app.py

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

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("abdulwaheed1/urdu_to_english_translation_mbart")
model = AutoModelForSeq2SeqLM.from_pretrained("abdulwaheed1/urdu_to_english_translation_mbart")

# Function to translate Urdu text to English
def translate_urdu_to_english(urdu_text):
    try:
        # Tokenize the input Urdu text
        inputs = tokenizer(urdu_text, return_tensors="pt", padding=True, truncation=True)

        # Generate translation using the model
        with torch.no_grad():
            translated_tokens = model.generate(**inputs, max_length=512)

        # Decode the generated tokens into English text
        translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)

        return translated_text

    except Exception as e:
        # Return an error message if something goes wrong
        return f"Error in translation: {str(e)}"

# Set up Gradio interface
iface = gr.Interface(
    fn=translate_urdu_to_english,  # Function to call
    inputs=gr.Textbox(label="Enter Urdu Text"),  # Textbox for user input
    outputs=gr.Textbox(label="Translated English Text"),  # Textbox for displaying output
    live=True  # Optionally, enable live translation (i.e., as the user types)
)

# Launch the Gradio interface
iface.launch()