import streamlit as st from transformers import pipeline st.set_page_config(page_title="Automated Question Answering System") st.title("Automated Question Answering System") st.subheader("Try") # """ # [![](https://img.shields.io/github/followers/OOlajide?label=OOlajide&style=social)](https://gitHub.com/OOlajide) # [![](https://img.shields.io/twitter/follow/sageOlamide?label=@sageOlamide&style=social)](https://twitter.com/sageOlamide) # """ # expander = st.sidebar.expander("About") # expander.write("This web app allows you to perform common Natural Language Processing tasks, select a task below to get started.") # st.sidebar.header("What will you like to do?") # option = st.sidebar.radio("", ["Text summarization", "Extractive question answering", "Text generation"]) @st.cache(show_spinner=False, allow_output_mutation=True) def question_model(): model_name = "deepset/tinyroberta-squad2" question_answerer = pipeline(model=model_name, tokenizer=model_name, task="question-answering") return question_answerer st.markdown("

Question Answering on Academic Essays

", unsafe_allow_html=True) st.markdown("

What is extractive question answering about?

", unsafe_allow_html=True) st.write("Extractive question answering is a Natural Language Processing task where text is provided for a model so that the model can refer to it and make predictions about where the answer to a question is.") st.markdown('___') source = st.radio("How would you upload the essay? Choose an option below", ["I want to input some text", "I want to upload a file"]) sample_question = "What is NLP?" if source == "I want to input some text": with open("sample.txt", "r") as text_file: sample_text = text_file.read() context = st.text_area("Use the example below or input your own text in English (10,000 characters max)", value=sample_text, max_chars=10000, height=330) question = st.text_input(label="Use the question below or enter your own question", value=sample_question) button = st.button("Get answer") if button: with st.spinner(text="Loading question model..."): question_answerer = question_model() with st.spinner(text="Getting answer..."): answer = question_answerer(context=context, question=question) # answer = answer["answer"] st.text(answer) elif source == "I want to upload a file": uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"]) if uploaded_file is not None: raw_text = str(uploaded_file.read(),"utf-8") context = st.text_area("", value=raw_text, height=330) question = st.text_input(label="Enter your question", value=sample_question) button = st.button("Get answer") if button: with st.spinner(text="Loading summarization model..."): question_answerer = question_model() with st.spinner(text="Getting answer..."): answer = question_answerer(context=context, question=question) answer = answer["answer"] st.text(answer)