chatterbox_tkinter.py

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)
    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 != '':
        Chatbox.config(state=NORMAL)
        Chatbox.insert(END, "You: " + msg + '\n\n')
        Chatbox.config(foreground="#442265", font=("Verdana", 12 ))
    
        res = chatbot_response(msg)
        Chatbox.insert(END, "Chatter Box: " + res + '\n\n')
            
        Chatbox.config(state=DISABLED)
        Chatbox.yview(END)
 

root = Tk()
root.title("Chatter Box")
root.geometry("400x500")
root.resizable(width=TRUE, height=TRUE)


Chatbox = Text(root, bd=0, bg="white", height="8", width="50", font="Arial",)

Chatbox.config(state=DISABLED)


scrollbar = Scrollbar(root, command=Chatbox.yview, cursor="star")
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="#0080ff", activebackground="#00bfff", fg='#ffffff',
                    command= send )



#Create the box to enter message
EntryBox = Text(root, bd=0, bg="white", width="28", 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=6, y=401, height=90, width=265)
SendButton.place(x=250, y=401, height=90)

root.mainloop()