app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load the model and tokenizer
model_name = "maulanayyy/codet5_code_translation"  # Ganti dengan nama model yang benar
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Function to perform inference
def translate_code(input_code):
    # Prepare the input text
    input_text = f"translate Java to C#: {input_code}"
    
    # Check if GPU is available
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    # Tokenize the input
    input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)  # Pastikan input_ids ada di GPU
    
    # Generate the output
    with torch.no_grad():
        outputs = model.generate(input_ids, max_length=512)
    
    # Decode the output
    translated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return translated_code

# Create