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.

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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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