train_chatbot.py

import nltk
nltk.download('punkt')
nltk.download('wordnet')
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()

import json
import pickle
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.optimizers import SGD
import random

words=[]
classes=[]
documents=[]
ignore_words=['?','!']
data_file=open('intents.json').read()
intents=json.loads(data_file)

for intent in intents['intents']:
    for pattern in intent['patterns']:
        # tokenizing words
        w=nltk.word_tokenize(pattern)
        words.extend(w)
        documents.append((w,intent['tag']))

        if intent['tag'] not in classes:
            classes.append(intent['tag'])

words=[lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
words=sorted(list(set(words)))

classes = sorted(list(set(classes)))
print(len(documents), "documents",documents)
print(len(classes), "classes", classes)
print(len(words), "unique lemmatized words", words)

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

# Creating Training Dataset
training = []
output_empty = [0]*len(classes)

for doc in documents:
    bag=[]
    pattern_words=doc[0]
    pattern_words=[lemmatizer.lemmatize(word.lower()) for word in pattern_words]

    for w in words:
        bag.append(1) if w in pattern_words else bag.append(0)

    output_row = list(output_empty)
    output_row[classes.index(doc[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("Training Data Created")

# Creating 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(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('chatbot_model.h5',hist)
print("model created")