app.py

import streamlit as st
from flair.models import TextClassifier
from flair.data import Sentence
import numpy as np
global tagger

def load_flair():
    return TextClassifier.load('en-sentiment')

def main():
    tagger = load_flair()

    st.markdown("<h1 style = 'textalign:center; color:blue;'> Sentiment Detection </h1>", unsafe_allow_html = True)
    st.write("Sentiment Detection from text is a classical problem. This is used when you try to predict the sentiment of comments.")

    input_sent = st.text_input("Input Sentence", "Although not well rated, the food in this restaurant was tasty and I enjoyed the meal!")

    s = Sentence(input_sent)
    tagger.predict(s)
    st.write("### Your Sentence is ", str(s.labels))

if __name__ == '__main__':
    main()