| import streamlit as st | |
| from transformers.pipelines import pipeline | |
| #from transformers.modeling_auto import AutoModelForQuestionAnswering | |
| #from transformers.tokenization_auto import AutoTokenizer | |
| # b) Load model & tokenizer | |
| #model = AutoModelForQuestionAnswering.from_pretrained(model_name) | |
| #tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| #classifier = pipeline("question-answering", model="deepset/roberta-base-squad2") | |
| model_name = "deepset/xlm-roberta-base-squad2" | |
| nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) | |
| #QA_input = { | |
| # 'question': 'Why is model conversion important?', | |
| # 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' | |
| #} | |
| #res = nlp(QA_input) | |
| def main(): | |
| st.title("Question & Answering") | |
| with st.form("text_field"): | |
| sentence_1= st.text_area('Enter question:') | |
| sentence_2= st.text_area('Enter context:') | |
| QA_input = {'question':sentence_1, 'context':sentence_2} | |
| #clicked==True only when the button is clicked | |
| clicked = st.form_submit_button("Submit") | |
| if clicked: | |
| results = nlp(QA_input) | |
| st.json(results) | |
| if __name__ == "__main__": | |
| main() |