app.py

from ast import Num
import streamlit as st
import preprocessor
import helper
import matplotlib.pyplot as plt
import seaborn as sns

st.sidebar.title("WhatsApp Chat Analyzer")

uploaded_file = st.sidebar.file_uploader("Choose a file")
if uploaded_file is not None:
    # To read file as bytes:
    bytes_data = uploaded_file.getvalue()
    data = bytes_data.decode("utf-8")
    df = preprocessor.preprocess(data)


    # fetch unique users
    user_list = df['user'].unique().tolist()
    user_list.remove('group_notification')
    user_list.sort()
    user_list.insert(0,"Overall")

    selected_user = st.sidebar.selectbox("Show Analysis wrt",user_list)

    if st.sidebar.button("Show Analysis"):

        #Stats Area
        num_messages,words,num_media_messages,num_links = helper.fetch_stats(selected_user,df)
        
        st.title("Top Statistics")
        col1, col2, col3, col4 = st.columns(4)

        with col1:
            st.header("Total Messages")
            st.title(num_messages)
        with col2:
            st.header("Total Words")
            st.title(words)
        with col3:
            st.header("Media Shared")
            st.title(num_media_messages)
        with col4:
            st.header("Links Shared")
            st.title(num_links)
        
        # monthly timeline
        st.title("Monthly Timeline")
        timeline = helper.monthly_timeline(selected_user,df)
        fig, ax = plt.subplots()
        ax.plot(timeline['time'],timeline['message'], color ='green')
        plt.xticks(rotation = 'vertical')
        st.pyplot(fig)

        # daily timeline
        st.title("Daily Timeline")
        daily_timeline = helper.daily_timeline(selected_user,df)
        fig, ax = plt.subplots()
        ax.plot(daily_timeline['only_date'],daily_timeline['message'], color ='black')
        plt.xticks(rotation = 'vertical')
        st.pyplot(fig)

        #activity map
        st.title("Activity Map")
        col1,col2 = st.columns(2)

        with col1:
            st.header("Most Busy Day")
            busy_day = helper.week_activity_map(selected_user,df)
            fig,ax = plt.subplots()
            ax.bar(busy_day.index, busy_day.values)
            plt.xticks(rotation = 'vertical')
            st.pyplot(fig)
        
        with col2:
            st.header("Most Busy Month")
            busy_month = helper.month_activity_map(selected_user,df)
            fig,ax = plt.subplots()
            ax.bar(busy_month.index, busy_month.values, color ="orange")
            plt.xticks(rotation = 'vertical')
            st.pyplot(fig)
        

        #activity heatmap
        st.title("Activity Heatmap")
        active_heatmap = helper.activity_heatmap(selected_user,df)
        fig,ax = plt.subplots()
        ax = sns.heatmap(active_heatmap)
        st.pyplot(fig)
        

        # finding the busiest users in the Group (Group Level)
        if selected_user=='Overall':
            st.title('Most Busy User')
            x,new_df = helper.most_busy_users(df)
            fig,ax = plt.subplots()
            
            col1, col2 = st.columns(2)

            with col1:
                ax.bar(x.index, x.values, color = 'red')
                plt.xticks(rotation =45)
                st.pyplot(fig)
            with col2:
                st.dataframe(new_df)
        

        # wordcloud
        st.title("WordCloud")
        image = helper.create_wordcloud(selected_user,df)
        fig,ax = plt.subplots()
        ax.imshow(image)
        st.pyplot(fig)

        # Most Common Words
        most_common_df = helper.most_common_words(selected_user,df)

        fig,ax = plt.subplots()

        ax.barh(most_common_df[0], most_common_df[1])
        plt.xticks(rotation='vertical')
        st.title('Most Common Words')
        st.pyplot(fig)

        #Emoji Analysis
        emoji_df = helper.emoji_helper(selected_user,df)
        st.title("Emoji Analysis")

        col1, col2 = st.columns(2)

        with col1:
            st.dataframe(emoji_df)
        with col2:
            fig, ax = plt.subplots()
            ax.pie(emoji_df[1].head(), labels = emoji_df[0].head(), autopct = "%0.2f")
            st.pyplot(fig)