import regex
import pandas as pd
import numpy as np
import emoji
import plotly.express as px
from collections import Counter
import matplotlib.pyplot as plt
from os import path
from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
% matplotlib inline

def startsWithDateAndTime(s):
    pattern = '^([0-9]+)(\/)([0-9]+)(\/)([0-9]+), ([0-9]+):([0-9]+)[ ]?(AM|PM|am|pm)? -' 
    result = re.match(pattern, s)
    if result:
        return True
    return False

def FindAuthor(s):
  s=s.split(":")
  if len(s)==2:
    return True
  else:
    return False

def getDataPoint(line):   
    splitLine = line.split(' - ') 
    dateTime = splitLine[0]
    date, time = dateTime.split(', ') 
    message = ' '.join(splitLine[1:])
    if FindAuthor(message): 
        splitMessage = message.split(': ') 
        author = splitMessage[0] 
        message = ' '.join(splitMessage[1:])
    else:
        author = None
    return date, time, author, message

from google.colab import files
uploaded = files.upload()
data = [] # List to keep track of data so it can be used by a Pandas dataframe
conversation = 'WhatsApp Chat (1).txt'
with open(conversation, encoding="utf-8") as fp:
    fp.readline() # Skipping first line of the file because contains information related to something about end-to-end encryption
    messageBuffer = [] 
    date, time, author = None, None, None
    while True:
        line = fp.readline() 
        if not line: 
            break
        line = line.strip() 
        if startsWithDateAndTime(line): 
            if len(messageBuffer) > 0: 
                parsedData.append([date, time, author, ' '.join(messageBuffer)]) 
            messageBuffer.clear() 
            date, time, author, message = getDataPoint(line) 
            messageBuffer.append(message) 
        else:
            messageBuffer.append(line)

df = pd.DataFrame(parsedData, columns=['Date', 'Time', 'Author', 'Message']) # Initialising a pandas Dataframe.
df["Date"] = pd.to_datetime(df["Date"])
df.tail(20)

df.Author.unique()

media_messages = df[df['Message'] == '<Media omitted>'].shape[0]
print(media_messages)
def split_count(text):

    emoji_list = []
    data = regex.findall(r'\X', text)
    for word in data:
        if any(char in emoji.UNICODE_EMOJI for char in word):
            emoji_list.append(word)

    return emoji_list

df["emoji"] = df["Message"].apply(split_count)
emojis = sum(df['emoji'].str.len())
print(emojis)
URLPATTERN = r'(https?://\S+)'
df['urlcount'] = df.Message.apply(lambda x: re.findall(URLPATTERN, x)).str.len()
links = np.sum(df.urlcount)
print("Data science Community")
print("Messages:",total_messages)
print("Media:",media_messages)
print("Emojis:",emojis)
print("Links:",links)

media_messages_df = df[df['Message'] == '<Media omitted>']
messages_df = df.drop(media_messages_df.index)
messages_df.info()
messages_df['Letter_Count'] = messages_df['Message'].apply(lambda s : len(s))
messages_df['Word_Count'] = messages_df['Message'].apply(lambda s : len(s.split(' ')))
messages_df["MessageCount"]=1

l = ["Aman Kharwal", "Sahil Pansare", "Sumehar"]
for i in range(len(l)):
  # Filtering out messages of particular user
  req_df= messages_df[messages_df["Author"] == l[i]]
  # req_df will contain messages of only one particular user
  print(f'Stats of {l[i]} -')
  # shape will print number of rows which indirectly means the number of messages
  print('Messages Sent', req_df.shape[0])
  #Word_Count contains of total words in one message. Sum of all words/ Total Messages will yield words per message
  words_per_message = (np.sum(req_df['Word_Count']))/req_df.shape[0]
  print('Words per message', words_per_message)
  #media conists of media messages
  media = media_messages_df[media_messages_df['Author'] == l[i]].shape[0]
  print('Media Messages Sent', media)
  # emojis conists of total emojis
  emojis = sum(req_df['emoji'].str.len())
  print('Emojis Sent', emojis)
  #links consist of total links
  links = sum(req_df["urlcount"])   
  print('Links Sent', links)   
  print()

total_emojis_list = list([a for b in messages_df.emoji for a in b])
emoji_dict = dict(Counter(total_emojis_list))
emoji_dict = sorted(emoji_dict.items(), key=lambda x: x[1], reverse=True)
for i in emoji_dict:
  print(i)

text = " ".join(review for review in messages_df.Message)
print ("There are {} words in all the messages.".format(len(text)))
stopwords = set(STOPWORDS)
# Generate a word cloud image
wordcloud = WordCloud(stopwords=stopwords, background_color="white").generate(text)
# Display the generated image:
# the matplotlib way:
plt.figure( figsize=(10,5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()

l = ["Aman Kharwal", "Sahil Pansare", "Sumehar"]
for i in range(len(l)):
  dummy_df = messages_df[messages_df['Author'] == l[i]]
  text = " ".join(review for review in dummy_df.Message)
  stopwords = set(STOPWORDS)
  #Generate a word cloud image
  print('Author name',l[i])
  wordcloud = WordCloud(stopwords=stopwords, background_color="white").generate(text)
  #Display the generated image   
  plt.figure( figsize=(10,5))
  plt.imshow(wordcloud, interpolation='bilinear')
  plt.axis("off")
  plt.show()