{"id":21915178,"url":"https://github.com/ethanlee928/pyconhk2023","last_synced_at":"2026-04-20T05:07:03.682Z","repository":{"id":204727953,"uuid":"699421761","full_name":"ethanlee928/PyConHK2023","owner":"ethanlee928","description":"Materials for PyCon Hong Kong 2023","archived":false,"fork":false,"pushed_at":"2023-11-05T07:56:07.000Z","size":20877,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-11-28T19:09:12.792Z","etag":null,"topics":["computer-vision","data-science","data-visualization","pycon","python","streamlit"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ethanlee928.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-10-02T15:47:03.000Z","updated_at":"2024-10-04T16:13:25.000Z","dependencies_parsed_at":"2023-11-05T08:25:47.531Z","dependency_job_id":null,"html_url":"https://github.com/ethanlee928/PyConHK2023","commit_stats":null,"previous_names":["ethanlee928/streamlit-playground","ethanlee928/pyconhk2023"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ethanlee928%2FPyConHK2023","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ethanlee928%2FPyConHK2023/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ethanlee928%2FPyConHK2023/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ethanlee928%2FPyConHK2023/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ethanlee928","download_url":"https://codeload.github.com/ethanlee928/PyConHK2023/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":235827036,"owners_count":19051172,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","data-science","data-visualization","pycon","python","streamlit"],"created_at":"2024-11-28T19:09:19.791Z","updated_at":"2026-04-20T05:07:03.637Z","avatar_url":"https://github.com/ethanlee928.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Streamlit \u0026mdash; Creating Interactive Websites with Ease\n\nMaterials for PyCon Hong Kong 2023\n\n## 1. Abstract\n\nStreamlit 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.\n\nThis 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.\nFor 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.\n\nLastly, 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.\n\nIn 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.\n\n## 2. How to Start\n\n**Remarks:**\n\n- The following instructions are only for `01-intro-page`, `02-simulation` and `03-computer-vision/haarcascade` example.\n- `03-computer-vision/jetson-inference` requires a Jetson Nano to run.\n\n### 2.0 Install the dependecies using virtual environment\n\n```bash\npython3 -m venv .venv\nsource .venv/bin/activate\npip3 install -r requirements.txt\n```\n\n### 2.1 Start 01-intro-page\n\n```bash\ncd 01-intro-page\nstreamlit run app.py\n```\n\n### 2.2 Start 02-simulation\n\n```bash\ncd 02-simulation\nstreamlit run app.py\n```\n\n### 2.3 Start haarcascade example\n\n```bash\ncd 03-computer-vision/haarcascade\nstreamlit run app.py\n```\n\n## 3. Streamlit with Jetson-Inference\n\n### 3.0 Prerequisite\n\n| **Device**       | Jetson Nano |\n| ---------------- | ----------- |\n| **JetPack Ver.** | 4.6         |\n| **Camera**       | USB Camera  |\n\n#### Docker Environment\n\nThis example has to be run inside a Docker environment.\n\n##### Build the Docker image\n\n```bash\ncd 03-computer-vision/jetson-inference/\ndocker build -t streamlit-jetson-inference .\n```\n\n##### Start the Docker container\n\n```bash\ncd 03-computer-vision/jetson-inference/\n./start.sh\n```\n\n### 3.1 Start examples\n\n**prerequisite**: Inside the docker container\n\n#### Download the models\n```bash\ncd /jetson-inference/tools\n./download-models.sh    # Select the models you want to download\n```\n\n```bash\ncd 03-computer-vision/jetson-inference\n\n# webrtc example\nstreamlit run webrtc.py\n\n# v4l2 example\nstreamlit run v4l2.py\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fethanlee928%2Fpyconhk2023","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fethanlee928%2Fpyconhk2023","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fethanlee928%2Fpyconhk2023/lists"}