Update app.py

app.py

@@ -18,6 +18,25 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
+# Define personality questions - THIS WAS MISSING!
+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]?"
+]
+
 # Enhanced CSS styling
 st.markdown("""
 <style>
@@ -144,165 +163,6 @@ HISTORY_FILE = "rag_chat_history.json"
 SESSIONS_FILE = "rag_chat_sessions.json"
 USERS_FILE = "online_users.json"
 
-# ================= PERSONALITY QUESTIONS =================
-
-# Replace the personality questions section (around line 760-780) with this fixed version:
-
-# Personality Questions Section
-st.header("🎭 Personality Questions")
-
-# Name input for personalizing questions
-name_input = st.text_input("Enter name for personalized questions:", placeholder="e.g., Sarah, Ahmed", help="Replace [name] in questions with this name")
-
-if name_input.strip():
-    name = name_input.strip()
-    st.markdown(f"""
-    <div class="personality-section">
-        <strong>💫 Quick Questions for {name}:</strong><br>
-        <small>Click any question to ask about {name}</small>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Display personality questions as clickable buttons
-    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 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("""
-    <div class="personality-section">
-        <strong>💫 Sample Questions:</strong><br>
-        <small>Enter a name above to personalize these questions</small>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # Show sample questions without names
-    for question in PERSONALITY_QUESTIONS[:5]:  # Show first 5 as examples
-        st.markdown(f"• {question}")
-
-# Then, modify the main chat processing section to handle personality questions
-# Add this right after the chat input section and before the existing chat processing:
-
-# 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)
-
-# Continue with the existing chat input processing...
-
 # ================= GITHUB INTEGRATION =================
 
 def clone_github_repo():