Untitled

import streamlit as st
from textblob import TextBlob
import pandas as pd
import altair as alt
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

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")
    st.subheader("Streamlit Projects")
    menu = ["Home", "About"]
    choice = st.sidebar.selectbox("Menu", menu)
    
    if choice == "Home":
        st.subheader("Home")
        with st.form("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',
                    color='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()