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()

    max_token_id = model.head.out_features - 1
    if max_token_id < 1:
        return  # Safeguard if model is still too small

    seed = torch.randint(0, max_token_id + 1, (1, 5), device=DEVICE)
    seed = seed[:, -128:]  # Clamp sequence length

    output = model(seed)

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

    # 🛡 Clamp predictions too
    preds = [min(max(p, 0), max_token_id) for p in preds]

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