Animated Graph using matplotlibHEADmain

1 files changed, 47 insertions, 30 deletions

diff --git a/consumer.py b/consumer.py
index 5542b9e..abbcf52 100644
--- a/consumer.py
+++ b/consumer.py
@@ -3,16 +3,13 @@ import json
 import re
 # from database_configuration import insert_tweet
 from nltk.sentiment.vader import SentimentIntensityAnalyzer
-import numpy as np
-import pandas as pd
+from time import sleep
+from multiprocessing import Process, Queue
 import matplotlib.pyplot as plt
+import matplotlib.animation as animation
 
 topic_name = 'twitterdata'
 
-# Initialize an empty dataframe to store the tweet id and sentiment
-tweets = pd.DataFrame(columns=['tweet_id', 'sentiment'])
-
-
 def sentence_score(rs):
     review_score = SentimentIntensityAnalyzer()
     return review_score.polarity_scores(rs)['compound']
@@ -40,38 +37,58 @@ def deEmojify(data):
                       "]+", re.UNICODE)
     return re.sub(emoj, '', data)
 
-consumer = KafkaConsumer(
+def consumer(q):
+    consumer = KafkaConsumer(
     topic_name,
-    auto_offset_reset='latest',
+    auto_offset_reset= 'earliest',
     enable_auto_commit=True,
     auto_commit_interval_ms =  1000,
     fetch_max_bytes = 128,
     max_poll_records = 100,
     value_deserializer=lambda x: json.loads(x.decode('utf-8')))
 
+    for msg in consumer:
+        tweet = ""
+        message = json.loads(json.dumps(msg.value))
+        if 'text' in message and 'RT @' not in message['text']:
+            if ('extended_tweet' in message) and 'full_text' in message['extended_tweet']:
+                tweet= deEmojify(message['extended_tweet']['full_text'].replace('\n', ' '))
+            else:
+                tweet= deEmojify(message['text'].replace('\n', ' '))
+        if tweet != '':
+            score = sentence_score(tweet)
+            sentiment = 'neutral'
+            if score > 0:
+                sentiment = 'positive'
+            elif score < 0:
+                sentiment = 'negative'
+            q.put(({'tweet_id': message['id'], 'tweet': tweet, 'sentiment': sentiment}))
+
+x_labels = ["Positive", "Negative", "Neutral"]
+y_data = [0, 0, 0]
 
-for msg in consumer:
-    tweet = ""
-    message = json.loads(json.dumps(msg.value))
-    if 'text' in message and 'RT @' not in message['text']:
-        if ('extended_tweet' in message) and 'full_text' in message['extended_tweet']:
-            tweet= deEmojify(message['extended_tweet']['full_text'].replace('\n', ' '))
+if __name__ == '__main__':
+    q = Queue()
+    p = Process(target=consumer, args=(q,))
+    p.start()
+
+    def update_data():
+        # Get new data from the queue
+        new_data = q.get()
+        # Check the sentiment of the tweet
+        if new_data['sentiment'] == 'positive':
+            y_data[0] += 1
+        elif new_data['sentiment'] == 'negative':
+            y_data[1] += 1
         else:
-            tweet= deEmojify(message['text'].replace('\n', ' '))
-    if tweet != '':
-        score = sentence_score(tweet)
-        sentiment = 'neutral'
-        if score > 0.5:
-            sentiment = 'positive'
-        elif score < -0.5:
-            sentiment = 'negative'
-        tweets = tweets.append({'tweet_id': message['id'], 'sentiment': sentiment}, ignore_index=True)
-        # Update the already shown plot with the new dataframe
-        tweets.groupby('sentiment').count()['tweet_id'].plot.bar()
-        plt.show()
-        plt.close('all')
+            y_data[2] += 1
+
+    fig = plt.figure()
+    plt.barh(x_labels, y_data, color=['green', 'red', 'blue'])
 
+    def animate(i):
+        update_data()
+        plt.barh(x_labels, y_data, color=['green', 'red', 'blue'])
 
-# End the consumer
-consumer.close()
-print('Done')
+    ani = animation.FuncAnimation(fig, animate, interval=1, frames=100)
+    plt.show()