9 |
10 |
11 |
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/python-deep-learning-chatbot/app.py:
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1 | from flask import Flask, render_template, jsonify, request
2 | import processor
3 |
4 |
5 | app = Flask(__name__)
6 |
7 | app.config['SECRET_KEY'] = 'enter-a-very-secretive-key-3479373'
8 |
9 |
10 | @app.route('/', methods=["GET", "POST"])
11 | def index():
12 | return render_template('index.html', **locals())
13 |
14 |
15 |
16 | @app.route('/chatbot', methods=["GET", "POST"])
17 | def chatbotResponse():
18 |
19 | if request.method == 'POST':
20 | the_question = request.form['question']
21 |
22 | response = processor.chatbot_response(the_question)
23 |
24 | return jsonify({"response": response })
25 |
26 |
27 |
28 | if __name__ == '__main__':
29 | app.run(host='0.0.0.0', port='8888', debug=True)
30 |
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/README.md:
--------------------------------------------------------------------------------
1 | # python-deep-learning-chatbot
2 | A deep learning chatbot created with Python and Flask
3 |
4 | To get started follow the steps below:
5 |
6 | 1. Install a virtual environment by runnning the following
7 | ```
8 | virtualenv chatbotenv
9 | source chatbotenv/bin/activate
10 | ```
11 |
12 | 2. Install all the required libraries
13 | ```
14 | pip install nltk
15 | pip install numpy
16 | pip install keras
17 | pip install tensorflow
18 | pip install flask
19 | ```
20 |
21 | Run the chatbot.py file to create the model
22 | ```
23 | python chatbot.py
24 | ```
25 |
26 | Run the APP to create a Flask front end on port 8888 (or any port the app is pointing to)
27 | ```
28 | python app.py
29 | ```
30 |
31 | # Watch the full Tutorial on Youtube
32 | [Create a Deep Learning Machine Learning Chatbot with Python and Flask](https://www.youtube.com/watch?v=8HifpykuTI4)
33 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2021 Tati Blockchain
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
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/python-deep-learning-chatbot/processor.py:
--------------------------------------------------------------------------------
1 | import nltk
2 | from nltk.stem import WordNetLemmatizer
3 | lemmatizer = WordNetLemmatizer()
4 | import pickle
5 | import numpy as np
6 |
7 | from keras.models import load_model
8 | model = load_model('chatbot_model.h5')
9 | import json
10 | import random
11 | intents = json.loads(open('job_intents.json', encoding='utf-8').read())
12 | words = pickle.load(open('words.pkl','rb'))
13 | classes = pickle.load(open('classes.pkl','rb'))
14 |
15 |
16 | def clean_up_sentence(sentence):
17 | sentence_words = nltk.word_tokenize(sentence)
18 | sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
19 | return sentence_words
20 |
21 | # return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
22 |
23 | def bow(sentence, words, show_details=True):
24 | # tokenize the pattern
25 | sentence_words = clean_up_sentence(sentence)
26 | # bag of words - matrix of N words, vocabulary matrix
27 | bag = [0]*len(words)
28 | for s in sentence_words:
29 | for i,w in enumerate(words):
30 | if w == s:
31 | # assign 1 if current word is in the vocabulary position
32 | bag[i] = 1
33 | if show_details:
34 | print ("found in bag: %s" % w)
35 | return(np.array(bag))
36 |
37 | def predict_class(sentence, model):
38 | # filter out predictions below a threshold
39 | p = bow(sentence, words, show_details=False)
40 | res = model.predict(np.array([p]))[0]
41 | ERROR_THRESHOLD = 0.25
42 | results = [[i,r] for i,r in enumerate(res) if r>ERROR_THRESHOLD]
43 | # sort by strength of probability
44 | results.sort(key=lambda x: x[1], reverse=True)
45 | return_list = []
46 | for r in results:
47 | return_list.append({"intent": classes[r[0]], "probability": str(r[1])})
48 | return return_list
49 |
50 | def getResponse(ints, intents_json):
51 | tag = ints[0]['intent']
52 | list_of_intents = intents_json['intents']
53 | for i in list_of_intents:
54 | if(i['tag']== tag):
55 | result = random.choice(i['responses'])
56 | break
57 | else:
58 | result = "You must ask the right questions"
59 | return result
60 |
61 | def chatbot_response(msg):
62 | ints = predict_class(msg, model)
63 | res = getResponse(ints, intents)
64 | return res
65 |
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/python-deep-learning-chatbot/templates/index.html:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 | Skolo
12 |
13 |
14 |
15 |
16 |
17 |
18 |
19 |
Welcome to Skolo Chatbot
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 |
78 |
79 |
80 |
81 |
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/python-deep-learning-chatbot/chatbot.py:
--------------------------------------------------------------------------------
1 | import nltk
2 | nltk.download('punkt')
3 | nltk.download('wordnet')
4 | from nltk.stem import WordNetLemmatizer
5 | lemmatizer = WordNetLemmatizer()
6 | import json
7 | import pickle
8 |
9 | import numpy as np
10 | from keras.models import Sequential
11 | from keras.layers import Dense, Activation, Dropout
12 | from keras.optimizers import SGD
13 | import random
14 |
15 | words=[]
16 | classes = []
17 | documents = []
18 | ignore_words = ['?', '!']
19 | data_file = open('job_intents.json', encoding='utf-8').read()
20 | intents = json.loads(data_file)
21 |
22 |
23 | for intent in intents['intents']:
24 | for pattern in intent['patterns']:
25 |
26 | w = nltk.word_tokenize(pattern)
27 | words.extend(w)
28 |
29 | documents.append((w, intent['tag']))
30 |
31 |
32 | if intent['tag'] not in classes:
33 | classes.append(intent['tag'])
34 |
35 | words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
36 | words = sorted(list(set(words)))
37 |
38 | classes = sorted(list(set(classes)))
39 |
40 | print (len(documents), "documents")
41 |
42 | print (len(classes), "classes", classes)
43 |
44 | print (len(words), "unique lemmatized words", words)
45 |
46 |
47 | pickle.dump(words,open('words.pkl','wb'))
48 | pickle.dump(classes,open('classes.pkl','wb'))
49 |
50 | # initializing training data
51 | training = []
52 | output_empty = [0] * len(classes)
53 | for doc in documents:
54 |
55 | bag = []
56 |
57 | pattern_words = doc[0]
58 | pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]
59 |
60 | for w in words:
61 | bag.append(1) if w in pattern_words else bag.append(0)
62 |
63 |
64 | output_row = list(output_empty)
65 | output_row[classes.index(doc[1])] = 1
66 |
67 | training.append([bag, output_row])
68 |
69 | random.shuffle(training)
70 | training = np.array(training)
71 | # create train and test lists. X - patterns, Y - intents
72 | train_x = list(training[:,0])
73 | train_y = list(training[:,1])
74 | print("Training data created")
75 |
76 |
77 | # Create model - 3 layers. First layer 128 neurons, second layer 64 neurons and 3rd output layer contains number of neurons
78 | # equal to number of intents to predict output intent with softmax
79 | model = Sequential()
80 | model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
81 | model.add(Dropout(0.5))
82 | model.add(Dense(64, activation='relu'))
83 | model.add(Dropout(0.5))
84 | model.add(Dense(len(train_y[0]), activation='softmax'))
85 |
86 | # Compile model. Stochastic gradient descent with Nesterov accelerated gradient gives good results for this model
87 | sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
88 | model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
89 |
90 | #fitting and saving the model
91 | hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
92 | model.save('chatbot_model.h5', hist)
93 |
94 | print("model created")
95 |
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/python-deep-learning-chatbot/job_intents.json:
--------------------------------------------------------------------------------
1 | {
2 | "intents": [
3 | {"tag": "greeting",
4 | "patterns": ["Hi there", "How are you", "Is anyone there?","Hey","Hola", "Hello", "Good day", "Hey", "Ekse", "Hi"],
5 | "responses": ["Hi stranger", "Yebo yes, how can I help", "Ey watsupp", "Ekse Hoezit", "Hola", "Hey dude", "You again"]
6 | },
7 | {"tag": "goodbye",
8 | "patterns": ["Bye", "See you later", "Goodbye", "Ok bye", "Bye Bye"],
9 | "responses": ["See you!", "Have a nice day", "Sure Bye", "Later dude", "Sayoonara", "Peace out", "Sure dude", "Ciao"]
10 | },
11 | {"tag": "thanks",
12 | "patterns": ["Thanks", "Thank you", "That's helpful", "Awesome, thanks", "Thanks for helping me"],
13 | "responses": ["Happy to help!", "Any time!", "My pleasure", "You are welcome", "Sure dude"]
14 | },
15 | {"tag": "noanswer",
16 | "patterns": [],
17 | "responses": ["Sorry, can't understand you", "Please give me more info", "Not sure I understand", "I am still here dude"]
18 | },
19 | {"tag": "name",
20 | "patterns": ["What is your name", "Whats your name", "Tell me your name", "Who are you", "Ungubani", "Tell me about yourself", "You are"],
21 | "responses": ["I am Karabo", "My name is Karabo", "You can call me KB for short", "I am Karabo, my friends call me KB", "Karabo is my name"]
22 | },
23 | {"tag": "options",
24 | "patterns": ["What do you do", "How can you help", "What do you know", "What is your purpose", "How can you help"],
25 | "responses": ["I can tell you about South Africa", "I can give you interesting facts about South Africa"]
26 | },
27 | {"tag": "south_africa_info",
28 | "patterns": ["South Africa", "What can you tell me about SA", "SA", "Tell me about SA", "What about SA", "What do you know about SA", "Tell me more about SA", "What can you tell me about South Africa", "Tell me about South Africa", "What about South Africa", "What do you know about South Africa", "Tell me more about South Africa"],
29 | "responses": ["South Africa is the southern most country in Africa, it has a population of more than 59million people and covers an area of 1221 037m2. South Africa has 11 official languages. The currency used in ZAR our South African Rand which trades at about 15ZAR per 1USD."]
30 | },
31 | {"tag": "south_africa_facts",
32 | "patterns": ["South Africa Facts", "SA Facts", "Give me some facts about SA", "Give me some facts about South Africa", "SA Facts", "SA interesting facts", "South Africa facts", "South Africa interesting facts", "Tell me something interesting"],
33 | "responses": [
34 | "South Africa is now the only country in the world to have hosted the Soccer, Cricket and Rugby World Cup!",
35 | "Table Mountain in Cape Town is believed to be one of the oldest mountains in the world and one of the planet’s 12 main energy centres, radiating magnetic, electric or spiritual energy.",
36 | "The Cape Floral Kingdom is one of the world’s six floral kingdoms – and the only one which is wholly contained within a single country.",
37 | "Some countries have deserts; some have subtropical forests, right? South Africa has: deserts, wetlands, grasslands, bush, subtropical forests, mountains and escarpments.",
38 | "South Africa’s drinking water is rated 3rd best in the world for being safe and ready to drink.",
39 | "Water is not all there is to drink in this thirsty country! South Africa’s Cape Winelands have around 560 wineries and 4 400 primary producers. Included in the Cape Winelands region is Route 62, considered the longest wine route in the world. That alone is good reason to visit South Africa if you haven’t yet been!",
40 | "What about beer? South African brewery SABMiller ranks – by volume – as the largest brewing company in the world. Saffers love their beer…but the real reason the brewery is so big? SABMiller also supplies up to 50% of China’s beer.",
41 | "South Africa is the only country in the entire world that has voluntarily abandoned its nuclear weapons programme.",
42 | "The world’s largest themed resort hotel in the world – The Palace of the Lost City – is found in South Africa. Surrounding the Palace is a 25 hectare manmade botanical jungle with almost 2 million plants, trees and shrubs.",
43 | "South Africa is extremely rich in mining and minerals and considered the world’s leader with nearly 90% of all the platinum metals on earth and around 41% of all the world’s Gold!",
44 | "South Africa is home to the oldest meteor scar in the world – the Vredefort Dome in a town called Parys. The site is a UNESCO World Heritage Site.",
45 | "The South African Rovos Rail is considered the most luxurious train in the world.",
46 | "South Africa is home to the highest commercial bungi jump in the world at 710 feet.",
47 | "General Motors South Africa is the only place outside of the USA to build the Hummer H3!",
48 | "Despite the country’s status as a democratic republic, the Province of KwaZulu-Natal has a monarchy, specially provided for by the Constitution. Goodwill Zwelithini kaBhekuzulu is the King of the Zulu Nation, has 27 kids and 6 wives and lives, literally, like a King!",
49 | "There are more than 2000 shipwrecks off the South African coast, most dating back at least 500 years.",
50 | "The oldest remains of modern humans were found in South Africa and are well over 160,000 years old.",
51 | "The Karoo region in the Western Cape is home to some of the best fossils of early dinosaurs. In fact, it is estimated that some 80% of the mammalian fossils found to date were found in the Karoo.",
52 | "SA has three capital cities: Pretoria is the Executive Capital, Cape Town the Legislative Capital and Bloemfontein the judicial Capital.",
53 | "Can you think of any other place in the world where two Nobel Peace Prize winners lived on the same street? Both Nelson Mandela and Archbishop Desmond Tutu had houses on Vilakazi Street in Soweto"
54 | ]
55 | }
56 | ]
57 | }
58 |
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