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
# from functools import partial # No longer needed if gr.State(value=plot_id) is used

# --- 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_ui_components # Import the new UI builder function
)
# 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, # For initializing plots
    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 Update Function (Original, generates figures) ---
def update_analytics_plots_figures(token_state_value, date_filter_option, custom_start_date, custom_end_date):
    """
    Prepares analytics data using external processing function and then generates plot figures.
    This function is primarily responsible for returning the Matplotlib figure objects.
    """
    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 # This should match the number of plots defined in plot_configs

    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")

    logging.info(f"Data for plotting - Filtered Merged Posts: {len(filtered_merged_posts_df)} rows, Filtered Mentions: {len(filtered_mentions_df)} rows.")
    logging.info(f"Date-Filtered Follower Stats: {len(date_filtered_follower_stats_df)} rows, Raw Follower Stats: {len(raw_follower_stats_df)} rows.")

    try:
        plot_figs = []
        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) # Original mention plot slot 1
        plot_figs.append(fig_mention_sentiment_shared) # Original mention plot slot 2

        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))
        
        # For the "Mention Analysis" section, we reuse the figures generated earlier
        plot_figs.append(fig_mentions_activity_shared) # New UI slot for mention volume, reuses figure
        plot_figs.append(fig_mention_sentiment_shared) # New UI slot for mention sentiment, reuses figure

        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."))
        
        while len(final_plot_figs) < num_expected_plots:
            logging.warning(f"Padding missing plot figure with placeholder. 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."))
        
        logging.info(f"Successfully generated {len(final_plot_figs)} plot figures for {num_expected_plots} UI slots.")
        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 (from URL - 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 (from URL - 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):
        logging.info(f"Initial load sequence triggered. Org URN: {org_urn_val}, URL Token: {'Present' if url_token else 'Absent'}")
        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. The 'Sync' button activates if new data needs to be fetched from LinkedIn based on the last sync times and data availability.")
            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 will appear here.</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 to filter analytics. Click 💣 for insights.")
            
            analytics_status_md = gr.Markdown("Analytics status will appear here...")

            with gr.Row():
                date_filter_selector = gr.Radio(
                    ["All Time", "Last 7 Days", "Last 30 Days", "Custom Range"],
                    label="Select Date Range", value="Last 30 Days"
                )
                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 the order of figures returned by update_analytics_plots_figures)
            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 number of plot configurations and expected plots."
            
            # --- Build Analytics Tab UI using the function from ui_generators ---
            # This function will create the gr.Markdown for sections and rows for plots.
            # It needs to be called within this gr.Blocks() context.
            plot_ui_objects = build_analytics_tab_ui_components(plot_configs)
            
            active_insight_plot_id_state = gr.State(None) # Stores the plot_id of the currently open insight panel

            # --- Bomb Button Click Handler ---
            def handle_bomb_click(plot_id_clicked, current_active_plot_id, current_token_state):
                logging.info(f"Bomb clicked for: {plot_id_clicked}. Currently active: {current_active_plot_id}")
                updates = [] 
                new_active_id = None

                if plot_id_clicked == current_active_plot_id:
                    new_active_id = None # Toggle off
                    logging.info(f"Closing insights for {plot_id_clicked}")
                else:
                    new_active_id = plot_id_clicked # Activate new one
                    logging.info(f"Opening insights for {plot_id_clicked}, closing others.")

                for p_id_iter, ui_obj_dict in plot_ui_objects.items():
                    is_target_one = (p_id_iter == new_active_id)
                    updates.append(gr.update(visible=is_target_one)) # For insights_col visibility

                    if is_target_one:
                        # TODO: Implement actual insight generation logic here
                        insight_text = f"**Insights for {ui_obj_dict['label']}**\n\n"
                        insight_text += f"Plot ID: `{p_id_iter}`.\n"
                        insight_text += "Detailed analysis would involve examining trends, anomalies, and correlations related to this specific chart.\n"
                        insight_text += "For example, for 'Posts Activity', we might look for days with unusually high or low activity and correlate with external events or content types."
                        updates.append(gr.update(value=insight_text))
                    else:
                        updates.append(gr.update(value=f"Click 💣 for insights on {ui_obj_dict['label']}...")) # Reset placeholder
                
                updates.append(new_active_id) # New value for active_insight_plot_id_state
                logging.info(f"Returning {len(updates)-1} UI updates. New active ID: {new_active_id}")
                return updates

            # --- Connect Bomb Buttons ---
            bomb_click_dynamic_outputs = []
            # The order of items in bomb_click_dynamic_outputs must match the order of iteration
            # in handle_bomb_click when it creates its `updates` list.
            # plot_ui_objects is a dictionary, so .keys() gives an arbitrary order if not Python 3.7+
            # To be safe, iterate based on plot_configs order for constructing outputs.
            for config in plot_configs:
                p_id_key = config["id"]
                bomb_click_dynamic_outputs.append(plot_ui_objects[p_id_key]["insights_col"])
                bomb_click_dynamic_outputs.append(plot_ui_objects[p_id_key]["insights_md"])
            bomb_click_dynamic_outputs.append(active_insight_plot_id_state)

            for config in plot_configs:
                plot_id = config["id"]
                components_dict = plot_ui_objects[plot_id]
                components_dict["bomb"].click(
                    fn=handle_bomb_click,
                    inputs=[gr.State(value=plot_id), active_insight_plot_id_state, token_state],
                    outputs=bomb_click_dynamic_outputs,
                    api_name=f"show_insights_{plot_id}" # Gradio handles None api_name if plot_id is None (though it shouldn't be)
                )
            
            # --- Function to Refresh All Analytics UI (Plots + Reset 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.")
                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]

                # Plot figure updates - iterate based on plot_configs to ensure order
                for i, config in enumerate(plot_configs):
                    p_id_key = config["id"]
                    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}"))
                
                # Insight column visibility and markdown content reset - iterate based on plot_configs
                for config in plot_configs:
                    p_id_key = config["id"]
                    ui_obj_dict_val = plot_ui_objects[p_id_key]
                    all_updates.append(gr.update(visible=False)) # Hide insights_col
                    all_updates.append(gr.update(value=f"Click 💣 for insights on {ui_obj_dict_val['label']}...")) # Reset insights_md

                all_updates.append(None) # Reset active_insight_plot_id_state
                return all_updates

            # --- Define outputs for the apply_filter_btn and sync.then() ---
            apply_filter_and_sync_outputs = [analytics_status_md]
            # Iterate based on plot_configs to ensure order
            for config in plot_configs: # Plot components
                apply_filter_and_sync_outputs.append(plot_ui_objects[config["id"]]["plot"])
            for config in plot_configs: # Insight column components
                apply_filter_and_sync_outputs.append(plot_ui_objects[config["id"]]["insights_col"])
            for config in plot_configs: # Insight markdown components
                apply_filter_and_sync_outputs.append(plot_ui_objects[config["id"]]["insights_md"])
            apply_filter_and_sync_outputs.append(active_insight_plot_id_state) # State component

            # --- 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"):
            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"):
            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, now that analytics outputs are known ---
    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, 
        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}' environment variable 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 environment variables not fully set.")

    try:
        logging.info(f"Matplotlib version: {matplotlib.__version__} found. 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)