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

Fine-Tuned Model Dataset Preparation

To effectively fine-tune our model, we need to prepare the training data in a specific format. In this section, we will walk you through the process of data preparation using a JSON file and the OpenAI CLI data preparations tool.

When preparing data for a fine-tuned model such as OpenAI’s, it’s essential to follow a structured process to ensure optimal performance and accurate results. The first step is to gather the relevant data that will be used to train the model. This data can come from a variety of sources, such as books, articles, or even specialized datasets.

To begin, create a new folder called Fine_Tune_Data on your desktop, and inside the folder, create a new file called train_data.json. For our book summary fine-tuned model, we will use one-sentence summaries for 30 different books. Those summaries will be written inside the file we just created in a JSON format:

[
{"prompt": "Book Summary: The Adventure...