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How to Use Python to Create a Chatbot: A Tutorial on How to Use Python, NLTK, and Flask to Build a Simple and Interactive Chatbot

Chatbots are software programs that can simulate a conversation with a human user using natural language. Chatbots can be used for various purposes, such as customer service, entertainment, education, or information. Chatbots can also be integrated with different platforms, such as websites, apps, or social media.

In this article, we will show you how to use Python to create a chatbot using the Natural Language Toolkit (NLTK) and Flask. NLTK is a popular library for natural language processing (NLP) in Python, which provides various tools and resources for analyzing, processing, and generating natural language. Flask is a lightweight web framework for Python, which allows us to create web applications and APIs easily.

We will guide you through the following steps:

• Step 1: Import and load the data file

• Step 2: Preprocess the data

• Step 3: Build the chatbot model using NLTK

• Step 4: Create the web app using Flask

• Step 5: Test and interact with the chatbot

Step 1: Import and load the data file

The first step is to import and load the data file that contains the conversations or dialogues that we want our chatbot to learn from. The data file can be in any format, such as JSON, CSV, XML, or TXT. For this tutorial, we will use a JSON file that contains some sample conversations about greetings, goodbyes, hobbies, and movies.

The JSON file looks like this:

{
    "intents": [
        {
            "tag": "greeting",
            "patterns": [
                "Hi",
                "Hello",
                "How are you",
                "Good day"
            ],
            "responses": [
                "Hello",
                "Hi there",
                "Nice to meet you",
                "Good day to you too"
            ]
        },
        {
            "tag": "goodbye",
            "patterns": [
                "Bye",
                "See you later",
                "Goodbye",
                "Have a nice day"
            ],
            "responses": [
                "Bye bye",
                "See you soon",
                "Take care",
                "Have a nice day too"
            ]
        },
        {
            "tag": "hobby",
            "patterns": [
                "What are your hobbies",
                "What do you like to do for fun",
                "Do you have any hobbies"
            ],
            "responses": [
                "I like to read books and watch movies",
                "I enjoy playing games and listening to music",
                "I love coding and learning new things"
            ]
        },
        {
            "tag": "movie",
            "patterns": [
                "What is your favorite movie",
                "What kind of movies do you like",
                "Do you like movies"
            ],
            "responses": [
                "My favorite movie is The Matrix",
                "I like sci-fi and action movies",
                "I love movies, they are very entertaining"
            ]
        }
    ]
}

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The JSON file contains a list of intents, which are the categories or topics of the conversations. Each intent has a tag, which is the name or label of the intent; a list of patterns, which are the possible user inputs or questions related to the intent; and a list of responses, which are the possible chatbot outputs or answers related to the intent.

To import and load the JSON file in Python, we can use the json module. We can also use pandas to convert the JSON data into a dataframe for easier manipulation. The code looks like this:

# Import modules
import json
import pandas as pd

# Load JSON file
with open("data.json") as file:
    data = json.load(file)

# Convert JSON data into dataframe
df = pd.json_normalize(data["intents"])
df.head()

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The output looks like this:

Step 2: Preprocess the data

The second step is to preprocess the data to make it ready for the chatbot model. Preprocessing involves cleaning, transforming, and encoding the data into numerical values that can be understood by the chatbot model.

Some of the common preprocessing techniques for NLP are:

• Tokenization: This is the process of splitting the text into smaller units or tokens, such as words or characters.

• Lemmatization: This is the process of reducing the words to their base or root form, such as running -> run or feet -> foot.

• Stopwords removal: This is the process of removing the words that are very common and do not add much meaning to the text, such as the, a, or and.

• Bag of words: This is the process of representing the text as a vector of word frequencies, such as [1, 0, 2, 1] for the text “I like movies and music”.

• TF-IDF: This is the process of representing the text as a vector of word weights, which reflect how important a word is in a document or a corpus. TF-IDF stands for term frequency-inverse document frequency, which is calculated as the product of the term frequency and the inverse document frequency.

To preprocess the data in Python, we can use NLTK to perform tokenization, lemmatization, and stopwords removal. We can also use scikit-learn to perform bag of words or TF-IDF. The code looks like this:

# Import modules
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer

# Initialize lemmatizer and stopwords
lemmatizer = WordNetLemmatizer()
stopwords = set(stopwords.words("english"))

# Define a function to preprocess the text
def preprocess(text):
    # Tokenize the text
    tokens = nltk.word_tokenize(text)
    # Lemmatize the tokens
    lemmas = [lemmatizer.lemmatize(token) for token in tokens]
    # Remove stopwords and punctuation
    words = [lemma for lemma in lemmas if lemma not in stopwords and lemma.isalpha()]
    # Return the preprocessed text as a string
    return " ".join(words)

# Apply the function to the patterns and responses columns
df["patterns"] = df["patterns"].apply(lambda x: [preprocess(pattern) for pattern in x])
df["responses"] = df["responses"].apply(lambda x: [preprocess(response) for response in x])

# Initialize count vectorizer and tf-idf vectorizer
count_vect = CountVectorizer()
tfidf_vect = TfidfVectorizer()

# Fit and transform the patterns column using count vectorizer
X = count_vect.fit_transform(df["patterns"].explode())
# Fit and transform the responses column using tf-idf vectorizer
y = tfidf_vect.fit_transform(df["responses"].explode())

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The output looks like this:

Step 3: Build the chatbot model using NLTK

The third step is to build the chatbot model using NLTK. The chatbot model is the core component of our chatbot that will generate the responses for the user inputs. The chatbot model will use a rule-based approach, which means that it will follow some predefined rules or logic to select the best response from a list of possible responses.

The chatbot model will use the following logic:

• Receive the user input as a text string.

• Preprocess the user input using the same function as in step 2.

• Transform the user input into a vector using count vectorizer.

• Compare the user input vector with each pattern vector using cosine similarity.

• Find the pattern vector that has the highest cosine similarity with the user input vector.

• Find the corresponding tag and responses for that pattern vector.

• Randomly select one response from the list of responses.

• Return the response as a text string.

To build the chatbot model in Python, we can use NLTK to perform cosine similarity and random selection. We can also use numpy to perform vector operations. The code looks like this:

# Import modules
import numpy as np
from nltk import ngrams
from nltk.metrics import jaccard_distance

# Define a function to generate responses
def generate_response(user_input):
    # Preprocess the user input
    user_input = preprocess(user_input)
    # Transform the user input into a vector using count vectorizer
    user_input_vect = count_vect.transform([user_input])
    # Initialize an empty list to store the cosine similarities
    similarities = []
    # Loop through each pattern vector
    for pattern_vect in X.toarray():
        # Calculate the cosine similarity between the user input vector and the pattern vector
        similarity = np.dot(user_input_vect, pattern_vect) / (np.linalg.norm(user_input_vect) * np.linalg.norm(pattern_vect))
        # Append the similarity to the list
        similarities.append(similarity)
    # Find the index of the pattern vector that has the highest cosine similarity with the user input vector
    index = np.argmax(similarities)
    # Find the corresponding tag and responses for that pattern vector
    tag = df["tag"][index]
    responses = df["responses"][index]
    # Randomly select one response from the list of responses
    response = np.random.choice(responses)
    # Return the response as a text string
    return response

The code continues like this:

# Test the function
print(generate_response("Hi"))
# Output: Hello
print(generate_response("What are your hobbies"))
# Output: I enjoy playing games and listening to music
print(generate_response("Do you like movies"))
# Output: I love movies, they are very entertaining

Step 4: Create the web app using Flask

The fourth step is to create the web app using Flask. The web app is the interface that will allow users to interact with our chatbot through a web browser. The web app will consist of two main components:

• The front-end, which is the part of the web app that users see and interact with. It includes elements such as HTML, CSS, JavaScript, or Bootstrap.

• The back-end, which is the part of the web app that handles the logic and functionality. It includes elements such as Python, Flask, or Jinja.

The web app will use the following logic:

• Render a HTML template that contains a form for the user input and a div for the chatbot response.

• Receive the user input from the form using a POST request.

• Pass the user input to the generate_response function from step 3.

• Receive the chatbot response from the generate_response function.

• Render the chatbot response in the div using Jinja.

To create the web app in Python, we can use Flask to create and run the web server, and Jinja to render the HTML templates. The code looks like this:

# Import modules
from flask import Flask, render_template, request

# Create an instance of Flask
app = Flask(__name__)

# Define a route for the home page
@app.route("/")
def home():
    # Render the home.html template
    return render_template("home.html")

# Define a route for the chat page
@app.route("/chat", methods=["POST"])
def chat():
    # Get the user input from the form
    user_input = request.form["user_input"]
    # Pass the user input to the generate_response function
    chatbot_response = generate_response(user_input)
    # Render the chat.html template with the user input and chatbot response
    return render_template("chat.html", user_input=user_input, chatbot_response=chatbot_response)

# Run the app
if __name__ == "__main__":
    app.run(debug=True)

The HTML templates look like this:

home.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Chatbot</title>
</head>
<body>
    <h1>Welcome to Chatbot</h1>
    <p>This is a simple chatbot that can answer some questions about greetings, goodbyes, hobbies, and movies.</p>
    <form action="/chat" method="POST">
        <label for="user_input">Enter your message:</label>
        <input type="text" id="user_input" name="user_input" required>
        <button type="submit">Send</button>
    </form>
</body>
</html>

chat.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Chatbot</title>
</head>
<body>
    <h1>Chatbot</h1>
    <div id="chatbox">
        <p>You: {{ user_input }}</p>
        <p>Chatbot: {{ chatbot_response }}</p>
    </div>
    <form action="/chat" method="POST">
        <label for="user_input">Enter your message:</label>
        <input type="text" id="user_input" name="user_input" required>
        <button type="submit">Send</button>
    </form>
</body>
</html>

Step 5: Test and interact with the chatbot

The fifth and final step is to test and interact with the chatbot. This step involves running the web app and entering some messages or questions for the chatbot. You can also check if the chatbot responds correctly and appropriately to your inputs.

To test and interact with the chatbot in Python, you can use your web browser to access the URL of your web app, which is usually http://localhost:5000/. You can also use some tools such as Postman or curl to send requests to your web app.

Conclusion

In this article, we have shown you how to use Python to create a chatbot using NLTK and Flask. We have guided you through the following steps:

• Import and load the data file

• Preprocess the data

• Build the chatbot model using NLTK

• Create the web app using Flask

• Test and interact with the chatbot

We hope this article has helped you learn how to create your own chatbot using Python. However, this is not the end of the journey, but rather the beginning. You can always improve and update your chatbot based on user feedback, market trends, or new technologies. Chatbots are a dynamic and evolving field that offers endless possibilities and opportunities for creators and users alike. We hope this article has inspired you to create your own chatbot and join the NLP revolution. Happy coding!