import pandas as pd 
import numpy as np
import streamlit as st
import altair as alt

from textblob import TextBlob
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

# Functions
def convert_to_df(sentiment):
    sentiment_dict = {"polarity":sentiment.polarity,"subjectivity":sentiment.subjectivity}
    sentiment_df = pd.DataFrame(sentiment_dict.items(),columns=["metric","value"])
    return sentiment_df

def analyze_token_sentiment(docx):
    analyzer = SentimentIntensityAnalyzer()
    pos_list = []
    neg_list = []
    neu_list = []
    for i in docx.split():
        res = analyzer.polarity_scores(i)["compound"]
        if res >= 0.1:
            pos_list.append(i)
            pos_list.append(res)

        elif res <= -0.1:
            neg_list.append(i)
            neg_list.append(res)
        else:
            neu_list.append(i)
    
    result = {"positives":pos_list, "negatives":neg_list, "neutral":neu_list}
    return result

def main():
    st.title("Sentiment Analysis NLP App using Streamlit")
    st.subheader("Reformation Team Project")
                 
    menu = ["Home","About"]
    choice = st.sidebar.selectbox("Menu",menu)

    if choice == "Home":
        st.subheader("Home")
        with st.form(key="nlpForm"):
            raw_text = st.text_area("Enter Text Here")
            submit_button = st.form_submit_button(label="Analyze")

        #layout
        col1, col2 = st.columns(2)
        if submit_button:

            with col1:
                st.info("Results")
                sentiment = TextBlob(raw_text).sentiment
                st.write(sentiment)

                #Emoji
                if sentiment.polarity > 0:
                    st.markdown("Sentiment:: Positive :smiley: ")
                elif sentiment.polarity <0:
                    st.markdown("Sentiment:: Negative :angry: ")
                else:
                    st.markdown("Sentiment:: Neutral :😐: ")

                # Dataframe
                result_df = convert_to_df(sentiment)
                st.dataframe(result_df)

                # Visualization
                c = alt.Chart(result_df).mark_bar().encode(
                    x="metric",
                    y="value",
                    colour="metric")
                st.altair_chart(c,use_container_width=True)

            with col2:
                st.info("Token Sentiment")

                token_sentiments = analyze_token_sentiment(raw_text)
                st.write(token_sentiments)

    else: 
        st.subheader("About")

if __name__ == "__main__":
    main()