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Summarizing_a_Paragraph_using_T5_architecture / app.py

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	from transformers import AutoTokenizer, AutoModelWithLMHead
	import gradio as grad
	text2text_tkn = AutoTokenizer.from_pretrained("deep-learning-analytics/wikihow-t5-small")
	mdl = AutoModelWithLMHead.from_pretrained("deep-learning-analytics/wikihow-t5-small")
	def text2text_summary(para):
	initial_txt = para.strip().replace("\n","")
	tkn_text = text2text_tkn.encode(initial_txt, return_tensors="pt")
	tkn_ids = mdl.generate(tkn_text, max_length=250, num_beams=5, repetition_penalty=2.5, early_stopping=Tru)
	response = text2text_tkn.decode(tkn_ids[0], skip_special_tokens=True)
	return response
	para=grad.Textbox(lines=10, label="Paragraph", placeholder="Copy paragraph")
	out=grad.Textbox(lines=1, label="Summary")
	grad.Interface(text2text_summary, inputs=para, outputs=out).launch()