chatbot.py

import random
import json
import pickle
import numpy as np

import nltk
nltk.download('wordnet')
nltk.download('omw-1.4')
from nltk.tokenize import wordpunct_tokenize
from nltk.stem import WordNetLemmatizer
from keras.models import load_model



lemmatizer=WordNetLemmatizer()
intents = json.loads(open('D:\AI and ML\Projects\Chat-Bot\intent.json').read())

words=pickle.load(open('D:\AI and ML\Projects\Chat-Bot\words.pkl','rb'))
classes=pickle.load(open('D:\AI and ML\Projects\Chat-Bot\classes.pkl','rb'))
model=load_model('D:\AI and ML\Projects\Chat-Bot\chatbotmodel.h5')

def clean_up_sentence(sentence):
    sentence_words=wordpunct_tokenize(sentence)
    sentence_words=[lemmatizer.lemmatize(word) for word in sentence_words]
    return sentence_words

def bag_of_words(sentence):
    sentence_words=clean_up_sentence(sentence)
    bag=[0]*len(words)
    for w in sentence_words:
        for i,word in enumerate(words):
            if word==w:
                bag[i]=1
    return np.array(bag)


def predict_class(sentence):
    bow=bag_of_words(sentence)
    res=model.predict(np.array([bow]))[0]
    ERROR_THRESHOLD=0.1
    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 get_response(intents_list,intents_json):
    result= None
    tag=intents_list[0]['intent']
    list_of_intents=intents_json['intents']
    for i in list_of_intents:
        if i['tag']==tag:
            result=random.choice(i['response'])
            break
    return result 
      

print("Go! Bot is running")

while True:
    message=input("")
    ints=predict_class(message)
    res=get_response(ints,intents)
    print(res)