app.py

from transformers import pipeline
import gradio as gr

# Load translation pipelines once
t5_translator = pipeline("translation_en_to_de", model="t5-small")

# ✅ Correct and public model for English → Tamil
ta_translator = pipeline("translation_en_to_ta", model="Helsinki-NLP/opus-mt-en-tam")

def translate_text(text):
    # Translate using T5-small (English → German)
    result_t5 = t5_translator(text, max_length=40)[0]['translation_text']
    # Translate using Helsinki-NLP (English → Tamil)
    result_ta = ta_translator(text, max_length=40)[0]['translation_text']
    return result_t5, result_ta

# Create Gradio interface
demo = gr.Interface(
    fn=translate_text,
    inputs=gr.Textbox(lines=2, placeholder="Enter English text here..."),
    outputs=[
        gr.Textbox(label="T5-small Translation (English → German)"),
        gr.Textbox(label="Helsinki-NLP Translation (English → Tamil)")
    ],
    title="📝 Text Translator",
    description="Enter English text and get translations using T5-small (English → German) and Helsinki-NLP (English → Tamil)"
)

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