Add the Natural Language Classifier to your application

from watson_developer_cloud import LanguageTranslationV2 as LanguageTranslation

from watson_developer_cloud import NaturalLanguageClassifierV1 as NaturalLanguageClassifier

  outputTxt = "TBD"

  targetlang = 'en'

  classification = None

  form = None 

          supportedModels = checkForTranslation(primarylang, targetlang)

          if supportedModels:

            logger.info("We have some supported translation models")

            englishTxt = performTranslation(data, primarylang, targetlang)

            outputTxt += ", which in english is %s. " % englishTxt

            classification = classifyTheText(englishTxt)            

          else:

            outputTxt += " ,which unfortunately we can't translate into English"

        else:

          classification = classifyTheText(data)

        if classification:

          outputTxt += "(and %s confident that it is %s classification)" % (classification['confidence'],

                                                                  classification['className'])         

  classification = {}

  nlc = getNLCService()

  classificationList = nlc.list()

  logger.info(json.dumps(classificationList, indent=2))

  if "classifiers" in classificationList: 

    logger.info(classificationList["classifiers"][0]['classifier_id'])

    if "classifier_id" in classificationList["classifiers"][0]:

       classID =  classificationList["classifiers"][0]['classifier_id']

       status = nlc.status(classID)

       logger.info(json.dumps(status, indent=2))              

       if "status" in status and "Available" == status["status"]:

         logger.info("Classifier is Ready to use")                 

         classes = nlc.classify(classID, txt)

         logger.info(json.dumps(classes, indent=2))

         if "classes" in classes:

           className = classes["classes"][0]["class_name"]

           confidence = classes["classes"][0]["confidence"]    

           classification = {"confidence": confidence,

                             "className" : className}

           logger.info(classification)            

  return classification

The following function first finds the Natural Language Classifier service, which you will code next:nlc = getNLCService()

It then fetches a list of all the classifiers that you created:classificationList = nlc.list()

If any are found, a status check is completed on the first classifier in the list:if "classifiers" in classificationList: 
status = nlc.status(classID)

If the classifier is showing a status of “ready,” the classification for the entered (or translated text) is completed:classes = nlc.classify(classID, txt)

The following function returns a list with the class for which the service has the highest confidence first in the list:className = classes["classes"][0]["class_name"]