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Building AI Applications with OpenAI APIs

Building AI Applications with OpenAI APIs - Second Edition

By : Martin Yanev
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Building AI Applications with OpenAI APIs

Building AI Applications with OpenAI APIs

By: Martin Yanev

Overview of this book

Unlock the power of AI in your applications with ChatGPT with this practical guide that shows you how to seamlessly integrate OpenAI APIs into your projects, enabling you to navigate complex APIs and ensure seamless functionality with ease. This new edition is updated with key topics such as OpenAI Embeddings, which’ll help you understand the semantic relationships between words and phrases. You’ll find out how to use ChatGPT, Whisper, and DALL-E APIs through 10 AI projects using the latest OpenAI models, GPT-3.5, and GPT-4, with Visual Studio Code as the IDE. Within these projects, you’ll integrate ChatGPT with frameworks and tools such as Flask, Django, Microsoft Office APIs, and PyQt. You’ll get to grips with NLP tasks, build a ChatGPT clone, and create an AI code bug-fixing SaaS app. The chapters will also take you through speech recognition, text-to-speech capabilities, language translation, generating email replies, creating PowerPoint presentations, and fine-tuning ChatGPT, along with adding payment methods by integrating the ChatGPT API with Stripe. By the end of this book, you’ll be able to develop, deploy, and monetize your own groundbreaking applications by harnessing the full potential of ChatGPT APIs.
Table of Contents (19 chapters)
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1
Part 1:Getting Started with OpenAI APIs
4
Part 2: Build Web Applications with ChatGPT API
8
Part 3: ChatGPT, DALL-E, and Whisper APIs for Desktop Apps Development
14
Part 4: Advanced Concepts for Powering ChatGPT Apps

Summary

In this chapter, we explored the Whisper API, a powerful tool for converting audio into text through advanced speech recognition and translation. The chapter provided step-by-step instructions on developing a language transcription project using Python, covering essential aspects such as handling audio files, installing necessary libraries, and setting up the API key. You learned how to transcribe and translate audio files using the Whisper API. The chapter also introduced a voice transcription application, integrating Tkinter and the Whisper API for real-time transcription.

You also learned how to use PyDub, a powerful audio processing library for Python, with the Whisper API to overcome the file size limitation of 25 MB. By leveraging PyDub’s capabilities, we can efficiently split large audio files into smaller segments, enabling the seamless transcription of lengthy recordings. You saw how to use PyDub and the Whisper API to process larger audio files in the language...

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Building AI Applications with OpenAI APIs
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