from transformers import MarianTokenizer, MarianMTModel
import gradio as gr

# Specify the model name for English to Urdu translation
model_name = 'Helsinki-NLP/opus-mt-en-ur'

# Load the tokenizer and model
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)

def translate_en_to_ur(text):
    # Tokenize the input text
    inputs = tokenizer(text, return_tensors='pt', padding=True)

    # Generate translation
    translated = model.generate(**inputs)

    # Decode the generated tokens back to text
    translated_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
    return translated_text[0]

# Define the Gradio interface
iface = gr.Interface(
    fn=translate_en_to_ur,
    inputs=gr.Textbox(lines=2, placeholder="Enter English text here..."),
    outputs="text",
    title="English to Urdu Translator",
    description="Hello, I am a newbie in Model Deployment. I was trying to design a language translator. I am using hugging face's Marian MT model "
)

# Launch the interface (this will be run by Hugging Face Spaces)
iface.launch(share=False) # share=False is important for deployment