[Administrator Intelligent Web Developers]

Nowadays Chatbots are integral parts of a website. It provides automatic user interaction and support. If you have developed a chatbot in Python, share your experience on the chatbot development steps.

[Tatwamashi Panda]

`import nltk
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’]:

#tokenize each word
w = nltk.word_tokenize(pattern)
words.extend(w)
#add documents in the corpus
documents.append((w, intent[‘tag’]))

# add to our classes list
if intent[‘tag’] not in classes:
classes.append(intent[‘tag’])

# lemmaztize and lower each word and remove duplicates
words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
words = sorted(list(set(words)))
# sort classes
classes = sorted(list(set(classes)))
# documents = combination between patterns and intents
print (len(documents), “documents”)
# classes = intents
print (len(classes), “classes”, classes)
# words = all words, vocabulary
print (len(words), “unique lemmatized words”, words)

pickle.dump(words,open(‘words.pkl’,’wb’))
pickle.dump(classes,open(‘classes.pkl’,’wb’))

# create our training data
training = []
# create an empty array for our output
output_empty = [0] * len(classes)
# training set, bag of words for each sentence
for doc in documents:
# initialize our bag of words
bag = []
# list of tokenized words for the pattern
pattern_words = doc[0]
# lemmatize each word – create base word, in attempt to represent related words
pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]
# create our bag of words array with 1, if word match found in current pattern
for w in words:
bag.append(1) if w in pattern_words else bag.append(0)

# output is a ‘0’ for each tag and ‘1’ for current tag (for each pattern)
output_row = list(output_empty)
output_row[classes.index(doc[1])] = 1

training.append([bag, output_row])
# shuffle our features and turn into np.array
random.shuffle(training)
training = np.array(training)
# create train and test lists. X – patterns, Y – intents
train_x = list(training[:,0])
train_y = list(training[:,1])
print(“Training data created”)

# Create model – 3 layers. First layer 128 neurons, second layer 64 neurons and 3rd output layer contains number of neurons
# equal to number of intents to predict output intent with softmax
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’))

# Compile model. Stochastic gradient descent with Nesterov accelerated gradient gives good results for this model
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss=’categorical_crossentropy’, optimizer=sgd, metrics=[‘accuracy’])

#fitting 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(“model created and saved”)

[Tatwamashi Panda]

import nltk
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
import pickle
import numpy as np

from tensorflow.keras.models import load_model
model = load_model(‘chatbot_model.h5’)
import json
import random
intents = json.loads(open(‘intents.json’).read())
words = pickle.load(open(‘words.pkl’,’rb’))
classes = pickle.load(open(‘classes.pkl’,’rb’))

def clean_up_sentence(sentence):
# tokenize the pattern – split words into array
sentence_words = nltk.word_tokenize(sentence)
# stem each word – create short form for word
sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
return sentence_words

# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence

def bow(sentence, words, show_details=True):
# tokenize the pattern
sentence_words = clean_up_sentence(sentence)
# bag of words – matrix of N words, vocabulary matrix
bag = [0]*len(words)
for s in sentence_words:
for i,w in enumerate(words):
if w == s:
# assign 1 if current word is in the vocabulary position
bag[i] = 1
if show_details:
print (“found in bag: %s” % w)
return(np.array(bag))

def predict_class(sentence, model):
# filter out predictions below a threshold
p = bow(sentence, words,show_details=False)
res = model.predict(np.array([p]))[0]
ERROR_THRESHOLD = 0.25
results = [[i,r] for i,r in enumerate(res) if r>ERROR_THRESHOLD]
# sort by strength of probability
results.sort(key=lambda x: x[1], reverse=True)
return_list = []
for r in results:
return_list.append({“intent”: classes[r[0]], “probability”: str(r[1])})
return return_list

def getResponse(ints, intents_json):
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
return result

def chatbot_response(msg):
ints = predict_class(msg, model)
res = getResponse(ints, intents)
return res

#Creating GUI with tkinter
import tkinter
from tkinter import *

def send():
msg = EntryBox.get(“1.0”,’end-1c’).strip()
EntryBox.delete(“0.0″,END)

if msg != ”:
ChatLog.config(state=NORMAL)
ChatLog.insert(END, “You: ” + msg + ‘\n\n’)
ChatLog.config(foreground=”#442265″, font=(“Verdana”, 12 ))

res = chatbot_response(msg)
ChatLog.insert(END, “Bot: ” + res + ‘\n\n’)

ChatLog.config(state=DISABLED)
ChatLog.yview(END)

base = Tk()
base.title(“Chatbot”)
base.geometry(“400×500″)
base.resizable(width=FALSE, height=FALSE)

#Create Chat window
ChatLog = Text(base, bd=0, bg=”white”, height=”8″, width=”50″, font=”Arial”,)

ChatLog.config(state=DISABLED)

#Bind scrollbar to Chat window
scrollbar = Scrollbar(base, command=ChatLog.yview, cursor=”heart”)
ChatLog[‘yscrollcommand’] = scrollbar.set

#Create Button to send message
SendButton = Button(base, font=(“Verdana”,12,’bold’), text=”Send”, width=”12″, height=5,
bd=0, bg=”#25cdf7″, activebackground=”#3c9d9b”,fg=’#ffffff’,
command= send )

#Create the box to enter message
EntryBox = Text(base, bd=0, bg=”white”,width=”29″, height=”5″, font=”Arial”)

#Place all components on the screen
scrollbar.place(x=376,y=6, height=386)
ChatLog.place(x=6,y=6, height=386, width=370)
EntryBox.place(x=128, y=401, height=90, width=265)
SendButton.place(x=6, y=401, height=90)

base.mainloop()

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