Write a simple chatbot with Python

Using Python to write a simple chatbot can start with the most basic logic and then gradually add more complex features. Here we will first implement a simple chatbot that can respond basicly based on the content entered by the user.

step:

Prepare the basic dialogue framework: First, implement a simple input-output function and return the corresponding reply based on the user's input.

Design some simple rules: set some simple rules for robots, such as common greetings, answers to common questions, etc.

Extended features: You can use the random library to add some diversity to chats, or use more complex natural language processing libraries such as nltk or spaCy to handle more complex conversations.

Example: A simple rule-driven chatbot

import random
 
# Simple greetings and answer rules for chatbotsresponses = {
    'Hello': ['Hello!  Nice to meet you!  ', 'Hi!  What's the point of doing?  ', 'Hello!  What can I do for you?  '],
    'goodbye': ['goodbye!  I wish you a wonderful day!  ', 'Bye-Bye!  Hope to see you next time!  ', 'Goodbye, take care of yourself!  '],
    'May I have your name': ['I am a robot and have no name yet.  ', 'My name is robot, you can call me a little assistant!  '],
    'What will you do': ['I can answer simple questions and help you do small things.  ', 'I can chat with you and answer some common questions.  '],
}
 
# Function: Get the robot responsedef get_bot_response(user_input):
    # Convert user input to lowercase to simplify matching    user_input = user_input.strip().lower()
    
    # If the user input contains known keywords, return a preset response    for key in responses:
        if key in user_input:
            return (responses[key])
    
    # If there is no matching keyword, return the default answer    return 'Sorry, I don't quite understand what you mean.  '
 
# Main chat functiondef chat():
    print("Hello, I'm your chatbot. Enter 'Goodbye' to end the conversation.")
    while True:
        user_input = input("you:")
        if 'goodbye' in user_input:  # If the user says "goodbye", end the conversation            print("robot:" + (responses['goodbye']))
            break
        else:
            response = get_bot_response(user_input)
            print("robot:" + response)
 
# Start the chatbotif __name__ == "__main__":
    chat()

Code parsing:

Predefined response rules: The responses dictionary stores some simple greetings, questions and answers, and the robot will return the corresponding response based on the user's input.

The user enters "Hello", and the robot will randomly select a response from the corresponding list of Hello.

The user enters "Goodbye" and the robot will terminate the conversation and return the farewell.

get_bot_response function: This function checks whether the input contains known keywords (such as "hello" and "what is your name") based on the user's input. If it is included, it returns the corresponding response. Otherwise, return to the default answer: "Sorry, I don't quite understand what you mean."

chat function: This function is the main control part of the chat, using an infinite loop to interact with the user until the user enters "Goodbye" to end the conversation.

Sample output:

Hello, I am your chatbot. Enter 'Goodbye' to end the conversation.
You: Hello
Robot: Hello! Nice to meet you!
You: What's your name
Robot: My name is robot, you can call me a little assistant!
You: What will you do
Robot: I can answer simple questions and help you do small things.
You: Goodbye
Robot: Goodbye, take care of yourself!

Further expansion:

This simple chatbot can be further expanded by:

Add more rules and responses: You can add more conversation rules to make the chat more fun.

Introducing natural language processing: Use nltk, spaCy, or other NLP libraries to process user input, analyze intent and entities in sentences, and achieve more complex dialogue.

Memory function: You can let the robot remember some user's personal information (such as name, interests, etc.) and use it in subsequent conversations.

Machine Learning: Introducing machine learning models (such as deep learning-based dialogue generation models) enable chatbots to learn by themselves and improve conversation quality.

This is the end of this article about writing a simple chatbot using Python. For more related Python chatbot content, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!