| import streamlit as st | |
| from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline | |
| model_name = "timpal0l/mdeberta-v3-base-squad2" | |
| model = AutoModelForQuestionAnswering.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| def get_answer(context, question): | |
| nlp = pipeline('question-answering', model=model, tokenizer=tokenizer) | |
| QA_input = {'question': question, 'context': context} | |
| res = nlp(QA_input) | |
| answer = res['answer'] | |
| return answer | |
| def main(): | |
| st.title("Question Answering App") | |
| st.markdown("Enter the context and question, then click on 'Get Answer' to retrieve the answer.") | |
| context = st.text_area("Context", "Enter the context here...") | |
| question = st.text_input("Question", "Enter the question here...") | |
| if st.button("Get Answer"): | |
| if context.strip() == "" or question.strip() == "": | |
| st.warning("Please enter the context and question.") | |
| else: | |
| answer = get_answer(context, question) | |
| st.success(f"Answer: {answer}") | |
| if __name__ == "__main__": | |
| main() | |