Update conversation_logic.py

conversation_logic.py

@@ -85,6 +85,74 @@ def detect_help_mode(text: str) -> str:
     return "explain"
 
 
+def _extract_text_from_history_item(item: Dict[str, Any]) -> str:
+    if not isinstance(item, dict):
+        return ""
+
+    for key in ("content", "text", "message", "question_text", "raw_user_text"):
+        value = item.get(key)
+        if isinstance(value, str) and value.strip():
+            return value.strip()
+
+    meta = item.get("meta")
+    if isinstance(meta, dict):
+        for key in ("question_text",):
+            value = meta.get(key)
+            if isinstance(value, str) and value.strip():
+                return value.strip()
+
+    return ""
+
+
+def _is_followup_hint_only(text: str) -> bool:
+    low = (text or "").strip().lower()
+    return low in {
+        "hint",
+        "another hint",
+        "next hint",
+        "next step",
+        "first step",
+        "continue",
+        "go on",
+        "walk me through it",
+        "step by step",
+    }
+
+
+def _recover_question_text_from_history(
+    raw_user_text: str,
+    question_text: Optional[str],
+    chat_history: Optional[List[Dict[str, Any]]],
+) -> str:
+    explicit = (question_text or "").strip()
+    if explicit:
+        return explicit
+
+    if not _is_followup_hint_only(raw_user_text):
+        return (raw_user_text or "").strip()
+
+    if not chat_history:
+        return (raw_user_text or "").strip()
+
+    for item in reversed(chat_history):
+        role = str(item.get("role", "")).lower()
+        text = _extract_text_from_history_item(item)
+
+        if not text:
+            continue
+
+        if role == "assistant":
+            continue
+
+        low = text.lower().strip()
+        if _is_followup_hint_only(low):
+            continue
+
+        return text
+
+    return (raw_user_text or "").strip()
+
+
 def _normalize_classified_topic(topic: Optional[str], category: Optional[str], question_text: str) -> str:
     t = (topic or "").strip().lower()
     q = (question_text or "").lower()
@@ -308,7 +376,6 @@ def _compose_reply(
     category: Optional[str] = None,
 ) -> str:
     steps = _safe_steps(result.steps or [])
-    topic = (result.topic or "").lower().strip()
     meta = result.meta or {}
 
     scaffold_reply = _build_scaffold_reply(
@@ -468,8 +535,13 @@ class ConversationEngine:
         options_text: Optional[List[str]] = None,
         **kwargs,
     ) -> SolverResult:
-        solver_input = (question_text or raw_user_text or "").strip()
         user_text = (raw_user_text or "").strip()
+        recovered_question_text = _recover_question_text_from_history(
+            raw_user_text=user_text,
+            question_text=question_text,
+            chat_history=chat_history,
+        )
+        solver_input = recovered_question_text
 
         category = normalize_category(kwargs.get("category"))
         classification = classify_question(question_text=solver_input, category=category)
@@ -540,6 +612,7 @@ class ConversationEngine:
         result.meta["can_reveal_answer"] = bool(
             result.solved and _is_direct_solve_request(user_text or solver_input, resolved_intent) and hint_stage >= 3
         )
+        result.meta["recovered_question_text"] = recovered_question_text
 
         if explainer_understood:
             reply = format_explainer_response(
@@ -574,7 +647,7 @@ class ConversationEngine:
         result.reply = reply
         result.help_mode = resolved_help_mode
         result.meta["intent"] = resolved_intent
-        result.meta["question_text"] = question_text or ""
+        result.meta["question_text"] = recovered_question_text or ""
         result.meta["options_count"] = len(options_text or [])
         result.meta["category"] = inferred_category
         result.meta["classified_topic"] = question_topic