| #from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline | |
| #import gradio as grad | |
| #import ast | |
| #mdl_name = "deepset/roberta-base-squad2" | |
| #my_pipeline = pipeline('question-answering', model=mdl_name,tokenizer=mdl_name) | |
| #def answer_question(question,context): | |
| #text= "{"+"'question': '"+question+"','context':'"+context+"'}" | |
| #di=ast.literal_eval(text) | |
| #response = my_pipeline(di) | |
| #return response | |
| #grad.Interface(answer_question, inputs=["text","text"],outputs="text").launch() | |
| from transformers import pipeline | |
| import gradio as grad | |
| mdl_name = "Helsinki-NLP/opus-mt-en-de" | |
| opus_translator = pipeline("translation", model=mdl_name) | |
| def translate(text): | |
| response = opus_translator(text) | |
| return response | |
| grad.Interface(translate, inputs=["text",], outputs="text").launch() | |