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Copy pathchatgui.py
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131 lines (107 loc) · 4.08 KB
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import nltk
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
import pickle
import numpy as np
from tensorflow.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])})
# print(return_list)
return return_list
def getResponse(ints, message, intents_json):
if(len(ints)==0):
result='Not found. Enter another query!'
text_file = open("UnsolvedQueries.txt", "a")
text_file.write(message + '\n')
text_file.close()
return result
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, msg, intents)
return res
#Creating GUI with tkinter
import tkinter
from tkinter import *
#from tkinter.ttk 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)
else:
ChatLog.config(state=NORMAL)
ChatLog.insert(END, "Bot: Please enter something! \n")
ChatLog.config(foreground="#442265", font=("Verdana", 12 ))
base = Tk()
base.title("ECA_Chatbot")
#p1 = PhotoImage(file = "eca.png")
#base.iconphoto(False, "eca.png")
base.geometry("400x500")
base.resizable(width=True, height=True)
#Create Chat window
ChatLog = Text(base, bd=2, bg="#a486c2", 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="10", height="2",
bd=5, bg="#5252f2", activebackground="#14f750",fg='#ffffff',
command= send )
#Create the box to enter message
EntryBox = Text(base, bd=5, bg="white",width="29", height="2", 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=5, y=401, height=90, width=265)
SendButton.place(x=250, y=400, height=90)
base.mainloop()