import streamlit as st
import spacy
from spacytextblob.spacytextblob import SpacyTextBlob

st.set_page_config(layout='wide', initial_sidebar_state='expanded')
st.title('Super cool NLP things for the whole family!')
st.markdown('Type some words in the text box below, and choose a processing option from the side menu.')
side = st.sidebar.selectbox("Select an option here", ("Sentiment", "Subjectivity", "NER"))
Text = st.text_input("Enter some words!")


@st.cache_data
def sentiment(text):
    nlp = spacy.load('en_core_web_sm')
    nlp.add_pipe('spacytextblob')
    doc = nlp(text)
    if len(Text) == 0:
        return "This setting will try to figure out the tone of the provided text."
    elif doc._.polarity<0:
        return "The text seems negative"
    elif doc._.polarity==0:
        return "The text seems neutral"
    else:
        return "The text seems positive"


@st.cache_data
def subjectivity(text):
    nlp = spacy.load('en_core_web_sm')
    nlp.add_pipe('spacytextblob')
    doc = nlp(text)
    if len(Text) == 0:
        return "This setting will try to figure out how opionionated the provided text is."
    if doc._.subjectivity > 0.5:
        return "This is a highly opinionated sentence"
    elif doc._.subjectivity < 0.5:
        return "This is a less opinionated sentence"
    else:
        return "This is a neutral sentence"

@st.cache_data
def ner(sentence):
    nlp = spacy.load("en_core_web_sm")
    doc = nlp(sentence)
    ents = [(e.text, e.label_) for e in doc.ents]
    if len(Text) == 0:
        return "This setting identifies and extracts named entities."
    else:
        return ents



def run():

    if side == "Sentiment":
        st.write(sentiment(Text))
    if side == "Subjectivity":
        st.write(subjectivity(Text))
    if side == "NER":
        st.write(ner(Text))
    
    

if __name__ == '__main__':
    run()