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consumer.py
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consumer.py
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from kafka import KafkaConsumer
from threading import Thread
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import matplotlib.animation as anim
from PIL import Image
import numpy as np
from os import path
class Consumer:
def __init__(self):
print('Starting Consumer...\n\n')
kafka_consumer = KafkaConsumer('ipl-topic', group_id='ipl-group')
for message in kafka_consumer:
decoded_message = message.value.decode('utf-8')
if path.isfile('./tweets.txt'): # if file exists
with open('tweets.txt', 'a', encoding='utf-8') as text_file:
print(decoded_message, file=text_file)
else: # if file does not exists
with open('tweets.txt', 'w', encoding='utf-8') as text_file:
print(decoded_message, file=text_file)
# decoded_key = message.key.decode('utf-8')
print('TOPIC: {}\nPARTITION: {}\nOFFSET: {}\nMESSAGE: {}\n\n\n' \
.format(message.topic, message.partition, message.offset, decoded_message))
# def generate_wordcloud(wordcloud_scheduler, fig):
def generate_wordcloud(fig):
stopWords = frozenset([
"a", "about", "above", "across", "after", "afterwards", "again", "against",
"all", "almost", "alone", "along", "already", "also", "although", "always",
"am", "among", "amongst", "amoungst", "amount", "an", "and", "another",
"any", "anyhow", "anyone", "anything", "anyway", "anywhere", "are",
"around", "as", "at", "back", "be", "became", "because", "become",
"becomes", "becoming", "been", "before", "beforehand", "behind", "being",
"below", "beside", "besides", "between", "beyond", "bill", "both",
"bottom", "but", "by", "call", "can", "cannot", "cant", "co", "con",
"could", "couldnt", "cry", "de", "describe", "detail", "do", "done",
"down", "due", "during", "each", "eg", "eight", "either", "eleven", "else",
"elsewhere", "empty", "enough", "etc", "even", "ever", "every", "everyone",
"everything", "everywhere", "except", "few", "fifteen", "fifty", "fill",
"find", "fire", "first", "five", "for", "former", "formerly", "forty",
"found", "four", "from", "front", "full", "further", "get", "give", "go",
"had", "has", "hasnt", "have", "he", "hence", "her", "here", "hereafter",
"hereby", "herein", "hereupon", "hers", "herself", "him", "himself", "his",
"how", "however", "hundred", "i", "ie", "if", "in", "inc", "indeed",
"interest", "into", "is", "it", "its", "itself", "keep", "last", "latter",
"latterly", "least", "less", "ltd", "made", "many", "may", "me",
"meanwhile", "might", "mill", "mine", "more", "moreover", "most", "mostly",
"move", "much", "must", "my", "myself", "name", "namely", "neither",
"never", "nevertheless", "next", "nine", "no", "nobody", "none", "noone",
"nor", "not", "nothing", "now", "nowhere", "of", "off", "often", "on",
"once", "one", "only", "onto", "or", "other", "others", "otherwise", "our",
"ours", "ourselves", "out", "over", "own", "part", "per", "perhaps",
"please", "put", "rather", "re", "same", "see", "seem", "seemed",
"seeming", "seems", "serious", "several", "she", "should", "show", "side",
"since", "sincere", "six", "sixty", "so", "some", "somehow", "someone",
"something", "sometime", "sometimes", "somewhere", "still", "such",
"system", "take", "ten", "than", "that", "the", "their", "them",
"themselves", "then", "thence", "there", "thereafter", "thereby",
"therefore", "therein", "thereupon", "these", "they", "thick", "thin",
"third", "this", "those", "though", "three", "through", "throughout",
"thru", "thus", "to", "together", "too", "top", "toward", "towards",
"twelve", "twenty", "two", "un", "under", "until", "up", "upon", "us",
"very", "via", "was", "we", "well", "were", "what", "whatever", "when",
"whence", "whenever", "where", "whereafter", "whereas", "whereby",
"wherein", "whereupon", "wherever", "whether", "which", "while", "whither",
"who", "whoever", "whole", "whom", "whose", "why", "will", "with",
"within", "without", "would", "yet", "you", "your", "yours", "yourself",
"yourselves","https","co","RT"])
text = open('tweets.txt', encoding='utf-8').read()
image_file = path.dirname(__file__)
logomask = np.array(Image.open(path.join(image_file, 'twitter.jpg')))
# lower max_font_size
wordcloud = WordCloud(stopwords=stopWords,mask = logomask, \
background_color='white', max_font_size=1000).generate(text)
#plt.clf()
plt.imshow(wordcloud, interpolation="bilinear")
if __name__ == '__main__':
consumer_thread1 = Thread(target=Consumer)
consumer_thread2 = Thread(target=Consumer)
consumer_thread1.start()
consumer_thread2.start()
fig = plt.figure()
plt.axis('off')
wordcloud_animation = anim.FuncAnimation(fig, generate_wordcloud, interval=5000)
plt.show()