| from transformers import AutoModelWithLMHead, AutoTokenizer | |
| import gradio as grad | |
| text2text_tkn = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap") | |
| mdl = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap") | |
| def text2text(answer,context): | |
| input_text = "answer: %s context: %s </s>" % (answer, context) | |
| features = text2text_tkn ([input_text], return_tensors='pt') | |
| output = mdl.generate(input_ids=features['input_ids'], | |
| attention_mask=features['attention_mask'], | |
| max_length=64) | |
| response=text2text_tkn.decode(output[0]) | |
| return response | |
| txt=grad.Textbox(lines=5, label="English", placeholder="Context") | |
| ans=grad.Textbox(lines=1, label="Answer") | |
| out=grad.Textbox(lines=1, label="Genereated Question") | |
| grad.Interface(text2text, inputs=[txt,ans], outputs=out).launch() | |