Developing LLM App Frontends with Streamlit
1h 43m 52s
English
Paid
This byte-sized course will teach Streamlit fundamentals and how to use Streamlit to create a frontend for your LLM-powered applications.
Read more about the course
In this project-based course you'll learn to use Streamlit to create a frontend for an LLM-powered Q&A application. Streamlit is an open-source Python library that simplifies the creation and sharing of custom frontends for machine learning and data science apps with the world.
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| # | Title | Duration |
|---|---|---|
| 1 | Introduction | 03:08 |
| 2 | Introduction to Streamlit | 04:15 |
| 3 | Streamlit Main Concepts | 05:28 |
| 4 | Displaying Data on the Screen: st.write() and Magic | 05:32 |
| 5 | Widgets Part 1: text_input, number_input, button | 05:10 |
| 6 | Widgets Part 2: checkbox, radio, select | 07:25 |
| 7 | Widgets Part 3: slider, file_uploader, camera_input, image | 08:15 |
| 8 | Layout: Sidebar | 01:57 |
| 9 | Layout: Columns | 04:30 |
| 10 | Layout: Expander | 02:10 |
| 11 | Displaying a Progress Bar | 03:08 |
| 12 | Session State | 07:35 |
| 13 | Callbacks | 06:08 |
| 14 | Project Introduction and Library Installation | 04:09 |
| 15 | Defining Functions | 06:14 |
| 16 | Creating the Sidebar | 06:04 |
| 17 | Reading, Chunking, and Embedding Data | 06:22 |
| 18 | Asking Questions and Getting Answers | 05:25 |
| 19 | Saving the Chat History | 06:00 |
| 20 | Clearing Session State History using Callback Functions | 04:57 |
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