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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")
io4 = gr.Interface.load("huggingface/t5-3b")
def inference(text, model):
if model == "t5-base":
outtext = io1(text)
elif model == "t5-small":
outtext = io2(text)
elif model == "t5-large":
outtext = io3(text)
else:
outtext = io4(text)
return outtext
gr.Interface(
inference,
[gr.inputs.Textbox(label="Input"),gr.inputs.Dropdown(choices=["t5-base","t5-small","t5-large","t5-3b"], type="value", default="t5-base", label="model")
],
gr.outputs.Textbox(label="Output"),
examples=examples,
article=article,
title=title,
description=description).launch(enable_queue=True) |