def can_response(self, **kwargs):
tags = kwargs.get('tags')
plain_text = kwargs.get('plain_text').lower()
service = ConversionService()
units = service.extractUnits(plain_text)
return len(units) > 1
def testExponentiation(self):
service = ConversionService()
inp = "I want two squared meters"
units = service.extractUnits(inp)
self.assertEqual(units, ['meters^2'])
inp = "I want two square meters"
units = service.extractUnits(inp)
self.assertEqual(units, ['meters^2'])
inp = "I want two sq meters"
units = service.extractUnits(inp)
self.assertEqual(units, ['meters^2'])
inp = "I want two cubic meters"
units = service.extractUnits(inp)
self.assertEqual(units, ['meters^3'])
inp = "I want two meters cubed"
units = service.extractUnits(inp)
self.assertEqual(units, ['meters^3'])
inp = "I want two meters to the fifth power"
units = service.extractUnits(inp)
self.assertEqual(units, ['meters^5'])
inp = "I want two meters to the fifth"
units = service.extractUnits(inp)
self.assertEqual(units, ['meters^5'])
def testReadability(self):
inp = "convert 0.000013 inches to centimeters"
target = pq.Quantity(0.000013, "inches")
target.units = "cm"
# Correctness of conversion
self.compareConversion(inp, target)
# Correctness of representation
service = ConversionService()
self.assertEqual(service.parseUnits(inp),
"3.3 times ten to the negative 5 cm")
def __init__(self):
self.tagger = PerceptronTagger()
self.alarm_ai = AlarmResponse()
self.converter_ai = ConversionResponse()
self.summarize_ai = SummarizerResponse()
self.news_ai = NewsResponse()
self.maps_ai = MapsResponse()
self.lemmatizer = WordNetLemmatizer()
self.parser = spy.English()
self.conversion = ConversionService()
self.ai = {
"alarm_api": self.alarm_ai,
"unit_conversion": self.converter_ai,
"summarization": self.summarize_ai,
"news": self.news_ai,
"maps_api": self.maps_ai
}
self.classifiers = []
with open('nb_dumped_classifier.pkl', 'rb') as fid:
self.classifiers.append(cPickle.load(fid))
with open('sgd_dumped_classifier.pkl', 'rb') as fid:
self.classifiers.append(cPickle.load(fid))
with open('pla_dumped_classifier.pkl', 'rb') as fid:
self.classifiers.append(cPickle.load(fid))
self.previous_ents = {"PERSON": "", "GPE": ""}
self.special_tags = {
'PERSON': ['his', 'her', 'him', 'he', 'she'],
'GPE': ['it', 'there']
}
def respond(self, **kwargs):
plain_text = kwargs.get('plain_text').lower()
service = ConversionService()
units = service.extractUnits(plain_text)
inf_eng = inflect.engine()
try:
original_num = re.findall(r'\d+', plain_text)
if len(original_num) == 0:
try:
converted_num = service.convert(plain_text)
return "Your ans is " + "%.2f" % (float(
str(service.convert(plain_text)).split()[0])
) + " " + units[1]
except TypeError:
plain_text = "one " + units[0] + " to " + units[1]
original_num = [1]
converted_num = float(
"%.2f" % (float(str(service.convert(plain_text)).split()[0])))
output = str(inf_eng.number_to_words(
original_num[0])) + " " + units[0] + " equals " + str(
inf_eng.number_to_words(converted_num)) + " " + units[1]
return output
except TypeError:
return "Sorry, I cannot convert these units."
except:
return "Something Wrong!"
def compareConversion(self, inp, target):
service = ConversionService()
result = service.convert(inp)
self.assertEqual(result.magnitude, target.magnitude)
self.assertEqual(result.units, target.units)
def testExtraction(self):
inp = "I want three pounds of eggs and two inches per squared foot"
service = ConversionService()
units = service.extractUnits(inp)
self.assertEqual(units, ['pounds', 'inches / foot^2'])