from altair.vegalite.v4.schema.core import Header
import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt

st.set_option('deprecation.showPyplotGlobalUse', False)

DATA_ = pd.read_csv("Tweets.csv")
st.title("Sentiment Analysis of Tweets about US Airlines")
st.sidebar.title("Sentiment Analysis of Tweets about US Airlines")
st.markdown("This application is a streamlit dashboard to analyze the sentiment of Tweets")
st.sidebar.markdown("This application is a streamlit dashboard to analyze the sentiment of Tweets")


def run():
    
    @st.cache(persist=True)
    def load_data():
        DATA_['tweet_created'] = pd.to_datetime(DATA_['tweet_created'])
        return DATA_
    data = load_data()
    
    st.sidebar.subheader("Show random tweet")
    random_tweet = st.sidebar.radio('Sentiment', ('positive', 'neutral', 'negative'))
    st.sidebar.markdown(data.query('airline_sentiment == @random_tweet')[["text"]].sample(n=1).iat[0,0])
    
    st.sidebar.markdown("### Number of tweets by sentiment")
    select = st.sidebar.selectbox('Visualization type', ['Histogram', 'Pie chart'])
    sentiment_count = data['airline_sentiment'].value_counts()
    sentiment_count = pd.DataFrame({'Sentiment':sentiment_count.index, 'Tweets':sentiment_count.values})
    
    if not st.sidebar.checkbox("Hide", True):
        st.markdown("### Number of tweets by sentiment")
        if select == "Histogram":
            fig = px.bar(sentiment_count, x='Sentiment', y='Tweets', color='Tweets', height=500)
            st.plotly_chart(fig)
        else:
            fig = px.pie(sentiment_count, values='Tweets', names='Sentiment')
            st.plotly_chart(fig)
            
    
    st.sidebar.subheader("When and Where are users tweeting from?")
    hour = st.sidebar.slider("Hour of day", 0,23)
    modified_data = data[data['tweet_created'].dt.hour == hour]
    if not st.sidebar.checkbox("Close", True, key='1'):
        st.markdown("### Tweets locations based on the time of date")
        st.markdown("%i tweets between %i:00 and %i:00" % (len(modified_data), hour, (hour+1)%24))
        st.map(modified_data)
        if st.sidebar.checkbox("Show Raw Data", False):
            st.write(modified_data)
    st.sidebar.subheader("Breakdown airline tweets by sentiment")
    choice = st.sidebar.multiselect('Pick airline', ('US Airways', 'United', 'American', 'Southwest', 'Delta', 'Virgin America'), key='0')

    if len(choice) > 0:
        choice_data = data[data.airline.isin(choice)]
        fig_choice = px.histogram(choice_data, x='airline',
        y='airline_sentiment',
        histfunc = 'count', color = 'airline_sentiment',
        facet_col='airline_sentiment',
        labels={'airline_sentiment':'tweets'}, height=600, width=800)
        st.plotly_chart(fig_choice)
    

    st.sidebar.header("Word Cloud")
    word_sentiment = st.sidebar.radio('Display word cloud for what sentiment?',('positive', 'neutral','negative'))

    if not st.sidebar.checkbox("Close", True, key='3'):
        st.header('Word cloud for %s sentiment' % (word_sentiment))
        df = data[data['airline_sentiment']==word_sentiment]
        words = ' '.join(df['text'])
        processed_words = ' '.join([word for word in words.split() if 'http' not in word and not word.startswith('@') and word !='RT'])
        wordcloud = WordCloud(stopwords=STOPWORDS,
        background_color='white', height=640, width=800).generate(processed_words)
        plt.imshow(wordcloud)
        plt.xticks([])
        plt.yticks([])
        st.pyplot() 
            
            
if __name__ == '__main__':
    run()