import gradio as gr

title = "MBart"

description = "Gradio Demo for MBart. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."

article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2001.08210' target='_blank'>Multilingual Denoising Pre-training for Neural Machine Translation</a></p>"

examples = [
    ["Paris is the capital of France","mbart-large-50-one-to-many-mmt"]
]

io1 = gr.Interface.load("huggingface/facebook/mbart-large-50-one-to-many-mmt")

io2 = gr.Interface.load("huggingface/facebook/mbart-large-50")

def inference(text, model):
    if model == "mbart-large-50-one-to-many-mmt":
        outtext = io1(text)
    else:
        outtext = io2(text)
    return outtext   
    
     

gr.Interface(
    inference, 
    [gr.inputs.Textbox(label="Input"),gr.inputs.Dropdown(choices=["mbart-large-50-one-to-many-mmt","mbart-large-50"], type="value", default="mbart-large-50-one-to-many-mmt", label="model")
], 
    gr.outputs.Textbox(label="Output"),
    examples=examples,
    article=article,
    title=title,
    description=description).launch(enable_queue=True)