Book Image

Building AI Applications with ChatGPT APIs

By : Martin Yanev
4.2 (5)
Book Image

Building AI Applications with ChatGPT APIs

4.2 (5)
By: Martin Yanev

Overview of this book

Combining ChatGPT APIs with Python opens doors to building extraordinary AI applications. By leveraging these APIs, you can focus on the application logic and user experience, while ChatGPT’s robust NLP capabilities handle the intricacies of human-like text understanding and generation. This book is a guide for beginners to master the ChatGPT, Whisper, and DALL-E APIs by building ten innovative AI projects. These projects offer practical experience in integrating ChatGPT with frameworks and tools such as Flask, Django, Microsoft Office APIs, and PyQt. Throughout this book, you’ll get to grips with performing NLP tasks, building a ChatGPT clone, and creating an AI-driven code bug fixing SaaS application. You’ll also cover speech recognition, text-to-speech functionalities, language translation, and generation of email replies and PowerPoint presentations. This book teaches you how to fine-tune ChatGPT and generate AI art using DALL-E APIs, and then offers insights into selling your apps by integrating ChatGPT API with Stripe. With practical examples available on GitHub, the book gradually progresses from easy to advanced topics, cultivating the expertise required to develop, deploy, and monetize your own groundbreaking applications by harnessing the full potential of ChatGPT APIs.
Table of Contents (19 chapters)
Free Chapter
1
Part 1:Getting Started with OpenAI APIs
4
Part 2: Building Web Applications with the ChatGPT API
8
Part 3: The ChatGPT, DALL-E, and Whisper APIs for Desktop Apps Development
14
Part 4:Advanced Concepts for Powering ChatGPT Apps

Building and Using the Fine-Tuned Model

In this section, we will explore the process of creating and utilizing a fine-tuned model using OpenAI’s CLI. OpenAI offers newcomers the opportunity to avail of a $5 credit to access the ChatGPT API and its fine-tuning services.

Fine-tuning involves building a specialized model based on an existing base model, and in our example, we will use the most advanced ChatGPT model available for fine-tuning called davinci. We will improve the performance of that model for book summarization tasks.

We will learn how to start a fine-tuning job, which uploads and processes the training data, and then we’ll monitor its progress until completion. Once the fine-tuning job is done, we will use the newly created fine-tuned model to generate text. We’ll learn how to make requests to the fine-tuned model using the completions API, and we’ll cover how to manage and delete fine-tuned models if needed.

We will begin by using our...