app.py

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import gradio as gr

# Load English to Urdu model
en_ur_model_name = "Helsinki-NLP/opus-mt-en-ur"
en_ur_tokenizer = AutoTokenizer.from_pretrained(en_ur_model_name)
en_ur_model = AutoModelForSeq2SeqLM.from_pretrained(en_ur_model_name)

# Load Urdu to English model
ur_en_model_name = "Helsinki-NLP/opus-mt-ur-en"
ur_en_tokenizer = AutoTokenizer.from_pretrained(ur_en_model_name)
ur_en_model = AutoModelForSeq2SeqLM.from_pretrained(ur_en_model_name)

# Translation function
def translate(text, direction):
    if not text.strip():
        return "Please enter some text."

    if direction == "English to Urdu":
        inputs = en_ur_tokenizer.encode(text, return_tensors="pt")
        outputs = en_ur_model.generate(inputs, max_length=512, num_beams=4, early_stopping=True)
        result = en_ur_tokenizer.decode(outputs[0], skip_special_tokens=True)
    else:  # Urdu to English
        inputs = ur_en_tokenizer.encode(text, return_tensors="pt")
        outputs = ur_en_model.generate(inputs, max_length=512, num_beams=4, early_stopping=True)
        result = ur_en_tokenizer.decode(outputs[0], skip_special_tokens=True)

    return result

# Gradio Interface
interface = gr.Interface(
    fn=translate,
    inputs=[
        gr.Textbox(label="Enter text", lines=3),
        gr.Radio(["English to Urdu", "Urdu to English"], label="Translation Direction")
    ],
    outputs=gr.Textbox(label="Translated text"),
    title="English ↔ Urdu Translator",
    description="Translate between English and Urdu using Helsinki-NLP models"
)

interface.launch()