import gradio as gr

title = "T5"

description = "Gradio Demo for T5. 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/1910.10683' target='_blank'>Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer</a></p>"

examples = [
    ['My name is Sarah and I live in London',"t5-base"]
]

io1 = gr.Interface.load("huggingface/t5-base")

io2 = gr.Interface.load("huggingface/t5-small")

io3 = gr.Interface.load("huggingface/t5-large")



def inference(text, model):
    if model == "t5-base":
        outtext = io1(text)
    elif model == "t5-small":
        outtext = io2(text)
    else: 
        outtext = io3(text)
    return outtext   
    
     

gr.Interface(
    inference, 
    [gr.inputs.Textbox(label="Input"),gr.inputs.Dropdown(choices=["t5-base","t5-small","t5-large"], type="value", default="t5-base", label="model")
], 
    gr.outputs.Textbox(label="Output"),
    examples=examples,
    article=article,
    title=title,
    description=description).launch(enable_queue=True, cache_examples=True)