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main.py
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import nltk #natural language tool kit for tokenization(hide meaningful data)
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()
import numpy
import tensorflow as tf
import tflearn
import random
import json
import pickle
with open("intents.json") as file: #open file
data = json.load(file)
try:
with open ("data.pickle","rb") as f:
words,classes,training, output = pickle.load(f)
except:
words = [] #broken up words
classes = []
docs_x = [] #list of patterns
docs_y = [] #list of tags
for intent in data["intents"]:
for pattern in intent["patterns"]:
wrds = nltk.word_tokenize(pattern)
words.extend(wrds)
docs_x.append(wrds)
docs_y.append(intent["tag"])
if intent["tag"] not in classes:
classes.append(intent["tag"])
words = [stemmer.stem(w.lower()) for w in words if w != "?"] #what is stemmer.stem
words = sorted(list(set(words)))
classes = sorted(classes) #sorted tags
training = []
output = []
out_empty = [0 for _ in range(len(classes))] #?
for x, doc in enumerate(docs_x): # x = position & docs_x = text
bag = []
wrds = [stemmer.stem(w) for w in doc]
for w in words:
if w in wrds:
bag.append(1)
else:
bag.append(0)
output_row = out_empty[:] #[:] = all elements of the array
output_row[classes.index(docs_y[x])] = 1
training.append(bag)
output.append(output_row)
with open ("data.pickle","wb") as f:
pickle.dump((words,classes,training, output),f)
traning = numpy.array(training)
output = numpy.array(output)
tf.compat.v1.reset_default_graph()
try:
model.load("model.tflearn")
except:
net = tflearn.input_data(shape=[None, len(training[0])]) #input data (length of training data)
net = tflearn.fully_connected(net,8) # hidden layer (8 nuerons, fully connected)
net = tflearn.fully_connected(net,8) # hidden layer (8 nuerons, fully connected)
net = tflearn.fully_connected(net,len(output[0]),activation="softmax") # output layer connects nueron with classes
net = tflearn.regression(net) #
model = tflearn.DNN(net) #DNN = type of nueral network
model.fit(training,output,n_epoch=1000,batch_size = 8,show_metric=True) #start passing training data, n_epoch = how many times it sees data
model.save("model.tflearn")
def bagOfWords(s, words):
bag = [0 for _ in range(len(words))]
s_words = nltk.word_tokenize(s)
s_words = [stemmer.stem(word.lower()) for word in s_words]
for se in s_words:
for i,w in enumerate(words):
if w == se:
bag[i] = 1
return numpy.array(bag)
def chat():
print("Start talking with the bot! (type quit to stop)")
while True:
inp = input("You ")
if inp.lower() == "quit":
break
results = model.predict([bagOfWords(inp,words)])
results_index = numpy.argmax(results)
tag = classes[results_index]
for tg in data["intents"]:
if tg["tag"] == tag:
responses = tg["responses"]
print(random.choice(responses))
chat()