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("Use the menu on the left to select a NLP task to do (click on > if closed).")

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("", ["Extractive question answering", "Text summarization", "Text generation", "Sentiment analysis"])

@st.cache(show_spinner=False, allow_output_mutation=True)
def question_model():
    model_name = "deepset/roberta-base-squad2"
    question_answerer = pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering")
    return question_answerer

@st.cache(show_spinner=False, allow_output_mutation=True)
def summarization_model():
    summarizer = pipeline("summarization")
    return summarizer

@st.cache(show_spinner=False, allow_output_mutation=True)
def generation_model():
    generator = pipeline("text-generation")
    return generator

@st.cache(show_spinner=False, allow_output_mutation=True)
def sentiment_model():
    sentiment_analysis = pipeline("sentiment-analysis")
    return sentiment_analysis

if option == "Extractive question answering":
    st.markdown("<h2 style='text-align: center; color:red;'>Extract answer from text</h2>", unsafe_allow_html=True)
    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"])
    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='Enter your question')
        button = st.button('Get answer')
        if button:
            question_answerer = question_model()
            with st.spinner(text="Getting answer..."):
                answer = question_answerer(context=context, question=question)
                answer = answer["answer"]
                html_str = f"<p style='color:red;'>{answer}</p>"
                st.markdown(html_str, unsafe_allow_html=True)
    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")
            button = st.button("Get answer")
            if button:
                question_answerer = question_model()
                with st.spinner(text="Getting answer..."):
                    answer = question_answerer(context=context, question=question)
                    answer = answer["answer"]
                    st.text(answer)

elif option == 'Text summarization':
    st.markdown("<h2 style='text-align: center; color:red;'>Summarize text</h2>", unsafe_allow_html=True)
    sample_text = "sample text"
    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"])
    if source == "I want to input some text":
        text = st.text_area('Input a text in English (between 1,000 and 10,000 characters)', value=sample_text, max_chars=10000, height=330)
        button = st.button('Get summary')
        if button:
            summarizer = summarization_model()
            with st.spinner(text="Summarizing text..."):
                summary = summarizer(text, max_length=130, min_length=30)
                st.write(summary)

    elif source == "I want to upload a file":
        uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"])
        button = st.button('Get summary')
        if button:
            summarizer = summarization_model()
            with st.spinner(text="Summarizing text..."):
                summary = summarizer(text, max_length=130, min_length=30)
                st.write(summary)
                
elif option == 'Text generation':
    st.markdown("<h2 style='text-align: center; color:grey;'>Generate text</h2>", unsafe_allow_html=True)
    text = st.text_input(label='Enter one line of text and let the NLP model generate the rest for you')
    button = st.button('Generate text')
    if button:
        generator = generation_model()
        with st.spinner(text="Generating text..."):
            generated_text = generator(text, max_length=50)
            st.write(generated_text[0]["generated_text"])
                
elif option == 'Sentiment analysis':
    st.markdown("<h2 style='text-align: center; color:grey;'>Classify review</h2>", unsafe_allow_html=True)
    text = st.text_input(label='Enter a sentence to get its sentiment analysis')
    button = st.button('Get sentiment analysis')
    if button:
        sentiment_analysis = sentiment_model()
        with st.spinner(text="Getting sentiment analysis..."):
            sentiment = sentiment_analysis(text)
            st.write(sentiment[0]["label"])