fixing cuda errors

dashboard/dashboard.py

@@ -1,4 +1,9 @@
 from flask import Flask, render_template
+import json
+import os
+import time
+import datetime
+import logging
 from model.brainmap import get_brainmap
 from model.journal import read_journal_entries
 from model.memory import load_dreams
@@ -7,10 +12,6 @@ from model.abstraction import cluster_vocab
 from model.memory import load_dreams
 from model.scheduler import get_time_until_next_action, get_next_action_label
 from context.context import load_context
-import json
-import os
-import time
-import datetime
 
 
 app = Flask(__name__)
@@ -126,4 +127,6 @@ def dreams():
 
 
 def run_dashboard():
+    log = logging.getLogger('werkzeug')
+    log.setLevel(logging.ERROR)
     app.run(host="0.0.0.0", port=5000, debug=False, use_reloader=False)

main.py

@@ -11,7 +11,6 @@ from model.rehearsal import simulate_conversation
 from model.scheduler import set_next_action
 from reader.reader import read_books_forever
 from dashboard.dashboard import run_dashboard
-import threading
 
 load_dotenv()
 TOKEN = os.getenv("DISCORD_TOKEN")
@@ -38,9 +37,6 @@ async def on_message(message):
     response = generate_response()
     await message.channel.send(response)
 
-# Launch Flask in background
-threading.Thread(target=run_dashboard, daemon=True).start()
-
 
 async def background_cleanup_loop():
     while True:

model/dynamic_expand.py

@@ -1,10 +1,12 @@
 import torch
+import threading
 from model.brain_architecture import TinyTransformer
 from model.brain_state import model, tokenizer, DEVICE
 
 optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
 
-_last_expansion_vocab_size = 0
+_last_vocab_size = 0
+_expand_lock = threading.Lock()
 
 
 def get_optimizer():
@@ -12,22 +14,24 @@ def get_optimizer():
 
 
 def expand_model_if_needed():
-    global model, optimizer, _last_expansion_vocab_size
+    global model, optimizer, _last_vocab_size
 
+    with _expand_lock:
         current_vocab_size = len(tokenizer.vocab) + 10
 
-    if current_vocab_size - _last_expansion_vocab_size < 5:
+        if current_vocab_size - _last_vocab_size < 10:
-        return  # Only expand every 5 words
+            return  # Expand only after 10 new words collected
 
         old_vocab_size = model.head.out_features
+
         if current_vocab_size <= old_vocab_size:
-        return  # No expansion needed
+            return
-    print(f"Expanding model from {old_vocab_size} -> {current_vocab_size}")
+
+        # print(f"Expanding model from {old_vocab_size} -> {current_vocab_size}")
 
         old_state = model.state_dict()
         new_model = TinyTransformer(vocab_size=current_vocab_size).to(DEVICE)
 
-    # Transfer matching parameters
         with torch.no_grad():
             for name, param in new_model.named_parameters():
                 if name in old_state and old_state[name].shape == param.shape:
@@ -35,5 +39,6 @@ def expand_model_if_needed():
 
         model = new_model
         optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
+        _last_vocab_size = current_vocab_size
 
-    print("Expansion complete.")
+        # print("Expansion complete.")