def api():
input_data = request.json
if ("summary" not in input_data) or ("review" not in input_data):
raise InvalidUsage("Must specify summary and review in the request",
status_code=422)
sanitized_text = utils.clean_text(input_data["summary"],
input_data["review"])
if sanitized_text:
language = utils.detect_language(sanitized_text)
prediction = utils.generate_prediction_from_text(sanitized_text)
return Response(
json.dumps({
"prediction":
prediction,
"language_detected":
language,
"disclaimer":
"Prediction not accurate for non-English reviews",
}))
else:
raise InvalidUsage(
"Review content cannot be blank (must provide either the summary or text section)",
status_code=422,
)
def main(argv):
parser = get_parser()
(options, args) = parser.parse_args(argv)
if not (options.input and options.output and options.model):
parser.error("Requires arguments not provided")
else:
lang = detect_language(options.input)
if lang not in ['en', 'es', 'it', 'nl']:
print >> sys.stderr, 'Language other than en, es, it, nl'
sys.exit(1)
else:
print "Loading model for lang {0}".format(lang)
age_gender_model=load_model(options.model,lang,'age_gender.model')
label_encoder_ag= load_label_encoder(options.model,lang,'age_gender.le')
#personlality
personality_model=load_model(options.model,lang,'personality.model')
label_encoder_per=load_label_encoder(options.model,lang,'personality.le')
print "Done loading model"
model_ag = Model(name='age_gender',clf=age_gender_model, label_encoder=label_encoder_ag, label_extractor=label_extractor_gender_age)
model_per = Model(name='personality',clf=personality_model, label_encoder=label_encoder_per, label_extractor=label_extractor_personality_values)
test =Test(model=[model_ag,model_per],path=options.input)
test.run()
test.save(options.output)
print "Done "
def main(argv):
parser = get_parser()
(options, args) = parser.parse_args(argv)
if not (options.input and options.output):
parser.error("Requires arguments not provided")
else:
lang = detect_language(options.input)
if lang not in ['en', 'es', 'it', 'nl']:
print >> sys.stderr, 'Language other than en, es, it, nl'
sys.exit(1)
else:
training_data = TrainingData(path=options.input).load_data() # load training data
#age gender
configure_obj_ag = configure_age_gender(lang) # apply which features to use
label_encoder_ag = preprocessing.LabelEncoder() # map feature name to integer index
log_reg = linear_model.LogisticRegression(dual=True, multi_class='multinomial',
solver='lbfgs') # multiclass classifier
model_age_gender = Train(configuration=configure_obj_ag,
classifier=log_reg) # create train pipeline
#personality
configure_obj_per=configure_personality(lang)
label_encoder_per = preprocessing.MultiLabelBinarizer()
# label_encoder_per = None
linear_reg=OneVsRestClassifier(linear_model.LogisticRegression(dual=True))
model_per = Train(configuration=configure_obj_per,
classifier=linear_reg)
print "Training ......."
model_age_gender.train(training_data.X,training_data.transform_label(label_extractor_age_gender,label_encoder_ag))
model_per.train(training_data.X,training_data.transform_label(label_extractor_personality,label_encoder_per))
print "Done "
print "Saving model in {0} ".format(options.output)
if not os.path.exists(os.path.join(options.output, lang)):
os.mkdir(os.path.join(options.output, lang))
model_age_gender.save(os.path.join(options.output, lang, 'age_gender.model'))
model_per.save(os.path.join(options.output, lang, 'personality.model'))
save_label_encoder(label_encoder_ag,options.output,lang,'age_gender.le')
save_label_encoder(label_encoder_per,options.output,lang,'personality.le')
print "Done saving model"
async def trans_detect(ctx):
global now
if utils.elapsed(now):
try:
phrase = ctx.content[ctx.content.index(' ')+1:]
lang = utils.detect_language(phrase)
print(f'translate {phrase}')
if lang == 'en':
response = utils.translate(phrase, 'en', 'ko')
elif lang == 'ko':
response = utils.translate(phrase, 'ko', 'en')
else:
raise Exception('Invalid language.')
print(response)
now = time()
await ctx.send(f'@{ctx.author.name}, {phrase} = {response.text}')
except ValueError:
await ctx.send(f'@{ctx.author.name}, usage: !translate <text>')
