helper.py

import re
import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS
import pandas as pd
from collections import Counter
import emoji
import seaborn as sb


def link_extractor(message):
	url_pattern = re.compile(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+')
	return url_pattern.findall(message)


def fetch_stats(selected_user, df):
	if selected_user == 'Overall':
		msg = df.shape[0]
		words = df['Message'].apply(lambda x: len(x.split())).sum()
		media = df[df['Message'].str.strip().str.lower().str.contains(
			'<media omitted>', na=False)].shape[0]
		links = df['Message'].apply(link_extractor).apply(len).sum()

	else:
		user = df[df['Sender'] == selected_user]
		msg = user.shape[0]
		words = user['Message'].apply(lambda x: len(x.split())).sum()
		media = user[user['Message'].str.strip().str.lower(
		).str.contains('<media omitted>', na=False)].shape[0]
		links = user['Message'].apply(link_extractor).apply(len).sum()
	return msg, words, media, links


def active_users(df):
	count = df['Sender'].value_counts()

	if 'System' in count.index:
		count = count.drop('System')

	prcnt = round((count / count.sum()) * 100, 2)
	# contribution = pd.DataFrame({
	# 	'User': count.index,
	# 	'Messages': count.values,
	# 	'Contribution(%)': prcnt.values
	# })

	if len(count) > 10:
		count = count.head(10)

	fig, ax = plt.subplots()
	ax.pie(count.values, labels=count.index, autopct='%1.1f%%', startangle=90)

	return fig


def Cloud(df, selected_user):
	with open('stop_hinglish.txt', 'r') as f:
		stop_hinglish = set(f.read().splitlines())

	stopwords = STOPWORDS.union(stop_hinglish)

	if selected_user == "Overall":
		user = df[df['Sender'] != 'System']
	else:
		user = df[df['Sender'] == selected_user]

	text = ' '.join(user['Message']).replace(
		"<Media omitted>", "").replace("This message was deleted", "").replace("You deleted this message", "").replace("Missed voice call","")

	wc = WordCloud(background_color='white', width=1600, height=800, stopwords=stopwords).generate(text)

	fig, ax = plt.subplots(figsize=(10, 10))
	ax.imshow(wc, interpolation='bilinear')
	ax.axis('off')
	return fig


def Common(df, selected_user):

	with open('stop_hinglish.txt', 'r') as f:
		stop_hinglish = set(f.read().splitlines())

	if selected_user == "Overall":
		user = df[df['Sender'] != 'System']
	else:
		user = df[df['Sender'] == selected_user]

	text = ' '.join(user['Message']).replace("<Media omitted>", "").replace(
		"This message was deleted", "").replace("You deleted this message", "").replace("Missed voice call","").lower()
	words = text.split()
	words = [word for word in words if word not in stop_hinglish]
	counts = Counter(words).most_common(20)
	words, word_counts = zip(*counts)

	fig, ax = plt.subplots(figsize=(10, 8))
	ax.barh(words, word_counts, color='skyblue')
	ax.set_xlabel('Count')
	ax.invert_yaxis()

	return fig


def emojis(df, selected_user):
	if selected_user == "Overall":
		user = df[df['Sender'] != 'System']
	else:
		user = df[df['Sender'] == selected_user]

	emoji_list = []
	for message in user['Message']:
		emoji_list.extend([emoji.emojize(e) for e in message if emoji.emoji_count(e)])

	counts = Counter(emoji_list).most_common(10)
	counts_df = pd.DataFrame(counts, columns=['Emoji', 'Counts'])
	counts_df = counts_df.astype({'Emoji': str, 'Counts': int})
	fig, ax = plt.subplots()
	ax.pie(counts_df['Counts'], labels=counts_df['Emoji'], startangle=90, autopct='%1.1f%%')
	ax.set_title('Emoji Distribution')

	return fig, counts_df

def monthly_timeline(df,selected_user):
	if selected_user == "Overall":
		user = df[df['Sender'] != 'System']
	else:
		user = df[df['Sender'] == selected_user]

	timeline = user.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
	fig,ax = plt.subplots()
	ax.plot(timeline['Time'],timeline['Message'])
	ax.set_xticklabels(timeline['Time'], rotation=45, ha='right')
	return fig

def daily_timeline(df,selected_user):
	if selected_user == "Overall":
		user = df[df['Sender'] != 'System']
	else:
		user = df[df['Sender'] == selected_user]

	timeline = user.groupby('Date').count()['Message'].reset_index()

	time = []
	fig,ax = plt.subplots(figsize=(15,10))
	ax.plot(timeline['Date'],timeline['Message'])
	ax.set_xticklabels(timeline['Date'], rotation=45, ha='right')
	return fig


def week_activity(df,selected_user):
	if selected_user == "Overall":
		user = df[df['Sender'] != 'System']
	else:
		user = df[df['Sender'] == selected_user]
	
	bzday= user['Day_name'].value_counts()
	fig,ax =plt.subplots()
	ax.bar(bzday.index,bzday.values)
	ax.set_xticklabels(bzday.index, rotation=45, ha='right')
	return fig


def month_activity(df,selected_user):
	if selected_user == "Overall":
		user = df[df['Sender'] != 'System']
	else:
		user = df[df['Sender'] == selected_user]
	bzmonth= user['Month'].value_counts()
	fig,ax =plt.subplots()
	ax.bar(bzmonth.index,bzmonth.values)
	ax.set_xticklabels(bzmonth.index, rotation=45, ha='right')
	return fig


def activity_heatmap(selected_user, df):
		if selected_user == "Overall":
				user = df[df['Sender'] != 'System']
		else:
				user = df[df['Sender'] == selected_user]
	
		user_heatmap = user.pivot_table(index='Day_name', columns='period', values='Message', aggfunc='count').fillna(0)
		
		cmap = plt.cm.Blues
		fig, ax = plt.subplots(figsize=(10,5))
		ax = sb.heatmap(user_heatmap, cmap=cmap, linewidths=.5, annot=True, fmt="g", square=True,cbar=False)
		ax.invert_yaxis()
		
		return fig

def search_messages(df, selected_user, word):
	if selected_user == "Overall":
			user = df[df['Sender'] != 'System']
	else:
			user = df[df['Sender'] == selected_user]
		
	temp = user[user['Message'].str.contains(word, case=False, na=False)].reset_index()

	temp['Full Date'] = (temp['Date'].astype(str) + '/' + 
                                temp['Month_Num'].astype(str) + '/' + 
                                temp['Year'].astype(str) + ', ' +
                                temp['Hour'].astype(str) + ':' +
                                temp['Minutes'].astype(str))

	return temp[['Sender', 'Message', 'Full Date']]