AI-ChatBot/main-host.py at master · anasselhoud/AI-ChatBot · 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
127
128
129
130
131
132
133
134
135
136
137
138
import nltk, os
nltk.download('punkt')

from nltk import word_tokenize,sent_tokenize

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

import numpy as np
import tflearn
import tensorflow as tf
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 = np.array(training)
    output = np.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)

if [os.path.isfile(i) for i in ["model.tflearn.meta", "model.tflearn.index"]] == [True, True]:
    model.load("model.tflearn")
else:
    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 np.array(bag)


from flask import Flask, render_template, request
import os

app = Flask(__name__)

@app.route('/')
def index():
    return render_template('index2.html')

@app.route('/process',methods=['POST'])
def process():

    user_input = request.form['messageText'].encode('utf-8').strip()

    inp = user_input

    result = model.predict([bag_of_words(inp, words)])[0]
    result_index = np.argmax(result)
    tag = labels[result_index]

    if result[result_index] > 0.7:
        for tg in data["intents"]:
          if tg['tag'] == tag:
              responses = tg['responses']

        bot_response=random.choice(responses)
        print(bot_response)
    else:
        bot_response = "I didnt get that. Can you explain or try again."
        print(bot_response)

    return render_template('index2.html',user_input=user_input, bot_response=bot_response)


if __name__ == '__main__':
    app.run(debug=True)