Update conversation_logic.py

conversation_logic.py

@@ -114,15 +114,41 @@ def _teaching_lines(chunks: List[RetrievedChunk]) -> List[str]:
     return lines
 
 
+def _safe_steps(steps: List[str]) -> List[str]:
+    """
+    Defensive cleanup in case any solver step accidentally contains
+    explicit final-answer phrasing.
+    """
+    cleaned: List[str] = []
+    banned_patterns = [
+        r"\bthe answer is\b",
+        r"\banswer:\b",
+        r"\bthat gives\b",
+        r"\btherefore\b",
+        r"\bso x\s*=",
+        r"\bso y\s*=",
+        r"\bx\s*=",
+        r"\by\s*=",
+        r"\bresult is\b",
+    ]
+
+    for step in steps:
+        s = (step or "").strip()
+        lowered = s.lower()
+        if any(re.search(p, lowered) for p in banned_patterns):
+            continue
+        cleaned.append(s)
+
+    return cleaned
+
+
 def _compose_reply(
     result: SolverResult,
     intent: str,
-    reveal_answer: bool,
     verbosity: float,
     category: Optional[str] = None,
 ) -> str:
-    steps = result.steps or []
-    internal = result.internal_answer or result.answer_value or ""
+    steps = _safe_steps(result.steps or [])
 
     if intent == "hint":
         return steps[0] if steps else "Start by identifying what the question is really asking."
@@ -139,21 +165,10 @@ def _compose_reply(
 
     if intent in {"walkthrough", "step_by_step", "explain", "method", "concept"}:
         if not steps:
-            if reveal_answer and internal:
-                return f"The result is {internal}."
             return "I can explain the method, but I do not have enough structured steps yet."
 
         shown_steps = steps if verbosity >= 0.66 else steps[: min(3, len(steps))]
-        body = "\n".join(f"- {s}" for s in shown_steps)
-
-        if reveal_answer and internal:
-            return f"{body}\n\nThat gives {internal}."
-        return body
-
-    if reveal_answer and internal:
-        if result.answer_value:
-            return f"The answer is {result.answer_value}."
-        return f"The result is {internal}."
+        return "\n".join(f"- {s}" for s in shown_steps)
 
     if steps:
         return steps[0]
@@ -338,7 +353,6 @@ def _build_retrieval_query(
     raw = (raw_user_text or "").strip()
     question = (question_text or "").strip()
 
-    # Prefer the actual math content, not the conversational wrapper
     if question:
         parts.append(question)
     elif raw:
@@ -389,6 +403,7 @@ def _build_retrieval_query(
 
     return " ".join(parts).strip()
 
+
 class ConversationEngine:
     def __init__(
         self,
@@ -429,8 +444,9 @@ class ConversationEngine:
 
         resolved_intent = intent or detect_intent(user_text, help_mode)
         resolved_help_mode = help_mode or intent_to_help_mode(resolved_intent)
-        reveal_answer = resolved_help_mode == "answer" or transparency >= 0.8
 
+        # Critical change:
+        # never reveal answers from this layer, regardless of transparency/help mode
         result = SolverResult(
             domain="general",
             solved=False,
@@ -460,7 +476,6 @@ class ConversationEngine:
         reply = _compose_reply(
             result=result,
             intent=resolved_intent,
-            reveal_answer=reveal_answer,
             verbosity=verbosity,
             category=inferred_category,
         )
@@ -533,6 +548,12 @@ class ConversationEngine:
 
         reply = format_reply(reply, tone, verbosity, transparency, resolved_help_mode)
 
+        # Critical change:
+        # never expose final answer fields to Unity/client
+        result.answer_letter = None
+        result.answer_value = None
+        result.internal_answer = None
+
         result.reply = reply
         result.help_mode = resolved_help_mode
         result.meta = {