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

# --- 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
)
# Corrected import for analytics_data_processing
from analytics_data_processing import prepare_filtered_analytics_data
from analytics_plot_generator import (
    generate_posts_activity_plot, generate_engagement_type_plot,
    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
)

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

# --- 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}")
    num_expected_plots = 23 

    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", "eb_labels")

    plot_figs = []
    try:
        plot_figs.append(generate_posts_activity_plot(filtered_merged_posts_df, date_column=date_column_posts))
        plot_figs.append(generate_engagement_type_plot(filtered_merged_posts_df))
        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)
        plot_figs.append(fig_mentions_activity_shared)
        plot_figs.append(fig_mention_sentiment_shared)
        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))
        plot_figs.append(fig_mentions_activity_shared) 
        plot_figs.append(fig_mention_sentiment_shared)

        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): # Check if it's a Matplotlib figure
                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."))
        
        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]

    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": "eb_labels"
    })

    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 💣 for insights.")
            
            analytics_status_md = gr.Markdown("Analytics status...")

            with gr.Row(): # Filters row
                date_filter_selector = gr.Radio(
                    ["All Time", "Last 7 Days", "Last 30 Days", "Custom Range"],
                    label="Select Date Range", value="Last 30 Days", 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]
            )

            # --- Define plot configurations (Order must match figure generation) ---
            plot_configs = [
                {"label": "Posts Activity Over Time", "id": "posts_activity", "section": "Posts & Engagement Overview"},
                {"label": "Post Engagement Types", "id": "engagement_type", "section": "Posts & Engagement Overview"},
                {"label": "Mentions Activity Over Time", "id": "mentions_activity", "section": "Mentions Overview"}, 
                {"label": "Mention Sentiment Distribution", "id": "mention_sentiment", "section": "Mentions Overview"}, 
                {"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 (Clicks)", "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) == 23, "Mismatch in plot_configs and expected plots."

            # --- Main layout for Analytics Tab: Plots Area and Global Insights Column ---
            with gr.Row(equal_height=False): # Main row for plots area and insights column
                with gr.Column(scale=8): # Column to hold all plot rows and section headers
                    # Build the plot area (section headers and rows of plot panels)
                    # This function is defined in ui_generators.py
                    # It will create gr.Markdown for sections and gr.Row for plot pairs
                    plot_ui_objects = build_analytics_tab_plot_area(plot_configs)
                
                # Global Insights Column (initially hidden)
                with gr.Column(scale=4, visible=False) as global_insights_column_ui:
                    gr.Markdown("### 💡 Generated Insights")
                    global_insights_markdown_ui = gr.Markdown("Click 💣 on a plot to see insights here.")

            active_insight_plot_id_state = gr.State(None) 

            # --- Bomb Button Click Handler ---
            def handle_bomb_click(plot_id_clicked, current_active_plot_id, token_state_val): # Added token_state_val
                logging.info(f"Bomb clicked for: {plot_id_clicked}. Currently active: {current_active_plot_id}")
                
                # Retrieve the label for the clicked plot
                clicked_plot_label = "Selected Plot" # Default
                if plot_id_clicked and plot_id_clicked in plot_ui_objects:
                    clicked_plot_label = plot_ui_objects[plot_id_clicked]["label"]

                if plot_id_clicked == current_active_plot_id: # Toggle off
                    new_active_id = None
                    insight_text_update = f"Insights for {clicked_plot_label} hidden. Click 💣 to show."
                    insights_col_visible = False
                    logging.info(f"Closing insights for {plot_id_clicked}")
                else: # Activate new one or switch
                    new_active_id = plot_id_clicked
                    # TODO: Implement actual insight generation logic here using plot_id_clicked and token_state_val
                    insight_text_update = f"**Insights for: {clicked_plot_label}**\n\n"
                    insight_text_update += f"Plot ID: `{plot_id_clicked}`.\n"
                    insight_text_update += "This is where detailed, AI-generated insights for this specific chart would appear, based on its data and trends.\n"
                    insight_text_update += "For instance, if this were 'Post Engagement Types', we might analyze which type is dominant and suggest content strategies."
                    insights_col_visible = True
                    logging.info(f"Opening insights for {plot_id_clicked}")
                
                return gr.update(visible=insights_col_visible), gr.update(value=insight_text_update), new_active_id

            # --- Connect Bomb Buttons ---
            # Outputs for each bomb click: global insights column visibility, its markdown content, and the state
            bomb_click_outputs = [global_insights_column_ui, global_insights_markdown_ui, active_insight_plot_id_state]

            for config in plot_configs:
                plot_id = config["id"]
                if plot_id in plot_ui_objects: # Ensure the UI object was created
                    components_dict = plot_ui_objects[plot_id]
                    components_dict["bomb_button"].click(
                        fn=handle_bomb_click,
                        inputs=[gr.State(value=plot_id), active_insight_plot_id_state, token_state], # Pass token_state
                        outputs=bomb_click_outputs,
                        api_name=f"show_insights_{plot_id}" 
                    )
            
            # --- Function to Refresh All Analytics UI (Plots + Reset Global Insights) ---
            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 insights.")
                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:]

                all_updates = [status_message_update] # For analytics_status_md

                # Plot figure updates - iterate based on plot_configs to ensure order
                for i, config in enumerate(plot_configs):
                    p_id_key = config["id"]
                    if p_id_key in plot_ui_objects: # Check if plot UI exists
                        if i < len(generated_plot_figures):
                            all_updates.append(generated_plot_figures[i])
                        else:
                            logging.error(f"Mismatch: Expected figure for {p_id_key} but not enough figures generated.")
                            all_updates.append(create_placeholder_plot("Figure Error", f"No figure for {p_id_key}"))
                    else:
                        # This case should ideally not happen if plot_configs and plot_ui_objects are in sync
                        logging.warning(f"Plot UI object for id {p_id_key} not found during refresh. Skipping its figure update.")


                # Reset Global Insights Column
                all_updates.append(gr.update(visible=False)) # Hide global_insights_column_ui
                all_updates.append(gr.update(value="Click 💣 on a plot to see insights here.")) # Reset global_insights_markdown_ui
                all_updates.append(None) # Reset active_insight_plot_id_state
                
                logging.info(f"Prepared {len(all_updates)} updates for analytics refresh.")
                return all_updates

            # --- Define outputs for the apply_filter_btn and sync.then() ---
            apply_filter_and_sync_outputs = [analytics_status_md]
            # Add plot components (must be in the order of plot_configs)
            for config in plot_configs:
                p_id_key = config["id"]
                if p_id_key in plot_ui_objects:
                     apply_filter_and_sync_outputs.append(plot_ui_objects[p_id_key]["plot_component"])
                else:
                    # Add a placeholder None if a plot component wasn't created, to maintain output list length.
                    # This helps prevent errors if plot_ui_objects somehow doesn't contain an expected key.
                    apply_filter_and_sync_outputs.append(None) 
                    logging.error(f"Plot component for {p_id_key} missing in plot_ui_objects for apply_filter_outputs.")


            # Add global insights components and state
            apply_filter_and_sync_outputs.extend([
                global_insights_column_ui, 
                global_insights_markdown_ui, 
                active_insight_plot_id_state
            ])
            
            logging.info(f"Total outputs for apply_filter/sync: {len(apply_filter_and_sync_outputs)}")


            # --- Connect Apply Filter Button ---
            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,
                show_progress="full"
            )

        with gr.TabItem("3️⃣ Mentions", id="tab_mentions"):
            # ... (Mentions tab content remains the same) ...
            refresh_mentions_display_btn = gr.Button("🔄 Refresh Mentions Display (from local data)", variant="secondary")
            mentions_html = gr.HTML("Mentions data loads from Bubble after sync. Click refresh to view current local 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"):
            # ... (Follower Stats tab content remains the same) ...
            refresh_follower_stats_btn = gr.Button("🔄 Refresh Follower Stats Display (from local data)", variant="secondary")
            follower_stats_html = gr.HTML("Follower statistics load from Bubble after sync. Click refresh to view current local data.")
            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"
            )
    
    # --- Define the full sync_click_event chain HERE ---
    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
    )
    # Connect to refresh analytics UI after sync
    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, 
        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)