configuration/kubernetes/deployment.yaml

@@ -64,10 +64,10 @@ spec:
         imagePullPolicy: Always
         resources:
           requests:
-            cpu: 64m
-            memory: 64Mi
+            cpu: 32m
+            memory: 32Mi
           limits:
-            cpu: 128m
+            cpu: 75m
             memory: 64Mi
         securityContext:
           capabilities:

src/main.py

@@ -18,18 +18,17 @@ analyser = sentimentAnalyser.get_sentiment()
 @app.route('/sentiment', methods=['GET'])
 def tweetPredict():
     tweet = request.args.get('tweet')
-    syncId = request.headers.get('X-CRYPTO-Sync-ID')
 
-    log("Receiving Tweet to classify [{}] for [{}]".format(tweet, syncId), 'INFO')
+    log("Receiving Tweet to classify {}".format(tweet), 'INFO')
 
     result = analyser.get_vader_sentiment(tweet)
 
-    log("Returning classification result of [{}]".format(result), 'INFO')
+    log("Returning classification result of {}".format(result), 'INFO')
 
     return json.dumps({'result': result, 'tweet': tweet}), 200, {'ContentType':'application/json'}
 
 @app.route('/sentimentProbeTest', methods=['GET'])
-def sentimentProbeTest():
+def sentimentProbeTrst():
     return json.dumps({'result':  {'Score': {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 'Compound': 0.0}, 'tweet': 'Fake Text'}), 200, {'ContentType':'application/json'}
 
 def callSentimentAnalyser():
@@ -43,7 +42,7 @@ def callProbes():
 if __name__ == '__main__':
     setup_logging()
 
-    log("Starting Sentiment Analyser...", 'INFO')
+    log("Starting Spam Filter...", 'INFO')
     sys.stdout.flush()
 
     Thread(target=callProbes).start()