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Main.py
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Main.py
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#imports
import nltk
from nltk.stem.lancaster import LancasterStemmer
import numpy
import tensorflow
import tflearn
import random
import pickle
import json
def train():
global stemmer,data,words,labels,training,output
stemmer = LancasterStemmer()
with open("intents.json") as file:
data = json.load(file)
try:
file=open('reporter.bin','rb')
changes=pickle.load(file)
if changes:
print(1/0)
else:
with open('data.pickle','rb') as f:
words,labels,training,output=pickle.load(f)
except:
words = []
labels = []
docs_x = []
docs_y = []
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 labels:
labels.append(intent['tag'])
words=[stemmer.stem(w.lower()) for w in words]
words=sorted(list(set(words)))
training = []
output = []
out_empty = [0 for _ in range(len(labels))]
for x,doc in enumerate(docs_x):
bag = []
wrds = [stemmer.stem(w) for w in doc if w!='?']
for w in words:
if w in wrds:
bag.append(1)
else:
bag.append(0)
output_row=out_empty[:]
output_row[labels.index(docs_y[x])]=1
training.append(bag)
output.append(output_row)
training = numpy.array(training)
output = numpy.array(output)
with open('data.pickle','wb') as f:
pickle.dump((words,labels,training,output),f)
tensorflow.reset_default_graph()
net = tflearn.input_data(shape=[None,len(training[0])])
net = tflearn.fully_connected(net,8)
net = tflearn.fully_connected(net,8)
net = tflearn.fully_connected(net,len(output[0]),activation='softmax')
net = tflearn.regression(net)
global model
model = tflearn.DNN(net)
try:
if not changes:
model.load('model.tflearn')
else:
print(1/0)
except:
model.fit(training,output,n_epoch=1000,batch_size=8,show_metric=False)
model.save('model.tflearn')
def bag_of_words(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(msg):
global words,model,labels
results=model.predict([bag_of_words(msg,words)])[0]
result_index=numpy.argmax(results)
tag=labels[result_index]
if results[result_index]>0.7:
for tg in data['intents']:
if tg['tag']==tag:
responses=tg['responses']
return random.choice(responses)
else:
return 'i do not understand'
if __name__=='__main__':
train()
print(chat('hello help me '))
input()