# -*- coding: utf-8 -*-
import gradio as gr
import json
import os
import logging

from Data_Fetching_and_Rendering import fetch_and_render_dashboard
from analytics_fetch_and_rendering import fetch_and_render_analytics
from mentions_dashboard import generate_mentions_dashboard
from gradio_utils import get_url_user_token
from Bubble_API_Calls import fetch_linkedin_token_from_bubble, bulk_upload_to_bubble

from Linkedin_Data_API_Calls import (
    fetch_linkedin_posts_core,
    fetch_comments,
    analyze_sentiment,
    compile_detailed_posts,
    prepare_data_for_bubble
)

def check_token_status(token_state):
    return "✅ Token available" if token_state and token_state.get("token") else "❌ Token not available"


def process_and_store_bubble_token(url_user_token, org_urn, token_state):
    new_state = token_state.copy() if token_state else {"token": None, "client_id": None, "org_urn": None}
    new_state.update({"token": None, "org_urn": org_urn})

    client_id = os.environ.get("Linkedin_client_id")
    if not client_id:
        print("❌ CRITICAL ERROR: 'Linkedin_client_id' environment variable not set.")
        new_state["client_id"] = "ENV VAR MISSING"
        return check_token_status(new_state), new_state

    new_state["client_id"] = client_id
    if not url_user_token or "not found" in url_user_token or "Could not access" in url_user_token:
        return check_token_status(new_state), new_state

    print(f"Attempting to fetch token from Bubble with user token: {url_user_token}")
    parsed = fetch_linkedin_token_from_bubble(url_user_token)

    if isinstance(parsed, dict) and "access_token" in parsed:
        new_state["token"] = parsed
        print("✅ Token successfully fetched from Bubble.")
    else:
        print("❌ Failed to fetch a valid token from Bubble.")

    return check_token_status(new_state), new_state

def guarded_fetch_posts(token_state):
    logging.info("Starting guarded_fetch_posts process.")
    if not token_state or not token_state.get("token"):
        logging.error("Access denied. No token available.")
        return "<p style='color:red; text-align:center;'>❌ Access denied. No token available.</p>"

    client_id = token_state.get("client_id")
    token_dict = token_state.get("token")
    org_urn = token_state.get('org_urn') # Ensure 'org_urn' is correctly fetched from token_state

    if not org_urn:
        logging.error("Organization URN (org_urn) not found in token_state.")
        return "<p style='color:red; text-align:center;'>❌ Configuration error: Organization URN missing.</p>"
    if not client_id:
        logging.error("Client ID not found in token_state.")
        return "<p style='color:red; text-align:center;'>❌ Configuration error: Client ID missing.</p>"


    try:
        # Step 1: Fetch core post data (text, summary, category) and their basic stats
        logging.info(f"Step 1: Fetching core posts for org_urn: {org_urn}")
        processed_raw_posts, stats_map, _ = fetch_linkedin_posts_core(client_id, token_dict, org_urn)
        # org_name is returned as the third item, captured as _ if not used directly here

        if not processed_raw_posts:
            logging.info("No posts found to process after step 1.")
            return "<p style='color:orange; text-align:center;'>ℹ️ No posts found to process.</p>"

        post_urns = [post["id"] for post in processed_raw_posts if post.get("id")]
        logging.info(f"Extracted {len(post_urns)} post URNs for further processing.")

        # Step 2: Fetch comments for these posts
        logging.info("Step 2: Fetching comments.")
        all_comments_data = fetch_comments(client_id, token_dict, post_urns, stats_map)

        # Step 3: Analyze sentiment of the comments
        logging.info("Step 3: Analyzing sentiment.")
        sentiments_per_post = analyze_sentiment(all_comments_data)

        # Step 4: Compile detailed post objects
        logging.info("Step 4: Compiling detailed posts.")
        detailed_posts = compile_detailed_posts(processed_raw_posts, stats_map, sentiments_per_post)

        # Step 5: Prepare data for Bubble
        logging.info("Step 5: Preparing data for Bubble.")
        li_posts, li_post_stats, li_post_comments = prepare_data_for_bubble(detailed_posts, all_comments_data)

        # Step 6: Bulk upload to Bubble
        logging.info("Step 6: Uploading data to Bubble.")
        bulk_upload_to_bubble(li_posts, "LI_posts")
        bulk_upload_to_bubble(li_post_stats, "LI_post_stats")
        bulk_upload_to_bubble(li_post_comments, "LI_post_comments")

        logging.info("Successfully fetched and uploaded posts and comments to Bubble.")
        return "<p style='color:green; text-align:center;'>✅ Posts and comments uploaded to Bubble.</p>"

    except ValueError as ve: # Catch specific errors like "Failed to fetch posts"
        logging.error(f"ValueError during LinkedIn data processing: {ve}")
        return f"<p style='color:red; text-align:center;'>❌ Error: {html.escape(str(ve))}</p>"
    except Exception as e:
        logging.exception("An unexpected error occurred in guarded_fetch_posts.") # Logs full traceback
        return "<p style='color:red; text-align:center;'>❌ An unexpected error occurred. Please check logs.</p>"



def guarded_fetch_dashboard(token_state):
    if not token_state or not token_state.get("token"):
        return "<p style='color:red; text-align:center;'>❌ Access denied. No token available.</p>"
    return fetch_and_render_dashboard(token_state.get("client_id"), token_state.get("token"))


def guarded_fetch_analytics(token_state):
    if not token_state or not token_state.get("token"):
        return ("<p style='color:red; text-align:center;'>❌ Access denied. No token available.</p>",
                None, None, None, None, None, None, None)

    return fetch_and_render_analytics(token_state.get("client_id"), token_state.get("token"))


def run_mentions_and_load(token_state):
    if not token_state or not token_state.get("token"):
        return ("<p style='color:red; text-align:center;'>❌ Access denied. No token available.</p>", None)
    return generate_mentions_dashboard(token_state.get("client_id"), token_state.get("token"))


with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
               title="LinkedIn Post Viewer & Analytics") as app:

    token_state = gr.State(value={"token": None, "client_id": None, "org_urn": None})

    gr.Markdown("# 🚀 LinkedIn Organization Post Viewer & Analytics")
    gr.Markdown("Token is supplied via URL parameter for Bubble.io lookup. Then explore dashboard and analytics.")

    url_user_token_display = gr.Textbox(label="User Token (from URL - Hidden)", interactive=False, visible=False)
    status_box = gr.Textbox(label="Overall Token Status", interactive=False)
    org_urn = gr.Textbox(visible=False)  # Needed for input, was missing from initial script

    app.load(fn=get_url_user_token, inputs=None, outputs=[url_user_token_display, org_urn])

    url_user_token_display.change(
        fn=process_and_store_bubble_token,
        inputs=[url_user_token_display, org_urn, token_state],
        outputs=[status_box, token_state]
    )


    app.load(fn=check_token_status, inputs=[token_state], outputs=status_box)
    gr.Timer(5.0).tick(fn=check_token_status, inputs=[token_state], outputs=status_box)

    with gr.Tabs():
        with gr.TabItem("1️⃣ Dashboard"):
            gr.Markdown("View your organization's recent posts and their engagement statistics.")
            
            sync_posts_to_bubble_btn = gr.Button("🔄 Fetch, Analyze & Store Posts to Bubble", variant="primary")
            dashboard_html_output = gr.HTML("<p style='text-align: center; color: #555;'>Click the button to fetch posts and store them in Bubble. Status will appear here.</p>")
            
            # Corrected: The click handler now calls guarded_fetch_posts
            # and dashboard_html_output is correctly defined in this scope.
            sync_posts_to_bubble_btn.click(
                fn=guarded_fetch_posts, 
                inputs=[token_state], 
                outputs=[dashboard_html_output]
            )

        with gr.TabItem("2️⃣ Analytics"):
            gr.Markdown("View follower count and monthly gains for your organization.")
            fetch_analytics_btn = gr.Button("📈 Fetch Follower Analytics", variant="primary")
            follower_count = gr.Markdown("<p style='text-align: center; color: #555;'>Waiting for token...</p>")

            with gr.Row():
                follower_plot, growth_plot = gr.Plot(), gr.Plot()
            with gr.Row():
                eng_rate_plot = gr.Plot()
            with gr.Row():
                interaction_plot = gr.Plot()
            with gr.Row():
                eb_plot = gr.Plot()
            with gr.Row():
                mentions_vol_plot, mentions_sentiment_plot = gr.Plot(), gr.Plot()

            fetch_analytics_btn.click(
                fn=guarded_fetch_analytics,
                inputs=[token_state],
                outputs=[follower_count, follower_plot, growth_plot, eng_rate_plot,
                         interaction_plot, eb_plot, mentions_vol_plot, mentions_sentiment_plot]
            )

        with gr.TabItem("3️⃣ Mentions"):
            gr.Markdown("Analyze sentiment of recent posts that mention your organization.")
            fetch_mentions_btn = gr.Button("🧠 Fetch Mentions & Sentiment", variant="primary")
            mentions_html = gr.HTML("<p style='text-align: center; color: #555;'>Waiting for token...</p>")
            mentions_plot = gr.Plot()
            fetch_mentions_btn.click(
                fn=run_mentions_and_load,
                inputs=[token_state],
                outputs=[mentions_html, mentions_plot]
            )

if __name__ == "__main__":
    if not os.environ.get("Linkedin_client_id"):
        print("WARNING: The 'Linkedin_client_id' environment variable is not set. The application may not function correctly.")
    app.launch(server_name="0.0.0.0", server_port=7860, share=True)