initial commit

.gitignore

@@ -0,0 +1,6 @@
+.venv/
+__pycache__/
+.idea/
+.ipynb_checkpoints/
+*.pyc
+*.txt~

.streamlit/config.toml

@@ -0,0 +1,7 @@
+[browser]
+gatherUsageStats = false
+
+[server]
+headless = true
+enableCORS = false # Recommended for production deployments to avoid potential CORS issues
+enableXsrfProtection = false # Recommended for production deployments

helper.py

@@ -0,0 +1,132 @@
+from urlextract import URLExtract
+from wordcloud import WordCloud
+import pandas as pd
+from collections import Counter
+import emoji
+
+
+def fetch_stats(selected_user,df):
+    if selected_user != 'Overall':
+        df = df[df['user'] == selected_user]
+
+    # fetch the number of messages
+    num_messages = df.shape[0]
+
+    # fetch the total number of words
+    words = []
+    for message in df['message']:
+        words.extend(message.split())
+
+    # fetch number of media messages
+    num_media_messages = df[df['message'] == '<Media omitted>\n'].shape[0]
+    
+    # fetch number of links shared
+    links = []
+    extract = URLExtract()
+    for message in df['message']:
+        links.extend(extract.find_urls(message))
+
+    return num_messages,len(words),num_media_messages,len(links)
+
+def most_busy_users(df):
+    top_users = df['user'].value_counts().head()
+    user_percent = round((df['user'].value_counts(normalize=True) * 100), 2).reset_index()
+    user_percent.columns = ['name', 'percent']
+    return top_users, user_percent
+
+
+def create_wordcloud(selected_user,df):
+
+    f = open('stop_hinglish.txt', 'r')
+    stop_words = f.read()
+
+    if selected_user != 'Overall':
+        df = df[df['user'] == selected_user]
+
+    temp = df[df['user'] != 'group_notification']
+    temp = temp[temp['message'] != '<Media omitted>\n']
+
+    def remove_stop_words(message):
+        y = []
+        for word in message.lower().split():
+            if word not in stop_words:
+                y.append(word)
+        return " ".join(y)
+
+    wc = WordCloud(width=500,height=500,min_font_size=10,background_color='white')
+    temp['message'] = temp['message'].apply(remove_stop_words)
+    df_wc = wc.generate(temp['message'].str.cat(sep=" "))
+    return df_wc
+    
+def most_common_words(selected_user,df):
+
+    f = open('stop_hinglish.txt','r')
+    stop_words = f.read()
+
+    if selected_user != 'Overall':
+        df = df[df['user'] == selected_user]
+
+    temp = df[df['user'] != 'group_notification']
+    temp = temp[temp['message'] != '<Media omitted>\n']
+
+    words = []
+
+    for message in temp['message']:
+        for word in message.lower().split():
+            if word not in stop_words:
+                words.append(word)
+
+    most_common_df = pd.DataFrame(Counter(words).most_common(20))
+    return most_common_df    
+
+
+def emoji_helper(selected_user,df):
+    if selected_user != 'Overall':
+        df = df[df['user'] == selected_user]
+
+    emojis = []
+    for message in df['message']:
+        #emojis.extend([c for c in message if c in emoji.EMOJI_DATA])
+        emojis.extend([c for c in message if emoji.is_emoji(c)])
+
+    emoji_df = pd.DataFrame(Counter(emojis).most_common(len(Counter(emojis))))
+
+    return emoji_df
+
+def monthly_timeline(selected_user,df):
+
+    if selected_user != 'Overall':
+        df = df[df['user'] == selected_user]
+
+    timeline = df.groupby(['year', 'month_num', 'month']).count()['message'].reset_index()
+
+    time = []
+    for i in range(timeline.shape[0]):
+        time.append(timeline['month'][i] + "-" + str(timeline['year'][i]))
+
+    timeline['time'] = time
+
+    return timeline
+
+def daily_timeline(selected_user,df):
+
+    if selected_user != 'Overall':
+        df = df[df['user'] == selected_user]
+
+    daily_timeline = df.groupby('only_date').count()['message'].reset_index()
+
+    return daily_timeline    
+
+def week_activity_map(selected_user,df):
+
+    if selected_user != 'Overall':
+        df = df[df['user'] == selected_user]
+
+    return df['day_name'].value_counts()
+
+def month_activity_map(selected_user,df):
+
+    if selected_user != 'Overall':
+        df = df[df['user'] == selected_user]
+
+    return df['month'].value_counts()

preprocessor.py

@@ -0,0 +1,48 @@
+import re
+import pandas as pd
+
+def preprocess(data):
+    print("Preprocess started")
+
+    pattern = r'\d{1,2}/\d{1,2}/\d{2,4},\s(?:1[0-2]|0?[1-9]):[0-5][0-9][\s\u202f\u00a0]?(?:AM|PM|am|pm)\s-\s'
+    messages = re.split(pattern, data)[1:]
+    date = re.findall(pattern, data)
+    print(f"Found {len(messages)} messages and {len(date)} dates")
+
+    dates = [d.replace('\u202f', ' ').replace('\u00a0', ' ') for d in date]
+    df = pd.DataFrame({'user_message': messages, 'message_date': dates})
+
+    try:
+        df['message_date'] = pd.to_datetime(df['message_date'], format='%d/%m/%y, %I:%M %p - ')
+    except Exception as e:
+        print("Date parsing error:", e)
+        return None
+
+    df.rename(columns={'message_date': 'date'}, inplace=True)
+
+    users = []
+    messages_list = []
+    for message in df['user_message']:
+        entry = re.split(r'([\w\W]+?):\s', message)
+        if entry[1:]:  # user exists
+            users.append(entry[1])
+            messages_list.append(" ".join(entry[2:]))
+        else:
+            users.append('group_notification')
+            messages_list.append(entry[0])
+
+    df['user'] = users
+    df['message'] = messages_list
+
+    df.drop(columns=['user_message'], inplace=True)
+    df['only_date'] = df['date'].dt.date
+    df['year'] = df['date'].dt.year
+    df['month_num'] = df['date'].dt.month
+    df['month'] = df['date'].dt.month_name()
+    df['day'] = df['date'].dt.day
+    df['day_name'] = df['date'].dt.day_name()
+    df['hour'] = df['date'].dt.hour
+    df['minute'] = df['date'].dt.minute
+
+    return df
+

stop_hinglish.txt

@@ -0,0 +1,1055 @@
+.
+..
+...
+?
+-
+--
+1
+2
+3
+4
+5
+6
+7
+8
+9
+0
+a
+aadi
+aaj
+aap
+aapne
+aata
+aati
+aaya
+aaye
+ab
+abbe
+abbey
+abe
+abhi
+able
+about
+above
+accha
+according
+accordingly
+acha
+achcha
+across
+actually
+after
+afterwards
+again
+against
+agar
+ain
+aint
+ain't
+aisa
+aise
+aisi
+alag
+all
+allow
+allows
+almost
+alone
+along
+already
+also
+although
+always
+am
+among
+amongst
+an
+and
+andar
+another
+any
+anybody
+anyhow
+anyone
+anything
+anyway
+anyways
+anywhere
+ap
+apan
+apart
+apna
+apnaa
+apne
+apni
+appear
+are
+aren
+arent
+aren't
+around
+arre
+as
+aside
+ask
+asking
+at
+aur
+avum
+aya
+aye
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+baar
+bad
+bahut
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+bc
+be
+became
+because
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+been
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+beforehand
+behind
+being
+below
+beside
+besides
+best
+better
+between
+beyond
+bhai
+bheetar
+bhi
+bhitar
+bht
+bilkul
+bohot
+bol
+bola
+bole
+boli
+bolo
+bolta
+bolte
+bolti
+both
+brief
+bro
+btw
+but
+by
+came
+can
+cannot
+cant
+can't
+cause
+causes
+certain
+certainly
+chahiye
+chaiye
+chal
+chalega
+chhaiye
+clearly
+c'mon
+com
+come
+comes
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+couldn
+couldnt
+couldn't
+d
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+dede
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+degi
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+dekha
+dekhe
+dekhi
+dekho
+denge
+dhang
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+did
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+didn't
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+diya
+diyaa
+diye
+diyo
+do
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+get
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+which
+while
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+whoever
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+willing
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+woh
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+yourself
+yourselves
+you've
+yup

streamlit_app.py

@@ -0,0 +1,141 @@
+import streamlit as st
+import preprocessor,helper
+import matplotlib.pyplot as plt
+
+st.markdown(
+    "<h1 style='text-align: center;'>Chat Analysis Space</h1>",
+    unsafe_allow_html=True
+    )
+
+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 uniquw 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"):
+        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 Message")
+            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(links)    
+
+        if selected_user=="overall":    
+            col1,col2=st.beta_columns(2)  
+        
+        # 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)
+
+
+
+        # 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')
+                #ax.set_xlabel('Users')
+                #ax.set_ylabel('Message Count')
+                #ax.set_title('Top 5 Most Active Users')
+                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 commmon 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)
+
+
+