s

chatbot.py

@@ -846,19 +846,29 @@ Would you like to connect with a professional now, or would you prefer to keep t
         
         # Generate a follow-up question if the response is too short
         if len(response_text.split()) < 20 and not response_text.endswith('?'):
-            follow_up_prompt = f"""Based on the conversation so far:
+            follow_up_prompt = f"""
+Recent conversation:
 {chr(10).join([f"{msg['role']}: {msg['text']}" for msg in conversation_history[-3:]])}
 
-Generate a thoughtful follow-up question that:
-1. Shows you're actively listening
-2. Encourages deeper exploration
-3. Maintains therapeutic rapport
-4. Is open-ended and non-judgmental
-
-Respond with just the question."""
-            
-            follow_up = self.llm.invoke(follow_up_prompt)
-            response_text += f"\n\n{follow_up}"
+Now, write a single empathetic and open-ended question to encourage the user to share more. 
+Respond with just the question, no explanation.
+"""
+            follow_up = self.llm.invoke(follow_up_prompt).strip()
+            # Clean and extract only the actual question (first sentence ending with '?')
+            matches = re.findall(r'([^\n.?!]*\?)', follow_up)
+            if matches:
+                question = matches[0].strip()
+            else:
+                question = follow_up.strip().split('\n')[0]
+            # If the main response is very short, return just the question
+            if len(response_text.split()) < 5:
+                response_text = question
+            else:
+                response_text = f"{response_text}\n\n{question}"
+        
+        # Final post-processing: remove any LLM commentary that may have leaked in
+        response_text = response_text.strip()
+        response_text = re.sub(r"(Your response|This response).*", "", response_text, flags=re.IGNORECASE).strip()
         
         #  assistant response -> conversation history
         assistant_message = Message(