app.py

# app.py

# Use a pipeline as a high-level helper
from transformers import pipeline
import gradio as gr

# Initialize the translation pipeline with the google-t5/t5-base model
text_translator = pipeline("translation", model="google/t5-base")

# Dictionary mapping destination languages to their T5-compatible names
language_mapping = {
    "German": "German",
    "Eastern Panjabi": "Punjabi",
    "Sanskrit": "Sanskrit",
    "Urdu": "Urdu",
    "Tamil": "Tamil",
    "Telugu": "Telugu",
    "Yue Chinese": "Chinese",
    "Chinese (Simplified)": "Chinese",
    "Chinese (Traditional)": "Chinese",
    "Hindi": "Hindi",
    "French": "French",
    "Spanish": "Spanish"
}

def translate_text(text, destination_language):
    dest_language = language_mapping.get(destination_language)
    
    if dest_language is None:
        return "Unsupported language selected."

    # T5 model requires a specific format for the translation task
    translation_prompt = f"translate English to {dest_language}: {text}"
    
    translation = text_translator(translation_prompt)
    return translation[0]["translation_text"]

gr.close_all()

demo = gr.Interface(
    fn=translate_text,
    inputs=[
        gr.Textbox(label="Input text to translate", lines=6), 
        gr.Dropdown(list(language_mapping.keys()), label="Select destination language")
    ],
    outputs=[gr.Textbox(label="Translated text", lines=4)],
    title="Language translator (model- Google T5 Base)",
    description="THIS APPLICATION WILL BE USED TO TRANSLATE ENGLISH TO MULTIPLE LANGUAGES",
    theme=gr.themes.Soft(),
    concurrency_limit=16
)

demo.launch()