main.py

# import nltk
# import numpy as np
# from nltk.stem.lancaster import LancasterStemmer
# from pandas.core.dtypes.common import classes
#
# stemmer = LancasterStemmer()
#
#
#
# import numpy
# import tflearn
# import tensorflow
# import random
# import json
#
# with open("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"] 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) 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 = np.array(output)

# import nltk
# # nltk.download()
# from nltk.stem.lancaster import LancasterStemmer
# stemmer = LancasterStemmer()
#
# import numpy
# import tflearn
# import tensorflow
# import random
# import json
#
#
# with open("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 != "?"]
# 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)
#
# from tensorflow.python.framework import ops
# ops.reset_default_graph()
# # 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)
#
# model = tflearn.DNN(net)
# model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
# model.save("model.tflearn")
# tensorflow.reset_default_graph()
# network model with probability (softmax)
# in input 45 layer
# in hiden layer is two layer = 8 cell
# in output are 6 layer return value probability = tag in file json = labels(where is come from)

# net = tflearn.input_data(shape=[None,len(training[0])])
# net = tflearn.fully_connected(net,8)
# layer in hidden layer are 8 cell
# net = tflearn.fully_connected(net,8)
# net = tflearn.fully_connected(net,len(output[0]),activation="softmax")
# net = tflearn.regression(net)
# finish a neural network after that training model
# model = tflearn.DNN(net)
# model.fit(training,output, n_epoch=1000, batch_size=8,show_metric=True)
# training data
# model.save("model.tflearn")
# last one coding
import nltk
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()

import numpy
import tflearn
import tensorflow
import random
import json
import pickle

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)))

    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)

from tensorflow.python.framework import ops
ops.reset_default_graph()
# 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)

model = tflearn.DNN(net)

try:
    model.load("model.tflearn")
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 chat():
    print("Start talking with the bot (type quit to stop)!")
    while True:
        inp = input("You: ")
        if inp.lower() == "quit":
            break

        results = model.predict([bag_of_words(inp, 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))


chat()