from nlc import NaturalLanguageClassifierInstance
import sys
import argparse
# 3rd party imports
import requests
if __name__ == "__main__":
sample_url = 'https://gateway.watsonplatform.net/natural-language-classifier/api'
user = ''
pw = ''
# Pass this as arguments (Example : Python nlc.py <usernamer> <password>)
parser = argparse.ArgumentParser()
parser.add_argument("user", help="User Name")
parser.add_argument("pw", help="Password")
parser.add_argument("csvfile", help="CSV file that will be used to generate the classifier (probably the training file generated from split.py)")
parser.add_argument("classifiername", help="Name of the classifier")
args = parser.parse_args()
nlc_instance = NaturalLanguageClassifierInstance(args.user, args.pw, sample_url)
# 2. Train a classifier
#moidfy the args to accept a csv file
nlc_instance.train_classifier(args.classifiername,training_file=args.csvfile)
sys.exit(0)
from nlc import NaturalLanguageClassifierInstance
import sys
import argparse
# 3rd party imports
import requests
if __name__ == "__main__":
sample_url = "https://gateway.watsonplatform.net/natural-language-classifier/api"
user = ""
pw = ""
# Pass this as arguments (Example : Python nlc.py <usernamer> <password>)
parser = argparse.ArgumentParser()
parser.add_argument("user", help="User Name")
parser.add_argument("pw", help="Password")
args = parser.parse_args()
nlc_instance = NaturalLanguageClassifierInstance(args.user, args.pw, sample_url)
# 2. Train a classifier
# moidfy the args to accept a csv file
print "Listing all classifiers : Total No. : %d" % len(nlc_instance.get_classifiers())
for nlInstance in nlc_instance.get_classifiers():
print nlInstance.get_id() + " " + nlInstance.get_name() + " " + nlInstance.get_created_date() + " " + nlInstance.get_status()
sys.exit(0)
request = urllib2.Request(classifier_url, data=data, headers=headers)
response = urllib2.urlopen(request)
json_response = json.loads(response.read())
tc_name = json_response["classes"][0]["class_name"]
tc_conf = json_response["classes"][0]["confidence"]
return tc_name, tc_conf
if __name__ == "__main__":
available = 0
not_available = 0
for bm_username, bm_password in NLC_container.iteritems():
nlc_instance = NaturalLanguageClassifierInstance(bm_username, bm_password, sample_url)
# print "Listing all classifiers : Total No. : %d" % len(nlc_instance.get_classifiers())
for nlcInstance in nlc_instance.get_classifiers():
# print nlcInstance.get_id() + ' ' + nlcInstance.get_name() + ' ' + nlcInstance.get_created_date() + ' ' + nlcInstance.get_status()
classifier_id = nlcInstance.get_id()
nlc_status = nlcInstance.get_status()
if nlc_status == "Available":
available += 1
else:
not_available += 1
# Checking status of classifier health (NLC container inventory)
tc_name, tc_conf = test(classifier_id, bm_username, bm_password, question_text)
