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)