app.py

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

# Load model và tokenizer
model_name = "Moleys/hirashiba-mt-tiny-vp-zh"
device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)

def translate_text(input_text):
    lines = input_text.split('\n')  # Tách từng dòng
    translated_lines = []
    
    for line in lines:
        raw_text = line.strip().lower()
        if not raw_text:
            translated_lines.append('')  # Giữ dòng trống
            continue
        
        # Tokenize input
        inputs = tokenizer(raw_text, return_tensors="pt", padding=True, truncation=True).to(device)
        
        # Dịch với mô hình (không cần tính gradient)
        with torch.no_grad():
            output_tokens = model.generate(**inputs, max_length=512)
        
        # Giải mã kết quả
        translated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
        translated_lines.append(translated_text)
    
    return '\n'.join(translated_lines)

if __name__ == '__main__':
    with gr.Blocks() as app:
        gr.Markdown('## Vietphrase (Book title) to Chinese Translation')
        
        with gr.Row():
            with gr.Column(scale=1):
                input_text = gr.Textbox(label='Input Vietnamese Text', lines=5, placeholder='Enter Vietnamese text here...')
                translate_button = gr.Button('Translate')
                output_text = gr.Textbox(label='Output Chinese Text', lines=5, interactive=False)
        
        translate_button.click(
            fn=translate_text,
            inputs=input_text,
            outputs=output_text
        )

    app.launch()