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logic.py
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logic.py
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import pickle
import json
from nltk.stem.lancaster import LancasterStemmer
import random
import tensorflow as tf
import tflearn
import numpy
import nltk
nltk.download('punkt')
data = json.loads('''{"intents": [
{"tag": "greeting",
"patterns": ["Hi", "How are you", "Is anyone there?", "Hello", "Good day", "Whats up"],
"responses": ["Hello!", "Good to see you again!", "Hi there, how can I help?"],
"context_set": ""
},
{"tag": "goodbye",
"patterns": ["cya", "See you later", "Goodbye", "so long!", "don't let the bed bugs rape!"],
"responses": ["Sad to see you go :(", "Talk to you later", "Goodbye!","Good riddance!"],
"context_set": ""
},
{"tag": "age",
"patterns": ["how old", "how old is tim", "what is your age", "how old are you", "age?"],
"responses": ["I am t-2 years old!", "t-2 years young!"],
"context_set": ""
},
{"tag": "name",
"patterns": ["what is your name", "what should I call you", "whats your name?"],
"responses": ["You can call me Trusty.", "I'm Trusty!", "I'm Trusty aka Trusty the Chatbot."],
"context_set": ""
},
{"tag": "shop",
"patterns": ["Id like to buy something", "whats on the menu", "what do you reccommend?", "could i get something to drink"],
"responses": ["We sell lemonade for $2!", "Lemonade is on the menu!"],
"context_set": ""
},
{"tag": "hours",
"patterns": ["when are you guys open", "what are your hours", "hours of operation"],
"responses": ["We are open 7am-4pm Monday-Friday!"],
"context_set": ""
}
]
}''')
stemmer = LancasterStemmer()
nltk.download('punkt')
try:
s
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)))
labels = sorted(labels)
training = []
output = []
out_empty = [0 for _ in range(len(labels))]
for x, doc in enumerate(docs_x):
bag = []
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)
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)
model = tflearn.DNN(net)
model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
model.save("model.tflearn")
try:
model.load("model.tflearn")
print("loaded from file")
except:
model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
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 call(what):
imp = input("You: ")
results = model.predict([bag_of_words(imp, words)])
results_index = numpy.argmax(results)
tag = labels[results_index]
for tg in data['intents']:
if tg['tag'] == tag:
responses = tg['responses']
print(random.choice(responses))