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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

ChatGPT clone conversation retention

We successfully created a simple ChatGPT clone using OpenAI’s GPT-3.5 engine and Flask. Now, let’s take our clone a step further by implementing a feature that retains conversation history and incorporates it into the context of the conversation.

Retaining conversation history allows our ChatGPT clone to maintain context across interactions. Each exchange between the user and the AI is stored in a list called conversation_history. The following list keeps track of both the user’s messages and the AI’s responses:

  1. We begin by initializing an empty list called conversation_history outside of our Flask application. This list will store all the messages exchanged between the user and the AI:
    conversation_history = []
    @app.route("/")
    def index():
        return render_template("index.html")
  2. After receiving a message from the user, we initially append it to the conversation_history...
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