# -*- coding: utf-8 -*-
import gradio as gr
import json
import requests # Added for API calls
import os # Added for environment variables
import urllib.parse # Added for URL encoding (though requests handles params well)

# Assuming these custom modules exist in your project directory or Python path
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

# Import the function from your utils file
from gradio_utils import get_url_user_token # Assuming gradio_utils.py is in the same directory
from Bubble_API_Calls import fetch_linkedin_token_from_bubble

# Shared state for token received via POST or Bubble
token_received = {"status": False, "token": None, "client_id": None}

# --- Handlers for token reception (POST) and status ---
def receive_token(accessToken: str, client_id: str):
    """
    Called by a hidden POST mechanism to supply the OAuth code/token and client ID.
    """
    try:
        token_dict = json.loads(accessToken.replace("'", '"'))
    except json.JSONDecodeError as e:
        print(f"Error decoding accessToken (POST): {e}")
        token_received["status"] = False
        token_received["token"] = None
        token_received["client_id"] = client_id
        return "❌ Invalid token format (POST)", "", client_id
    
    token_received["status"] = True
    token_received["token"] = token_dict # This should be the dict like {"access_token": "value"}
    token_received["client_id"] = client_id
    print(f"Token (from POST) received successfully. Client ID: {client_id}")
    # Update status box, token display, client display directly
    return check_status(), show_token(), show_client()


def check_status():
    return "✅ Token available" if token_received["status"] else "❌ Waiting for token…"

def show_token(): # Shows access_token if available
    if token_received["status"] and token_received["token"] and isinstance(token_received["token"], dict):
        return token_received["token"].get("access_token", "Access token key missing in dict")
    elif token_received["status"] and token_received["token"]: # If token is a raw string (should not happen with new logic)
        return str(token_received["token"]) # Fallback, but ideally token_received["token"] is always a dict if status is True
    return ""

def show_client():
    return token_received["client_id"] if token_received["status"] and token_received["client_id"] else ""



# --- Guarded fetch functions (using token from POST or Bubble) ---
# These functions expect token_received["token"] to be a dictionary 
# like {"access_token": "actual_token_value", ...}
def guarded_fetch_dashboard():
    if not token_received["status"]:
        return "<p style='color:red; text-align:center;'>❌ Access denied. No token available. Please send token first or ensure URL token is valid.</p>"
    html = fetch_and_render_dashboard(
        token_received["client_id"],
        token_received["token"] 
    )
    return html

def guarded_fetch_analytics():
    if not token_received["status"]:
        return (
            "<p style='color:red; text-align:center;'>❌ Access denied. No token available.</p>",
            None, None, None, None, None, None, None 
        )
    count_md, plot, growth_plot, avg_post_eng_rate, interaction_metrics, eb_metrics, mentions_vol_metrics, mentions_sentiment_metrics = fetch_and_render_analytics(
        token_received["client_id"],
        token_received["token"] 
    )
    return count_md, plot, growth_plot, avg_post_eng_rate, interaction_metrics, eb_metrics, mentions_vol_metrics, mentions_sentiment_metrics

def run_mentions_and_load():
    if not token_received["status"]:
        return ("<p style='color:red; text-align:center;'>❌ Access denied. No token available.</p>", None)
    html, fig = generate_mentions_dashboard(
        token_received["client_id"],
        token_received["token"] 
    )
    return html, fig

# --- Build the Gradio UI ---
with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
               title="LinkedIn Post Viewer & Analytics") as app:
    gr.Markdown("# 🚀 LinkedIn Organization Post Viewer & Analytics")
    gr.Markdown("Token can be supplied via URL parameter (for Bubble.io lookup) or hidden POST. Then explore dashboard and analytics.")

    # Hidden elements: simulate POST endpoint for OAuth token
    hidden_token_input = gr.Textbox(visible=False, elem_id="hidden_token") 
    hidden_client_input = gr.Textbox(visible=False, elem_id="hidden_client_id") 
    hidden_btn = gr.Button(visible=False, elem_id="hidden_btn")

    # --- Display elements ---
    url_user_token_display = gr.Textbox(
        label="User Token (from URL - Hidden)", 
        interactive=False, 
        placeholder="Attempting to load from URL...",
        visible=False 
    )
    
    parsed_token_dict = gr.Textbox(label="Bubble API Call Status", interactive=False, placeholder="Waiting for URL token...")

    status_box = gr.Textbox(label="Overall Token Status", interactive=False) 
    token_display = gr.Textbox(label="Access Token (Active)", interactive=False)
    client_display = gr.Textbox(label="Client ID (Active)", interactive=False)

    # --- Load URL parameter on app start & Link to Bubble Fetch ---
    app.load(
        fn=get_url_user_token, 
        inputs=None, 
        outputs=[url_user_token_display] 
    )

    url_user_token_display.change(
        fn=fetch_linkedin_token_from_bubble,
        inputs=[url_user_token_display],
        outputs=[parsed_token_dict]
    )

    hidden_btn.click(
        fn=receive_token,
        inputs=[hidden_token_input, hidden_client_input],
        outputs=[status_box, token_display, client_display] 
    )
    
    app.load(fn=check_status, outputs=status_box)
    app.load(fn=show_token, outputs=token_display)
    app.load(fn=show_client, outputs=client_display)
    
    timer = gr.Timer(2.0) 
    timer.tick(fn=check_status, outputs=status_box)
    timer.tick(fn=show_token, outputs=token_display)
    timer.tick(fn=show_client, outputs=client_display)

    # Tabs for functionality
    with gr.Tabs():
        with gr.TabItem("1️⃣ Dashboard"):
            gr.Markdown("View your organization's recent posts and their engagement statistics.")
            fetch_dashboard_btn = gr.Button("📊 Fetch Posts & Stats", variant="primary")
            dashboard_html = gr.HTML(value="<p style='text-align: center; color: #555;'>Waiting for token...</p>")
            fetch_dashboard_btn.click(
                fn=guarded_fetch_dashboard,
                inputs=[],
                outputs=[dashboard_html]
            )

        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 = gr.Plot(visible=True) 
                growth_rate_plot = gr.Plot(visible=True)

            with gr.Row():
                post_eng_rate_plot = gr.Plot(visible=True)

            with gr.Row():
                interaction_data = gr.Plot(visible=True)

            with gr.Row():
                eb_data = gr.Plot(visible=True)

            with gr.Row():
                mentions_vol_data = gr.Plot(visible=True)
                mentions_sentiment_data = gr.Plot(visible=True)
            
            fetch_analytics_btn.click(
                fn=guarded_fetch_analytics,
                inputs=[],
                outputs=[follower_count, follower_plot, growth_rate_plot, post_eng_rate_plot, interaction_data, eb_data, mentions_vol_data, mentions_sentiment_data]
            )

        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(value="<p style='text-align: center; color: #555;'>Waiting for token...</p>")
            mentions_plot = gr.Plot(visible=True)
            fetch_mentions_btn.click(
                fn=run_mentions_and_load,
                inputs=[],
                outputs=[mentions_html, mentions_plot]
            )

# Launch the app
if __name__ == "__main__":
    # Ensure the 'Bubble_API' environment variable is set where this app is run.
    # For local testing, you can set it in your terminal before running:
    # export Bubble_API="YOUR_ACTUAL_BUBBLE_API_KEY"
    # python app.py
    app.launch(server_name="0.0.0.0", server_port=7860, share=True)