from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

# Load CodeT5 or similar model
model_name = "Salesforce/codet5-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def fix_code(code: str) -> str:
    input_text = f"fix: {code}"
    inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        output = model.generate(**inputs, max_length=512)
    return tokenizer.decode(output[0], skip_special_tokens=True)