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chatbot.py
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chatbot.py
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import nltk
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()
import numpy
import tensorflow
import random
from keras.models import Sequential
from keras.layers import Dense, Activation
import pickle
import json
with open('intents.json') as file:
data = json.load(file)
try:
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 if w!= "?" ]
words = sorted(list(set(words)))
#print("words:", words)
training = []
output = []
out_empty = [ 0 for _ in range(len(labels))]
for x, doc in enumerate(docs_x):
bag = []
#print("doc:", doc)
wrds = [stemmer.stem(w.lower()) for w in doc]
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()
model = Sequential()
model.add(Dense(32, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(6, activation='softmax'))
#model.add(Activation('relu'))
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(training, output, epochs=1000, batch_size=8)
#input= [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
#input=numpy.array(input)
#print(input)
#print(input.shape)
#results = model.predict(input)
#print("results:", results)
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():
print("Start talking with the bot (type quit to stop)!")
while True:
inp = input("You: ")
if inp.lower() == "quit":
break
print(bag_of_words(inp, words))
results = model.predict([[bag_of_words(inp, words)]])
print("Results:", results)
results_index = numpy.argmax(results)
print("Result_index:", results_index)
tag = labels[results_index]
print("tag:", tag)
for tg in data["intents"]:
if tg['tag'] == tag:
responses = tg['responses']
print(random.choice(responses))
#chat()
from flask import Flask, request, render_template
app = Flask(__name__)
@app.route('/')
def my_form():
return render_template('home.html')
@app.route('/', methods=['POST'])
def my_form_post():
text = request.form['text']
results = model.predict([[bag_of_words(text, words)]])
results_index = numpy.argmax(results)
tag = labels[results_index]
for tg in data["intents"]:
if tg['tag'] == tag:
responses = tg['responses']
return render_template('home.html') + random.choice(responses)
if __name__ == '__main__':
app.run(debug=True)