chat_app.py

#!/usr/bin/env python
# coding: utf-8




from email import message
import nltk
#nltk.download('punkt')
#nltk.download('wordnet')
#nltk.download('omw-1.4')
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
from nltk.stem.lancaster import LancasterStemmer
stemmer=LancasterStemmer()

import pickle
import numpy as np
from tensorflow.keras.models import load_model
model = load_model('chatbot_model2.h5')
import json
import random
import tkinter
from tkinter import *






intents = json.loads(open('intents.json').read())
words = pickle.load(open('words2.pkl','rb'))
classes = pickle.load(open('classes2.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) ]
    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(text):
    ints = predict_class(text, model)
    res = getResponse(ints, intents)
    return res

'''while True:
   message = input("You: ")
   ints = predict_class(message, model)
   res = getResponse(ints, intents)
   print(res)'''