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chatbot #4

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s464951 merged 11 commits from nlu-model into master 2024-06-14 11:55:59 +02:00
Conversation 0 Commits 11 Files Changed 23 +58 -102
6 changed files with 58 additions and 102 deletions
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3
chatbot/data/intents.json
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@ -4,5 +4,8 @@
"(?i)\\b(kim|czym)\\s+(jesteś|jestes|jest)\\b",
"(?i)\\b(cześć|czesc|witaj|witam|hej|siema|helo|hello)\\b",
"(?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"
],
"ask_price": [
"(?i)\\b(jaka jest cena produktu)\\b"
]
}

3
chatbot/data/response.json
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@ -20,5 +20,8 @@
"Wygląda na to, że krążymy wokół tego samego tematu. Czy możemy przejść do czegoś innego?",
"Znowu to samo pytanie, może zmienimy temat?",
"Ponownie pytasz o to samo, czy jest coś innego, o co chciałbyś zapytać?"
],
"give_price": [
"Cena to 20zl"
]
}

8
chatbot/data/test_dialog.conllu
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@ -49,8 +49,8 @@
6 dowiedzieć, request NoLabel
7 jakie request NoLabel
8 są request NoLabel
9 opcje request B-delivery-method
10 dostawy? request I-delivery-method
9 opcje request B-delivery_method
10 dostawy? request I-delivery_method
# text: Oczywiście! Oferujemy dostawę kurierem za 10 zł oraz odbiór osobisty w naszym sklepie.
# intent: inform
@ -93,5 +93,5 @@
1 Proszę request NoLabel
2 podać request NoLabel
3 ceny request B-price
4 weza request B-product
5 ogrodowego. request I-product
4 weza request B-item
5 ogrodowego. request I-item

66
chatbot/data/train_dialog.conllu
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@ -1134,67 +1134,5 @@
1 Proszę request NoLabel
2 podać request NoLabel
3 cene request B-price
4 fotela request B-product
5 ogrodowego. request I-product
# text: Czesc chcialabym kupic szampon do wlosow
# intent: request
# slots:
1 Czesc request NoLabel
2 chcialabym request NoLabel
3 kupic request NoLabel
4 szampon request B-item
5 do request I-item
6 wlosow request I-item
# text: Czesc jaka jest cena le¿aka?
# intent: request
# slots:
1 Czesc request NoLabel
2 jaka request NoLabel
3 jest request NoLabel
4 cena request B-price
5 le¿aka? request B-item
# text: Ile kosztuje krzeslo?
# intent: request
# slots:
1 Ile request NoLabel
2 kosztuje request B-price
3 krzeslo? request B-item
# text: Jaka cena mleka?
# intent: request
# slots:
1 Jaka request NoLabel
2 cena request B-price
3 mleka? request B-item
# text: Ile kosztuje ten produkt?
# intent: request
# slots:
1 Ile request NoLabel
2 kosztuje request B-price
3 ten request NoLabel
4 produkt? request B-item
# text: Chce kupic doniczke
# intent: request
# slots:
1 Chce request NoLabel
2 kupic request NoLabel
3 doniczke request B-item
# text: Jaka cena dostawy?
# intent: request
# slots:
1 Jaka request NoLabel
2 cena request B-price
3 dostawy? request B-delivery_method
# text: Ul. Poznanska 2323
# intent: inform
# slots:
1 Ul. inform B-address
2 Poznanska inform I-address
3 2323 inform I-address
4 fotela request B-item
5 ogrodowego. request I-item

10
chatbot/models/nlu_train2.py
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@ -112,10 +112,10 @@ class Model:
trainset = list(parse_incr(f, fields=['id', 'form', 'frame', 'slot'], field_parsers=field_parsers))
with open(self.test_dataset, encoding='utf-8') as f:
testset = list(parse_incr(f, fields=['id', 'form', 'frame', 'slot'], field_parsers=field_parsers))
print('TRAINSET:', trainset)
corpus = Corpus(train=conllu2flair(trainset, label_type), test=conllu2flair(testset, label_type))
label_dictionary = corpus.make_label_dictionary(label_type=label_type)
print('LABEL:' ,label_dictionary)
embedding_types = [
WordEmbeddings('pl'),
FlairEmbeddings('pl-forward'),
@ -133,6 +133,6 @@ class Model:
model = Model(train_dataset='../data/test_dialog.conllu', test_dataset='../data/test_dialog.conllu')
model.train_model('frame')
model.train_model('slot', field_parsers={'slot': nolabel2o})
#model = Model(train_dataset='../data/test_dialog.conllu', test_dataset='../data/test_dialog.conllu')
# model2 = Model(train_dataset='../data/test_dialog.conllu', test_dataset='../data/test_dialog.conllu')
# model2.train_model('slot', field_parsers={'slot': nolabel2o})

68
chatbot/modules/nlu.py
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@ -1,38 +1,50 @@
import os
import jsgf
from flair.models import SequenceTagger
import sys
sys.path.append("..")
from models.nlu_train2 import predict_frame, predict_slot
import logging
logging.getLogger('flair').setLevel(logging.CRITICAL)
class NLU:
def __init__(self):
self.grammars = [
jsgf.parse_grammar_file(f"grammars/{file_name}")
for file_name in os.listdir("grammars")
]
self.frame_model = SequenceTagger.load('../models/frame-model/final-model.pt')
self.slot_model = SequenceTagger.load('../models/slot-model/final-model.pt')
def get_dialog_act(self, rule):
def get_intent(self, text: str):
return predict_frame(self.frame_model, text.split(), 'frame')
def get_slot(self, text: str):
pred = predict_slot(self.slot_model, text.split(), 'slot')
slots = []
self.get_slots(rule.expansion, slots)
return {"act": rule.grammar.name, "slots": slots}
current_slot = None
current_slot_value = []
def get_slots(self, expansion, slots):
if expansion.tag != "":
slots.append((expansion.tag, expansion.current_match))
return
for frame in pred:
slot = frame["slot"]
if slot.startswith("B-"):
if current_slot:
slots.append({'name': current_slot, 'value': " ".join(current_slot_value)})
current_slot = slot[2:]
current_slot_value = [frame["form"]]
elif slot.startswith("I-"):
current_slot_value.append(frame["form"])
for child in expansion.children:
self.get_slots(child, slots)
if current_slot:
slots.append({'name': current_slot, 'value': " ".join(current_slot_value)})
if not expansion.children and isinstance(expansion, jsgf.NamedRuleRef):
self.get_slots(expansion.referenced_rule.expansion, slots)
return slots
def match(self, utterance):
list_of_illegal_character = [",", ".", "'", "?", "!", ":", "-", "/"]
for illegal_character in list_of_illegal_character[:-2]:
utterance = utterance.replace(f"{illegal_character}", "")
for illegal_character in list_of_illegal_character[-2:]:
utterance = utterance.replace(f"{illegal_character}", " ")
def analyze(self, text: str):
intent = self.get_intent(text)
slots = self.get_slot(text)
print({'intent': intent,
'slots': slots})
return {
'intent': intent,
'slots': slots
}
for grammar in self.grammars:
matched = grammar.find_matching_rules(utterance.lower())
if matched:
return self.get_dialog_act(matched[0])
return {"act": "null", "slots": []}
nlu = NLU()
nlu.analyze("Chce kupic lakier do pazanokci")