Update app.py

app.py

@@ -7,10 +7,15 @@ 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
-
-from Linkedin_Data_API_Calls import fetch_linkedin_posts, fetch_linkedin_comments, analyze_sentiment, prepare_data_for_bubble, bulk_upload_to_bubble
+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"
@@ -42,22 +47,68 @@ def process_and_store_bubble_token(url_user_token, org_urn, token_state):
     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_post")
+        bulk_upload_to_bubble(li_post_stats, "LI_post_stats")
+        bulk_upload_to_bubble(li_post_comments, "LI_post_comments")
 
-    posts = fetch_linkedin_posts(client_id, token_dict)
-    comments_data = fetch_linkedin_comments(client_id, token_dict, [post["id"] for post in posts])
-    sentiments = analyze_sentiment(comments_data)
-    li_posts, li_post_stats, li_post_comments = prepare_data_for_bubble(posts, sentiments)
+        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>"
 
-    bulk_upload_to_bubble(li_posts, "LI_post")
-    bulk_upload_to_bubble(li_post_stats, "LI_post_stats")
-    bulk_upload_to_bubble(li_post_comments, "LI_post_comments")
+    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>"
 
-    return "<p style='color:green; text-align:center;'>✅ Posts and comments uploaded to Bubble.</p>"
 
 
 def guarded_fetch_dashboard(token_state):