Claude 8aa1b0d859 Fix dashboard translation issues and template variable interpolation
This commit resolves three critical translation/localization issues in the bakery dashboard:

1. **Health Status Translation Keys**: Fixed HealthStatusCard's translateKey function to properly handle `dashboard.health.*` keys by correctly stripping the `dashboard.` prefix while preserving the `health.` namespace path. This ensures checklist items like "production_on_schedule" and "all_ingredients_in_stock" display correctly in Spanish.

2. **Reasoning Translation Keys**: Updated backend dashboard_service.py to use the correct i18n key prefixes:
   - Purchase orders now use `reasoning.purchaseOrder.*` instead of `reasoning.types.*`
   - Production batches now use `reasoning.productionBatch.*`
   - Added context parameter to `_get_reasoning_type_i18n_key()` method for proper namespace routing

3. **Template Variable Interpolation**: Fixed template variable replacement in action cards:
   - Added array preprocessing logic in both backend and frontend to convert `product_names` arrays to `product_names_joined` strings
   - Updated ActionQueueCard's translateKey to preprocess array parameters before i18n interpolation
   - Fixed ProductionTimelineCard to properly handle reasoning namespace prefix removal

These fixes ensure that:
- Health status indicators show translated text instead of raw keys (e.g., "Producción a tiempo" vs "dashboard.health.production_on_schedule")
- Purchase order reasoning displays with proper product names and stockout days instead of literal template variables (e.g., "Stock bajo para Harina. El stock se agotará en 7 días" vs "Stock bajo para {{product_name}}")
- All dashboard components consistently handle i18n key namespaces and parameter interpolation

Affected files:
- frontend/src/components/dashboard/HealthStatusCard.tsx
- frontend/src/components/dashboard/ActionQueueCard.tsx
- frontend/src/components/dashboard/ProductionTimelineCard.tsx
- services/orchestrator/app/services/dashboard_service.py
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2025-07-17 14:34:24 +02:00
2025-11-14 20:27:39 +01:00
2025-09-23 12:49:35 +02:00
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2025-11-06 11:04:50 +01:00

🍞 BakeWise - Multi-Service Architecture

Welcome to BakeWise, an advanced AI-powered platform for bakery management and optimization. This project implements a microservices architecture with multiple interconnected services to provide comprehensive bakery management solutions.

🚀 Quick Start

Prerequisites

  • Docker Desktop with Kubernetes enabled
  • Docker Compose
  • Node.js (for frontend development)

Running the Application

  1. Clone the repository:

    git clone <repository-url>
    cd bakery-ia
    
  2. Set up environment variables:

    cp .env.example .env
    # Edit .env with your specific configuration
    
  3. Run with Docker Compose:

    docker-compose up --build
    
  4. Or run with Kubernetes (Docker Desktop):

    # Enable Kubernetes in Docker Desktop
    # Run the setup script
    ./scripts/setup-kubernetes-dev.sh
    

🏗️ Architecture Overview

The project follows a microservices architecture with the following main components:

  • Frontend: React-based dashboard for user interaction
  • Gateway: API gateway handling authentication and routing
  • Services: Multiple microservices handling different business domains
  • Infrastructure: Redis, RabbitMQ, PostgreSQL databases

🐳 Kubernetes Infrastructure

🛠️ Services

The project includes multiple services:

  • Auth Service: Authentication and authorization
  • Tenant Service: Multi-tenancy management
  • Sales Service: Sales processing
  • External Service: Integration with external systems
  • Training Service: AI model training
  • Forecasting Service: Demand forecasting
  • Notification Service: Notifications and alerts
  • Inventory Service: Inventory management
  • Recipes Service: Recipe management
  • Suppliers Service: Supplier management
  • POS Service: Point of sale
  • Orders Service: Order management
  • Production Service: Production planning
  • Alert Processor: Background alert processing

📊 Monitoring

The system includes comprehensive monitoring with:

  • Prometheus for metrics collection
  • Grafana for visualization
  • ELK stack for logging (planned)

🚀 Production Deployment

For production deployment on clouding.io with Kubernetes:

  1. Set up your clouding.io Kubernetes cluster
  2. Update image references to your container registry
  3. Configure production-specific values
  4. Deploy using the production kustomization:
    kubectl apply -k infrastructure/kubernetes/environments/production/
    

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Submit a pull request

🔧 Troubleshooting

macOS "too many open files" error

If you encounter the "too many open files" error when running the application on macOS:

failed to create fsnotify watcher: too many open files

This is related to system limits on file system watchers. The kind configuration has been updated to handle this, but if you still encounter issues:

  1. Restart your Kind cluster with the updated configuration:

    kind delete cluster --name bakery-ia-local
    kind create cluster --config kind-config.yaml --name bakery-ia-local
    
  2. If needed, you can also increase the macOS system limits (though this shouldn't be necessary with the updated kind configuration):

    # Check current limits
    sysctl kern.maxfiles kern.maxfilesperproc
    
    # These are usually set high enough by default, but if needed:
    # sudo sysctl -w kern.maxfiles=65536
    # sudo sysctl -w kern.maxfilesperproc=65536
    

📄 License

This project is licensed under the MIT License.

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