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Building AI Applications with OpenAI APIs

You're reading from   Building AI Applications with OpenAI APIs Leverage ChatGPT, Whisper, and DALL-E APIs to build 10 innovative AI projects

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781835884003
Length 252 pages
Edition 2nd Edition
Languages
Concepts
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Author (1):
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Martin Yanev Martin Yanev
Author Profile Icon Martin Yanev
Martin Yanev
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1:Getting Started with OpenAI APIs FREE CHAPTER
2. Chapter 1: Getting Started with the ChatGPT API for NLP Tasks 3. Chapter 2: Building a ChatGPT Clone 4. Part 2: Build Web Applications with ChatGPT API
5. Chapter 3: Creating and Deploying a Code Bug-Fixing Application Using Flask 6. Chapter 4: Integrating the Code Bug-Fixing Application with a Payment Service 7. Chapter 5: Quiz Generation App with ChatGPT and Django 8. Part 3: ChatGPT, DALL-E, and Whisper APIs for Desktop Apps Development
9. Chapter 6: Language Translation Desktop App with the ChatGPT API and Microsoft Word 10. Chapter 7: Building an Outlook Email Reply Generator 11. Chapter 8: Essay Generation Tool with PyQt and the ChatGPT API 12. Chapter 9: Integrating the ChatGPT and DALL-E APIs: Building an End-to-End PowerPoint Presentation Generator 13. Chapter 10: Speech Recognition and Text-to-Speech with the Whisper API 14. Part 4: Advanced Concepts for Powering ChatGPT Apps
15. Chapter 11: Choosing the Right ChatGPT API Model 16. Chapter 12: Fine-Tuning ChatGPT to Create Unique API Models 17. Index 18. Other Books You May Enjoy

Summary

In this chapter of the book, you were introduced to the process of building a ChatGPT clone, which is a chatbot that utilizes OpenAI’s language model to generate human-like responses to user input. The application was built using Flask, a lightweight web framework for Python, and was customizable to allow for the use of different OpenAI models and other options such as the length of the generated text.

We also covered topics such as creating and generating the frontend HTML for the ChatGPT clone, intercepting ChatGPT API endpoints, passing user input from the frontend to the backend using AJAX, and displaying the generated text in the frontend.

We learned how to retain conversation history to enable our ChatGPT clone to become more contextually aware, providing more coherent and relevant responses based on previous interactions.

In Chapter 3, you will learn how to create and deploy an AI-powered code bug-fixing SaaS application using Flask and the ChatGPT language...

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