Update app.py

app.py

@@ -18,23 +18,13 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
-# Define personality questions - THIS WAS MISSING!
+# Define personality questions - reduced to general ones
 PERSONALITY_QUESTIONS = [
     "What is [name]'s personality like?",
-    "What are [name]'s favorite hobbies?",
     "What does [name] do for work?",
-    "What are [name]'s strengths?",
-    "What makes [name] unique?",
-    "What is [name]'s educational background?",
-    "What are [name]'s life goals?",
-    "What challenges has [name] overcome?",
-    "What is [name]'s family role?",
-    "What are [name]'s values?",
-    "What does [name] enjoy doing in free time?",
-    "What skills does [name] have?",
-    "What motivates [name]?",
-    "What are [name]'s achievements?",
-    "How would friends describe [name]?"
+    "What are [name]'s hobbies?",
+    "What makes [name] special?",
+    "Tell me about [name]"
 ]
 
 # Enhanced CSS styling
@@ -807,9 +797,10 @@ with st.sidebar:
         for i, question in enumerate(PERSONALITY_QUESTIONS):
             formatted_question = question.replace("[name]", name)
             if st.button(formatted_question, key=f"pq_{i}", use_container_width=True):
-                # Add the question to chat
+                # Add the question to chat and set flag to process it
                 user_message = {"role": "user", "content": formatted_question}
                 st.session_state.messages.append(user_message)
+                st.session_state.process_personality_question = formatted_question
                 st.rerun()
     else:
         st.markdown("""
@@ -1036,6 +1027,120 @@ for message in st.session_state.messages:
         else:
             st.markdown(message["content"])
 
+# Check if we need to process a personality question
+if hasattr(st.session_state, 'process_personality_question'):
+    prompt = st.session_state.process_personality_question
+    del st.session_state.process_personality_question  # Clear the flag
+    
+    # Display user message
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    
+    # Process the question using the same logic as chat input
+    # Update user tracking
+    update_online_users()
+    
+    # Get RAG response
+    with st.chat_message("assistant"):
+        if rag_system and rag_system.model and rag_system.get_collection_count() > 0:
+            # Search documents first
+            search_results = rag_system.search(prompt, n_results=5)
+            
+            # Debug output for troubleshooting
+            if search_results:
+                st.info(f"🔍 Found {len(search_results)} potential matches. Best similarity: {search_results[0]['similarity']:.3f}")
+            else:
+                st.warning("🔍 No search results returned from vector database")
+            
+            # Check if we found relevant documents (very low threshold)
+            if search_results and search_results[0]['similarity'] > 0.001:  # Ultra-low threshold
+                # Generate document-based answer
+                result = rag_system.generate_answer(
+                    prompt, 
+                    search_results, 
+                    use_ai_enhancement=use_ai_enhancement,
+                    unlimited_tokens=unlimited_tokens
+                )
+                
+                # Display AI answer or extracted answer
+                if use_ai_enhancement and result['has_both']:
+                    answer_text = result['ai_answer']
+                    st.markdown(f"🤖 **AI Enhanced Answer:** {answer_text}")
+                    
+                    # Also show extracted answer for comparison if different
+                    if result['extracted_answer'] != answer_text:
+                        with st.expander("📄 View Extracted Answer"):
+                            st.markdown(result['extracted_answer'])
+                else:
+                    answer_text = result['extracted_answer']
+                    st.markdown(f"📄 **Document Answer:** {answer_text}")
+                    
+                    # Show why AI enhancement wasn't used
+                    if use_ai_enhancement and not result['has_both']:
+                        st.info("💡 AI enhancement failed - showing extracted answer from documents")
+                
+                # Show RAG info with more details
+                if show_sources and result['sources']:
+                    confidence_text = f"{result['confidence']*100:.1f}%" if show_confidence else ""
+                    st.markdown(f"""
+                    <div class="rag-attribution">
+                        <strong>📁 Sources:</strong> {', '.join(result['sources'])}<br>
+                        <strong>🎯 Confidence:</strong> {confidence_text}<br>
+                        <strong>📊 Found:</strong> {len(search_results)} relevant sections<br>
+                        <strong>🔍 Best Match:</strong> {search_results[0]['similarity']:.3f} similarity
+                    </div>
+                    """, unsafe_allow_html=True)
+                
+                # Add to messages with RAG info
+                assistant_message = {
+                    "role": "assistant", 
+                    "content": answer_text,
+                    "rag_info": {
+                        "sources": result['sources'],
+                        "confidence": result['confidence'],
+                        "extracted_answer": result['extracted_answer'],
+                        "has_ai": result['has_both']
+                    }
+                }
+                
+            else:
+                # No relevant documents found - show debug info
+                if search_results:
+                    st.warning(f"📄 Found documents but similarity too low (best: {search_results[0]['similarity']:.3f}). Using general AI...")
+                else:
+                    st.warning("📄 No documents found in search. Using general AI...")
+                    
+                general_response = get_general_ai_response(prompt, unlimited_tokens=unlimited_tokens)
+                st.markdown(f"💬 **General AI:** {general_response}")
+                
+                assistant_message = {
+                    "role": "assistant", 
+                    "content": general_response,
+                    "rag_info": {"sources": [], "confidence": 0, "mode": "general"}
+                }
+        
+        else:
+            # RAG system not ready - use general AI
+            if rag_system and rag_system.get_collection_count() == 0:
+                st.warning("No documents indexed. Sync from GitHub or upload documents first...")
+            else:
+                st.error("RAG system not ready. Using general AI mode...")
+            
+            general_response = get_general_ai_response(prompt, unlimited_tokens=unlimited_tokens)
+            st.markdown(f"💬 **General AI:** {general_response}")
+            
+            assistant_message = {
+                "role": "assistant", 
+                "content": general_response,
+                "rag_info": {"sources": [], "confidence": 0, "mode": "general"}
+            }
+    
+    # Add assistant message to history
+    st.session_state.messages.append(assistant_message)
+    
+    # Auto-save
+    save_chat_history(st.session_state.messages)
+
 # Chat input
 if prompt := st.chat_input("Ask questions about your documents..."):
     # Update user tracking