training.py

#importing the libraries
import numpy as np
import nltk
import json
import pickle
import random
from nltk.stem import WordNetLemmatizer
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.optimizers import SGD
#making a chatbot using NLTK

nltk.download('wordnet')
lemmatizer = WordNetLemmatizer()
with open('intents.json','r') as file:
    intents = json.load(file)
print(type(intents))
words = []
classes = []
documents = []
ignore_letters = ['?', '!', '.', ',']

for intent in intents["intents"]:
    for pattern in intent['patterns']:
        word_list = nltk.word_tokenize(pattern)
        words.extend(word_list)
        documents.append((word_list, intent['tag']))
        if intent['tag'] not in classes:
            classes.append(intent['tag'])
#edit chatbot to play games in games.py file's functions when user asks to play games
words = [lemmatizer.lemmatize(word) for word in words if word not in ignore_letters]
words = sorted(set(words))
print(words)
classes = sorted(set(classes))

pickle.dump(words, open('words.pkl', 'wb'))
pickle.dump(classes, open('classes.pkl', 'wb'))

#training data
training = []
output_empty = [0] * len(classes)
print(output_empty)
print(type(output_empty))
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])
print(train_x)
print(train_y)

#building the model
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(Dense(len(train_y[0]), activation='softmax'))

#compiling the model
sgd = tf.keras.optimizers.legacy.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])

#training and saving the model
hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
model.save('chatbot_model.h5', hist)
print("-------------Completed Successfully-------------")