import json
import pickle
import random
from codecs import StreamReaderWriter
from typing import Any, Union, List
import numpy as np
import nltk
import self as self
lemmatizer = nltk.WordNetLemmatizer()
nltk.download('punkt')
nltk.download('wordnet')
from nltk.stem import WordNetLemmatizer
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.optimizers import SGD
import codecs
intents: Union[StreamReaderWriter, Any]
with codecs.open('intents.json', encoding='utf-8') as intents:
intents = json.load(intents)
# intents = json.loads(open('intents.json').read())
# for line in f:
# print(line)
words = []
classes = []
documents = []
ignore_letters = ['?', '!', '.', ',']
for intent in intents['intents']:
for pattern in intent['patterns']:
word_list = nltk.word_tokenize(pattern)
words.append(word_list)
documents.append((word_list, intent['tag']))
if intent['tag'] not in classes:
classes.append(intent['tag'])
lemmatizer = WordNetLemmatizer
words = [lemmatizer.lemmatize(self, word) for word in words if word not in ignore_letters]
words = sorted(set(words))
classes = sorted(set(classes))
pickle.dump(words, open('words.pkl', 'wb'))
pickle.dump(words, open('classes.pkl', 'wb'))
training = []
output_empty = [0] * len(classes)
for document in documents:
bag = []
word_patterns = document[0]
word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns]
for word in words:
bag.append(1) if word in word_patterns else bag.append(0)
output_row = list(output_empty)
output_row[classes.index(document[1])] = 1
training.append([bag, output_row])
random.shuffle(training)
training = np.array(training)
train_x = list(training[:, 0])
train_y = list(training[:, 1])
model = Sequential()
model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(train_y[0]), activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0/9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.fit(np.aray(train_x), np.array(train_y), epochs=200, batch_size=5,verbose=1)
model.save('chatbot_model.model')
print("Done")