Fixed a circular import

main.py

@@ -3,7 +3,7 @@ import asyncio
 import threading
 from dotenv import load_dotenv
 import os
-from model.train import train_on_message
+from model.trainer import train_on_message
 from model.brain import generate_response
 from model.cleanup import full_cleanup
 from model.dream_replay import replay_dreams

model/brain.py

@@ -1,93 +1,32 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
 import random
-from model.tokenizer import Tokenizer
+import torch
+import torch.nn.functional as F
 from model.memory import save_dream
-from model.train import train_on_message
+from model.brain_state import model, tokenizer, DEVICE
-from model.journal import record_to_journal
-
 
 recent_dreams = []
 
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-tokenizer = Tokenizer()
-VOCAB_SIZE = 10000  # Temporary cap, grows dynamically
-EMBED_DIM = 128
-
-
-class MultiHeadSelfAttention(nn.Module):
-    def __init__(self, embed_dim, heads):
-        super().__init__()
-        assert embed_dim % heads == 0
-        self.heads = heads
-        self.head_dim = embed_dim // heads
-        self.scale = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32))
-
-        self.to_qkv = nn.Linear(embed_dim, embed_dim * 3)
-        self.out = nn.Linear(embed_dim, embed_dim)
-
-    def forward(self, x):
-        B, T, C = x.shape
-        qkv = self.to_qkv(x).view(B, T, self.heads, 3 * self.head_dim)
-        q, k, v = qkv.chunk(3, dim=-1)
-
-        attn_scores = (q @ k.transpose(-2, -1)) / self.scale
-        attn_weights = torch.softmax(attn_scores, dim=-1)
-
-        out = attn_weights @ v
-        out = out.transpose(1, 2).contiguous().view(B, T, C)
-        return self.out(out)
-
-
-class TransformerBlock(nn.Module):
-    def __init__(self, embed_dim, heads):
-        super().__init__()
-        self.attn = MultiHeadSelfAttention(embed_dim, heads)
-        self.norm1 = nn.LayerNorm(embed_dim)
-        self.ff = nn.Sequential(
-            nn.Linear(embed_dim, embed_dim * 4),
-            nn.ReLU(),
-            nn.Linear(embed_dim * 4, embed_dim)
-        )
-        self.norm2 = nn.LayerNorm(embed_dim)
-
-    def forward(self, x):
-        x = x + self.attn(self.norm1(x))
-        x = x + self.ff(self.norm2(x))
-        return x
-
-
-class TinyTransformer(nn.Module):
-    def __init__(self, vocab_size=VOCAB_SIZE, embed_dim=256, depth=4, heads=8):
-        super().__init__()
-        self.token_embed = nn.Embedding(vocab_size, embed_dim)
-        self.pos_embed = nn.Parameter(torch.randn(1, 128, embed_dim))
-        self.blocks = nn.Sequential(*[TransformerBlock(embed_dim, heads) for _ in range(depth)])
-        self.norm = nn.LayerNorm(embed_dim)
-        self.head = nn.Linear(embed_dim, vocab_size)
-
-    def forward(self, x):
-        B, T = x.shape
-        tok = self.token_embed(x)
-        pos = self.pos_embed[:, :T, :]
-        x = tok + pos
-        x = self.blocks(x)
-        x = self.norm(x)
-        return self.head(x)
-
-
-model = TinyTransformer().to(DEVICE)
-optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
-loss_fn = nn.CrossEntropyLoss()
-
 
 def generate_response():
-    seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE)
+    model.eval()
-    output = model(seed.unsqueeze(0))
+    # Pick a real known word to seed from context memory
+    context_texts = get_recent_context(5)
+    if context_texts:
+        start = random.choice(context_texts)
+        seed_tokens = tokenizer.tokenize(start)
+        if seed_tokens:
+            seed = torch.tensor([seed_tokens[-1]], device=DEVICE).unsqueeze(0)
+        else:
+            seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE).unsqueeze(0)
+    else:
+        seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE).unsqueeze(0)
+
+    output = model(seed)
     pred = torch.argmax(output, dim=-1).squeeze().tolist()
+
     if not isinstance(pred, list):
         pred = [pred]
+
     return tokenizer.detokenize(pred)
 
 
@@ -118,8 +57,10 @@ def daydream():
 
     if score > 0.45:
         save_dream(sentence, score)
+        from model.journal import record_to_journal
         record_to_journal(sentence)
+        from model.trainer import train_on_message
         train_on_message(sentence)
-        recent_dreams.append((score, sentence))
+
-        if len(recent_dreams) > 10:
+    if len(recent_dreams) > 10:
-            recent_dreams.pop(0)
+        recent_dreams.pop(0)

model/brain_architecture.py

@@ -0,0 +1,63 @@
+import torch
+import torch.nn as nn
+
+
+class MultiHeadSelfAttention(nn.Module):
+    def __init__(self, embed_dim, heads):
+        super().__init__()
+        assert embed_dim % heads == 0
+        self.heads = heads
+        self.head_dim = embed_dim // heads
+        self.scale = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32))
+
+        self.to_qkv = nn.Linear(embed_dim, embed_dim * 3)
+        self.out = nn.Linear(embed_dim, embed_dim)
+
+    def forward(self, x):
+        B, T, C = x.shape
+        qkv = self.to_qkv(x).view(B, T, self.heads, 3 * self.head_dim)
+        q, k, v = qkv.chunk(3, dim=-1)
+
+        attn_scores = (q @ k.transpose(-2, -1)) / self.scale
+        attn_weights = torch.softmax(attn_scores, dim=-1)
+
+        out = attn_weights @ v
+        out = out.transpose(1, 2).contiguous().view(B, T, C)
+        return self.out(out)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, embed_dim, heads):
+        super().__init__()
+        self.attn = MultiHeadSelfAttention(embed_dim, heads)
+        self.norm1 = nn.LayerNorm(embed_dim)
+        self.ff = nn.Sequential(
+            nn.Linear(embed_dim, embed_dim * 4),
+            nn.ReLU(),
+            nn.Linear(embed_dim * 4, embed_dim)
+        )
+        self.norm2 = nn.LayerNorm(embed_dim)
+
+    def forward(self, x):
+        x = x + self.attn(self.norm1(x))
+        x = x + self.ff(self.norm2(x))
+        return x
+
+
+class TinyTransformer(nn.Module):
+    def __init__(self, vocab_size, embed_dim=256, depth=4, heads=8):
+        super().__init__()
+        self.token_embed = nn.Embedding(vocab_size, embed_dim)
+        self.pos_embed = nn.Parameter(torch.randn(1, 128, embed_dim))
+        self.blocks = nn.Sequential(*[TransformerBlock(embed_dim, heads) for _ in range(depth)])
+        self.norm = nn.LayerNorm(embed_dim)
+        self.head = nn.Linear(embed_dim, vocab_size)
+
+    def forward(self, x):
+        B, T = x.shape
+        tok = self.token_embed(x)
+        pos = self.pos_embed[:, :T, :]
+        x = tok + pos
+        x = self.blocks(x)
+        x = self.norm(x)
+        return self.head(x)

model/brain_state.py

@@ -0,0 +1,13 @@
+import torch
+import torch.nn as nn
+from model.brain_architecture import TinyTransformer
+from model.tokenizer import Tokenizer
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+tokenizer = Tokenizer()
+VOCAB_SIZE = 10000  # Expandable if needed
+
+model = TinyTransformer(vocab_size=VOCAB_SIZE).to(DEVICE)
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
+loss_fn = nn.CrossEntropyLoss()

model/dream_replay.py

@@ -1,7 +1,6 @@
 import random
-import torch
 from model.memory import load_dreams
-from model.train import train_on_message
+from model.trainer import train_on_message
 
 
 def replay_dreams():

model/journal.py

@@ -1,6 +1,6 @@
 import os
 import time
-from model.train import train_on_message
+from model.trainer import train_on_message
 import random
 
 JOURNAL_PATH = "data/memory/journal.txt"

model/rehearsal.py

@@ -1,6 +1,6 @@
 import torch
 from model.brain import model, tokenizer, DEVICE
-from model.train import train_on_message
+from model.trainer import train_on_message
 
 
 def simulate_conversation():

model/trainer.py

@@ -1,11 +1,7 @@
 import torch
-import torch.nn as nn
-import random
 import time
-from model.brain import model, tokenizer, DEVICE, optimizer, loss_fn, daydream
+from model.brain_state import model, tokenizer, DEVICE, optimizer, loss_fn
-from context.context import get_recent_context, add_to_context
+from context.context import add_to_context, get_recent_context
-
-_last_thought = time.time()
 
 LOSS_FILE = "data/logs/loss.log"
 
@@ -16,7 +12,6 @@ def log_loss(value: float):
 
 
 def train_on_message(text: str):
-    global _last_thought
     model.train()
     context_texts = get_recent_context(3)
     augmented_text = " ".join(context_texts + [text])
@@ -30,16 +25,10 @@ def train_on_message(text: str):
 
     output = model(input_tensor)
     loss = loss_fn(output.view(-1, output.size(-1)), target_tensor.view(-1))
-    log_loss(loss.item())
 
     optimizer.zero_grad()
     loss.backward()
     optimizer.step()
 
+    log_loss(loss.item())
     add_to_context(text)
-
-    now = time.time()
-    if now - _last_thought > 15:
-        for _ in range(3):
-            daydream()
-        _last_thought = now

reader/reader.py

@@ -1,6 +1,6 @@
 import os
 import asyncio
-from model.train import train_on_message
+from model.trainer import train_on_message
 from reader.filter import is_valid_line
 
 BOOK_DIR = "data/books"