How to create chatbot using Python

How to create chatbot using Python: Hello Everyone! Welcome to this article. If you are here you might be looking to create a chatbot using python. But you don’t know working with python.

Don’t panic. I’ll tell you how to create a chatbot using python. You might be thinking about how we can do this. Believe me, it’s easy to do.

What is chatbot

A system or computer software that uses artificial intelligence to maintain interactions with people is known as a chatbot.

They are in charge of acting out a dialogue amongst people. Their goal is to assist all business's various customer service divisions in making financial savings and ensuring a better customer experience. There are considerable employee benefits as well, even though the majority of chatbot attention focuses on customer-facing events. A chatbot can speed up processes and help staff with onboarding, data retrieval, and employee satisfaction surveys.

How to create chatbot using Python

We will take a step-by-step approach and break down the Python chat boot creation process. To create a chatbot in Python, you need to import all the necessary packages and start the variables you want to use in your chatbot project. Also remember that when working with text data, you need to pre-process the data on your dataset before designing the ML model. This is where tokenizing helps with text data - it helps split large text datasets into smaller, readable parts (like words). Once this is done, you can also go for limitation which converts a word into its lima form. It then creates a pickle file to store the Python items used to predict the boot response.

1. Develop dependence.

The first step in creating a chatbot in Python with a chatbot library is to install the library on your system. It is best if you create and use a new Python virtual environment for installation. To do this, you need to type and execute this command in your Python terminal.

pip install chatterbot

pip install chatterbot_corpus

Now that your setup is ready, we can move on to the next step in creating a chat boot using python.

2. Import classes.

Importing Classes is the second step in the Python chat boot process. All you need to do is import two classes - Chat Boot to Chat Boot and Chat Boot Trainers to List Trainer.

from chatterbot import ChatBot

from chatterbot.trainers import List Trainer

3. Create and train chatbots.

This is the third step in creating a chatbot in Python. The chatbot you are creating will be an example of the class "ChatBot". After creating a new ChatterBot example, you can train the bot to improve its performance. Training ensures that the bot has enough knowledge to get started with specific answers to specific information. You must now execute the following command:

my_bot = ChatBot (name='PyBot', read_only=True, logic_adapters= ['chatterbot. logic.MathematicalEvaluation', 'chatterbot. logic. BestMatch'])

Here, the argument (which corresponds to the parameter name) represents the name of your Python chatbot. If you want to disable boot learning after training, you can add the command "read_only = True". The "logic_adapters" command displays a list of adapters used to train chatbots. While "chatterbot.logic.MathematicalEvaluation" helps bot to solve math problems, "chatterbot.logic.BestMatch" helps to choose the best match from the list of answers we have provided. Since you need to provide a list of answers, you can do so by listing the strings that can later be used to train your Python chatbot and find the best match for each question. Can be done. An example of an answer is that you can train your chatbot to learn via python:

small_talk = ['hi there!',

"hi!',

"how do you do?',

 "how are you?', 

'i\'m cool.', 

'fine, you?', 

'always cool.', 

'i\'m ok', 

'glad to hear that.', 

'il'm fine', 

'glad to hear that.', 

'i feel awesome', 

'excellent, glad to hear that.', 

'not so good', 

'sorry to hear that.', 

'what's your name?', 

'i\'m pybot. ask me a math question, please.']

math_talk_1 = ['pythagorean theorem',

'a squared plus b squared equals c squared.']

math_talk_2 = ['law of cosines',

'C**2 = a**2 + b**2 - 2 * a * b * cos(gamma)']

You can also create and train a boot by writing an example of "ListTrainer" and providing it with a list of such wires:

list_trainer = List Trainer(my_bot)

for item in (small_talk, math_talk_1, math_talk_2):

list_trainer.train(item)

Now, your Python chatbot is ready to chat.

4. Chat with Python Chatbot.

To interact with your Python chat boot, you can use the .get_response () function. However, it is important to understand that a chatbot using python may not know how to answer all your questions. Since his knowledge and training is still very limited, you have to give him time and provide more training data to further train him.

5. Train your Python Chatbot with data corpus.

In this final step of creating a chatbot in Python, you can use an existing corpus of data to further train your Python chatbot. Here's an example of how to train your Python chatbot with the data corpus provided by the bot:

from chatterbot.trainers import ChatterBotCorpusTrainer

corpus_trainer = ChatterBotCorpusTrainer(my_bot) corpus_trainer.train('chatterbot.corpus.English)

Fortunately, ChatterBot offers this functionality in many different languages. So, you can also tell the corpus subset in the language of your choice. This is how we create chatbots in Python.

Conclusion:

So, this was all about "How to create chatbot using Python". We have inserted a lot of effort into bringing this in front of you. If you want to ask something, or you have any queries feel free to comment below. We will definitely respond back. Thank you very much for being here.