helper.py

from urlextract import URLExtract 
extractor = URLExtract()
from wordcloud import WordCloud
from collections import Counter
import pandas as pd
import emoji
import seaborn as sns 


def fetch_stats(selected_user, df):
    #BLOCK 1
    if selected_user != 'Overall':
        df = df[df['user'] == selected_user]

    num_msg = df.shape[0]
    words = []
    for message in df['message']:
        words.extend(message.split())


    # BLOCK 2
    # if selected_user == 'Overall':
    #     # Fetching the no of messages
    #     num_msg = df.shape[0]

    #     # fetching no of words
    #     words = []
    #     for message in df['message']:
    #         words.extend(message.split())
    #     return num_msg, len(words)
    # else:
    #     new_df = df[df['user'] == selected_user]
    #     num_msg = new_df.shape[0]
    #     words = []
    #     for message in new_df['message']:
    #         words.extend(message.split())
    #     return num_msg, len(words)



    # NO OF MEDIA MESSAGES
    num_media_msg = df[df['message'] == '<Media omitted>\n'].shape[0]


    # NO OF URL LINKS
    links = []
    for message in df['message']:
        links.extend(extractor.find_urls(message))

    return num_msg, len(words), num_media_msg, len(links)




def most_active_users(df):
    x = df['user'].value_counts().head()
    df = round((df['user'].value_counts()/ df.shape[0])*100, 2).reset_index().rename(columns={'index':'name', 'user':'percent'})
    return x,df


def create_worcloud(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_stopWord(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_stopWord)
    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

# WILL IMPLEMENT THIS PART LATER
# def emoji_analyse(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([char for char in message if char in emoji.UNICODE_EMOJI['en']])

#     emoji_df = pd.DataFrame(Counter(emojis).most_common(len(Counter(emojis))))
#     # emoji_df = pd.DataFrame(Counter(emojis).most_common(), columns=['emojis', 'counts'])
#     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() #reset_index is used to convert something into dataframe

    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()


def activity_heatmap(selected_user, df):
    if selected_user != 'Overall':
        df = df[df['user'] == selected_user]        

    # Create a pivot table
    user_heatmap = df.pivot_table(index='day_name', columns='period', values='message', aggfunc='count').fillna(0)

    return user_heatmap