mrk2.py

import nltk
from nltk.stem.lancaster import LancasterStemmer
import numpy as np
import tflearn
import random
import json


stemmer = LancasterStemmer()

with open("Documents/GitHub/ai-project/intents.json") as file:
	data = json.load(file)

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 not in "?"]
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) 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 = np.array(training)
output = np.array(output)


input_size = len(training[0])
output_size = len(output[0])
print(training.dtype)
print(output.dtype)

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=600,batch_size=8,show_metric=True)
model.save("Documents/GitHub/ai-project/model.tflearn")