import gradio as gr
from transformers import AutoModelForSeq2SeqLM, NllbTokenizer

# Load the pre-trained model and tokenizer
model_name = "sarahai/nllb-ru-uz"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = NllbTokenizer.from_pretrained(model_name)

def translate(text, src_lang="rus_Cyrl", tgt_lang="uzn_Latn"):
    """Translates text from source to target language."""
    inputs = tokenizer(text, return_tensors="pt")
    translated = model.generate(**inputs)
    return tokenizer.batch_decode(translated, skip_special_tokens=True)[0]

# Define the Gradio interface
interface = gr.Interface(
    fn=translate,
    inputs=[gr.Textbox(label="Text to Translate"), gr.Dropdown(choices=["ru", "uz"], label="Source Language"), gr.Dropdown(choices=["uz"], label="Target Language")],
    outputs="textbox",
    title="Russian to Uzbek Translator",
    description="Translate text from Russian to Uzbek using the `sarahai/nllb-ru-uz` model.",
)

# Launch the Gradio app
interface.launch(share=True, debug=True)  # Set share=True to create a Hugging Face Space