Study-Bot/studybot.py at main · ifti-77/Study-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
import random
import json
import pickle
import numpy as np

import nltk
nltk.download('punkt')
nltk.download('punkt_tab')
nltk.download('wordnet')

from nltk.stem import WordNetLemmatizer

from tensorflow.keras.models import load_model

lemmartizer = WordNetLemmatizer()
intents = json.loads(open('intents.json').read())

words = pickle.load(open('words.pkl','rb'))
classes = pickle.load(open('classes.pkl','rb'))
model = load_model('StudyBot.h5')

def clean_up_sentence(sentence):
    sentence_words = nltk.word_tokenize(sentence)
    sentence_words = [lemmartizer.lemmatize(word) for word in sentence_words]
    return sentence_words

def bag_of_words(sentence):
    sentence_words = clean_up_sentence(sentence)
    bag = [0]*len(words)
    for w in sentence_words:
        for i,word in enumerate(words):
            if word == w:
                bag[i]= 1
    return np.array(bag)
def predict_class(sentence):
    bow = bag_of_words(sentence)
    res = model.predict(np.array([bow]))[0]
    ERROR_THRESHOLD = 0.25
    result = [[i,r] for i,r in enumerate(res) if r>ERROR_THRESHOLD]

    result.sort(key=lambda x:x[1], reverse=True)
    return_list = []
    for r in result:
        return_list.append({'intent':classes[r[0]], 'probability':str(r[1])})
    return return_list

def get_response(intents_list, intents_json):
    tag = intents_list[0]['intent']
    list_of_intents = intents_json['intents']
    for i in list_of_intents:
        if i['tag'] == tag:
            result = random.choice(i['responses'])
            break
    return result
print("bot is running....!")

while True:
    message = input("")
    ints = predict_class(message)
    res = get_response(ints, intents)
    print("Bot:",res)