How to Build an AI Chatbot for WhatsApp with Python, Twilio, and OpenAI: A Step-by-Step Guide

Since, in this tutorial series, we focus on the full-stack development of the chatbot, we will not go through the AI part in too much detail. We will create a very simple python server that listens to requests using a POST Request. Once we created our account on Crisp, we will need to retrieve our live chat code.

We then load the data from the file and preprocess it using the preprocess function. The function tokenizes the data, converts all words to lowercase, removes stopwords and punctuation, and lemmatizes the words. Remember, building chatbots is as much an art as it is a science.

Communicating with the Python chatbot

Chatbots relying on logic adapters work best for simple applications where there are not so many dialog variations and the conversation flow is easy to control. To demonstrate how to create a chatbot in Python using a ready-to-use library, we decided to apply the ChatterBot library. All these specifics make the transformer model faster for text processing tasks than architectures based on recurrent or convolutional layers. In this tutorial, we will be using the Chatterbot Python library to build an AI-based Chatbot. Conversational chatbot Python uses Logic Adapters to determine the logic for how a response to a given input statement is selected.

Google adopted Python back in 2006, and they’ve used it for many platforms and applications since. There is a lot of hype around Python at the moment, especially. Complete Jupyter Notebook File- How to create a Chatbot using Natural Language Processing Model and Python Tkinter GUI Library. How to create a Tkinter App in Python is out of the scope of this article but you can refer to the official documentation for more information. Interested in learning Python, read ‘Python API Requests- A Beginners Guide On API Python 2022‘. Learn how to use Huggingface transformers and PyTorch libraries to summarize long text, using pipeline API and T5 transformer model in Python.

In API.json file

It is mostly used by companies to gauge the sentiments of their users and customers. By understanding how they feel, companies can improve user/customer service and experience. Here, the input can either be text or speech and the chatbot acts accordingly.

A chatbot is a piece of software or a computer program that mimics human interaction via voice or text exchanges. More users are using chatbot virtual assistants to complete basic activities or get a solution addressed in business-to-business (B2B) and business-to-consumer (B2C) settings. Process of converting words into numbers by generating vector embeddings from the tokens generated above. This is given as input to the neural network model for understanding the written text. Today, almost all companies have chatbots to engage their users and serve customers by catering to their queries.

The get_retriever function will create a retriever based on data we extracted in the previous step using scrape.py. The StreamHandler class will be used for streaming the responses from ChatGPT to our application. Individual consumers and businesses both are increasingly employing chatbots today, making life convenient with their 24/7 availability. Not only this, it also saves time for companies majorly as their customers do not need to engage in lengthy conversations with their service reps.

Its versatility and an array of robust libraries make it the go-to language for chatbot creation. To start off, you’ll learn how to export data from a WhatsApp chat conversation. In lines 9 to 12, you set up the first training round, where you pass a list of two strings to trainer.train().

The best part about using Python for building AI chatbots is that you don’t have to be a programming expert to begin. You can be a rookie, and a beginner developer, and still be able to use it efficiently. As these commands are run in your terminal application, ChatterBot is installed along with its dependencies in a new Python virtual environment. In a breakthrough announcement, OpenAI recently introduced the ChatGPT API to developers and the public. Particularly, the new “gpt-3.5-turbo” model, which powers ChatGPT Plus has been released at a 10x cheaper price, and it’s extremely responsive as well.

• The choice between AI and ML is in part a choice between levels of chatbot complexity.
• The model consists of an embedding layer, a dropout layer, a convolutional layer, a max pooling layer, an LSTM layer, and two dense layers.
• With each new question asked, the bot is being trained to create new modules and linkages to cover 80% of the questions in a domain or a given scenario.
• The right choice of the library depends on the specific requirements of the chatbot project.

If you created your OpenAI account earlier, you may have free credit worth $18. After the free credit is exhausted, you will have to pay for the API access. Next, run python main.py a couple of times, changing the human message and id as desired with each run. You should have a full conversation input and output with the model. Next, we need to update the main function to add new messages to the cache, read the previous 4 messages from the cache, and then make an API call to the model using the query method. It'll have a payload consisting of a composite string of the last 4 messages.

And you'll need to make many decisions that will be critical to the success of your app. Don’t forget to notice that we have used a Dropout layer which helps in preventing overfitting during training. The next step is the usual one where we will import the relevant libraries, the significance of which will become evident as we proceed. After this, we have to represent our sentences using this vocabulary and its size. In our case, we have 17 words in our library, So, we will represent each sentence using 17 numbers. We will mark ‘1’ where the word is present and ‘0’ where the word is absent.

This is important if we want to hold context in the conversation. In the next section, we will focus on communicating with the AI model and handling the data transfer between client, server, worker, and the external API. In order to use Redis JSON's ability to store our chat history, we need to install rejson provided by Redis labs. Next, to run our newly created Producer, update chat.py and the WebSocket /chat endpoint like below.

Building a basic chatbot using Python

Do you want to take your customer interactions to the next level? With the

power of Artificial Intelligence development, you can now make your own

chatbot. Built by OpenAI, the ChatGPT API allows businesses to integrate

advanced NLP models into their applications and websites, enabling dynamic and

human-like conversations with users. You've learned how to make your first AI in Python by making a chatbot that chooses random responses from a list and keeps track of keywords and responses it learns using lists. We'll add an if statement inside the while loop but outside of the for loop to check if keyword_found is false.

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