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Building LLM Powered  Applications

You're reading from   Building LLM Powered Applications Create intelligent apps and agents with large language models

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835462317
Length 342 pages
Edition 1st Edition
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Author (1):
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Valentina Alto Valentina Alto
Author Profile Icon Valentina Alto
Valentina Alto
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Table of Contents (16) Chapters Close

Preface 1. Introduction to Large Language Models FREE CHAPTER 2. LLMs for AI-Powered Applications 3. Choosing an LLM for Your Application 4. Prompt Engineering 5. Embedding LLMs within Your Applications 6. Building Conversational Applications 7. Search and Recommendation Engines with LLMs 8. Using LLMs with Structured Data 9. Working with Code 10. Building Multimodal Applications with LLMs 11. Fine-Tuning Large Language Models 12. Responsible AI 13. Emerging Trends and Innovations 14. Other Books You May Enjoy
15. Index

Developing the front-end with Streamlit

Now that we have seen the logic behind an LLM-powered StoryScribe, it is time to give our application a GUI. To do so, we will once again leverage Streamlit. As always, you can find the whole Python code in the GitHub book repository at https://github.com/PacktPublishing/Building-LLM-Powered-Applications.

As per the previous sections, you need to create a .py file to run in your terminal via streamlit run file.py. In our case, the file will be named storyscribe.py.

The following are the main steps to set up the front-end:

  1. Configuring the application webpage:
    st.set_page_config(page_title="StoryScribe", page_icon="")
    st.header(' Welcome to StoryScribe, your story generator and promoter!')
    load_dotenv()
    openai_api_key = os.environ['OPENAI_API_KEY']
    
  2. Initialize the dynamic variables to be used within the placeholders of the prompts:
    topic = st.sidebar.text_input...
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