app.py

# -*- 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

# 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 ""

# --- Function to fetch LinkedIn Token from Bubble.io ---
def fetch_linkedin_token_from_bubble(url_user_token_str):
    """
    Fetches LinkedIn access token from Bubble.io API using the state value (url_user_token_str).
    The token is expected in a 'Raw_text' field as a JSON string, which is then parsed.
    Updates the global token_received state if successful.
    Returns status messages for UI update.
    """
    # Initial UI states (in case of early exit or error)
    current_status = check_status()
    current_token_display = show_token()
    current_client_display = show_client()

    bubble_api_key = os.environ.get("Bubble_API")
    if not bubble_api_key:
        error_msg = "❌ Bubble API Error: The 'Bubble_API' environment variable is not set."
        print(error_msg)
        return error_msg, current_status, current_token_display, current_client_display

    if not url_user_token_str or "not found" in url_user_token_str or "Could not access" in url_user_token_str:
        return f"ℹ️ No valid user token from URL to query Bubble. ({url_user_token_str})", current_status, current_token_display, current_client_display

    base_url = "https://app.ingaze.ai/version-test/api/1.1/obj/Linkedin_access"
    constraints = [{"key": "state", "constraint_type": "equals", "value": url_user_token_str}]
    params = {'constraints': json.dumps(constraints)}
    headers = {"Authorization": f"Bearer {bubble_api_key}"} 
    
    bubble_api_status_msg = f"Attempting to fetch token from Bubble for state: {url_user_token_str}..."
    print(bubble_api_status_msg)

    response = None 
    try:
        response = requests.get(base_url, params=params, headers=headers, timeout=15) # Increased timeout slightly
        response.raise_for_status() 
        
        data = response.json()
        results = data.get("response", {}).get("results", [])
        
        if results:
            raw_text_from_bubble = results[0].get("Raw_text", None)
            parsed_token_dict = None 

            if raw_text_from_bubble and isinstance(raw_text_from_bubble, str):
                try:
                    parsed_token_dict = json.loads(raw_text_from_bubble)
                    if not isinstance(parsed_token_dict, dict):
                        bubble_api_status_msg = (f"⚠️ Bubble API: 'Raw_text' field did not contain a valid JSON dictionary string. "
                                                 f"Content type: {type(raw_text_from_bubble)}, Value: {raw_text_from_bubble}")
                        print(bubble_api_status_msg)
                        parsed_token_dict = None 
                    # If it is a dict, parsed_token_dict is now the token dictionary itself
                except json.JSONDecodeError as e:
                    bubble_api_status_msg = (f"⚠️ Bubble API: Error decoding 'Raw_text' JSON string: {e}. "
                                             f"Content: {raw_text_from_bubble}")
                    print(bubble_api_status_msg)
                    parsed_token_dict = None 
            elif raw_text_from_bubble: # It exists but is not a string
                 bubble_api_status_msg = (f"⚠️ Bubble API: 'Raw_text' field was not a string. "
                                         f"Type: {type(raw_text_from_bubble)}, Value: {raw_text_from_bubble}")
                 print(bubble_api_status_msg)


            if parsed_token_dict and "access_token" in parsed_token_dict:
                token_received["status"] = True
                token_received["token"] = parsed_token_dict # Store the entire parsed dictionary
                token_received["client_id"] = f"Bubble (state: {url_user_token_str})"
                bubble_api_status_msg = f"✅ LinkedIn Token successfully fetched and parsed from Bubble 'Raw_text' for state: {url_user_token_str}"
                print(bubble_api_status_msg)
            elif raw_text_from_bubble and not parsed_token_dict:
                # Error message already set by parsing logic if raw_text_from_bubble existed but parsing failed.
                # If bubble_api_status_msg wasn't set by specific parsing errors, use a general one.
                if not bubble_api_status_msg.startswith("⚠️"): # Avoid overwriting specific parsing error
                    bubble_api_status_msg = f"⚠️ Bubble API: 'Raw_text' found but could not be parsed into a valid token dictionary for state: {url_user_token_str}."
                print(bubble_api_status_msg)
            elif not raw_text_from_bubble:
                 bubble_api_status_msg = (f"⚠️ Bubble API: Token field ('Raw_text') "
                                         f"not found or is null in response for state: {url_user_token_str}. Result: {results[0]}")
                 print(bubble_api_status_msg)
            elif parsed_token_dict and "access_token" not in parsed_token_dict: # Parsed OK, but missing the crucial key
                 bubble_api_status_msg = (f"⚠️ Bubble API: 'access_token' key missing in parsed 'Raw_text' dictionary for state: {url_user_token_str}. Parsed: {parsed_token_dict}")
                 print(bubble_api_status_msg)
            # If none of the above, the initial bubble_api_status_msg will be used or an error below will catch it.

        else: # No results from Bubble for the given state
            bubble_api_status_msg = f"❌ Bubble API: No results found for state: {url_user_token_str}"
            print(bubble_api_status_msg)
            
    except requests.exceptions.HTTPError as http_err:
        error_details = response.text if response else "No response content"
        bubble_api_status_msg = f"❌ Bubble API HTTP error: {http_err} - Response: {error_details}"
        print(bubble_api_status_msg)
    except requests.exceptions.Timeout:
        bubble_api_status_msg = "❌ Bubble API Request timed out."
        print(bubble_api_status_msg)
    except requests.exceptions.RequestException as req_err:
        bubble_api_status_msg = f"❌ Bubble API Request error: {req_err}"
        print(bubble_api_status_msg)
    except json.JSONDecodeError as json_err: # Error decoding the main Bubble response, not Raw_text
        error_details = response.text if response else "No response content"
        bubble_api_status_msg = f"❌ Bubble API main response JSON decode error: {json_err}. Response: {error_details}"
        print(bubble_api_status_msg)
    except Exception as e:
        bubble_api_status_msg = f"❌ An unexpected error occurred while fetching from Bubble: {str(e)}"
        print(bubble_api_status_msg)

    # Return values to update all relevant UI components
    return bubble_api_status_msg, check_status(), show_token(), show_client()


# --- 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 
    )
    
    bubble_status_display = 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=[bubble_status_display, status_box, token_display, client_display]
    )

    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)