Developing LLM App Frontends with Streamlit

1h 43m 52s
English
Paid

Course description

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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#1: Introduction

All Course Lessons (20)

#Lesson TitleDurationAccess
1
Introduction Demo
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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