Chatbot Using Deep Learning in Python

Chatbot Using Deep Learning in Python

In this tutorial program, we will learn about building a Chatbot using deep learning, the language used is Python. So here I am going to discuss what are the basic steps of this deep learning problem and how to approach it.

For this Chatbot, we are going to use Natural Language Processing(NLP).

Natural Language Processing:

  1. Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural language, like speech and text, by software.
  2. It is part of Deep Learning, The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers.

Here is the link to the dataset consist of the required files to run the chatbot:

We need different files for this project including:

interns.json – input to chatbot and responses to train the bot.

intens.json (file)

{"intents": [
        {"tag": "greeting",
         "patterns": ["Hi there", "How are you", "Is anyone there?","Hey","Hola", "Hello", "Good day","Hi"],
         "responses": ["Hello, thanks for asking", "Good to see you again", "Hi there, how can I help?","Hello"],
         "context": [""]
        {"tag": "goodbye",
         "patterns": ["Bye", "See you later", "Goodbye", "Nice chatting to you, bye", "Till next time"],
         "responses": ["See you!", "Have a nice day", "Bye! Come back again soon.","see you soon"],
         "context": [""]
        {"tag": "thanks",
         "patterns": ["Thanks", "Thank you", "That's helpful", "Awesome, thanks", "Thanks for helping me","Thanku very much"],
         "responses": ["Happy to help!", "Any time!", "My pleasure","your welcome"],
         "context": [""]
        {"tag": "noanswer",
         "patterns": [],
         "responses": ["Sorry, can't understand you", "Please give me more info", "Not sure I understand"],
         "context": [""]
        {"tag": "options",
         "patterns": ["How you could help me?", "What you can do?", "What help you provide?", "How you can be helpful?", "What support is offered"],
         "responses": ["I can guide you through Adverse drug reaction list, Blood pressure tracking, Hospitals and Pharmacies", "Offering support for Adverse drug reaction, Blood pressure, Hospitals and Pharmacies,symptoms,information related to diseaeas"],
         "context": [""]
        {"tag": "adverse_drug",
         "patterns": ["How to check Adverse drug reaction?", "Open adverse drugs module", "Give me a list of drugs causing adverse behavior", "List all drugs suitable for patient with adverse reaction", "Which drugs dont have adverse reaction?" ],
         "responses": ["Navigating to Adverse drug reaction module"],
         "context": [""]
        {"tag": "blood_pressure",
         "patterns": ["Open blood pressure module", "Task related to blood pressure", "Blood pressure data entry", "I want to log blood pressure results", "Blood pressure data management" ],
         "responses": ["Navigating to Blood Pressure module"],
         "context": [""]
        {"tag": "blood_pressure_search",
         "patterns": ["I want to search for blood pressure result history", "Blood pressure for patient", "Load patient blood pressure result", "Show blood pressure results for patient", "Find blood pressure results by ID","history related to patient"],
         "responses": ["Please provide Patient ID", "Patient ID?"],
         "context": ["search_blood_pressure_by_patient_id"]
        {"tag": "search_blood_pressure_by_patient_id",
         "patterns": [],
         "responses": ["Loading Blood pressure result for Patient"],
         "context": [""]
        {"tag": "pharmacy_search",
         "patterns": ["Find me a pharmacy", "Find pharmacy", "List of pharmacies nearby", "Locate pharmacy", "Search pharmacy","pharmacy places" ],
         "responses": ["Please provide pharmacy name"],
         "context": ["search_pharmacy_by_name"]
        {"tag": "search_pharmacy_by_name",
         "patterns": [],
         "responses": ["Loading pharmacy details"],
         "context": [""]
        {"tag": "hospital_search",
         "patterns": ["Lookup for hospital", "Searching for hospital to transfer patient", "I want to search hospital data", "Hospital lookup for patient", "Looking up hospital details","find nearby hospitals" ],
         "responses": ["Please provide hospital name or location"],
         "context": ["search_hospital_by_params"]
        {"tag": "search_hospital_by_params",
         "patterns": [],
         "responses": ["Please provide hospital type","i need more information"],
         "context": ["search_hospital_by_type"]
        {"tag": "search_hospital_by_type",
         "patterns": [],
         "responses": ["Loading hospital details","getting the required information"],
         "context": [""]
  {"tag": "symptoms_params",
         "patterns": ["heavy breathing","headache","fever"],
         "responses": ["Please provide more details","i need more information"],
         "context": ["disease_params"]
  {"tag": "disease_params",
         "patterns": [],
         "responses": ["loading details","please provide patient ID"],
         "context": [""]


To Train the chatbot run the file

import json
import nltk
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

from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()

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)
        #add documents in the corpus
        documents.append((w, intent['tag']))

        # add to our classes list
        if intent['tag'] not in classes:

# 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)


# 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
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")

# 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(Dense(64, activation='relu'))
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 =, np.array(train_y), epochs=200, batch_size=5, verbose=1)'chatbot_model.h5', hist)

print("model is created")

Output creates the model that is:
Chatbot_model.h5 consist of the code for building the chatbot and also desktop application build using a Python interface called Tkinter.

run the below code using python3 command

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 - 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)

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]
    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'])
    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()

    if msg != '':
        ChatLog.insert(END, "You: " + msg + '\n\n')
        ChatLog.config(foreground="#442265", font=("maroon", 12 ))
        res = chatbot_response(msg)
        ChatLog.insert(END, "Bot: " + res + '\n\n')

base = Tk()
base.resizable(width=FALSE, height=FALSE)

#Create Chat window
ChatLog = Text(base, bd=0, bg="MistyRose2", height="10", width="100", font="Arial",)


#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=("slate gray",12,'bold'), text="Send", width="15", height=7,
                    bd=0, bg="#32de97", activebackground="#3c9d9b",fg='#ffffff',
                    command= send )

#Create the box to enter message
EntryBox = Text(base, bd=0, bg="blanched almond",width="20", height="7", font="Arial")
#EntryBox.bind("<Return>", send)

#Place all components on the screen,y=6, height=400),y=6, height=386, width=370), y=426, height=90, width=265), y=426, height=90)


Also read: Python program to create a simple chat box.

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