def get(self, request):
level = request.GET.get('level', None)
access_levels = ['district', 'warehouse', 'ip', 'facility']
access_areas = []
if level and level.lower() in access_levels:
access_areas = pydash.reject(Score.objects.values_list(level, flat=True).distinct(), lambda x: len(x) < 1)
return Response(access_areas)
def get(self, request):
level = request.GET.get('level', None)
access_levels = ['district', 'warehouse', 'ip', 'facility']
access_areas = []
if level and level.lower() in access_levels:
access_areas = pydash.reject(
Score.objects.values_list(level, flat=True).distinct(),
lambda x: len(x) < 1)
return Response(access_areas)
def get(self, request):
level = request.GET.get("level", None)
access_levels = ["district", "warehouse", "ip", "facility"]
access_areas = []
if level and level.lower() in access_levels:
access_areas = pydash.reject(
Score.objects.values_list(level, flat=True).distinct(),
lambda x: len(x) < 1,
)
return Response(access_areas)
def get_bot_response(text):
sentences = (chain(re.split(
PUNCTUATION_REGEX, text)).reject(is_empty).map(clean_sentence).value())
if len(sentences) <= 0:
return NO_INTENT_DETECTED_ANSWER
lemmatized_sentences = reject(
[lemmatize_sentence(nlp(sentence)) for sentence in sentences],
is_empty,
)
X = [
sentence_to_feature_vector(sentence, bag_of_words)
for sentence in lemmatized_sentences
]
predictions = model.predict(X)
largest_index = np.argmax(predictions, axis=1)
largest_values = np.take_along_axis(predictions,
np.expand_dims(largest_index, axis=-1),
axis=-1)
# last user intent was not detected correctly
if largest_values[-1][0] < MINIMUM_THRESHOLD:
return NO_INTENT_DETECTED_ANSWER
answers = [
random.choice(intents_json[idx]["responses"]) for idx in largest_index
]
for idx in largest_index:
if "date" in intents_json[idx]["extract"]:
extracted_date = get_date(sentences[-1])
answers[-1] = answers[-1].replace("<date>", str(extracted_date))
if "place" in intents_json[idx]["extract"]:
extracted_places = get_places(sentences[-1], nlp)
answers[-1] = answers[-1].replace("<place>",
",".join(extracted_places))
if "number" in intents_json[idx]["extract"]:
extracted_numbers = get_numbers(sentences[-1])
answers[-1] = answers[-1].replace("<number>",
",".join(extracted_numbers))
return {
"answer": " ".join(answers),
"intent": intents_json[largest_index[-1]]["tag"],
"nextStates": intents_json[largest_index[-1]]["nextStates"],
}
def persist_multiple_order_records(report):
facilities_with_multiple_orders = pydash.reject(
report.locs, lambda f: facility_has_single_order(f))
all = pydash.map_(facilities_with_multiple_orders, build_mof(report))
MultipleOrderFacility.objects.filter(cycle=report.cycle).delete()
MultipleOrderFacility.objects.bulk_create(all)
def test_reject(case, expected):
assert _.reject(*case) == expected
def persist_multiple_order_records(report):
facilities_with_multiple_orders = pydash.reject(report.locs, lambda f: facility_has_single_order(f))
all = pydash.collect(facilities_with_multiple_orders, build_mof(report))
MultipleOrderFacility.objects.filter(cycle=report.cycle).delete()
MultipleOrderFacility.objects.bulk_create(all)