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output.py
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136 lines (110 loc) · 4.28 KB
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import nltk
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
import pickle
import numpy as np
from textblob import TextBlob
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'))
import pyttsx3
engine=pyttsx3.init('sapi5')
voice=engine.getProperty("voices")
engine.setProperty('voice',voice[1].id)
engine.setProperty('rate',150)
import speech_recognition as sr
import pyaudio
def speak(msg):
engine.say(msg)
engine.runAndWait()
def clean_up_sentence(sentence):
sentence_words = nltk.word_tokenize(sentence)
sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
return sentence_words
def bag_of_words(sentence, words, show_details=True):
sentence_words = clean_up_sentence(sentence)
bag = [0]*len(words)
for s in sentence_words:
for i,word in enumerate(words):
if word == s:
bag[i] = 1
if show_details:
print ("found in bag: %s" % word)
return(np.array(bag))
def predict_class(sentence):
p = bag_of_words(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]
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):
if (ints):
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
else:
return "Sorry Data Not Available"
def autocorrect(msg2):
text=TextBlob(msg2)
msg2=str(text.correct())
return msg2
import tkinter
from tkinter import *
def send(e):
j=e
msg1 = EntryBox.get("1.0",'end-1c').strip()
msg = autocorrect(msg1)
EntryBox.delete("0.0",END)
if msg != '':
ChatBox.config(state=NORMAL)
ChatBox.insert(END, "\nYou: " + msg + '\n\n')
ChatBox.config(foreground="#446665", font=("Verdana", 12 ))
ints = predict_class(msg)
res = getResponse(ints, intents)
ChatBox.insert(END, "Bot: " + res + '\n\n')
ChatBox.config(state=NORMAL)
ChatBox.yview(END)
#speak(res)
def buttonclick():
r = sr.Recognizer()
r.pause_threshold = 0.7
r.energy_threshold = 400
with sr.Microphone() as source:
try:
audio = r.listen(source, timeout=5)
msg = str(r.recognize_google(audio))
msge = autocorrect(msg)
EntryBox.insert("1.0",msge)
except sr.UnknownValueError:
ChatBox.insert(END, "Cant recognize")
root = Tk()
root.title("JIO SUPPORT ASSISTANT")
root.geometry("450x450")
root.resizable(width=TRUE, height=TRUE)
ChatBox = Text(root, bd=0, bg="white", height="80", width="200", font="Arial",wrap=WORD,highlightbackground="black")
ChatBox.config(state=NORMAL)
photo = PhotoImage(file = r"E:\Project\Jio\download.png")
photoimage = photo.subsample(9,9)
mic = Button(root, font=("Sans Serif",12,'bold italic'), text="MIC", command=buttonclick, bd=0, bg='white',activebackground="#3c9d9b", overrelief='groove', relief='sunken', image= photoimage)
SendButton = Button(root, font=("Sans Serif",12,'bold italic'), text="SEND", width="12", height=5,
bd=0,bg='green',activebackground="#3c9d9b",fg='#000000',
command= lambda: send(2), overrelief='groove', relief='sunken')
EntryBox = Text(root, bd=0, bg="white",width="29", height="5", font="Arial" )
EntryBox.bind("<Return>",send)
ChatBox.place(x=6,y=6, height=390, width=450)
EntryBox.place(x=6, y=401, height=45, width=265)
SendButton.place(x=270, y=401, height=45)
mic.place(x=398, y=401, height=45, width=50)
root.mainloop()