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import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
class CodeGenerator:
def __init__(self, model_name):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
def generate_code(self, nl_input, max_length=256, num_beams=4, early_stopping=True):
inputs = self.tokenizer(nl_input, return_tensors="pt").to(self.device)
outputs = self.model.generate(
**inputs,
max_length=max_length,
num_beams=num_beams,
early_stopping=early_stopping,
)
generated_code = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_code
if __name__ == "__main__":
model_name = "S-Dreamer/PyCodeT5"
generator = CodeGenerator(model_name)
nl_input = "Write a Python function to calculate the factorial of a number."
generated_code = generator.generate_code(nl_input)
print(generated_code) |