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 

# --- 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
)
# 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
    # --- Import new plot functions ---
    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
)

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

# --- Analytics Tab: Plot Update Function ---
def update_analytics_plots(token_state_value, date_filter_option, custom_start_date, custom_end_date):
    """
    Prepares analytics data using external processing function and then generates plots.
    """
    logging.info(f"Updating analytics plots. Filter: {date_filter_option}, Custom Start: {custom_start_date}, Custom End: {custom_end_date}")

    # --- Increased number of expected plots ---
    num_expected_plots = 18 # Was 13, added 5 new plots

    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:
        # prepare_filtered_analytics_data might need to be updated if new DFs are required for new plots (e.g. comment sentiment)
        # For now, we assume it returns the same set of DFs and new plots will try to use them or handle missing data.
        (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
            )
        
        # Hypothetical: If prepare_filtered_analytics_data was updated to return comment sentiment data:
        # filtered_comments_with_sentiment_df = ... # (This would be the 7th item in the tuple)
        # For now, we will pass filtered_merged_posts_df to generate_comments_sentiment_breakdown_plot,
        # and that function will handle missing sentiment columns by showing a placeholder.
        # Or, if you have comment sentiment data in another DataFrame in token_state, retrieve it here.
        # e.g., comments_df_with_sentiment = token_state_value.get("bubble_comments_sentiment_df", pd.DataFrame())

    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")
    # config_date_col_followers_source = token_state_value.get("config_date_col_followers", "date") 


    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:
        # Existing plots
        plot_posts_activity = generate_posts_activity_plot(filtered_merged_posts_df, date_column=date_column_posts)
        plot_engagement_type = generate_engagement_type_plot(filtered_merged_posts_df) 
        plot_mentions_activity = generate_mentions_activity_plot(filtered_mentions_df, date_column=date_column_mentions)
        plot_mention_sentiment = generate_mention_sentiment_plot(filtered_mentions_df) 

        plot_followers_count = generate_followers_count_over_time_plot(
            date_filtered_follower_stats_df, 
            type_filter_column='follower_count_type',
            type_value='follower_gains_monthly' 
        )
        plot_followers_growth_rate = generate_followers_growth_rate_plot(
            date_filtered_follower_stats_df,
            type_filter_column='follower_count_type',
            type_value='follower_gains_monthly'
        )

        plot_followers_by_location = generate_followers_by_demographics_plot(raw_follower_stats_df, category_col='category_name', type_filter_column='follower_count_type', type_value='follower_geo', plot_title="Followers by Location")
        plot_followers_by_role = generate_followers_by_demographics_plot(raw_follower_stats_df, category_col='category_name', type_filter_column='follower_count_type', type_value='follower_function', plot_title="Followers by Role")
        plot_followers_by_industry = generate_followers_by_demographics_plot(raw_follower_stats_df, category_col='category_name', type_filter_column='follower_count_type', type_value='follower_industry', plot_title="Followers by Industry")
        plot_followers_by_seniority = generate_followers_by_demographics_plot(raw_follower_stats_df, category_col='category_name', type_filter_column='follower_count_type', type_value='follower_seniority', plot_title="Followers by Seniority")

        plot_engagement_rate = generate_engagement_rate_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts, engagement_rate_col='engagement')
        plot_reach_over_time = generate_reach_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts, reach_col='clickCount')
        plot_impressions_over_time = generate_impressions_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts, impressions_col='impressionCount')

        # --- Generate new plots ---
        plot_likes_over_time = generate_likes_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts, likes_col='likeCount')
        plot_clicks_over_time = generate_clicks_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts, clicks_col='clickCount')
        plot_shares_over_time = generate_shares_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts, shares_col='shareCount')
        plot_comments_over_time = generate_comments_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts, comments_col='commentCount')
        
        # For comment sentiment, pass a DataFrame that is expected to have comment-level sentiment.
        # If `filtered_merged_posts_df` is passed and lacks 'comment_sentiment' column, the plot function will show a placeholder.
        # If you have a specific df for this, e.g., `filtered_comments_with_sentiment_df` from `prepare_filtered_analytics_data` (if modified)
        # or from `token_state_value.get("bubble_comments_sentiment_df")`, use that one.
        # For this example, we assume `filtered_merged_posts_df` is passed and the plot function handles it.
        plot_comments_sentiment_breakdown = generate_comments_sentiment_breakdown_plot(
            filtered_merged_posts_df, # Or your specific df with comment sentiments
            sentiment_column='sentiment' # Assuming 'sentiment' column in post_df might be a proxy, or change to 'comment_sentiment' if that column exists
                                         # The plot function will show a placeholder if this column isn't suitable or found.
        )


        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})"
        
        all_generated_plots = [
            plot_posts_activity, plot_engagement_type, plot_mentions_activity, plot_mention_sentiment,
            plot_followers_count, plot_followers_growth_rate,
            plot_followers_by_location, plot_followers_by_role, plot_followers_by_industry, plot_followers_by_seniority,
            plot_engagement_rate, plot_reach_over_time, plot_impressions_over_time,
            # --- Add new plot objects to the list ---
            plot_likes_over_time, plot_clicks_over_time,
            plot_shares_over_time, plot_comments_over_time,
            plot_comments_sentiment_breakdown
        ]
        num_plots_generated = sum(1 for p in all_generated_plots if p is not None and not isinstance(p, str)) 
        logging.info(f"Successfully generated {num_plots_generated} plots out of {num_expected_plots} expected.")
        
        # Ensure the number of returned plots matches num_expected_plots, padding with placeholders if necessary
        # This is crucial if some plot functions might return None on error and we need to match the Gradio outputs list length
        final_plots_list = []
        for p in all_generated_plots:
            if p is not None and not isinstance(p, str): # isinstance check for safety, though plots should be figs
                final_plots_list.append(p)
            else: # If a plot failed and returned None or an error string (which it shouldn't, should be placeholder fig)
                logging.warning(f"A plot generation failed or returned unexpected type, using placeholder. Plot: {p}")
                final_plots_list.append(create_placeholder_plot(title="Plot Error", message="Failed to generate this plot."))

        # If fewer plots were generated than expected (e.g. due to early exit or major error in a plot function)
        while len(final_plots_list) < num_expected_plots:
            logging.warning(f"Padding missing plot with placeholder. Expected {num_expected_plots}, got {len(final_plots_list)} so far.")
            final_plots_list.append(create_placeholder_plot(title="Missing Plot", message="Plot could not be generated."))
            if len(final_plots_list) > num_expected_plots + 5: # Safety break
                 logging.error("Too many placeholders added, breaking loop.")
                 break


        return [message] + final_plots_list[:num_expected_plots] # Ensure correct number of outputs

    except Exception as e:
        error_msg = f"❌ Error generating analytics plots: {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(),
        # Consider adding "bubble_comments_sentiment_df": pd.DataFrame() if you plan to fetch this data
        "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" 
    })

    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"
            )
            
            sync_click_event = sync_data_btn.click(
                fn=sync_all_linkedin_data_orchestrator,
                inputs=[token_state],
                outputs=[sync_status_html_output, token_state], 
                show_progress="full"
            ).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 
            ).then(
                fn=display_main_dashboard, 
                inputs=[token_state],
                outputs=[dashboard_display_html],
                show_progress=False
            )


        with gr.TabItem("2️⃣ Analytics", id="tab_analytics"):
            gr.Markdown("## 📈 LinkedIn Performance Analytics")
            gr.Markdown("Select a date range to filter Posts and Mentions analytics. Follower demographic plots show overall latest data. Follower time-series plots respect the selected date range if applicable to their data source (e.g. monthly gains).")
            
            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 (for Posts, Mentions, and some Follower time-series)",
                    value="Last 30 Days"
                )
                custom_start_date_picker = gr.DateTime(label="Start Date (Custom)", visible=False, include_time=False, type="datetime") # Changed to datetime
                custom_end_date_picker = gr.DateTime(label="End Date (Custom)", visible=False, include_time=False, type="datetime") # Changed to 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]
            )

            gr.Markdown("### Posts & Engagement Overview (Filtered by Date)")
            with gr.Row():
                posts_activity_plot = gr.Plot(label="Posts Activity Over Time")
                engagement_type_plot = gr.Plot(label="Post Engagement Types")
            
            gr.Markdown("### Mentions Overview (Filtered by Date)")
            with gr.Row():
                mentions_activity_plot = gr.Plot(label="Mentions Activity Over Time")
                mention_sentiment_plot = gr.Plot(label="Mention Sentiment Distribution")

            gr.Markdown("### Follower Dynamics")
            with gr.Row():
                followers_count_plot = gr.Plot(label="Followers Count Over Time (e.g., Monthly Gains)")
                followers_growth_rate_plot = gr.Plot(label="Followers Growth Rate (e.g., Monthly Gains)")
            
            gr.Markdown("### Follower Demographics (Overall Latest Data)")
            with gr.Row():
                followers_by_location_plot = gr.Plot(label="Followers by Location")
                followers_by_role_plot = gr.Plot(label="Followers by Role (Function)")
            with gr.Row():
                followers_by_industry_plot = gr.Plot(label="Followers by Industry")
                followers_by_seniority_plot = gr.Plot(label="Followers by Seniority")

            gr.Markdown("### Post Performance Insights (Filtered by Date)")
            with gr.Row(): 
                engagement_rate_plot = gr.Plot(label="Engagement Rate Over Time")
                reach_over_time_plot = gr.Plot(label="Reach Over Time (Clicks)") # This was originally in its own row
            with gr.Row(): # Moved impressions to be paired with reach if desired, or keep separate
                impressions_over_time_plot = gr.Plot(label="Impressions Over Time")
                # New plots will start here, keeping 2 per row
                likes_over_time_plot = gr.Plot(label="Reactions (Likes) Over Time")

            gr.Markdown("### Detailed Post Engagement Over Time (Filtered by Date)")
            with gr.Row():
                clicks_over_time_plot = gr.Plot(label="Clicks Over Time")
                shares_over_time_plot = gr.Plot(label="Shares Over Time")
            with gr.Row():
                comments_over_time_plot = gr.Plot(label="Comments Over Time")
                # For the 5th new plot, "Breakdown of Comments by Sentiment"
                # It will be alone in this row, or you can add another plot next to it later.
                comments_sentiment_plot = gr.Plot(label="Breakdown of Comments by Sentiment")


            analytics_plot_outputs = [
                analytics_status_md, posts_activity_plot, engagement_type_plot, 
                mentions_activity_plot, mention_sentiment_plot,
                followers_count_plot, followers_growth_rate_plot,
                followers_by_location_plot, followers_by_role_plot, 
                followers_by_industry_plot, followers_by_seniority_plot,
                engagement_rate_plot, reach_over_time_plot, impressions_over_time_plot,
                # --- Add new plot components to the output list in the correct order ---
                likes_over_time_plot, clicks_over_time_plot,
                shares_over_time_plot, comments_over_time_plot,
                comments_sentiment_plot
            ]

            apply_filter_btn.click(
                fn=update_analytics_plots,
                inputs=[token_state, date_filter_selector, custom_start_date_picker, custom_end_date_picker],
                outputs=analytics_plot_outputs,
                show_progress="full"
            )
            
            # Also update analytics after sync
            sync_click_event.then( 
                fn=update_analytics_plots,
                inputs=[token_state, date_filter_selector, custom_start_date_picker, custom_end_date_picker],
                outputs=analytics_plot_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"
            )
    

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)