3fec052c12
Organized the application into separate frontend and backend directories for cleaner deployment and better separation of concerns. Frontend Directory (frontend/): - index.html: Single-page web interface (renamed from web_ui.html) - README.md: Frontend deployment guide - Total size: ~113 KB (self-contained) - Smart base path detection (works at / or /ai_qc/) - No configuration changes required Backend Directory (backend/): - All Python files (api_server.py, llm_config.py, etc.) - visual_qc_apps/: 33 QC check modules - profiles/: 6 QC profile configurations - brand_guidelines/: Reference asset storage - config/: Environment configurations - scripts/: Deployment automation - uploads/, output/: Data directories - requirements.txt, ai_qc.service, apache_config.conf - Complete documentation New Documentation: - FOLDER_STRUCTURE.md: Comprehensive guide to new structure - frontend/README.md: Frontend deployment instructions - backend/BACKEND_README.md: Backend deployment guide Deployment Mapping: - frontend/ → /var/www/html/ai_qc/ (web root) - backend/ → /opt/ai_qc/ (application directory) Benefits: - Clear separation of concerns - Backend code not in web-accessible directory - Independent frontend/backend updates - Matches server's existing patterns (/opt/veo3, /opt/voice2text) - Industry-standard architecture - Easy to deploy and maintain Original files preserved in root directory for reference. Ready for production deployment following MIGRATION_GUIDE.md. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
117 lines
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3.4 KiB
Bash
Executable file
117 lines
No EOL
3.4 KiB
Bash
Executable file
#!/bin/bash
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# Script to deploy Visual AI QC application to production server
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set -e # Exit on any error
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echo "==========================================="
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echo " Visual AI QC - Production Deployment"
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echo "==========================================="
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# Configuration - UPDATE THESE VALUES FOR YOUR SERVER
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PROD_SERVER="your-server-ip-or-domain"
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PROD_USER="your-username"
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PROD_PATH="/path/to/production/ai_qc"
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BACKUP_PATH="/path/to/backups"
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# Get the script directory and app root
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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APP_DIR="$(dirname "$SCRIPT_DIR")"
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echo "Local app directory: $APP_DIR"
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echo "Production server: $PROD_USER@$PROD_SERVER:$PROD_PATH"
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# Change to app directory
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cd "$APP_DIR"
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# Run pre-deployment tests
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echo ""
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echo "Step 1: Running pre-deployment tests..."
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if [ -f "scripts/test-system.sh" ]; then
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bash scripts/test-system.sh
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if [ $? -ne 0 ]; then
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echo "ERROR: Pre-deployment tests failed!"
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exit 1
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fi
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echo "✅ All tests passed"
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else
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echo "Warning: No test script found, skipping tests"
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fi
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# Confirm deployment
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echo ""
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echo "Step 2: Deployment confirmation"
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echo "This will deploy to: $PROD_USER@$PROD_SERVER:$PROD_PATH"
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read -p "Continue with deployment? (y/N): " confirm
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if [[ ! $confirm =~ ^[Yy]$ ]]; then
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echo "Deployment cancelled"
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exit 0
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fi
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# Create backup of production
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echo ""
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echo "Step 3: Creating production backup..."
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BACKUP_NAME="backup_$(date +%Y%m%d_%H%M%S)"
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ssh "$PROD_USER@$PROD_SERVER" "mkdir -p $BACKUP_PATH && cp -r $PROD_PATH $BACKUP_PATH/$BACKUP_NAME"
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echo "✅ Backup created: $BACKUP_PATH/$BACKUP_NAME"
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# Sync files to production (excluding development-specific files)
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echo ""
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echo "Step 4: Syncing files to production..."
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rsync -avz --progress \
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--exclude="uploads-dev/" \
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--exclude="output-dev/" \
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--exclude="config/development.env" \
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--exclude="scripts/run-local.sh" \
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--exclude="venv/" \
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--exclude=".git/" \
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--exclude="__pycache__/" \
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--exclude="*.pyc" \
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--exclude=".DS_Store" \
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./ "$PROD_USER@$PROD_SERVER:$PROD_PATH/"
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echo "✅ Files synced to production"
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# Set production environment and restart service
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echo ""
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echo "Step 5: Configuring production environment..."
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ssh "$PROD_USER@$PROD_SERVER" << 'EOF'
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cd /path/to/production/ai_qc
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# Set production environment
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export ENVIRONMENT=production
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# Ensure production folders exist
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mkdir -p uploads output
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# Install/update requirements
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if [ -f requirements.txt ]; then
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pip install -r requirements.txt
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fi
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# Restart the service (adapt this command for your setup)
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# Option 1: If using systemd
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# sudo systemctl restart ai-qc
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# Option 2: If using PM2
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# pm2 restart ai-qc
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# Option 3: If running directly (kill and restart)
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# pkill -f "python.*api_server.py" || true
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# nohup python api_server.py > server.log 2>&1 &
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echo "Production service restarted"
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EOF
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# Verify deployment
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echo ""
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echo "Step 6: Verifying deployment..."
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# Add health check here - ping the production server
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# curl -f http://your-production-domain.com/api/health || echo "Warning: Health check failed"
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echo ""
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echo "🎉 Deployment completed successfully!"
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echo "Backup location: $PROD_USER@$PROD_SERVER:$BACKUP_PATH/$BACKUP_NAME"
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echo "Production URL: http://$PROD_SERVER"
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echo ""
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echo "If anything goes wrong, you can rollback with:"
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echo "ssh $PROD_USER@$PROD_SERVER 'rm -rf $PROD_PATH && mv $BACKUP_PATH/$BACKUP_NAME $PROD_PATH'" |