Update common/ResponseValidation.py

common/ResponseValidation.py

@@ -6,15 +6,16 @@ from RespondentAgent import *
 from langchain_groq import ChatGroq
 
 
-def matches_user_speaking_style(answer, transcript_text, processor_llm, user_profile, agent_question):
+def matches_user_speaking_style(answer, processor_llm, user_profile, agent_question):
     """
-    Uses the LLM to determine if the answer matches the tone and style of the user's prior speaking style in the transcript.
-    Returns True if similar and in first person, False otherwise.
+    Uses the LLM to determine if the answer matches the expected tone and style 
+    based on the user's communication profile.
+    Returns True if it is first-person and stylistically aligned, False otherwise.
     """
     logging.info("[Style Match Check] Entry")
-    
+
     try:
-        # First-person perspective check
+        # --- Step 1: First-person check ---
         fp_prompt = f"""
 You are an expert in analysing writing style and narrative perspective.
 Determine whether the following response is written from a first-person point of view.
@@ -23,6 +24,7 @@ Say "First Person: Yes" only if clearly first-person, else say "First Person: No
 
 Response:
 \"\"\"{answer}\"\"\"
+
 Output format:
 First Person: Yes
 or
@@ -34,77 +36,69 @@ First Person: No
             logging.warning("[Style Match Check] Failed first-person test")
             return False
 
-        # Get communication profile
-        style = user_profile.get_field("Communication", "Style")
-        tone = user_profile.get_field("Communication", "Tone")
-        length = user_profile.get_field("Communication", "Length")
-        topics = user_profile.get_field("Communication", "Topics")
-
-        # Identify factual-type question
+        # --- Step 2: Skip style check for factual questions ---
         factual_keywords = [
             "name", "age", "where are you from", "where do you live", "occupation",
             "birthplace", "what do you do", "how old", "which city", "which country"
         ]
         lower_q = agent_question.strip().lower()
         is_factual = any(kw in lower_q for kw in factual_keywords)
-
         if is_factual:
-            logging.info("[Style Match Check] Question is factual — skipping style comparison")
+            logging.info("[Style Match Check] Question is factual — skipping style evaluation")
             return True
 
-        # Style match prompt
-        prompt = f"""
-You are a writing style and tone analyst.
+        # --- Step 3: Extract user communication profile ---
+        style = user_profile.get_field("Communication", "Style")
+        tone = user_profile.get_field("Communication", "Tone")
+        length = user_profile.get_field("Communication", "Length")
+        topics = user_profile.get_field("Communication", "Topics")
 
-Your job is to assess whether a new response sounds like it was written by the same person who spoke in the interview transcript — considering phrasing, vocabulary, tone, and sentence structure.
+        # --- Step 4: Style validation prompt ---
+        style_check_prompt = f"""
+You are a communication coach and writing style analyst.
 
----
-### Prior Interview Transcript (how the user usually talks):
-\"\"\"{transcript_text}\"\"\"
+Evaluate whether the following response aligns with the given communication profile.
 
 ---
-### New Response:
+### Response:
 \"\"\"{answer}\"\"\"
 
----
-### Style Profile Reference:
+### Communication Profile:
 - Style: {style}
 - Tone: {tone}
 - Preferred Length: {length}
-- Topics: {topics}
+- Common Topics: {topics}
 
 ---
 ### Instructions:
-- Check if the *tone*, *style*, and *language* of the new response align with the transcript.
-- Use the style profile for reference.
-- Focus on phrasing, formality, sentence structure, expressiveness, and personal flair.
-- Ignore topic similarity — you’re assessing delivery style.
-- Reply only with one of the following:
+- Does the writing match the tone, style, and sentence structure described?
+- Is the language aligned in formality, expressiveness, and delivery?
+- Respond only with one of the following:
 
 Style Match: Yes  
 or  
 Style Match: No
 """
+        logging.info("[Style Match Check] Invoking LLM for profile-based style check")
+        style_response = processor_llm.invoke(style_check_prompt)
+        style_result = style_response.content.strip().lower()
 
-        logging.info("[Style Match Check] Invoking LLM with style comparison prompt")
-        response = processor_llm.invoke(prompt)
-        result = response.content.strip().lower()
-
-        if "style match: yes" in result:
+        if "style match: yes" in style_result:
             logging.info("[Style Match Check] Match confirmed")
             return True
-        elif "style match: no" in result:
+        elif "style match: no" in style_result:
             logging.info("[Style Match Check] Style mismatch detected")
             return False
         else:
-            logging.warning(f"[Style Match Check] Unexpected output format: {result}")
+            logging.warning(f"[Style Match Check] Unexpected output format: {style_result}")
             return False
 
     except Exception as e:
-        logging.error(f"[Style Match Check] LLM failed during comparison: {e}")
+        logging.error(f"[Style Match Check] Exception occurred: {e}")
         return False
 
 
+
 def validate_response(question, answer, user_profile_str, fast_facts_str, interview_transcript_text, respondent_type, ai_evaluator_agent, processor_llm):
     llm_mode_prompt = f"""
 You are an expert in market research interview analysis. Given the following question, determine if it is:
@@ -193,22 +187,3 @@ Accuracy Rating: <0-10>
         return False
 
 
-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 = matches_user_speaking_style(
-            answer=styled_answer,
-            transcript_text=interview_transcript_text,
-            processor_llm=processor_llm,
-            user_profile=user_profile,
-            agent_question=agent_question
-        )
-        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")
-        return False