import torch
from model.brain import model, tokenizer, DEVICE
from model.trainer import train_on_message
from model.dynamic_expand import expand_model_if_needed


def simulate_conversation():
    expand_model_if_needed()

    model.eval()
    seed = torch.randint(0, tokenizer.next_id, (1, 5), device=DEVICE)
    seed = seed[:, -128:]  # Safety clamp
    output = model(seed)

    preds = torch.argmax(output, dim=-1).squeeze().tolist()
    if isinstance(preds, int):
        preds = [preds]

    text = tokenizer.detokenize(preds)
    if text and len(text.split()) >= 3:
        train_on_message(text)