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Building Chatbot Project using Python

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Chatbots are extremely helpful for business organizations and also the customers. The majority of people prefer to talk directly from a chatbox instead of calling service centers. Facebook released data that proved the value of bots. More than 2 billion messages are sent between people and companies monthly. The HubSpot research tells us that 71% of people want to get customer support from messaging apps. It is a quick way to get their problems solved so chatbots have a bright future in organizations.

Today we are going to build an exciting project on Chatbot. We will implement a chatbot from scratch that will be able to understand what the user is talking about and give an appropriate response.


To implement the chatbot, we will be using Keras, which is a Deep Learning library, NLTK, which is a Natural Language Processing toolkit, and some helpful libraries. Run the below command to make sure all the libraries are installed:

pip install tensorflow keras pickle nltk 

If you want to learn Python for free, then here is the Master guide to learn Python for free

How do Chatbots Work?

Chatbots are nothing but an intelligent piece of software that can interact and communicate with people just like humans. Interesting, isn’t it? So now let’s see how they actually work.

All chatbots come under the NLP (Natural Language Processing) concepts. NLP is composed of two things:

  • NLU (Natural Language Understanding): The ability of machines to understand human language like English.
  • NLG (Natural Language Generation): The ability of a machine to generate text similar to human written sentences.

Imagine a user asking a question to a chatbot: “Hey, what’s on the news today?”

The chatbot will break down the user sentence into two things: intent and an entity. The intent for this sentence could be get_news as it refers to an action the user wants to perform. The entity tells specific details about the intent, so “today” will be the entity. So this way, a machine learning model is used to recognize the intents and entities of the chat.

Project File Structure

After the project is complete, you will be left with all these files. Lets quickly go through each of them. It will give you an idea of how the project will be implemented.

  • Train_chatbot.py — In this file, we will build and train the deep learning model that can classify and identify what the user is asking to the bot.
  • Gui_Chatbot.py — This file is where we will build a graphical user interface to chat with our trained chatbot.
  • Intents.json — The intents file has all the data that we will use to train the model. It contains a collection of tags with their corresponding patterns and responses.
  • Chatbot_model.h5 — This is a hierarchical data format file in which we have stored the weights and the architecture of our trained model.
  • Classes.pkl — The pickle file can be used to store all the tag names to classify when we are predicting the message.
  • Words.pkl — The words.pkl pickle file contains all the unique words that are the vocabulary of our model.

Download the source code and the dataset: https://drive.google.com/drive/folders/1r6MrrdE8V0bWBxndGfJxJ4Om62dJ2OMP?usp=sharing

How to Build Your Own Chatbot

I’ve simplified the building of this chatbot in 5 steps:

Step 1. Import Libraries and Load the Data

Create a new python file and name it as train chatbot and then we are going to import all the required modules. After that, we will read the JSON data file in our Python program

import numpy as np 
from keras.models import Sequential 
from keras.layers import Dense, Activation, Dropout 
from keras.optimizers import SGD 
import random 
import nltk 
from nltk.stem import WordNetLemmatizer 
lemmatizer = WordNetLemmatizer() 
import json 
import pickle 
intents_file = open('intents.json').read() 
intents = json.loads(intents_file) 


Step 2. Preprocessing the Data

The model cannot take the raw data. It has to go through a lot of pre-processing for the machine to easily understand. For textual data, there are many preprocessing techniques available. The first technique is tokenizing, in which we break the sentences into words.

By observing the intents file, we can see that each tag contains a list of patterns and responses. We tokenize each pattern and add the words in a list. Also, we create a list of classes and documents to add all the intents associated with patterns.

classes = [] 
documents = [] 
ignore_letters = ['!', '?', ',', '.'] 
for intent in intents['intents']: 
for pattern in intent['patterns']: 
#tokenize each word 
word = nltk.word_tokenize(pattern) 
#add documents in the corpus 
documents.append((word, intent['tag'])) 
# add to our classes list 
if intent['tag'] not in classes: 
Another technique is Lemmatization. We can convert words into the lemma form so that we can reduce all the canonical words. For example, the words play, playing, plays, played, etc. will all be replaced with play. This way, we can reduce the number of total words in our vocabulary. So now we lemmatize each word and remove the duplicate words.
# lemmaztize and lower each word and remove duplicates 
words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_letters] 
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) 

In the end, the words contain the vocabulary of our project and classes contain the total entities to classify. To save the python object in a file, we used the pickle.dump() method. These files will be helpful after the training is done and we predict the chats.

Step 3. Create Training and Testing Data

To train the model, we will convert each input pattern into numbers. First, we will lemmatize each word of the pattern and create a list of zeroes of the same length as the total number of words. We will set value 1 to only those indexes that contain the word in the patterns. In the same way, we will create the output by setting 1 to the class input the pattern belongs to.

# create the training data 
training = [] 
# create empty array for the output 
output_empty = [0] * len(classes) 
# training set, bag of words for every sentence 
for doc in documents: 
# initializing bag of words 
bag = [] 
# list of tokenized words for the pattern 
word_patterns = doc[0] 
# lemmatize each word - create base word, in attempt to represent related words 
word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns] 
create the bag of words array with 1, if word is found in current pattern 
for word in words: 
bag.append(1) if word in word_patterns 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 the features and make numpy array 
training = np.array(training) 
# create training and testing lists. X - patterns, Y – intents 
train_x = list(training[:,0]) 
train_y = list(training[:,1]) 
print("Training data is created")

Step 4. Training the Model

The architecture of our model will be a neural network consisting of 3 dense layers. The first layer has 128 neurons, the second one has 64 and the last layer will have the same neurons as the number of classes. The dropout layers are introduced to reduce overfitting of the model. We have used the SGD optimizer and fit the data to start the training of the model. After the training of 200 epochs is completed, we then save the trained model using the Keras model.save(“chatbot_model.h5”) function.

# deep neural networds model 
model = Sequential() 
model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu')) 
model.add(Dense(64, activation='relu')) 
model.add(Dense(len(train_y[0]), activation='softmax')) 
# Compiling model. SGD 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']) 
#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("model is created")

Step 5. Interacting With the Chatbot

Our model is ready to chat, so now let’s create a nice graphical user interface for our chatbot in a new file. You can name the file as gui_chatbot.py

In our GUI file, we will be using the Tkinter module to build the structure of the desktop application and then we will capture the user message and again perform some preprocessing before we input the message into our trained model.

The model will then predict the tag of the user’s message, and we will randomly select the response from the list of responses in our intents file.

Here’s the full source code for the GUI file.

import nltk 
from nltk.stem import WordNetLemmatizer 
lemmatizer = WordNetLemmatizer() 
import pickle 
import numpy as np 
from 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 - splitting words into array 
sentence_words = nltk.word_tokenize(sentence) 
# stemming every word - reducing to base form 
sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words] 
return sentence_words 
# return bag of words array: 0 or 1 for words that exist in sentence 
def bag_of_words(sentence, words, show_details=True): 
# tokenizing patterns 
sentence_words = clean_up_sentence(sentence) 
# bag of words - vocabulary matrix 
bag = [0]*len(words)   
for s in sentence_words: 
for i,word in enumerate(words): 
if word == s:  
# assign 1 if current word is in the vocabulary position 
bag[i] = 1 
if show_details: 
print ("found in bag: %s" % word) 
def predict_class(sentence): 
# filter below  threshold predictions 
p = bag_of_words(sentence, words,show_details=False) 
res = model.predict(np.array([p]))[0] 
results = [[i,r] for i,r in enumerate(res) if r>ERROR_THRESHOLD] 
# sorting strength 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']) 
return result 
#Creating tkinter GUI 
import tkinter 
from tkinter import * 
def send(): 
msg = EntryBox.get("1.0",'end-1c').strip() 
if msg != '': 
ChatBox.insert(END, "You: " + msg + '\n\n') 
ChatBox.config(foreground="#446665", font=("Verdana", 12 ))  
ints = predict_class(msg) 
res = getResponse(ints, intents) 
ChatBox.insert(END, "Bot: " + res + '\n\n')           
root = Tk() 
root.resizable(width=FALSE, height=FALSE) 
#Create Chat window 
ChatBox = Text(root, bd=0, bg="white", height="8", width="50", font="Arial",) 
#Bind scrollbar to Chat window 
scrollbar = Scrollbar(root, command=ChatBox.yview, cursor="heart") 
ChatBox['yscrollcommand'] = scrollbar.set 
#Create Button to send message 
SendButton = Button(root, font=("Verdana",12,'bold'), text="Send", width="12", height=5, 
bd=0, bg="#f9a602", activebackground="#3c9d9b",fg='#000000', 
command= send ) 
#Create the box to enter message 
EntryBox = Text(root, bd=0, bg="white",width="29", height="5", font="Arial") 
#EntryBox.bind("<Return>", send) 
#Place all components on the screen 
scrollbar.place(x=376,y=6, height=386) 
ChatBox.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) 

Running the Chatbot

Now we have two separate files, one is the train_chatbot.py, which we will use first to train the model.

python train_chatbot.py 

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