OpenMorningstar screenshot

OpenMorningstar

Author Avatar Theme by Henryji529
Updated: 23 Jan 2025
10 Stars

晨星小站

Categories

Overview

This product is a web application developed using a stack of front-end and back-end technologies. It includes features for development, deployment, testing, and integration. The application utilizes various tools and frameworks such as Sass, TailwindCSS, DaisyUI, Vue3, NaiveUI, Django, DRF, Redis, MySQL, Docker, Nginx, Fabric, Supervisor, Coverage, Vitest, Github Action, PyTorch, and TensorBoard. The deployment process involves setting up the environment, installing necessary dependencies, and automating deployments using provided scripts.

Features

  • Front-end: Sass, TailwindCSS, DaisyUI, Vue3, NaiveUI
  • Back-end: Django, DRF, Redis, MySQL
  • Deployment: Docker, Nginx, Fabric, Supervisor
  • Testing: Coverage, Vitest
  • Integration: Github Action
  • AI: PyTorch, TensorBoard

Installation

To install the theme locally, follow these steps:

  1. Clone or download the source code.
  2. In the task.sh file, you will find common development shortcuts.
  3. In the task.sh file, you will also find common deployment shortcuts.
  4. For general deployment on a bare-metal environment:
    • Transfer .env and scripts/deploy/deploy.sh using scp.
    • Execute the deploy.sh script to install the required dependencies.
  5. Install the following:
    • Oh-my-bash: oh-my-bash
    • Vim and VimPlus: vim and vimPlus
    • Code-server: code-server
    • Docker and docker-compose: docker and docker-compose
    • Supervisor: supervisor
    • Nvm and Node: nvm and node
  6. Additional productivity tools can be installed using the upgradeProd command in task.sh.

Summary

This web application uses a wide range of technologies and frameworks to offer front-end features like Sass, TailwindCSS, DaisyUI, Vue3, and NaiveUI, as well as back-end capabilities using Django, DRF, Redis, and MySQL. The deployment process involves Docker, Nginx, Fabric, and Supervisor, and testing is conducted using Coverage and Vitest. Integration with Github Action is supported, and AI functionalities are provided through PyTorch and TensorBoard. The installation guide includes steps for setting up the environment and installing the necessary dependencies.