feat(run): support docker build and run

.github/workflows/default.yaml

@@ -0,0 +1,40 @@
+# This is a basic workflow to help you get started with Actions
+
+name: CI
+
+# Controls when the workflow will run
+on:
+  # Triggers the workflow on push or pull request events but only for the "master" branch
+  push:
+    branches: [ "master" ]
+  pull_request:
+    branches: [ "master" ]
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+  # This workflow contains a single job called "build"
+  build:
+    # The type of runner that the job will run on
+    runs-on: ubuntu-latest
+
+    # Steps represent a sequence of tasks that will be executed as part of the job
+    steps:
+      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
+      - uses: actions/checkout@v3
+
+      # Runs a set of commands using the runners shell
+      - name: Get pwd
+        run: |
+          pwd
+          ls -alh
+      - name: Run Docker Build Script
+        env:
+          DOCKER_USERNAME: ${{ secrets.DOCKERHUB_USERNAME }}
+          DOCKER_PASSWORD: ${{ secrets.DOCKERHUB_PASSWORD }}
+          DOCKER_REPO: ${{ secrets.DOCKERHUB_REPO }}
+        run: |
+          chmod +x build.sh
+          ./build.sh

README.md

@@ -48,12 +48,27 @@ Through the above introduction and Demo demonstration, you must be curious about
 
 ## Quick Start
 
-1. Clone the latest code or select a released version, Python3.7 or later is ready.
-2. Generate the configuration file: Copy `env.yaml.tpl` and rename it to `env.yaml`.
-3. Modify the configuration file: Edit `env.yaml` and add the necessary information such as GPT Token (refer to [documentation link](docs/DOCUMENT.md) for detailed instructions).
-4. Run the service: Execute `sh run.sh` on Linux or Mac, or double-click `run.bat` on Windows.
-5. Access the service: Access the service through a browser (check the startup log for the access address, default is http://127.0.0.1:8080).
-6. Complete requirement development: Follow the instructions on the page to complete requirement development, and view the generated code in the `./workspace` directory.
+1. Run with source code
+    ```
+    1. Clone the latest code or select a released version, Python3.7 or later is ready.
+    2. Generate the configuration file: Copy `env.yaml.tpl` and rename it to `env.yaml`.
+    3. Modify the configuration file: Edit `env.yaml` and add the necessary information such as GPT Token (refer to [documentation link](docs/DOCUMENT.md) for detailed instructions).
+    4. Run the service: Execute `sh run.sh` on Linux or Mac, or double-click `run.bat` on Windows.
+    5. Access the service: Access the service through a browser (check the startup log for the access address, default is http://127.0.0.1:8080).
+    6. Complete requirement development: Follow the instructions on the page to complete requirement development, and view the generated code in the `./workspace` directory.
+    ```
+2. Run with Docker
+    ```shell
+    1. Create a directory: mkdir -p workspace
+    2. Copy 'env.yaml.tpl' from the repository to the current directory and rename it to 'env.yaml'
+    3. Modify the configuration file: edit 'env.yaml' and add necessary information such as GPT Token.
+    4. docker run -it \
+        -v$PWD/workspace:/app/workspace \
+        -v$PWD/env.yaml:/app/env.yaml \
+        -p8080:8080 -p8081:8081 kuafuai/devopsgpt:latest
+    5. Access the service: Access the service through a browser (access address provided in the startup log, the default is http://127.0.0.1:8080).
+    6. Complete the requirement development: complete the requirement development according to the guidance of the page, and view the generated code in the './workspace 'directory
+    ```
 
 **For detailed documentation and configuration parameters, please refer to the [documentation link](docs/DOCUMENT.md).**
 

build.sh

@@ -0,0 +1,20 @@
+#!/bin/bash
+
+# Get the current commit SHA
+COMMIT_SHA=$(git rev-parse HEAD)
+
+# Get the current date in the format YYYYMMDD
+CURRENT_DATE=$(date +%Y%m%d)
+
+# Construct the new tag
+NEW_TAG="${CURRENT_DATE}-${COMMIT_SHA:0:7}"
+
+# Build the Docker image with the new tag
+docker build -t "${DOCKER_REPO}:${NEW_TAG}" -t "${DOCKER_REPO}:latest" -f Dockerfile .
+
+# Login to Docker Hub
+echo "$DOCKER_PASSWORD" | docker login -u "$DOCKER_USERNAME" --password-stdin
+
+# Push the Docker image with the new tag
+docker push "${DOCKER_REPO}:${NEW_TAG}"
+docker push "${DOCKER_REPO}:latest"

docs/DOCUMENT.md

@@ -75,22 +75,4 @@ APPS contains information about the applications you need to develop. The first
   - setp1Task (used for breaking down tasks).
 - service.lib: Libraries/packages available for the service.
   - setpReqChooseLib (analysis of libraries/packages used together).
-- service.specification: Specification for using library packages.
-
-## Docker Build & Run
-
-1. **Build the Docker Image**: 
-   ```
-   docker build . -t devopsgpt
-   ```
-2. **Setup Workspace**:
-   ```
-   mkdir -p workspace
-   ```
-3. **Run the Docker Container**:
-   ```
-   docker run -it \
-    -v$PWD/workspace:/app/workspace \
-    -v$PWD/env.yaml:/app/env.yaml \
-    -p8080:8080 -p8081:8081 devopsgpt
-   ```
+- service.specification: Specification for using library packages.

docs/README_CN.md

@@ -57,12 +57,27 @@
 
 ## 快速开始
 
-1. 克隆最新代码或选择已发布的版本，准备好 Python3.7 或以上版本。
-2. 生成配置文件：复制 `env.yaml.tpl` 并重命名为 `env.yaml`。
-3. 修改配置文件：编辑 `env.yaml`，添加GPT Token等必要信息。
-4. 运行服务：在 Linux 或 Mac 上执行 `sh run.sh`，在 Windows 上双击运行 `run.bat`。
-5. 访问服务：通过浏览器访问服务（启动日志中提供的访问地址，默认为 http://127.0.0.1:8080）。
-6. 完成需求开发：按照页面引导完成需求开发，在 `./workspace` 目录下查看生成的代码。
+1. 源码运行
+    ```
+    1. 克隆最新代码或选择已发布的版本，准备好 Python3.7 或以上版本。
+    2. 生成配置文件：复制 `env.yaml.tpl` 并重命名为 `env.yaml`。
+    3. 修改配置文件：编辑 `env.yaml`，添加GPT Token等必要信息。
+    4. 运行服务：在 Linux 或 Mac 上执行 `sh run.sh`，在 Windows 上双击运行 `run.bat`。
+    5. 访问服务：通过浏览器访问服务（启动日志中提供的访问地址，默认为 http://127.0.0.1:8080）。
+    6. 完成需求开发：按照页面引导完成需求开发，在 `./workspace` 目录下查看生成的代码。
+    ```
+2. Docker运行
+    ```shell
+    1. 创建一个目录：mkdir -p workspace
+    2. 从仓库中复制 `env.yaml.tpl` 到当前目录，并重命名为 `env.yaml`
+    3. 修改配置文件：编辑 `env.yaml`，添加GPT Token等必要信息。
+    4. docker run -it \
+        -v$PWD/workspace:/app/workspace \
+        -v$PWD/env.yaml:/app/env.yaml \
+        -p8080:8080 -p8081:8081 kuafuai/devopsgpt:latest
+    5. 访问服务：通过浏览器访问服务（启动日志中提供的访问地址，默认为 http://127.0.0.1:8080）
+    6. 完成需求开发：按照页面引导完成需求开发，在 `./workspace` 目录下查看生成的代码
+    ```
 
 **详细文档和配置参数请参考 [文档链接](./DOCUMENT_CN.md)。**