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 
# No longer need timedelta here if all date logic is in analytics_data_processing
# from datetime import datetime, timedelta 

# --- 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
)
import analytics_plot_generators 
# NEW: Import for data processing functions
import analytics_data_processing

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

    if not token_state_value or not token_state_value.get("token"):
        message = "❌ Access denied. No token. Cannot generate analytics."
        logging.warning(message)
        return message, None, None, None, None, None 

    # --- Prepare Data (Moved to analytics_data_processing) ---
    try:
        filtered_posts_df, filtered_mentions_df, follower_stats_df, start_dt_for_msg, end_dt_for_msg = \
            analytics_data_processing.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)
        return error_msg, None, None, None, None, None

    # Date column names (still needed for plot generators)
    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")
    date_column_followers = token_state_value.get("config_date_col_followers", "date") 

    logging.info(f"Data for plotting - Filtered posts: {len(filtered_posts_df)} rows, Filtered Mentions: {len(filtered_mentions_df)} rows.")
    logging.info(f"Follower stats (unfiltered by global range): {len(follower_stats_df)} rows.")

    # --- Generate Plots ---
    try:
        plot_posts_activity = analytics_plot_generators.generate_posts_activity_plot(filtered_posts_df, date_column_posts)
        plot_engagement_type = analytics_plot_generators.generate_engagement_type_plot(filtered_posts_df) 
        plot_mentions_activity = analytics_plot_generators.generate_mentions_activity_plot(filtered_mentions_df, date_column_mentions)
        plot_mention_sentiment = analytics_plot_generators.generate_mention_sentiment_plot(filtered_mentions_df) 
        plot_follower_growth = analytics_plot_generators.generate_follower_growth_plot(follower_stats_df, date_column_followers)

        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})"
        
        num_plots_generated = sum(1 for p in [plot_posts_activity, plot_engagement_type, plot_mentions_activity, plot_mention_sentiment, plot_follower_growth] if p is not None)
        logging.info(f"Successfully generated {num_plots_generated} plots.")

        return message, plot_posts_activity, plot_engagement_type, plot_mentions_activity, plot_mention_sentiment, plot_follower_growth
    except Exception as e:
        error_msg = f"❌ Error generating analytics plots: {e}"
        logging.error(error_msg, exc_info=True)
        return error_msg, None, None, None, None, None


# --- 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(), "fetch_count_for_api": 0,
        "bubble_mentions_df": pd.DataFrame(),
        "bubble_follower_stats_df": pd.DataFrame(),
        "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 analytics show overall trends and are not affected by this date filter.")
            
            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)",
                    value="Last 30 Days"
                )
                custom_start_date_picker = gr.DatePicker(label="Start Date (Custom)", visible=False)
                custom_end_date_picker = gr.DatePicker(label="End Date (Custom)", visible=False)
            
            apply_filter_btn = gr.Button("🔍 Apply Filter & Refresh Analytics", variant="primary")

            def toggle_custom_date_pickers(selection):
                return gr.update(visible=selection == "Custom Range"), gr.update(visible=selection == "Custom Range")

            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 Overview (Not Filtered by Date Range Selector)")
            with gr.Row():
                follower_growth_plot = gr.Plot(label="Follower Growth Over Time")

            apply_filter_btn.click(
                fn=update_analytics_plots,
                inputs=[token_state, date_filter_selector, custom_start_date_picker, custom_end_date_picker],
                outputs=[analytics_status_md, posts_activity_plot, engagement_type_plot, mentions_activity_plot, mention_sentiment_plot, follower_growth_plot],
                show_progress="full"
            )
            
            sync_click_event.then(
                fn=update_analytics_plots,
                inputs=[token_state, date_filter_selector, custom_start_date_picker, custom_end_date_picker],
                outputs=[analytics_status_md, posts_activity_plot, engagement_type_plot, mentions_activity_plot, mention_sentiment_plot, follower_growth_plot],
                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)