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import nltk
import random
import numpy as np
import json
import pickle
from nltk.stem import WordNetLemmatizer
from tensorflow.keras.models import load_model
lemmatizer = WordNetLemmatizer()
with open('intents.json') as json_file:
intents = json.load(json_file)
words = pickle.load(open('words.pkl', 'rb'))
classes = pickle.load(open('classes.pkl', 'rb'))
model = load_model('chatbotmodel.h5')
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):
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 = 0.25
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 get_response(intents_list, intents_json):
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['responses'])
break
return result
def chat(text):
ints = predict_class(text)
res = get_response(ints, intents)
return res
print("GO! BOT IS RUNNING")
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