app.py

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

st.sidebar.title("Whatsapp Chat Analysis")

uploaded_file = st.sidebar.file_uploader("Choose a file")
if uploaded_file is not None:
    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, color = 'orange')
            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)

        if selected_user != 'OverAll':
            st.title("Weekly Activity Map")
            user_heatmap = helper.activity_heatmap(selected_user, df)
            ax = sns.heatmap(user_heatmap)
            plt.yticks(rotation='horizontal')
            st.pyplot(fig)



        # find 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='vertical')
                st.pyplot(fig)

            with col2:
                st.dataframe(new_df)

        # Word Cloud
        st.title('Word Cloud')
        df_wc = helper.create_wordcloud(selected_user, df)
        fig, ax = plt.subplots()
        ax.imshow(df_wc)
        st.pyplot(fig)


        # most common Words
        st.title('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], color = 'yellow')
        st.pyplot(fig)


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

        col1, col2 = st.columns(2)

        with col1:
            st.dataframe(emoji_df)

        with col2:
            fig, ax = plt.subplots()
            ax.pie(emoji_df['Total'].head(),labels=emoji_df['emoji'].head(), autopct="%0.2f")
            st.pyplot(fig)