data fix
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2139011821
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166707dd02
@ -4,5 +4,8 @@
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"(?i)\\b(kim|czym)\\s+(jesteś|jestes|jest)\\b",
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"(?i)\\b(cześć|czesc|witaj|witam|hej|siema|helo|hello)\\b",
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"(?i)\\b(proszę|powiedz|opowiedz|opisz)\\s+(mi|nam)\\s+(o|więcej|coś)\\s+(o|na temat)\\s*(twoim|ciebie|twoje)\\s*(imieniu|imieniem|imię|nazwisko)\\b"
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],
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"ask_price": [
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"(?i)\\b(jaka jest cena produktu)\\b"
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]
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}
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@ -20,5 +20,8 @@
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"Wygląda na to, że krążymy wokół tego samego tematu. Czy możemy przejść do czegoś innego?",
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"Znowu to samo pytanie, może zmienimy temat?",
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"Ponownie pytasz o to samo, czy jest coś innego, o co chciałbyś zapytać?"
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]
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],
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"give_price": [
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"Cena to 20zl"
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]
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}
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@ -49,8 +49,8 @@
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6 dowiedzieć, request NoLabel
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7 jakie request NoLabel
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8 są request NoLabel
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9 opcje request B-delivery-method
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10 dostawy? request I-delivery-method
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9 opcje request B-delivery_method
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10 dostawy? request I-delivery_method
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# text: Oczywiście! Oferujemy dostawę kurierem za 10 zł oraz odbiór osobisty w naszym sklepie.
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# intent: inform
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@ -93,5 +93,5 @@
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1 Proszę request NoLabel
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2 podać request NoLabel
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3 ceny request B-price
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4 weza request B-product
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5 ogrodowego. request I-product
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4 weza request B-item
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5 ogrodowego. request I-item
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@ -1134,67 +1134,5 @@
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1 Proszę request NoLabel
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2 podać request NoLabel
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3 cene request B-price
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4 fotela request B-product
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5 ogrodowego. request I-product
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# text: Czesc chcialabym kupic szampon do wlosow
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# intent: request
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# slots:
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1 Czesc request NoLabel
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2 chcialabym request NoLabel
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3 kupic request NoLabel
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4 szampon request B-item
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5 do request I-item
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6 wlosow request I-item
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# text: Czesc jaka jest cena le¿aka?
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# intent: request
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# slots:
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1 Czesc request NoLabel
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2 jaka request NoLabel
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3 jest request NoLabel
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4 cena request B-price
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5 le¿aka? request B-item
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# text: Ile kosztuje krzeslo?
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# intent: request
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# slots:
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1 Ile request NoLabel
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2 kosztuje request B-price
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3 krzeslo? request B-item
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# text: Jaka cena mleka?
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# intent: request
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# slots:
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1 Jaka request NoLabel
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2 cena request B-price
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3 mleka? request B-item
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# text: Ile kosztuje ten produkt?
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# intent: request
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# slots:
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1 Ile request NoLabel
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2 kosztuje request B-price
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3 ten request NoLabel
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4 produkt? request B-item
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# text: Chce kupic doniczke
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# intent: request
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# slots:
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1 Chce request NoLabel
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2 kupic request NoLabel
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3 doniczke request B-item
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# text: Jaka cena dostawy?
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# intent: request
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# slots:
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1 Jaka request NoLabel
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2 cena request B-price
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3 dostawy? request B-delivery_method
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# text: Ul. Poznanska 2323
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# intent: inform
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# slots:
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1 Ul. inform B-address
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2 Poznanska inform I-address
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3 2323 inform I-address
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4 fotela request B-item
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5 ogrodowego. request I-item
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@ -112,10 +112,10 @@ class Model:
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trainset = list(parse_incr(f, fields=['id', 'form', 'frame', 'slot'], field_parsers=field_parsers))
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with open(self.test_dataset, encoding='utf-8') as f:
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testset = list(parse_incr(f, fields=['id', 'form', 'frame', 'slot'], field_parsers=field_parsers))
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print('TRAINSET:', trainset)
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corpus = Corpus(train=conllu2flair(trainset, label_type), test=conllu2flair(testset, label_type))
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label_dictionary = corpus.make_label_dictionary(label_type=label_type)
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print('LABEL:' ,label_dictionary)
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embedding_types = [
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WordEmbeddings('pl'),
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FlairEmbeddings('pl-forward'),
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@ -133,6 +133,6 @@ class Model:
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model = Model(train_dataset='../data/test_dialog.conllu', test_dataset='../data/test_dialog.conllu')
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model.train_model('frame')
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model.train_model('slot', field_parsers={'slot': nolabel2o})
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#model = Model(train_dataset='../data/test_dialog.conllu', test_dataset='../data/test_dialog.conllu')
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# model2 = Model(train_dataset='../data/test_dialog.conllu', test_dataset='../data/test_dialog.conllu')
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# model2.train_model('slot', field_parsers={'slot': nolabel2o})
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@ -1,38 +1,50 @@
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import os
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import jsgf
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from flair.models import SequenceTagger
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import sys
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sys.path.append("..")
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from models.nlu_train2 import predict_frame, predict_slot
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import logging
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logging.getLogger('flair').setLevel(logging.CRITICAL)
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class NLU:
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def __init__(self):
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self.grammars = [
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jsgf.parse_grammar_file(f"grammars/{file_name}")
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for file_name in os.listdir("grammars")
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]
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self.frame_model = SequenceTagger.load('../models/frame-model/final-model.pt')
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self.slot_model = SequenceTagger.load('../models/slot-model/final-model.pt')
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def get_dialog_act(self, rule):
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def get_intent(self, text: str):
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return predict_frame(self.frame_model, text.split(), 'frame')
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def get_slot(self, text: str):
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pred = predict_slot(self.slot_model, text.split(), 'slot')
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slots = []
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self.get_slots(rule.expansion, slots)
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return {"act": rule.grammar.name, "slots": slots}
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current_slot = None
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current_slot_value = []
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def get_slots(self, expansion, slots):
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if expansion.tag != "":
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slots.append((expansion.tag, expansion.current_match))
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return
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for frame in pred:
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slot = frame["slot"]
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if slot.startswith("B-"):
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if current_slot:
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slots.append({'name': current_slot, 'value': " ".join(current_slot_value)})
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current_slot = slot[2:]
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current_slot_value = [frame["form"]]
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elif slot.startswith("I-"):
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current_slot_value.append(frame["form"])
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for child in expansion.children:
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self.get_slots(child, slots)
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if current_slot:
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slots.append({'name': current_slot, 'value': " ".join(current_slot_value)})
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if not expansion.children and isinstance(expansion, jsgf.NamedRuleRef):
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self.get_slots(expansion.referenced_rule.expansion, slots)
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return slots
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def match(self, utterance):
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list_of_illegal_character = [",", ".", "'", "?", "!", ":", "-", "/"]
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for illegal_character in list_of_illegal_character[:-2]:
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utterance = utterance.replace(f"{illegal_character}", "")
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for illegal_character in list_of_illegal_character[-2:]:
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utterance = utterance.replace(f"{illegal_character}", " ")
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def analyze(self, text: str):
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intent = self.get_intent(text)
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slots = self.get_slot(text)
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print({'intent': intent,
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'slots': slots})
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return {
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'intent': intent,
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'slots': slots
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}
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for grammar in self.grammars:
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matched = grammar.find_matching_rules(utterance.lower())
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if matched:
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return self.get_dialog_act(matched[0])
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return {"act": "null", "slots": []}
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nlu = NLU()
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nlu.analyze("Chce kupic lakier do pazanokci")
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