class NLClassifier(object):
def __init__(self, username, password, classifier):
# Setup Watson SDK
self.natural_language_classifier = NLC(username=username,password=password)
# Classifier information
self.classifier = {}
self.classifier['name'] = classifier['name']
self.classifier['training_file'] = classifier['training_file']
c = self.natural_language_classifier.list_classifiers()
if any(d['name'] == self.classifier['name'] for d in c['classifiers'] ):
self.classifier['id'] = [ d['classifier_id'] for d in c['classifiers'] if d['name'] == self.classifier['name'] ][0]
print 'Found classifier id %s ' % self.classifier['id']
self.classifier['status'] = self.natural_language_classifier.status(self.classifier['id'])['status']
else:
print 'No classifier found, creating new from training set'
self.classifier['id'] = self.create_classifier()
print 'New classifier id: %s ' % self.classifier['id']
### Method to train the Watson Natural Language Classifier
# The training set is delivered as a CSV file as specified in the Developer Guide
# https://www.ibm.com/watson/developercloud/doc/nl-classifier/data_format.shtml
def create_classifier(self):
training_data = open(self.classifier['training_file'], 'rb')
training_result = self.natural_language_classifier.create( training_data=training_data, name=self.classifier['name'] )
if training_result['status'] == "Training":
self.classifier['status'] = "Training"
return training_result['classifier_id']
else:
print training_result
return "Error"
def classify(self,text):
# Typically in a production system Watson NLC will be fully trained and verified by a data scientist before the system is ever
# exposed in production. However because this is a demo application where Watson NLC is trained at application deployment time,
# we will need to have a check to verify that the training is completed.
if self.classifier['status'] == "Training":
r = self.natural_language_classifier.status(self.classifier['id'])
if r['status'] == "Training":
return {"error": "Classifier still in training. Please try again in a few minutes."}
elif r['status'] == "Available":
self.classifier['status'] = 'Available'
else:
return {"error": "Unknown status for classifier", "message": r['status']}
return self.natural_language_classifier.classify(self.classifier['id'], text)
