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modeling_twentyq.py

@@ -70,9 +70,11 @@ class TwentyQForCausalLM(PreTrainedModel, GenerationMixin):
         text = raw_bytes.decode("utf-8", errors="replace")
 
         # Parse conversation and get next response
-        answers, qnum, last_was_guess, game_over_msg = self._parse_conversation(text)
+        answers, qnum, last_was_guess, game_over_msg, unrecognized = self._parse_conversation(text)
 
-        if game_over_msg:
+        if unrecognized:
+            response = f"I didn't understand that. Please answer: {unrecognized}"
+        elif game_over_msg:
             response = game_over_msg
         else:
             # Seed RNG from conversation for deterministic play
@@ -90,6 +92,7 @@ class TwentyQForCausalLM(PreTrainedModel, GenerationMixin):
         qnum = 0
         last_was_guess = False
         game_over_msg = None
+        unrecognized = None  # set to hint string if last answer wasn't understood
 
         # Split into turns by [A] and [U] markers
         parts = text.replace("\r", "").split("\n")
@@ -120,6 +123,9 @@ class TwentyQForCausalLM(PreTrainedModel, GenerationMixin):
                     if avm_code:
                         answers.append((0, avm_code, False))
                         qnum += 1
+                        unrecognized = None
+                    else:
+                        unrecognized = "Animal, Vegetable, Mineral, or Other"
                     i += 2
 
                 elif a_msg.lower().startswith("i'm guessing"):
@@ -130,14 +136,14 @@ class TwentyQForCausalLM(PreTrainedModel, GenerationMixin):
                     ans_code = ANSWER_WORDS.get(u_lower, 0)
                     if ans_code == 2:  # Yes — correct guess
                         game_over_msg = f"I win! Got it in {qnum + 1} questions."
-                    elif ans_code == -1:  # Close
-                        if t_idx >= 0:
-                            answers.append((t_idx, 0, True))
-                        qnum += 1
-                    else:  # No — wrong
+                        unrecognized = None
+                    elif ans_code == 1 or ans_code == -1:  # No or Close
                         if t_idx >= 0:
                             answers.append((t_idx, 0, True))
                         qnum += 1
+                        unrecognized = None
+                    else:
+                        unrecognized = "Yes, No, or Close"
                     i += 2
 
                 elif a_msg.lower().startswith("i win") or a_msg.lower().startswith("i'm stumped"):
@@ -149,17 +155,19 @@ class TwentyQForCausalLM(PreTrainedModel, GenerationMixin):
                     # Regular question
                     q_text = a_msg.rstrip("?").strip()
                     q_idx = self._q_lookup.get(q_text.lower(), -1)
-                    ans_code = ANSWER_WORDS.get(u_lower, 6)
-                    if ans_code == -1:
-                        ans_code = 6  # treat "close" as unknown for regular q
-                    if q_idx >= 0:
-                        answers.append((q_idx, ans_code, False))
-                    qnum += 1
+                    ans_code = ANSWER_WORDS.get(u_lower, 0)
+                    if ans_code == -1 or ans_code == 0:
+                        unrecognized = "Yes, No, Probably, Doubtful, Maybe, or Unknown"
+                    else:
+                        unrecognized = None
+                        if q_idx >= 0:
+                            answers.append((q_idx, ans_code, False))
+                        qnum += 1
                     i += 2
             else:
                 i += 1
 
-        return answers, qnum, last_was_guess, game_over_msg
+        return answers, qnum, last_was_guess, game_over_msg, unrecognized
 
     def _next_move(self, answers, qnum, last_was_guess):
         if qnum == 0: