import torch
from transformers import ByT5Tokenizer, T5ForConditionalGeneration

# Use ByT5 for the ByT5 model
tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")
model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")




def segment_text(text):
    """
    Use a neural network model to segment text into words.
    """
    # Encode the input text for the model as UTF-8 bytes
    inputs = tokenizer.encode("segment: " + text, return_tensors="pt")

    # Generate predictions
    with torch.no_grad():
        outputs = model.generate(inputs)

    # Decode the generated tokens back to text
    segmented_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return segmented_text