I will make chatbot using ai in python with integration
I will make chatbot using ai in python with integration
Sure! Chatbots are a great way to automate customer service and provide quick responses to frequently asked questions. Python has several libraries that make it easy to build chatbots with AI capabilities. Some popular libraries include ChatterBot, Rasa, and OpenAI’s GPT-3 engine123.
Get chatbot using ai in python with integration
Table of Contents
- Demo
- Project Overview
- Prerequisites
- Step 1: Create a Chatbot Using Python ChatterBot
- Step 2: Begin Training Your Chatbot
- Step 3: Export a WhatsApp Chat
- Step 4: Clean Your Chat Export
- Step 5: Train Your Chatbot on Custom Data and Start Chatting
- Conclusion
- Next Steps
Chatbots can provide real-time customer support and are therefore a valuable asset in many industries. When you understand the basics of the ChatterBot library, you can build and train a self-learning chatbot with just a few lines of Python code.
You’ll get the basic chatbot up and running right away in step one, but the most interesting part is the learning phase, when you get to train your chatbot. The quality and preparation of your training data will make a big difference in your chatbot’s performance.
To simulate a real-world process that you might go through to create an industry-relevant chatbot, you’ll learn how to customize the chatbot’s responses. You’ll do this by preparing WhatsApp chat data to train the chatbot. You can apply a similar process to train your bot from different conversational data in any domain-specific topic.
In this tutorial, you’ll learn how to:
- Build a command-line chatbot with ChatterBot
- Train the chatbot to customize its responses
- Export your WhatsApp chat history
- Perform data cleaning on the chat export using regular expressions
- Retrain the chatbot with industry-specific data
- You’ll also learn how ChatterBot stores your training data, and you’ll find suggestions and pointers for next steps, so you can start collecting real user data and let the chatbot learn from it.
Overall, in this tutorial, you’ll quickly run through the basics of creating a chatbot with ChatterBot and learn how Python allows you to get fun and useful results without needing to write a lot of code.
Step 1: Create a Chatbot Using Python ChatterBot
In this step, you’ll set up a virtual environment and install the necessary dependencies. You’ll also create a working command-line chatbot that can reply to you—but it won’t have very interesting replies for you yet.
To get started with your chatbot project, create and activate a virtual environment, then install chatterbot and pytz:
ChatterBot: Build a Chatbot With Python
ChatterBot: Build a Chatbot With Python
by Martin Breuss Oct 12, 2022 9 Comments data-science intermediate projects
Tweet Share Email
Table of Contents
Demo
Project Overview
Prerequisites
Step 1: Create a Chatbot Using Python ChatterBot
Step 2: Begin Training Your Chatbot
Step 3: Export a WhatsApp Chat
Step 4: Clean Your Chat Export
Step 5: Train Your Chatbot on Custom Data and Start Chatting
Conclusion
Next Steps
Remove ads
Chatbots can provide real-time customer support and are therefore a valuable asset in many industries. When you understand the basics of the ChatterBot library, you can build and train a self-learning chatbot with just a few lines of Python code.
You’ll get the basic chatbot up and running right away in step one, but the most interesting part is the learning phase, when you get to train your chatbot. The quality and preparation of your training data will make a big difference in your chatbot’s performance.
To simulate a real-world process that you might go through to create an industry-relevant chatbot, you’ll learn how to customize the chatbot’s responses. You’ll do this by preparing WhatsApp chat data to train the chatbot. You can apply a similar process to train your bot from different conversational data in any domain-specific topic.
In this tutorial, you’ll learn how to:
Build a command-line chatbot with ChatterBot
Train the chatbot to customize its responses
Export your WhatsApp chat history
Perform data cleaning on the chat export using regular expressions
Retrain the chatbot with industry-specific data
You’ll also learn how ChatterBot stores your training data, and you’ll find suggestions and pointers for next steps, so you can start collecting real user data and let the chatbot learn from it.
Overall, in this tutorial, you’ll quickly run through the basics of creating a chatbot with ChatterBot and learn how Python allows you to get fun and useful results without needing to write a lot of code.
Source Code: Click here to download the free source code that you’ll use to build a chatbot.
Demo
At the end of this tutorial, you’ll have a command-line chatbot that can respond to your inputs with semi-meaningful replies:
You’ll achieve that by preparing WhatsApp chat data and using it to train the chatbot. Beyond learning from your automated training, the chatbot will improve over time as it gets more exposure to questions and replies from user interactions.
Remove ads
Project Overview
The ChatterBot library combines language corpora, text processing, machine learning algorithms, and data storage and retrieval to allow you to build flexible chatbots.
You can build an industry-specific chatbot by training it with relevant data. Additionally, the chatbot will remember user responses and continue building its internal graph structure to improve the responses that it can give.
Attention: While ChatterBot is still a popular open source solution for building a chatbot in Python, it hasn’t been actively maintained for a while and has therefore accumulated a significant number of issues.
There are multiple forks of the project that implement fixes and updates to the existing codebase, but you’ll have to personally pick the fork that implements the solution you’re looking for and then install it directly from GitHub. A fork might also come with additional installation instructions.
To get started, however, you won’t use a fork. Instead, you’ll use a specific pinned version of the library, as distributed on PyPI. You’ll find more information about installing ChatterBot in step one.
In this tutorial, you’ll start with an untrained chatbot that’ll showcase how quickly you can create an interactive chatbot using Python’s ChatterBot. You’ll also notice how small the vocabulary of an untrained chatbot is.
Next, you’ll learn how you can train such a chatbot and check on the slightly improved results. The more plentiful and high-quality your training data is, the better your chatbot’s responses will be.
Therefore, you’ll either fetch the conversation history of one of your WhatsApp chats or use the provided chat.txt file that you can download here:
Source Code: Click here to download the free source code that you’ll use to build a chatbot.
It’s rare that input data comes exactly in the form that you need it, so you’ll clean the chat export data to get it into a useful input format. This process will show you some tools you can use for data cleaning, which may help you prepare other input data to feed to your chatbot.
After data cleaning, you’ll retrain your chatbot and give it another spin to experience the improved performance.
When you work through this process from start to finish, you’ll get a good idea of how you can build and train a Python chatbot with the ChatterBot library so that it can provide an interactive experience with relevant replies.
Prerequisites
Before you get started, make sure that you have a Python version available that works for this ChatterBot project. What version of Python you need depends on your operating system:
Windows
Linux
macOS
You need to use a Python version below 3.8 to successfully work with the recommended version of ChatterBot in this tutorial. You can install Python 3.7.9 using pyenv-win.
If you’ve installed the right Python version for your operating system, then you’re ready to get started. You’ll touch on a handful of Python concepts while working through the tutorial:
Conditional statements
while loops for iteration
Lists and tuples
Python functions
Substring checks and substring replacement
File input/output
Python comprehensions and generator expressions
Regular expressions (regex) using re
If you’re comfortable with these concepts, then you’ll probably be comfortable writing the code for this tutorial. If you don’t have all of the prerequisite knowledge before starting this tutorial, that’s okay! In fact, you might learn more by going ahead and getting started. You can always stop and review the resources linked here if you get stuck.
Step 1: Create a Chatbot Using Python ChatterBot
In this step, you’ll set up a virtual environment and install the necessary dependencies. You’ll also create a working command-line chatbot that can reply to you—but it won’t have very interesting replies for you yet.
To get started with your chatbot project, create and activate a virtual environment, then install chatterbot and pytz:
Windows
Linux + macOS
PS> python -m venv venv
PS> venv\Scripts\activate
(venv) PS> python -m pip install chatterbot==1.0.4 pytz
Running these commands in your terminal application installs ChatterBot and its dependencies into a new Python virtual environment.
Note: At the time of writing, the ChatterBot library hasn’t seen a lot of maintenance for a while. It’s therefore facing some issues that can get annoying quickly.
For this tutorial, you’ll use ChatterBot 1.0.4, which also works with newer Python versions on macOS and Linux. On Windows, you’ll have to stay on a Python version below 3.8. ChatterBot 1.0.4 comes with a couple of dependencies that you won’t need for this project. However, you’ll quickly run into more problems if you try to use a newer version of ChatterBot or remove some of the dependencies.
So just relax into this selected version and give it a spin. If you’re hooked and you need more, then you can switch to a newer version later on.
After the installation is complete, running python -m pip freeze should bring up list of installed dependencies that’s similar to what you can find in the provided sample code’s requirements.txt file:
Source Code: Click here to download the free source code that you’ll use to build a chatbot.
With the installation out of the way, and ignoring some of the issues that the library currently has, you’re ready to get started! Create a new Python file, call it bot.py, and add the code that you need to get a basic chatbot up and running:
# bot.py
from chatterbot import ChatBot
chatbot = ChatBot("Chatpot")
exit_conditions = (":q", "quit", "exit")
while True:
query = input("> ")
if query in exit_conditions:
break
else:
print(f"🪴 {chatbot.get_response(query)}")
After importing ChatBot in line 3, you create an instance of ChatBot in line 5. The only required argument is a name, and you call this one "Chatpot". No, that’s not a typo—you’ll actually build a chatty flowerpot chatbot in this tutorial! You’ll soon notice that pots may not be the best conversation partners after all.
In line 8, you create a while loop that’ll keep looping unless you enter one of the exit conditions defined in line 7. Finally, in line 13, you call .get_response() on the ChatBot instance that you created earlier and pass it the user input that you collected in line 9 and assigned to query.
The call to .get_response() in the final line of the short script is the only interaction with your chatbot. And yet—you have a functioning command-line chatbot that you can take for a spin.
When you run bot.py, ChatterBot might download some data and language models associated with the NLTK project. It’ll print some information about that to your console. Python won’t download this data again during subsequent runs.
Note: The NLTK project installs the data that ChatterBot uses into a default location on your operating system:
Windows: C:\nltk_data\
Linux: /usr/share/nltk_data/
macOS: /Users/<username>/nltk_data/
NLTK will automatically create the directory during the first run of your chatbot.
If you’re ready to communicate with your freshly homegrown Chatpot, then you can go ahead and run the Python file:
$ python bot.py
After the language models are set up, you’ll see the greater than sign (>) that you defined in bot.py as your input prompt. You can now start to interact with your chatty pot:
> hello
🪴 hello
> are you a plant?
🪴 hello
> can you chat, pot?
🪴 hello
Well … your chat-pot is responding, but it’s really struggling to branch out. Tough to expect more from a potted plant—after all, it’s never gotten to see the world!
Note: On Windows PowerShell, the potted plant emoji (🪴) might not render correctly. Feel free to replace it with any other prompt you like.
Even if your chat-pot doesn’t have much to say yet, it’s already learning and growing. To test this out, stop the current session. You can do this by typing one of the exit conditions—":q", "quit", or "exit". Then start the chatbot another time. Enter a different message, and you’ll notice that the chatbot remembers what you typed during the previous run:
> hi
🪴 hello
> what's up?
🪴 are you a plant?
During the first run, ChatterBot created a SQLite database file where it stored all your inputs and connected them with possible responses. There should be three new files that have popped up in your working directory:
./
├── bot.py
├── db.sqlite3
├── db.sqlite3-shm
└── db.sqlite3-wal
ChatterBot uses the default SQLStorageAdapter and creates a SQLite file database unless you specify a different storage adapter.
Note: The main database file is db.sqlite3, while the other two, ending with -wal and -shm, are temporary support files.
Because you said both hello and hi at the beginning of the chat, your chat-pot learned that it can use these messages interchangeably. That means if you chat a lot with your new chatbot, it’ll gradually have better replies for you. But improving its responses manually sounds like a long process!
Now that you’ve created a working command-line chatbot, you’ll learn how to train it so you can have slightly more interesting conversations.
BASIC : $750
I'll create basic chatbots like FAQ, article suggestion, and Q&A bots, among others.
STANDARD : $2,000
l will assist you in creating chatbots for lead generation, making arrangements,
PREMIUM : $7,000
