Update researchsimulation/InteractiveInterviewChatbot.py

researchsimulation/InteractiveInterviewChatbot.py

@@ -413,20 +413,22 @@ def validate_generic_answer(agent_name, agent_question, generic_answer, user_pro
         logging.exception("[validate_generic_answer] Exception during validation")
         return False
 
-def validate_styled_answer(agent_name, agent_question, styled_answer, user_profile, processor_llm):
+def validate_styled_answer(agent_name, agent_question, styled_answer, user_profile, processor_llm, interview_transcript_text):
+    """
+    Validates whether the styled answer matches the user's typical speaking style using prior interview transcript and communication profile.
+    Returns True if stylistically aligned, False otherwise.
+    """
     logging.info("[validate_styled_answer] Entry")
     try:
-        is_valid = validate_response(
-            question=agent_question,
+        # Use transcript-based style matching logic
+        is_valid = matches_user_speaking_style(
             answer=styled_answer,
-            user_profile_str=str(user_profile),
-            fast_facts_str="", 
-            interview_transcript_text="",  
-            respondent_type=agent_name,
-            ai_evaluator_agent=None,
-            processor_llm=processor_llm
+            transcript_text=interview_transcript_text,
+            processor_llm=processor_llm,
+            user_profile=user_profile,
+            agent_question=agent_question
         )
-        logging.info(f"[validate_styled_answer] Result: {is_valid}")
+        logging.info(f"[validate_styled_answer] Style validation result: {is_valid}")
         return is_valid
     except Exception as e:
         logging.exception("[validate_styled_answer] Exception during style validation")
@@ -477,7 +479,7 @@ def ask_interview_question(respondent_agents_dict, last_active_agent, question,
             generic_answer = generate_generic_answer(agent_name, agent_question, respondent_agent)
 
             if not validate_generic_answer(agent_name, agent_question, generic_answer, user_profile, processor_llm):
-                responses.append(f"**PreData Moderator**: The generated answer for {agent_name} did not meet our content standards.")
+                responses.append(f"**PreData Moderator**: Please ask {agent_name} again.")
                 continue
 
             def generator():