app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer_code2desc = AutoTokenizer.from_pretrained("microsoft/codebert-base")
model_code2desc = AutoModelForCausalLM.from_pretrained("microsoft/codebert-base")

def code_to_description(code: str) -> str:
    inputs = tokenizer_code2desc.encode("summarize: " + code, return_tensors="pt", max_length=512, truncation=True)
    outputs = model_code2desc.generate(inputs, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2, do_sample=True, top_k=50, top_p=0.95, temperature=0.8)
    description = tokenizer_code2desc.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
    return description

iface = gr.Interface(fn=code_to_description, inputs="text", outputs="text", share=True)
iface.launch()