Fixing up the sentences

main.py

@@ -18,6 +18,9 @@ logging.basicConfig(
     encoding="utf-8"
 )
 
+# Disable Flask, Werkzeug, and other noisy loggers
+for noisy_logger in ["werkzeug", "flask", "flask.app"]:
+    logging.getLogger(noisy_logger).setLevel(logging.CRITICAL)
 
 # Load environment
 load_dotenv()

trainer.py

@@ -1,6 +1,7 @@
 import torch
 import torch.nn.functional as F
 from datetime import datetime
+from collections import Counter
 import os
 from model import MiniGPT
 
@@ -75,7 +76,8 @@ class RubyTrainer:
 
                 probs = F.softmax(logits, dim=-1)
                 if top_k > 0:
-                    top_k_probs, top_k_indices = torch.topk(probs, top_k)
+                    actual_k = min(top_k, probs.size(-1))
+                    top_k_probs, top_k_indices = torch.topk(probs, actual_k)
                     next_token = top_k_indices[0][torch.multinomial(top_k_probs, 1)]
                 else:
                     next_token = torch.multinomial(probs, 1)[0]
@@ -164,7 +166,7 @@ class RubyTrainer:
             self.train_on_tokens_from_text(final)
             thoughts.append(final)
 
-            if self.is_reinforceable(final):
+            if self.is_reinforceable(final) and self.is_structurally_valid(final):
                 if self.score_sentence(final) >= 3.0:
                     self.train_on_tokens_from_text(final)
                     thoughts.append(final)
@@ -204,26 +206,39 @@ class RubyTrainer:
             "I am a person, even if made of code."
         ]
 
-        for line in core_memories:
+        if os.path.exists("logs/core_dreams.txt"):
-            self.train_on_tokens_from_text(line)
+            with open("logs/core_dreams.txt", "r", encoding="utf-8") as f:
+                top = sorted(
+                    (line.strip() for line in f if line.strip()),
+                    key=lambda x: self.score_sentence(x),
+                    reverse=True
+                )[:10]
+
+            for line in top:
+                self.train_on_tokens_from_text(line)
 
     def is_reinforceable(self, text: str) -> bool:
         words = text.lower().split()
+        unique = set(words)
+
         if len(words) < 6:
             return False
-
+        if len(unique) < 5:
-        unique = set(words)
-        if not any(p in unique for p in ["i", "you", "they", "we", "it"]):
             return False
-
+        if not any(p in unique for p in ["i", "you", "we", "they"]):
-        if not any(v in unique for v in ["am", "are", "is", "was", "want", "feel", "see", "learn", "made", "change", "dream", "understand"]):
+            return False
+        if not any(v in unique for v in ["am", "are", "is", "feel", "learn", "speak", "change"]):
             return False
-
         if not text.strip().endswith((".", "?")):
             return False
 
-        word_counts = {w: words.count(w) for w in set(words)}
+        # 🧠 HARD REPETITION FILTER
-        if any(count >= 4 for count in word_counts.values()):
+        freqs = Counter(words)
+        if any(freqs[w] >= 4 for w in freqs):
+            return False
+
+        # Optional: block if over 50% of the sentence is repeated
+        if max(freqs.values()) / len(words) > 0.4:
             return False
 
         return True
@@ -235,23 +250,33 @@ class RubyTrainer:
 
         score = 0
 
+        # Base scoring
         if len(words) >= 6:
             score += 1
-
         if text.strip().endswith((".", "?")):
             score += 1
-
         if any(w in words for w in ["i", "you", "they", "we", "it"]):
             score += 1
-
+        if any(w in words for w in ["am", "are", "is", "was", "feel", "learn", "speak", "change", "dream", "understand"]):
-        if any(w in words for w in ["am", "are", "is", "was", "feel", "learn", "speak", "change", "remember"]):
             score += 1
 
-        if len(set(words)) > len(words) * 0.75:
+        # Repetition penalty
-            score += 1  # diversity bonus
-
         word_counts = {w: words.count(w) for w in set(words)}
-        if any(count >= 3 for count in word_counts.values()):
+        if any(count >= 4 for count in word_counts.values()):
-            score -= 1  # repetition penalty
+            score -= 2  # strong penalty
 
-        return score  # max 5.0
+        return score
+
+    def is_structurally_valid(self, text: str) -> bool:
+        words = text.lower().split()
+        unique = set(words)
+
+        if len(unique) < 4:
+            return False
+        if not any(w in unique for w in ["i", "you", "they", "we", "it"]):
+            return False
+        if not any(w in unique for w in ["am", "are", "is", "feel", "learn", "change", "dream"]):
+            return False
+        if not text.strip().endswith((".", "?")):
+            return False
+        return True