app.py

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

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

uploaded_file = st.sidebar.file_uploader("Choose a file")
if uploaded_file is not None:
    bytes_data = uploaded_file.read()

    # Detect the encoding
    result = chardet.detect(bytes_data)
    encoding = result['encoding']

    # Fallback to utf-8 if encoding is None
    if encoding is None:
        encoding = 'utf-8'  # or 'latin1' if utf-8 fails

    # Decode with the detected or fallback encoding
    try:
        data = bytes_data.decode(encoding)
    except UnicodeDecodeError:
        # If utf-8 decoding fails, fallback to latin1
        data = bytes_data.decode('latin1')

    df = preprocessor.preprocess(data)

    # Fetch unique users
    user_list = df['user'].unique().tolist()
    if 'group_notification' in user_list:
        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='purple')
            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)

        st.title("Weekly Activity Map")
        user_heatmap = helper.activity_heatmap(selected_user, df)
        fig, ax = plt.subplots()
        ax = sns.heatmap(user_heatmap)
        st.pyplot(fig)

        # Finding the busiest users in the group (Group level)
        if selected_user == 'Overall':
            st.title('Most Busy Users')
            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)

        # WordCloud
        st.title("Wordcloud")
        df_wc = helper.create_wordcloud(selected_user, df)
        fig, ax = plt.subplots()
        ax.imshow(df_wc)
        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)