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Updated to dream

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This commit is contained in:
Dani 2025-04-24 13:23:54 -04:00
parent a069e9b7dd
commit 699a21ad84
5 changed files with 86 additions and 3 deletions
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9
dashboard/dashboard.py
View File

@ -1,13 +1,20 @@
from flask import Flask, render_template
from model.memory import load_dreams
from model.tokenizer import Tokenizer
import threading
app = Flask(__name__)
tokenizer = Tokenizer()
@app.route("/")
def index():
return render_template("index.html")
dreams = load_dreams()
top_dreams = dreams[:5]
return render_template("index.html",
vocab_size=len(tokenizer.vocab),
top_dreams=top_dreams)
def run_dashboard():

9
dashboard/templates/index.html
View File

@ -5,6 +5,13 @@
</head>
<body>
<h1>Ruby is running</h1>
<p>Vocabulary Size: {{ vocab_size }}</p>
<p><strong>Vocabulary Size:</strong> {{ vocab_size }}</p>
<h2>🏆 Highest Scoring Dreams</h2>
<ul>
{% for dream in top_dreams %}
<li><strong>{{ dream.score }}</strong> | {{ dream.sentence }}</li>
{% endfor %}
</ul>
</body>
</html>

37
model/brain.py
View File

@ -2,6 +2,11 @@ import torch
import torch.nn as nn
import random
from model.tokenizer import Tokenizer
import torch.nn.functional as F
from model.memory import save_dream
import time
recent_dreams = []
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = Tokenizer()
@ -34,3 +39,35 @@ def generate_response():
if not isinstance(pred, list):
pred = [pred]
return tokenizer.detokenize(pred)
def score_sentence(sentence: str) -> float:
words = sentence.strip().split()
length = len(words)
diversity = len(set(words)) / (length + 1)
if length < 4:
return 0.0
return diversity * min(length, 20)
def daydream():
model.eval()
seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE).unsqueeze(0)
dream = []
for _ in range(12): # generate 12-word thought
out = model(seed)
logits = out[:, -1, :]
probs = F.softmax(logits, dim=-1)
token = torch.multinomial(probs, num_samples=1)
dream.append(token.item())
seed = torch.cat([seed, token], dim=1)
sentence = tokenizer.detokenize(dream)
score = score_sentence(sentence)
if score > 0.3:
save_dream(sentence, score)
recent_dreams.append((score, sentence))
if len(recent_dreams) > 10:
recent_dreams.pop(0)

19
model/memory.py
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@ -0,0 +1,19 @@
import json
import os
DREAM_LOG_PATH = "data/memory/dreams.json"
def load_dreams():
if not os.path.exists(DREAM_LOG_PATH):
return []
with open(DREAM_LOG_PATH, "r", encoding="utf-8") as f:
return json.load(f)
def save_dream(sentence: str, score: float):
dreams = load_dreams()
dreams.append({"sentence": sentence, "score": round(score, 2)})
dreams = sorted(dreams, key=lambda x: x["score"], reverse=True)[:100]
with open(DREAM_LOG_PATH, "w", encoding="utf-8") as f:
json.dump(dreams, f, indent=2)

15
model/train.py
View File

@ -1,8 +1,14 @@
import torch
from model.brain import model, optimizer, loss_fn, tokenizer, DEVICE
import torch.nn as nn
import random
import time
from model.brain import model, tokenizer, DEVICE, optimizer, loss_fn, daydream
_last_thought = time.time()
def train_on_message(text: str):
global _last_thought
model.train()
tokens = tokenizer.tokenize(text)
if len(tokens) < 2:
@ -17,3 +23,10 @@ def train_on_message(text: str):
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Idle dreaming every 15 seconds
now = time.time()
if now - _last_thought > 15:
for _ in range(3):
daydream()
_last_thought = now