{"intents": [
{"tag": "greeting",
"patterns": ["Hi", "How are you", "Is anyone there?", "Hello", "Good day"],
"responses": ["Hello, thanks for visiting", "Good to see you again", "Hi there, how can I help?"],
"context_set": ""
},
{"tag": "goodbye",
"patterns": ["Bye", "See you later", "Goodbye"],
"responses": ["See you later, thanks for visiting", "Have a nice day", "Bye! Come back again soon."]
},
{"tag": "thanks",
"patterns": ["Thanks", "Thank you", "That's helpful"],
"responses": ["Happy to help!", "Any time!", "My pleasure"]
},
{"tag": "hours",
"patterns": ["What hours are you open?", "What are your hours?", "When are you open?" ],
"responses": ["We're open every day 9am-9pm", "Our hours are 9am-9pm every day"]
},
{"tag": "menu",
"patterns": ["I want to order", "What kinds of food are there?", "What is your delicous food?" ],
"responses": ["Are you ready to order? Our restaurant have pizza, vegetable pasta, beefsteack. These is special of our restaurant", "Can I help you? This is menu. Please ask me if you have any question."]
},
...........

import nltk
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()

import numpy as np
import tflearn
import tensorflow as tf
import random

import json
with open('intents.json') as json_data:
intents = json.load(json_data)

words = []
classes = []
documents = []
stop_words = ['?', 'a', 'an', 'the']

for intent in intents['intents']:
for pattern in intent['patterns']:

w = nltk.word_tokenize(pattern)
words.extend(w)
documents.append((w, intent['tag']))
if intent['tag'] not in classes:
classes.append(intent['tag'])

words = [stemmer.stem(w.lower()) for w in words if w not in stop_words]
words = sorted(list(set(words)))

classes = sorted(list(set(classes)))

# create our training data
training = []
output = []
output_empty = [0] * len(classes)

# training set, bag of words for each sentence
for doc in documents:
bag = []
pattern_words = doc[0]
pattern_words = [stemmer.stem(word.lower()) for word in pattern_words]

for w in words:
bag.append(1) if w in pattern_words else bag.append(0)

# output is a '0' for each tag and '1' for current tag
output_row = list(output_empty)
output_row[classes.index(doc[1])] = 1

training.append([bag, output_row])
random.shuffle(training)
training = np.array(training)

# create train and test lists
train_x = list(training[:,0])
train_y = list(training[:,1])

train_x example: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]

tf.reset_default_graph()
# Build neural network
net = tflearn.input_data(shape=[None, len(train_x[0])])
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, len(train_y[0]), activation='softmax')
net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy')

# Define model and setup tensorboard
model = tflearn.DNN(net, tensorboard_dir='tflearn_logs')
# Start training
model.fit(train_x, train_y, n_epoch=1000, batch_size=8, show_metric=True)
model.save('model.tflearn')

import pickle
pickle.dump( {'words':words, 'classes':classes, 'train_x':train_x, 'train_y':train_y}, open( "training_data", "wb" ) )

# restore our data structures
import pickle
data = pickle.load( open( "training_data", "rb" ) )
words = data['words']
classes = data['classes']
train_x = data['train_x']
train_y = data['train_y']

# import intents file
import json
with open('intents.json') as json_data:
intents = json.load(json_data)

def clean_up_sentence(sentence):
sentence_words = nltk.word_tokenize(sentence)
sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
return sentence_words

# bag of words
def bow(sentence, words, show_details=False):
sentence_words = clean_up_sentence(sentence)
# bag of words
bag = [0]*len(words)
for s in sentence_words:
for i,w in enumerate(words):
if w == s:
bag[i] = 1
if show_details:
print ("found in bag: %s" % w)

return(np.array(bag))

bow('I want to special food', words)
>>> array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 0, 1, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0])

# data structure to hold user context
context = {}

ERROR_THRESHOLD = 0.25
def classify(sentence):
results = model.predict([bow(sentence, words)])[0]
results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD]
results.sort(key=lambda x: x[1], reverse=True)
return_list = []
for r in results:
return_list.append((classes[r[0]], r[1]))
return return_list

def response(sentence, userID='1', show_details=False):
results = classify(sentence)
if results:
while results:
for i in intents['intents']:
if i['tag'] == results[0][0]:
if 'context_set' in i:
if show_details: print ('context:', i['context_set'])
context[userID] = i['context_set']
if not 'context_filter' in i or \
(userID in context and 'context_filter' in i and i['context_filter'] == context[userID]):
if show_details: print ('tag:', i['tag'])
return print(random.choice(i['responses']))

results.pop(0)

classify('Hi, have any waiter')
>>> [('greeting', 0.8708275)]

response('Hi, have any waiter')
>>> Hi there, how can I help?

response('is your shop open today?')
>>> We're open every day from 9am-9pm

response('I want to special food')
>>> Are you ready to order? Our restaurant have pizza, vegetable pasta, beefsteack. These is special of our restaurant

response('thankyou, I want beefsteack and 2 glass of water')
>>> Thankyou for ordering, Please wait me some minutes.

response('do you take cash?')
>>> We accept most major credit cards

response('Goodbye, see you later')
>>> See you later, thanks for visiting