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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://gitHub.com/OOlajide)
# [](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("<h2 style='text-align: center; color:grey;'>Question Answering on Academic Essays</h2>", unsafe_allow_html=True)
st.markdown("<h3 style='text-align: left; color:#F63366; font-size:18px;'><b>What is extractive question answering about?<b></h3>", 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)
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