Chat-Analysis/app.py at main · ayuclan/Chat-Analysis · GitHub

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


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.getvalue()
        data = bytes_data.decode("utf-8")
        df = preprocessor.preprocess(data)

        # Fetch unique users
        user_list = df['user'].unique().tolist()
        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, 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)  # Assuming words is an integer
            with col3:
                st.header("Media Shared")
                st.title(num_media_messages)
            with col4:
                st.header("Links Shared")
                st.title(links)  # Assuming links is a list

            # 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)
            if not user_heatmap.empty and np.any(user_heatmap.values):
                fig, ax = plt.subplots()
                ax = sns.heatmap(user_heatmap)
                st.pyplot(fig)
            else:
                st.write("No data available for heatmap.")

            # 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
            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])
            plt.xticks(rotation='vertical')
            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)