_updates.append(None)

                for cfg in plot_configs: # 19 plots
                    pid = cfg["id"]
                    if pid in plot_ui_objects:
                        all_updates.append(gr.update(value=BOMB_ICON))
                        all_updates.append(gr.update(value=FORMULA_ICON))
                        all_updates.append(gr.update(value=EXPLORE_ICON))
                        all_updates.append(gr.update(visible=True))
                    else:
                        all_updates.extend([None, None, None, None])
                
                all_updates.append(None)
                logging.info(f"Prepared {len(all_updates)} updates for analytics refresh.")
                return all_updates

            apply_filter_and_sync_outputs_list = [analytics_status_md]
            
            for config_item_filter_sync in plot_configs: # 19 plots
                pid_filter_sync = config_item_filter_sync["id"]
                if pid_filter_sync in plot_ui_objects and "plot_component" in plot_ui_objects[pid_filter_sync]:
                    apply_filter_and_sync_outputs_list.append(plot_ui_objects[pid_filter_sync]["plot_component"])
                else:
                    apply_filter_and_sync_outputs_list.append(None)
            
            apply_filter_and_sync_outputs_list.extend([
                global_actions_column_ui,
                global_actions_markdown_ui,
                active_panel_action_state
            ])
            
            for cfg_filter_sync_btns in plot_configs: # 19 plots
                pid_filter_sync_btns = cfg_filter_sync_btns["id"]
                if pid_filter_sync_btns in plot_ui_objects:
                    apply_filter_and_sync_outputs_list.append(plot_ui_objects[pid_filter_sync_btns]["bomb_button"])
                    apply_filter_and_sync_outputs_list.append(plot_ui_objects[pid_filter_sync_btns]["formula_button"])
                    apply_filter_and_sync_outputs_list.append(plot_ui_objects[pid_filter_sync_btns]["explore_button"])
                    apply_filter_and_sync_outputs_list.append(plot_ui_objects[pid_filter_sync_btns]["panel_component"])
                else:
                    apply_filter_and_sync_outputs_list.extend([None, None, None, None])

            apply_filter_and_sync_outputs_list.append(explored_plot_id_state)
            
            logging.info(f"Total outputs for apply_filter/sync: {len(apply_filter_and_sync_outputs_list)}") # Expected: 1 + 19 + 3 + (19*4) + 1 = 1 + 19 + 3 + 76 + 1 = 100

            apply_filter_btn.click(
                fn=refresh_all_analytics_ui_elements,
                inputs=[token_state, date_filter_selector, custom_start_date_picker, custom_end_date_picker],
                outputs=apply_filter_and_sync_outputs_list,
                show_progress="full"
            )

        with gr.TabItem("3️⃣ Mentions", id="tab_mentions"):
            refresh_mentions_display_btn = gr.Button("🔄 Refresh Mentions Display", variant="secondary")
            mentions_html = gr.HTML("Mentions data...")
            mentions_sentiment_dist_plot = gr.Plot(label="Mention Sentiment Distribution")
            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 Follower Stats", id="tab_follower_stats"):
            refresh_follower_stats_btn = gr.Button("🔄 Refresh Follower Stats Display", variant="secondary")
            follower_stats_html = gr.HTML("Follower statistics...")
            with gr.Row():
                fs_plot_monthly_gains = gr.Plot(label="Monthly Follower Gains")
            with gr.Row():
                fs_plot_seniority = gr.Plot(label="Followers by Seniority (Top 10 Organic)")
                fs_plot_industry = gr.Plot(label="Followers by Industry (Top 10 Organic)")

            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"
            )

    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_part3 = sync_event_part2.then(
        fn=display_main_dashboard,
        inputs=[token_state], outputs=[dashboard_display_html], show_progress=False
    )
    sync_event_final = sync_event_part3.then(
        fn=refresh_all_analytics_ui_elements,
        inputs=[token_state, date_filter_selector, custom_start_date_picker, custom_end_date_picker],
        outputs=apply_filter_and_sync_outputs_list, show_progress="full"
    )

if __name__ == "__main__":
    if not os.environ.get(LINKEDIN_CLIENT_ID_ENV_VAR):
        logging.warning(f"WARNING: '{LINKEDIN_CLIENT_ID_ENV_VAR}' env var not set.")
    if not os.environ.get(BUBBLE_APP_NAME_ENV_VAR) or \
       not os.environ.get(BUBBLE_API_KEY_PRIVATE_ENV_VAR) or \
       not os.environ.get(BUBBLE_API_ENDPOINT_ENV_VAR):
        logging.warning("WARNING: Bubble env vars not fully set.")

    try:
        logging.info(f"Matplotlib version: {matplotlib.__version__}, Backend: {matplotlib.get_backend()}")
    except ImportError:
        logging.error("Matplotlib is not installed. Plots will not be generated.")

    app.launch(server_name="0.0.0.0", server_port=7860, debug=True)