English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 20 Lessons (1h 44m) | 279 MB
This byte-sized course will teach Streamlit fundamentals and how to use Streamlit to create a frontend for your LLM-powered applications.
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.
WHAT YOU’LL LEARN
- How to utilize Streamlit to develop intuitive frontends for machine learning and data science applications, making your projects accessible to a wider audience
- The basics of Streamlit, including its installation and core features, tailored for beginners to quickly start building interactive web apps
- Integrating Large Language Models (LLMs) with Streamlit to create consumer-facing Q&A applications, leveraging the power of AI to answer user queries in real-time
- Transitioning from Jupyter Notebooks to a production-ready web app using Streamlit, enabling you to share your LLM-powered applications with the world beyond the developer community
Table of Contents
1 Introduction
2 Introduction to Streamlit
3 Streamlit Main Concepts
4 Displaying Data on the Screen st.write() and Magic
5 Widgets Part 1 text_input, number_input, button
6 Widgets Part 2 checkbox, radio, select
7 Widgets Part 3 slider, file_uploader, camera_input, image
8 Layout Sidebar
9 Layout Columns
10 Layout Expander
11 Displaying a Progress Bar
12 Session State
13 Callbacks
14 Project Introduction and Library Installation
15 Defining Functions
16 Creating the Sidebar
17 Reading, Chunking, and Embedding Data
18 Asking Questions and Getting Answers
19 Saving the Chat History
20 Clearing Session State History using Callback Functions
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