training.py

import random
import json
import pickle
import numpy as np
import nltk
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

lemmatizer = WordNetLemmatizer()

intents = json.loads(open('intents.json').read())

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'])

#print(documents)
words = [lemmatizer.lemmatize(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(classes, open('classes.pkl','wb'))
#print(words)
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])


##neuralnet

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'])

hist = model.fit(np.array(train_x),np.array(train_y),epochs=200,batch_size=5,verbose=1)
model.save('chatbotmodel.h5',hist)
print("Eureka")