Step by step guide to create customized chatbot by using spaCy (Python NLP library)

Hi Community,
In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

Step1: Install required libraries

Step2: Create patterns and responses file

Step3: Train the Model

Step4: Create ChatBot Application based on the trained model

So Let us start.

Step1: Install required libraries

First of all, we need to install the required python libraries, which we can achieve by running the below command:

pip3 install spacy nltk
pip3 install torch torchvision torchaudio –index-url https://download.pytorch.org/whl/cpu

Step2: Create patterns and responses file

We need to create intents.json file containing question patterns and responses, below is an example of some of the question patterns and responses

{
“intents”: [
{
“tag”: “greeting”,
“patterns”: [
“Hi”,
“Hey”,
“How are you”,
“Is anyone there?”,
“Hello”,
“Good day”
],
“responses”: [
“Hey :-)”,
“Hello, thanks for visiting”,
“Hi there, what can I do for you?”,
“Hi there, how can I help?”
]
},
{
“tag”: “goodbye”,
“patterns”: [“Bye”, “See you later”, “Goodbye”],
“responses”: [
“See you later, thanks for visiting”,
“Have a nice day”,
“Bye! Come back again soon.”
]
},
{
“tag”: “thanks”,
“patterns”: [“Thanks”, “Thank you”, “That’s helpful”, “Thank’s a lot!”],
“responses”: [“Happy to help!”, “Any time!”, “My pleasure”]
},
{
“tag”: “items”,
“patterns”: [
“tell me about this app”,
“What kinds of technology used?”,
“What do you have?”
],
“responses”: [
“Write something about the app.”
]
},
{
“tag”: “funny”,
“patterns”: [
“Tell me a joke!”,
“Tell me something funny!”,
“Do you know a joke?”
],
“responses”: [
“Why did the hipster burn his mouth? He drank the coffee before it was cool.”,
“What did the buffalo say when his son left for college? Bison.”
]
}
]
}

Step3: Train the Model

3.1- Create a NeuralNet model.py file which will be used to train the model

#model.py file
import torch.nn as nn

class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.l2 = nn.Linear(hidden_size, hidden_size)
self.l3 = nn.Linear(hidden_size, num_classes)
self.relu = nn.ReLU()

def forward(self, x):
out = self.l1(x)
out = self.relu(out)
out = self.l2(out)
out = self.relu(out)
out = self.l3(out)
# no activation and no softmax at the end
return out

3.2 – We need nltk_utils.py file which uses “nltk” python library (a platform used for building Python programs that work with human language data for applying in statistical natural language processing (NLP)). 

#nltk_utils.py
#nltk library used for building Python programs that work with human language data for applying in
#statistical natural language processing (NLP)
import numpy as np
import nltk
from nltk.stem.porter import PorterStemmer
stemmer = PorterStemmer()

def tokenize(sentence):
“””
split sentence into array of words/tokens
a token can be a word or punctuation character, or number
“””
return nltk.word_tokenize(sentence)

def stem(word):
“””
stemming = find the root form of the word
examples:
words = [“organize”, “organizes”, “organizing”]
words = [stem(w) for w in words]
-> [“organ”, “organ”, “organ”]
“””
return stemmer.stem(word.lower())

def bag_of_words(tokenized_sentence, words):
“””
return bag of words array:
1 for each known word that exists in the sentence, 0 otherwise
example:
sentence = [“hello”, “how”, “are”, “you”]
words = [“hi”, “hello”, “I”, “you”, “bye”, “thank”, “cool”]
bog = [ 0 , 1 , 0 , 1 , 0 , 0 , 0]
“””
# stem each word
sentence_words = [stem(word) for word in tokenized_sentence]
# initialize bag with 0 for each word
bag = np.zeros(len(words), dtype=np.float32)
for idx, w in enumerate(words):
if w in sentence_words:
bag[idx] = 1
return bag

3.3 – We need to train the model, We will use the train.py file to create our model

#train.py
import numpy as np
import json,torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from nltk_utils import bag_of_words, tokenize, stem
from model import NeuralNet

with open(‘intents.json’, ‘r’) as f:
intents = json.load(f)

all_words = []
tags = []
xy = []
# loop through each sentence in our intents patterns
for intent in intents[‘intents’]:
tag = intent[‘tag’]
# add to tag list
tags.append(tag)
for pattern in intent[‘patterns’]:
# tokenize each word in the sentence
w = tokenize(pattern)
# add to our words list
all_words.extend(w)
# add to xy pair
xy.append((w, tag))

# stem and lower each word
ignore_words = [‘?’, ‘.’, ‘!’]
all_words = [stem(w) for w in all_words if w not in ignore_words]
# remove duplicates and sort
all_words = sorted(set(all_words))
tags = sorted(set(tags))

print(len(xy), “patterns”)
print(len(tags), “tags:”, tags)
print(len(all_words), “unique stemmed words:”, all_words)

# create training data
X_train = []
y_train = []
for (pattern_sentence, tag) in xy:
# X: bag of words for each pattern_sentence
bag = bag_of_words(pattern_sentence, all_words)
X_train.append(bag)
# y: PyTorch CrossEntropyLoss needs only class labels, not one-hot
label = tags.index(tag)
y_train.append(label)

X_train = np.array(X_train)
y_train = np.array(y_train)

# Hyper-parameters
num_epochs = 1000
batch_size = 8
learning_rate = 0.001
input_size = len(X_train[0])
hidden_size = 8
output_size = len(tags)
print(input_size, output_size)

class ChatDataset(Dataset):
def __init__(self):
self.n_samples = len(X_train)
self.x_data = X_train
self.y_data = y_train

# support indexing such that dataset[i] can be used to get i-th sample
def __getitem__(self, index):
return self.x_data[index], self.y_data[index]

# we can call len(dataset) to return the size
def __len__(self):
return self.n_samples

dataset = ChatDataset()
train_loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)

device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)
model = NeuralNet(input_size, hidden_size, output_size).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Train the model
for epoch in range(num_epochs):
for (words, labels) in train_loader:
words = words.to(device)
labels = labels.to(dtype=torch.long).to(device)
# Forward pass
outputs = model(words)
# if y would be one-hot, we must apply
# labels = torch.max(labels, 1)[1]
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (epoch+1) % 100 == 0:
print (f’Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}‘)

print(f’final loss: {loss.item():.4f}‘)
data = {
“model_state”: model.state_dict(),
“input_size”: input_size,
“hidden_size”: hidden_size,
“output_size”: output_size,
“all_words”: all_words,
“tags”: tags
}
FILE = “chatdata.pth”
torch.save(data, FILE)
print(f’training complete. file saved to {FILE}‘)

3.4 – Run the above train.py file to train the model

Train.py file will create chatdata.pth file which will be used in our chat.py file

Step4: Create ChatBot Application based on the trained model

As Our model is created. In the final step, we will create a chat.py file which we can use in our chatbot.

#chat.py
import os,random,json,torch
from model import NeuralNet
from nltk_utils import bag_of_words, tokenize

device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)

with open(“intents.json”) as json_data:
intents = json.load(json_data)

data_dir = os.path.join(os.path.dirname(__file__))
FILE = os.path.join(data_dir, ‘chatdata.pth’)
data = torch.load(FILE)

input_size = data[“input_size”]
hidden_size = data[“hidden_size”]
output_size = data[“output_size”]
all_words = data[‘all_words’]
tags = data[‘tags’]
model_state = data[“model_state”]

model = NeuralNet(input_size, hidden_size, output_size).to(device)
model.load_state_dict(model_state)
model.eval()

bot_name = “iris-NLP”
def get_response(msg):
sentence = tokenize(msg)
X = bag_of_words(sentence, all_words)
X = X.reshape(1, X.shape[0])
X = torch.from_numpy(X).to(device)

output = model(X)
_, predicted = torch.max(output, dim=1)

tag = tags[predicted.item()]

probs = torch.softmax(output, dim=1)
prob = probs[0][predicted.item()]
if prob.item() > 0.75:
for intent in intents[‘intents’]:
if tag == intent[“tag”]:
return random.choice(intent[‘responses’])
return “I do not understand…”
if __name__ == “__main__”:
print(“Let’s chat! (type ‘quit’ to exit)”)
while True:
sentence = input(“You: “)
if sentence == “quit”:
break
resp = get_response(sentence)
print(resp)

Run chat.py file and ask some questions which already defined above  in our intents.json file

For more details please visit IRIS-GenLab open exchange application page

Thanks