from transformers import pipeline
import gradio as gr

# Load translation pipelines once
de_translator = pipeline("translation_en_to_de", model="Helsinki-NLP/opus-mt-en-de")
hi_translator = pipeline("translation_en_to_hi", model="Helsinki-NLP/opus-mt-en-hi")

def translate_text(text):
    # Translate English → German
    result_de = de_translator(text, max_length=40)[0]['translation_text']
    # Translate English → Hindi
    result_hi = hi_translator(text, max_length=40)[0]['translation_text']
    return result_de, result_hi

# Gradio interface
demo = gr.Interface(
    fn=translate_text,
    inputs=gr.Textbox(lines=2, placeholder="Enter English text here..."),
    outputs=[
        gr.Textbox(label="Helsinki-NLP Translation (English → German)"),
        gr.Textbox(label="Helsinki-NLP Translation (English → Hindi)")
    ],
    title="📝 Text Translator",
    description="Enter English text and get translations into German and Hindi using public Helsinki-NLP models"
)

if __name__ == "__main__":
    demo.launch()