import nltk

from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()

import pickle

import numpy as np

import sys

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

if __name__ == "__main__":

text = sys.argv[1]

print(text)

#'/home/hp/Desktop/bill_13.jpg'

ans = chatbot_response(text)

print(ans)

# #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()