Python Chatbot Project – Learn to build your first chatbot using NLTK & Keras

Free Python course with 25 projects (coupon code: DATAFLAIR_PYTHON) Start Now

Hey Siri, What’s the meaning of Life?

As per all the evidence, it’s chocolate for you.

Soon as I heard this reply from Siri, I knew I found a perfect partner to savour my hours of solitude. From stupid questions to some pretty serious advice, Siri has been always there for me.

How amazing it is to tell someone everything and anything and not being judged at all. A top class feeling it is and that’s what the beauty of a chatbot is.

This is the 9th project in the 20 Python projects series by DataFlair and make sure to bookmark other interesting projects:

  1. Fake News Detection Python Project
  2. Parkinson’s Disease Detection Python Project
  3. Color Detection Python Project
  4. Speech Emotion Recognition Python Project
  5. Breast Cancer Classification Python Project
  6. Age and Gender Detection Python Project
  7. Handwritten Digit Recognition Python Project
  8. Chatbot Python Project
  9. Driver Drowsiness Detection Python Project
  10. Traffic Signs Recognition Python Project
  11. Image Caption Generator Python Project

What is Chatbot?

A chatbot is an intelligent piece of software that is capable of communicating and performing actions similar to a human. Chatbots are used a lot in customer interaction, marketing on social network sites and instantly messaging the client. There are two basic types of chatbot models based on how they are built; Retrieval based and Generative based models.

Python chatbot project

1. Retrieval based Chatbots

A retrieval-based chatbot uses predefined input patterns and responses. It then uses some type of heuristic approach to select the appropriate response. It is widely used in the industry to make goal-oriented chatbots where we can customize the tone and flow of the chatbot to drive our customers with the best experience.

2. Generative based Chatbots

Generative models are not based on some predefined responses.

They are based on seq 2 seq neural networks. It is the same idea as machine translation. In machine translation, we translate the source code from one language to another language but here, we are going to transform input into an output. It needs a large amount of data and it is based on Deep Neural networks.

About the Python Project – Chatbot

In this Python project with source code, we are going to build a chatbot using deep learning techniques. The chatbot will be trained on the dataset which contains categories (intents), pattern and responses. We use a special recurrent neural network (LSTM) to classify which category the user’s message belongs to and then we will give a random response from the list of responses.

Let’s create a retrieval based chatbot using NLTK, Keras, Python, etc.

The Dataset

The dataset we will be using is ‘intents.json’. This is a JSON file that contains the patterns we need to find and the responses we want to return to the user. The link to the project is available below:

Python Chatbot Project Dataset

Prerequisites

The project requires you to have good knowledge of Python, Keras, and Natural language processing (NLTK). Along with them, we will use some helping modules which you can download using the python-pip command.

pip install tensorflow, keras, pickle, nltk

How to Make Chatbot in Python?

Now we are going to build the chatbot using Python but first, let us see the file structure and the type of files we will be creating:

Types of files - Python Chatbot

  • Intents.json – The data file which has predefined patterns and responses.
  • train_chatbot.py – In this Python file, we wrote a script to build the model and train our chatbot.
  • Words.pkl – This is a pickle file in which we store the words Python object that contains a list of our vocabulary.
  • Classes.pkl – The classes pickle file contains the list of categories.
  • Chatbot_model.h5 – This is the trained model that contains information about the model and has weights of the neurons.
  • Chatgui.py – This is the Python script in which we implemented GUI for our chatbot. Users can easily interact with the bot.

Here are the 5 steps to create a chatbot in Python from scratch:

  1. Import and load the data file
  2. Preprocess data
  3. Create training and testing data
  4. Build the model
  5. Predict the response

1. Import and load the data file

First, make a file name as train_chatbot.py. We import the necessary packages for our chatbot and initialize the variables we will use in our Python project.

The data file is in JSON format so we used the json package to parse the JSON file into Python.

import nltk
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
import json
import pickle

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

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

This is how our intents.json file looks like.

intents data file - Python chatbot project

2. Preprocess data

When working with text data, we need to perform various preprocessing on the data before we make a machine learning or a deep learning model. Tokenizing is the most basic and first thing you can do on text data. Tokenizing is the process of breaking the whole text into small parts like words.

Here we iterate through the patterns and tokenize the sentence using nltk.word_tokenize() function and append each word in the words list. We also create a list of classes for our tags.

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'])

Now we will lemmatize each word and remove duplicate words from the list. Lemmatizing is the process of converting a word into its lemma form and then creating a pickle file to store the Python objects which we will use while predicting.

# lemmatize, 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'))

3. Create training and testing data

Now, we will create the training data in which we will provide the input and the output. Our input will be the pattern and output will be the class our input pattern belongs to. But the computer doesn’t understand text so we will convert text into numbers.

# 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")

Want to master Python programming?

Check out 270+ Python Tutorials by DataFlair

4. Build the model

We have our training data ready, now we will build a deep neural network that has 3 layers. We use the Keras sequential API for this. After training the model for 200 epochs, we achieved 100% accuracy on our model. Let us save the model as ‘chatbot_model.h5’.

# 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")

5. Predict the response (Graphical User Interface)

Now to predict the sentences and get a response from the user to let us create a new file ‘chatapp.py’.

We will load the trained model and then use a graphical user interface that will predict the response from the bot. The model will only tell us the class it belongs to, so we will implement some functions which will identify the class and then retrieve us a random response from the list of responses.

Again we import the necessary packages and load the ‘words.pkl’ and ‘classes.pkl’ pickle files which we have created when we trained our model:

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'))

To predict the class, we will need to provide input in the same way as we did while training. So we will create some functions that will perform text preprocessing and then predict the class.

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

After predicting the class, we will get a random response from the list of intents.

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(text):
    ints = predict_class(text, model)
    res = getResponse(ints, intents)
    return res

Now we will code a graphical user interface. For this, we use the Tkinter library which already comes in python. We will take the input message from the user and then use the helper functions we have created to get the response from the bot and display it on the GUI. Here is the full source code for the GUI.

#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("Hello")
base.geometry("400x500")
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="#32de97", 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")
#EntryBox.bind("<Return>", send)


#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()

6. Run the chatbot

To run the chatbot, we have two main files; train_chatbot.py and chatapp.py.

First, we train the model using the command in the terminal:

python train_chatbot.py

If we don’t see any error during training, we have successfully created the model. Then to run the app, we run the second file.

python chatgui.py

The program will open up a GUI window within a few seconds. With the GUI you can easily chat with the bot.

Screenshots:

Python chatbot result

chat between user & bot - Python Chatbot

Summary

In this Python data science project, we understood about chatbots and implemented a deep learning version of a chatbot in Python which is accurate. You can customize the data according to business requirements and train the chatbot with great accuracy. Chatbots are used everywhere and all businesses is looking forward to implementing bot in their workflow.

Cracking Python interview is now easy!!

Practice the Top Python Interview Questions by DataFlair

I hope you will practice by customizing your own chatbot using Python and don’t forget to show us your work. And, if you found the article useful, do share the project with your friends and colleagues.

114 Responses

  1. sadhna says:

    Hi, such an amazing code for chat-bot. Can you please help me that how can I make delay in bot response. Actually I’ve tried a lot with time sleep.but it’s delaying sending the message of user and I want delay in bot’s reply. Could you please help ASAP.

  2. Ayushi says:

    How can we use this chatbot on messaging apps like whatsapp,facebook.

  3. Rr says:

    Time to make my own chat bot then:)

  4. Manoj says:

    I am getting an error telling
    name ‘chatbot_respons” is not defined

    Exception in Tkinter callback
    Traceback (most recent call last):
    File “C:\Users\manoj\miniconda3\envs\tensorflow\lib\tkinter\__init__.py”, line 1705, in __call__
    return self.func(*args)
    File “”, line 14, in send
    res = chatbot_response(msg)
    NameError: name ‘chatbot_response’ is not defined

  5. Manoj says:

    I am getting an error like:

    Exception in Tkinter callback
    Traceback (most recent call last):
    File “C:\Users\manoj\miniconda3\envs\tensorflow\lib\tkinter\__init__.py”, line 1705, in __call__
    return self.func(*args)
    File “”, line 14, in send
    res = chatbot_response(msg)
    NameError: name ‘chatbot_response’ is not defined

  6. Toriq Ahmad Salam says:

    Hello, thanks for sharing
    Can you explain where LSTM applied on codes? And why you the technique belong to RNN LSTM?
    I need your response to learning more about this and develop this model.

    Thank you so much

  7. Michelle says:

    hi ,

    I have just started the chatbot project which looks very interesting. But I cannot install the pickle library, so I have stuck now. I am using Python 3.7 with Anaconda latest version.
    I appreciate if you can help. thanks.

  8. MM says:

    Hi ,

    Can you also help with error message:

    File “C:\Users\mm\Anaconda3\lib\site-packages\nltk\corpus\reader\panlex_lite.py”, line 15, in
    import sqlite3
    File “C:\Users\mm\Anaconda3\lib\sqlite3\__init__.py”, line 23, in
    from sqlite3.dbapi2 import *
    File “C:\Users\mm\Anaconda3\lib\sqlite3\dbapi2.py”, line 27, in
    from _sqlite3 import *
    ImportError: DLL load failed: The specified module could not be found.
    PS C:\Python\python-project-chatbot-codes>

    I have looked up the error and some suggest that I don’t have sqlite3.dll install. can someone direct me please?

    thanks

  9. ruchi says:

    hi! anyone knows how to save the responses inside mysql. I only need to save the responses make by the user. Hope an answer. Thank you

  10. Hakku says:

    Hello,

    Regarding the error with the pickle library in python 3.x. I found that if you do pip install pickle-mixin it works just fine. You might get errors from nltk, missing wordnet and some other package. If you do, just follow the instructions the error comes with. I also recommend using venv.

  11. Ashit Chuphal says:

    Exception in Tkinter callback
    Traceback (most recent call last):
    File “C:\Users\ashit\Anaconda3\lib\tkinter\__init__.py”, line 1705, in __call__
    return self.func(*args)
    File “”, line 13, in send
    res = chatbot_response(msg)
    NameError: name ‘chatbot_response’ is not defined

  12. Vaish says:

    It would be great if we can have web crawler option for the bot to go and check on a specific sharepoint site or website. Also, any possibility for the bot to find for files in a folder which is maintained internally.

  13. DEBJIT GHOSH says:

    I am getting an error saying : “name ‘msg’ is not defined.”…Please help.

  14. Vaibhav Jain says:

    I am getting the same error :

    “name ‘msg’ is not defined.”

    Did u get the solution for this error !

  15. Vindhya Prakash says:

    Indentation Error. Leave a space.
    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 ))

  16. Sanjaya Prakash says:

    After entering the pharmacy name how do we get the next output.

    Chatbot says “Good to see you again”.

    How to process the pharmacy name and get the details?

  17. Jackson Harry says:

    We Want to Publish an Article  on your website      https://data-flair.training/
    please contect me
    jacksonharry778 @ gmail . com

  18. khushi shah says:

    the if msg!=” should be inside the send function

  19. khushi says:

    Hello. the bot only answers one thing. what is the fix? please reply

  20. Max says:

    how can the performance of the chatbot be assessed?

  21. Joy Victor says:

    Hi, I want to make a chatbot for my school project. I really appreciate the code here but I’d like to make the bot on my own, what do you suggest I learn? BTW, I already know a little bit of python and how to make GUIs. thanks

  22. lakshmi says:

    bring that if into that def(),it is out of that

    def()
    ….
    ..
    if msg…this is the mistake
    we need to make it
    def()
    …..
    ….
    if msg…
    this is who i got it solved

  23. vinayak says:

    res = chatbot_response(msg)

    How is this function from other file called in chatgui.py?

    I am getting error stating
    “File “chatgui.py”, line 14, in send
    res = chatbot_response(msg)
    NameError: name ‘chatbot_response’ is not defined”

  24. Darvena Rohilla says:

    HI. I need to create a chat bot that can pull reports from power BI. Please help.

  25. Jade says:

    Hello the gui will not work for me. The window pops up, the buttons are all there, but I can’t type anything out to the bot. The installation and training was successful, so I don’t know what could be wrong.

  26. trinh do says:

    Traceback (most recent call last):
    File “C:\Program Files\Sublime Text 3\chatbot\train_chatbot.py”, line 33, in
    words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
    File “C:\Program Files\Sublime Text 3\chatbot\train_chatbot.py”, line 33, in
    words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
    TypeError: lemmatize() missing 1 required positional argument: ‘word’Traceback (most recent call last):
    File “C:\Program Files\Sublime Text 3\chatbot\train_chatbot.py”, line 33, in
    words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
    File “C:\Program Files\Sublime Text 3\chatbot\train_chatbot.py”, line 33, in
    words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
    TypeError: lemmatize() missing 1 required positional argument: ‘word’

    Please help!!!

  27. trinh do says:

    Traceback (most recent call last):
    File “C:\Program Files\Sublime Text 3\chatbot\train_chatbot.py”, line 33, in
    words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
    File “C:\Program Files\Sublime Text 3\chatbot\train_chatbot.py”, line 33, in
    words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
    TypeError: lemmatize() missing 1 required positional argument: ‘word’

    Please help!!!

  28. Sourav says:

    Is there any way to increase the accuracy of the chatbot if we have 1000+ patterns in intents.json?

  29. Sourav says:

    Is there any way to increase the accuracy of responses If we are using 1000+ tickets.

Leave a Reply

Your email address will not be published. Required fields are marked *

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.