| 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=True | |
| ) | |
| 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() | |