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chat_app.py
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chat_app.py
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#!/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)'''