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https://github.com/ethanlee928/pyconhk2023
Materials for PyCon Hong Kong 2023
https://github.com/ethanlee928/pyconhk2023
computer-vision data-science data-visualization pycon python streamlit
Last synced: 15 days ago
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Materials for PyCon Hong Kong 2023
- Host: GitHub
- URL: https://github.com/ethanlee928/pyconhk2023
- Owner: ethanlee928
- Created: 2023-10-02T15:47:03.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-11-05T07:56:07.000Z (over 1 year ago)
- Last Synced: 2024-11-28T19:09:12.792Z (2 months ago)
- Topics: computer-vision, data-science, data-visualization, pycon, python, streamlit
- Language: Python
- Homepage:
- Size: 19.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Streamlit — Creating Interactive Websites with Ease
Materials for PyCon Hong Kong 2023
## 1. Abstract
Streamlit is a Python library for creating interactive web applications with ease. It provides built-in tools and widgets for adding interactivity, integrates with popular data science libraries like pandas and scikit-learn, and allows for the deployment of web apps with just a few lines of code.
This talk will explore the different potentials of using Streamlit with three examples: creating a static page, a page for data visualization, and a page for real-time computer vision applications. First, we will discuss the basics of Streamlit by building a static page, which is suitable even for Python beginners. It aims to illustrate the convenience of Streamlit by showing how one can create a styled, responsive self-introduction page in minutes.
For data visualization, we will create an interactive page of Monte-Carlo Simulation on the stock market, which aims to illustrate the integration between Streamlit and some popular data science libraries.
Lastly, we will utilize the open-source library streamlit-webrtc, which allows users to easily use real-time video/audio streams on Streamlit apps, to build a real-time computer vision application.
In short, this talk introduces an emerging Python library—Streamlit. It is a powerful tool for data scientists and machine learning engineers for research and development.
## 2. How to Start
**Remarks:**
- The following instructions are only for `01-intro-page`, `02-simulation` and `03-computer-vision/haarcascade` example.
- `03-computer-vision/jetson-inference` requires a Jetson Nano to run.
### 2.0 Install the dependecies using virtual environment
```bash
python3 -m venv .venv
source .venv/bin/activate
pip3 install -r requirements.txt
```
### 2.1 Start 01-intro-page
```bash
cd 01-intro-page
streamlit run app.py
```
### 2.2 Start 02-simulation
```bash
cd 02-simulation
streamlit run app.py
```
### 2.3 Start haarcascade example
```bash
cd 03-computer-vision/haarcascade
streamlit run app.py
```
## 3. Streamlit with Jetson-Inference
### 3.0 Prerequisite
| **Device** | Jetson Nano |
| ---------------- | ----------- |
| **JetPack Ver.** | 4.6 |
| **Camera** | USB Camera |
#### Docker Environment
This example has to be run inside a Docker environment.
##### Build the Docker image
```bash
cd 03-computer-vision/jetson-inference/
docker build -t streamlit-jetson-inference .
```
##### Start the Docker container
```bash
cd 03-computer-vision/jetson-inference/
./start.sh
```
### 3.1 Start examples
**prerequisite**: Inside the docker container
#### Download the models
```bash
cd /jetson-inference/tools
./download-models.sh # Select the models you want to download
```
```bash
cd 03-computer-vision/jetson-inference
# webrtc example
streamlit run webrtc.py
# v4l2 example
streamlit run v4l2.py
```