Create chat bot - JO PARIS 2024
victor_dalet
Posted on August 25, 2024
In this article, I show how to create a simple chat bot with tensorflow.
For the data, I use a kaggle dataset from PARIS JO JO 2024 to get sentences at the training stage.
You can obtain the finish code in my github : https://github.com/victordalet/Kaggle_analysis/tree/feat/paris_2024_olympics
I - Default chat bot dataset
A tensorflow dataset on chat bots looks like this.
We can find a tag, a pattern and the various responses.
Our goal will be to add the different sequences from the JO betting dataset and add them to a file like this.
{
"intents": [
{
"tag": "google",
"patterns": [
"google",
"search",
"internet"
],
"responses": [
"Redirecting to Google..."
]
},
II - Data processing
I read a chat bot dataset in default json and JO's csv and split and processed it to add the sentence in the json
import json
class CreateDataset:
def __init__(self):
self.json_path = 'data.json'
self.csv_path = '../paris-2024-faq.csv'
with open(self.json_path) as file:
self.dataset = json.load(file)
f = open(self.csv_path, 'r')
dataset_split = f.read().split(";")
question = False
for data in dataset_split:
if question:
question = False
self.dataset["intents"][-1]["responses"].append(data)
if "?" in data:
question = True
self.dataset["intents"].append({
"tag": "",
"patterns": [
data
],
"responses": [
]
})
with open(self.json_path, 'w') as f:
json.dump(self.dataset, f)
III - Training
For training purposes, I've edited a tensorflow example.
If you take my code to run it, add in the first argument the number of epochs you want.
Create a save directory where your model will go, and add inside the classes.pkl and words.pkl files that are in the github like at the beginning of this article.
import random
import json
import pickle
import numpy as np
import sys
import nltk
from nltk.stem import WordNetLemmatizer
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.optimizers import SGD
class Train:
words: list
classes: list
documents: list
ignore_letters: list
training: list
output_empty: list
train_x: list
train_y: list
model: Sequential
epochs: int
def __init__(self):
self.lemmatizer = WordNetLemmatizer()
self.intents = json.loads(open('data.json').read())
self.words = []
self.classes = []
self.documents = []
self.training = []
self.ignore_letters = ['?', '!']
self.epochs = int(sys.argv[1])
def run(self):
self.download_nltk_data()
self.load_training_data()
self.prepare_training_data()
self.build_neural_network()
self.train()
@staticmethod
def download_nltk_data():
nltk.download('punkt')
nltk.download('wordnet')
def load_training_data(self):
for intent in self.intents['intents']:
for pattern in intent['patterns']:
word_list = nltk.word_tokenize(pattern)
self.words.extend(word_list)
self.documents.append((word_list, intent['tag']))
if intent['tag'] not in self.classes:
self.classes.append(intent['tag'])
def prepare_training_data(self):
self.words = [self.lemmatizer.lemmatize(word)
for word in self.words
if word not in self.ignore_letters]
self.words = sorted(set(self.words))
self.classes = sorted(set(self.classes))
pickle.dump(self.words, open('saves/words.pkl', 'wb'))
pickle.dump(self.classes, open('saves/classes.pkl', 'wb'))
self.output_empty = [0] * len(self.classes)
for document in self.documents:
bag = []
word_patterns = document[0]
word_patterns = [self.lemmatizer.lemmatize(word.lower())
for word in word_patterns]
for word in self.words:
bag.append(1) if word in word_patterns else bag.append(0)
output_row = list(self.output_empty)
output_row[self.classes.index(document[1])] = 1
self.training.append([bag, output_row])
random.shuffle(self.training)
self.training = np.array(self.training)
self.train_x = list(self.training[:, 0])
self.train_y = list(self.training[:, 1])
def build_neural_network(self):
self.model = Sequential()
self.model.add(Dense(128, input_shape=(len(self.train_x[0]),),
activation='relu'))
self.model.add(Dropout(0.5))
self.model.add(Dense(64, activation='relu'))
self.model.add(Dropout(0.5))
self.model.add(Dense(len(self.train_y[0]), activation='softmax'))
sgd = SGD(lr=0.01, momentum=0.9, nesterov=True)
self.model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
def train(self):
self.model.fit(np.array(self.train_x),
np.array(self.train_y),
epochs=self.epochs,
batch_size=5,
verbose=1)
self.model.save('saves/chatbot_model.model')
if __name__ == "__main__":
Train().run()
IV - Test
I create a ChatBot class, with a test method that takes a random message.
You can use the get_response method to add this chatbot to your application, for example I call it in one of my projects in a flask api to have my chatbot in a website.
import random
import json
import pickle
import numpy as np
import nltk
from nltk.stem import WordNetLemmatizer
from tensorflow.keras.models import load_model
class ChatBot:
lemmatizer: WordNetLemmatizer
intents: dict
words: list
classes: list
model: load_model
ERROR_THRESHOLD = 0.25
def __init__(self):
self.download_nltk_data()
self.lemmatizer = WordNetLemmatizer()
self.intents = json.loads(open('data.json').read())
self.words = pickle.load(open('saves/words.pkl', 'rb'))
self.classes = pickle.load(open('saves/classes.pkl', 'rb'))
self.model = load_model('saves/chatbot_model.model')
@staticmethod
def download_nltk_data():
nltk.download('punkt')
nltk.download('wordnet')
def clean_up_sentence(self, sentence):
sentence_words = nltk.word_tokenize(sentence)
sentence_words = [self.lemmatizer.lemmatize(word)
for word in sentence_words]
return sentence_words
def bag_of_words(self, sentence):
sentence_words = self.clean_up_sentence(sentence)
bag = [0] * len(self.words)
for w in sentence_words:
for i, word in enumerate(self.words):
if word == w:
bag[i] = 1
return np.array(bag)
def predict_class(self, sentence):
bow = self.bag_of_words(sentence)
res = self.model.predict(np.array([bow]))[0]
results = [[i, r]
for i, r in enumerate(res)
if r > self.ERROR_THRESHOLD]
results.sort(key=lambda x: x[1], reverse=True)
return_list = []
for r in results:
return_list.append({'intent': self.classes[r[0]],
'probability': str(r[1])})
return return_list
def get_response(self, intents_list):
intents_json = self.intents
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
def test(self):
while True:
message = input("")
ints = self.predict_class(message)
res = self.get_response(ints)
print(res)
💖 💪 🙅 🚩
victor_dalet
Posted on August 25, 2024
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