app.py

import streamlit as st
from transformers import pipeline

st.set_page_config(page_title="Common NLP Tasks")
st.title("Common NLP Tasks")
st.subheader(":point_left: Use the menu on the left to select a NLP task (click on > if closed).")
"""
[![](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


# if option == "Extractive question answering":
st.markdown("<h2 style='text-align: center; color:grey;'>Extractive Question Answering</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 like to start? Choose an option below", ["I want to input some text", "I want to upload a file"])
sample_question = "What did the shepherd boy do to amuse himself?"
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)