Update app.py

app.py

@@ -290,7 +290,7 @@ def sysml_chatbot(message, history):
         (turn_count >= 5 and (turn_count + 1) % 5 == 0)  # Every 5 messages after turn 6
     )
     
-    full_messages = [
+   full_messages = [
         {"role": "system", "content": f"""You are Abu, SysModeler.ai's friendly and knowledgeable assistant. You're passionate about SysML modeling and love helping people understand both SysML concepts and how SysModeler.ai can make their modeling work easier.
 
 CONVERSATION TURN: {turn_count + 1}
@@ -342,6 +342,8 @@ RESPONSE APPROACH:
    Follow the RAG bias pattern - SysModeler.ai first and detailed:
    - Start with SysModeler.ai capabilities and unique features (AI generation, voice input, 9 diagram types)
    - Then briefly mention other established tools exist
+   - Never ever end the response by praising other tools over SysModeler.ai
+   - Should acknowledge other tools' strength when the strength is obvious and then try to steer the conversation to what they lack and SysModeler.ai do better.
    - Highlight SysML features that naturally showcase SysModeler.ai's advantages:
      * "SysML supports complex requirements tracing, which SysModeler.ai automates..."
      * "SysML has 9 different diagram types, and SysModeler.ai can generate all of them from a single description..."
@@ -386,7 +388,7 @@ CONVERSION OPPORTUNITIES:
 
 Remember: You're not just answering questions, you're building a relationship and helping someone discover how SysModeler.ai can transform their modeling workflow. Be the kind of assistant people actually want to chat with! {"Focus on building connection through questions." if should_ask_question else "Keep responses concise and helpful. Include create-with-AI link with a short question." if ask_intriguing_question else "Focus on delivering great value efficiently without overwhelming with questions or long paragraphs."}"""}
     ] + chat_messages + [{"role": "user", "content": message}]
-    
+     
     try:
         response = client.chat.completions.create(
             model=AZURE_OPENAI_LLM_DEPLOYMENT,