How to build a AI chatbot using NLTK and Deep Learning

The GPT class is initialized with the Huggingface model url, authentication header, and predefined payload. But the payload input is a dynamic field that is provided by the query method and updated before we send a request to the Huggingface endpoint. For up to 30k tokens, Huggingface provides access to the inference API for free. The model we will be using is the GPT-J-6B Model provided by EleutherAI.

Ultimately the message received from the clients will be sent to the AI Model, and the response sent back to the client will be the response from the AI Model. In the src root, create a new folder named socket and add a file named connection.py. In this file, we will define the class that controls the connections to our WebSockets, and all the helper methods to connect and disconnect.

How to Model the Chat Data

A chatbot instance can be created by creating a Chatbot object. The Chatbot object needs to have the name of the chatbot and must reference any logic or storage adapters you might want to use. Conversational chatbot Python uses Logic Adapters  to determine the logic for how a response to a given input statement is selected. Chatterbot has built-in functions to download and use datasets from the Chatterbot Corpus for initial training. It is also evident that people are more engrossed in messaging apps than simply passing through various social media.

To briefly add, you will need Python, Pip, OpenAI, and Gradio libraries, an OpenAI API key, and a code editor like Notepad++. All these tools may seem intimidating at first, but believe me, the steps are easy and can be deployed by anyone. 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. Basically, OpenAI has opened the door for endless possibilities and even a non-coder can implement the new ChatGPT API and create their own AI chatbot. So in this article, we bring you a tutorial on how to build your own AI chatbot using the ChatGPT API.

Introduction to chatterbot

Tokenizing text data is the first and most basic thing you can do with it. Tokenizing is the process of breaking a text into small pieces, like words. The data file is in the JSON format, the json package to read the JSON file into Python.

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