deploy.yml

name: Build and Deploy

on:
  push:
    branches:
      - main
      - master
  pull_request:
    branches:
      - main
      - master
  workflow_dispatch:
    inputs:
      environment:
        description: 'Deployment environment'
        required: true
        default: 'production'
        type: choice
        options:
          - production

env:
  AWS_REGION: ${{ secrets.AWS_REGION || 'ap-northeast-2' }}
  CODE_DEPLOY_APPLICATION_NAME: ${{ secrets.CODE_DEPLOY_APPLICATION_NAME }}
  CODE_DEPLOY_DEPLOYMENT_GROUP: ${{ secrets.CODE_DEPLOY_DEPLOYMENT_GROUP }}

jobs:
  test:
    name: Run Tests
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v4
        with:
          submodules: recursive
          token: ${{ secrets.PAT_TOKEN || secrets.GITHUB_TOKEN }}

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.10'
          cache: 'pip'

      - name: Verify Python version
        run: |
          python --version
          python -c "import sys; assert sys.version_info[:2] == (3, 10), f'Expected Python 3.10, got {sys.version}'"

      - name: Clean up disk space
        run: |
          # 디스크 공간 확인
          df -h
          # pip 캐시 정리
          pip cache purge || true
          # 기타 캐시 정리
          sudo rm -rf /usr/share/dotnet
          sudo rm -rf /opt/ghc
          sudo rm -rf /usr/local/share/boost
          sudo rm -rf "$AGENT_TOOLSDIRECTORY"
          # 디스크 공간 재확인
          df -h

      - name: Install numpy first (pre-built wheel)
        run: |
          python -m pip install --upgrade pip setuptools wheel
          python -m pip install --no-cache-dir --only-binary numpy "numpy>=1.26,<2.0"

      - name: Install dependencies
        run: |
          python -m pip install --no-cache-dir --no-build-isolation -r echoshot_ai_server/requirements.txt

      - name: Install PyTorch (CPU for testing)
        run: |
          pip install --no-cache-dir torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1

      # 테스트 비활성화화
      # - name: Run tests
      #   run: |
      #     pytest echoshot_ai_server/ -v --tb=short || true
      #   continue-on-error: true

  build-and-deploy:
    name: Build and Deploy
    runs-on: ubuntu-latest
    needs: test
    if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/master')
    steps:
      - name: Checkout code
        uses: actions/checkout@v4
        with:
          submodules: recursive
          token: ${{ secrets.PAT_TOKEN || secrets.GITHUB_TOKEN }}

      # 1) Docker Hub 로그인
      - name: Login to Docker Hub
        uses: docker/login-action@v3
        with:
          username: ${{ secrets.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_TOKEN }}

      # 2) 베이스 이미지 Pull (OpenCV CUDA 포함)
      - name: Pull base image
        run: |
          echo "베이스 이미지를 pull합니다..."
          docker pull ${{ secrets.DOCKERHUB_USERNAME }}/opencv-cuda-t4:4.10.0 || echo "베이스 이미지가 없습니다. 먼저 빌드해야 합니다."

      # 3) GPU 버전 Docker 이미지 빌드 (베이스 이미지 사용)
      - name: Build GPU Docker image
        run: |
          echo "GPU 버전 애플리케이션 이미지를 빌드합니다..."
          docker build \
            --build-arg DOCKERHUB_USERNAME=${{ secrets.DOCKERHUB_USERNAME }} \
            --build-arg OPENCV_VERSION=4.10.0 \
            -t ${{ secrets.DOCKERHUB_USERNAME }}/echoshot-worker:gpu-fsrcnn-t4 \
            -t ${{ secrets.DOCKERHUB_USERNAME }}/echoshot-worker:latest \
            .

      # 4) Docker Hub로 푸시
      - name: Push Docker images
        run: |
          echo "이미지를 Docker Hub에 푸시합니다..."
          docker push ${{ secrets.DOCKERHUB_USERNAME }}/echoshot-worker:gpu-fsrcnn-t4
          docker push ${{ secrets.DOCKERHUB_USERNAME }}/echoshot-worker:latest
      
      - name: Configure AWS credentials
        uses: aws-actions/configure-aws-credentials@v4
        with:
          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: ${{ env.AWS_REGION }}

      - name: Create deployment package
        run: |
          # 서브모듈 초기화 확인
          if [ -d "EchoShotX-ai-private" ]; then
            echo "서브모듈이 확인되었습니다."
            ls -la EchoShotX-ai-private/
          else
            echo "WARNING: 서브모듈이 없습니다."
          fi
          
          mkdir -p deploy-package
          cp -r echoshot_ai_server deploy-package/
          cp -r scripts deploy-package/
          cp -r EchoShotX-ai-private deploy-package/  # 서브모듈 포함
          cp appspec.yml deploy-package/
          cp Dockerfile deploy-package/
          cp docker-compose.yml deploy-package/  # Docker Compose 파일 포함
          cd deploy-package
          zip -r ../deploy-package.zip .
          cd ..

      - name: Upload to S3
        run: |
          # S3 버킷 이름 설정 (Secret이 있으면 사용, 없으면 자동 생성)
          if [ -n "${{ secrets.CODEDEPLOY_S3_BUCKET }}" ]; then
            S3_BUCKET="${{ secrets.CODEDEPLOY_S3_BUCKET }}"
          else
            S3_BUCKET="codedeploy-echoshot-$(echo ${{ env.AWS_REGION }} | tr -d '-')"
            # S3 버킷이 없으면 생성 (선택적)
            aws s3 mb s3://$S3_BUCKET --region ${{ env.AWS_REGION }} || true
          fi
          
          S3_KEY="deployments/echoshot-worker/${{ github.sha }}.zip"
          
          # 배포 패키지 업로드
          aws s3 cp deploy-package.zip s3://$S3_BUCKET/$S3_KEY
          
          echo "S3_BUCKET=$S3_BUCKET" >> $GITHUB_ENV
          echo "S3_KEY=$S3_KEY" >> $GITHUB_ENV

      - name: Create CodeDeploy deployment
        id: create-deployment
        run: |
          DEPLOYMENT_ID=$(aws deploy create-deployment \
            --application-name ${{ env.CODE_DEPLOY_APPLICATION_NAME }} \
            --deployment-group-name ${{ env.CODE_DEPLOY_DEPLOYMENT_GROUP }} \
            --s3-location bucket=${{ env.S3_BUCKET }},key=${{ env.S3_KEY }},bundleType=zip \
            --query 'deploymentId' \
            --output text)
          
          echo "deployment_id=$DEPLOYMENT_ID" >> $GITHUB_OUTPUT
          echo "Deployment created: $DEPLOYMENT_ID"

      - name: Wait for deployment
        run: |
          DEPLOYMENT_ID="${{ steps.create-deployment.outputs.deployment_id }}"
          MAX_WAIT=1800  # 30분
          ELAPSED=0
          INTERVAL=30
          
          while [ $ELAPSED -lt $MAX_WAIT ]; do
            STATUS=$(aws deploy get-deployment \
              --deployment-id $DEPLOYMENT_ID \
              --query 'deploymentInfo.status' \
              --output text)
            
            echo "Deployment status: $STATUS"
            
            if [ "$STATUS" = "Succeeded" ]; then
              echo "Deployment succeeded!"
              exit 0
            elif [ "$STATUS" = "Failed" ] || [ "$STATUS" = "Stopped" ]; then
              echo "Deployment failed!"
              exit 1
            fi
            
            sleep $INTERVAL
            ELAPSED=$((ELAPSED + INTERVAL))
          done
          
          echo "Deployment timeout!"
          exit 1