Python-Chat-Bot-/bot.py at main · YOUSSEF-YL/Python-Chat-Bot- · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126


from tensorflow.python.framework import ops
import nltk
from  nltk.stem.lancaster import  LancasterStemmer
from numpy.lib.function_base import piecewise
streamer = LancasterStemmer()
import numpy
import  tflearn
import random
import  json
import  pickle


#nltk.download('punkt')

with open("brain.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 = []
        training =[]
        output =[]

        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 = [streamer.stem(w.upper()) for w in words if w != "?"]
        words = sorted(list(set(words)))

        labels = sorted(labels)

        out_empty =[0 for _ in range(len(labels))]

        for x, doc in enumerate(docs_X):
            bag = []

            wrds = [streamer.stem(w.lower) for w in doc]

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

                out_row = out_empty[:]
                out_row[labels.index(docs_Y[x])] =1

                training.append(bag)
                output.append(out_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()
ops.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 words_bag(s,words):
    bag = [0 for _ in range(len(words))]

    s_words = nltk.word_tokenize(s)
    s_words = [streamer.stem(word.upper()) for word in s_words]

    for s in s_words:
        for i,w in enumerate(words):
            if w == s:
                bag[i] = 1

    return numpy.array(bag)

def chat():
    print("START TALKING WITH THE BOT (Type Q to quit)")
    while True:
        inp = input("You: ")
        if inp.lower() == "Q":
            break

# link top probiblity num to its word

        results = model.predict([words_bag(inp,words)])
        results_index = numpy.argmax(results)
        tag = labels[results_index]

        for tg in data["intents"]:
            if tg['tag'] == tag:
                responses = tg['responses']

        print(tag)

        #print(random.choice(responses))


chat()