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intents.py
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intents.py
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from abc import ABCMeta, abstractmethod
import random
import json
import pickle
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import nltk
from nltk.stem import WordNetLemmatizer
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.optimizers.legacy import SGD ####### change
from tensorflow.keras.models import load_model
import pandas as pd ######### change
nltk.download('punkt', quiet=True)
nltk.download('wordnet', quiet=True)
class IAssistant(metaclass=ABCMeta):
@abstractmethod
def train_model(self):
""" Implemented in child class """
@abstractmethod
def request_tag(self, message):
""" Implemented in child class """
@abstractmethod
def get_tag_by_id(self, id):
""" Implemented in child class """
@abstractmethod
def request_method(self, message):
""" Implemented in child class """
@abstractmethod
def request(self, message):
""" Implemented in child class """
class GenericAssistant(IAssistant):
def __init__(self, intents, intent_methods={}, model_name="assistant_model"):
self.intents = intents
self.intent_methods = intent_methods
self.model_name = model_name
if intents.endswith(".json"):
self.load_json_intents(intents)
self.lemmatizer = WordNetLemmatizer()
def load_json_intents(self, intents):
self.intents = json.loads(open(intents).read())
def train_model(self):
self.words = []
self.classes = []
documents = []
ignore_letters = ['!', '?', ',', '.']
for intent in self.intents['intents']:
for pattern in intent['patterns']:
word = nltk.word_tokenize(pattern)
self.words.extend(word)
documents.append((word, intent['tag']))
if intent['tag'] not in self.classes:
self.classes.append(intent['tag'])
self.words = [self.lemmatizer.lemmatize(w.lower()) for w in self.words if w not in ignore_letters]
self.words = sorted(list(set(self.words)))
self.classes = sorted(list(set(self.classes)))
training = []
output_empty = [0] * len(self.classes)
for doc in documents:
bag = []
word_patterns = doc[0]
word_patterns = [self.lemmatizer.lemmatize(word.lower()) for word in word_patterns]
for word in self.words:
bag.append(1) if word in word_patterns else bag.append(0)
output_row = list(output_empty)
output_row[self.classes.index(doc[1])] = 1
training.append([bag, output_row])
random.shuffle(training)
# training = np.array(training) ######### change
df = pd.DataFrame(training, columns=['patterns', 'intents'])
training = df.to_numpy()
train_x = list(training[:, 0])
train_y = list(training[:, 1])
self.model = Sequential()
self.model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
self.model.add(Dropout(0.5))
self.model.add(Dense(64, activation='relu'))
self.model.add(Dropout(0.5))
self.model.add(Dense(len(train_y[0]), activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
self.model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
self.hist = self.model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
def save_model(self, model_name=None):
if model_name is None:
self.model.save(f"{self.model_name}.h5", self.hist)
pickle.dump(self.words, open(f'{self.model_name}_words.pkl', 'wb'))
pickle.dump(self.classes, open(f'{self.model_name}_classes.pkl', 'wb'))
else:
self.model.save(f"{model_name}.h5", self.hist)
pickle.dump(self.words, open(f'{model_name}_words.pkl', 'wb'))
pickle.dump(self.classes, open(f'{model_name}_classes.pkl', 'wb'))
def load_model(self, model_name=None):
if model_name is None:
self.words = pickle.load(open(f'{self.model_name}_words.pkl', 'rb'))
self.classes = pickle.load(open(f'{self.model_name}_classes.pkl', 'rb'))
self.model = load_model(f'{self.model_name}.h5')
else:
self.words = pickle.load(open(f'{model_name}_words.pkl', 'rb'))
self.classes = pickle.load(open(f'{model_name}_classes.pkl', 'rb'))
self.model = load_model(f'{model_name}.h5')
def _clean_up_sentence(self, sentence):
sentence_words = nltk.word_tokenize(sentence)
sentence_words = [self.lemmatizer.lemmatize(word.lower()) for word in sentence_words]
return sentence_words
def _bag_of_words(self, sentence, words):
sentence_words = self._clean_up_sentence(sentence)
bag = [0] * len(words)
for s in sentence_words:
for i, word in enumerate(words):
if word == s:
bag[i] = 1
return np.array(bag)
def _predict_class(self, sentence):
p = self._bag_of_words(sentence, self.words)
res = self.model.predict(np.array([p]))[0]
# print('**********probabilities(-1):', res)
ERROR_THRESHOLD = 0.1
results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]
# print('**********probabilities(0):', results)
results.sort(key=lambda x: x[1], reverse=True)
# print('**********probabilities(1):', results)
return_list = []
for r in results:
return_list.append({'intent': self.classes[r[0]], 'probability': str(r[1])})
return return_list
def _get_response(self, ints, intents_json):
try:
tag = ints[0]['intent']
list_of_intents = intents_json['intents']
for i in list_of_intents:
if i['tag'] == tag:
result = random.choice(i['responses'])
break
except IndexError:
result = "I don't understand!"
return result
def request_tag(self, message):
pass
def get_tag_by_id(self, id):
pass
def request_method(self, message):
pass
def request(self, message):
ints = self._predict_class(message)
# print('********probabilities:', ints)
if ints[0]['intent'] in self.intent_methods.keys():
self.intent_methods[ints[0]['intent']]()
return
else:
return self._get_response(ints, self.intents)