Claude bedd4868ac Fix invalid unit_of_measure 'dozen' causing 422 API errors
**Issue:**
Creating ingredients failed with 422 error:
```
Input should be 'kg', 'g', 'l', 'ml', 'units', 'pcs', 'pkg', 'bags' or 'boxes'
input: "dozen"
```

**Root Cause:**
Frontend was using units not supported by backend UnitOfMeasure enum:
- "dozen" (docena)
- "cup" (taza)
- "tbsp" (cucharada)
- "tsp" (cucharadita)
- "piece" → should be "pcs"
- "package" → should be "pkg"
- "bag" → should be "bags"
- "box" → should be "boxes"

**Backend Supported Units (inventory.ts:28-38):**
kg, g, l, ml, units, pcs, pkg, bags, boxes

**Solution:**
Replaced all invalid units with backend-compatible ones across codebase.

**Files Modified:**

1. **UploadSalesDataStep.tsx:604**
   - Before: ['kg', 'g', 'L', 'ml', 'units', 'dozen']
   - After: ['kg', 'g', 'l', 'ml', 'units', 'pcs', 'pkg', 'bags', 'boxes']

2. **BatchAddIngredientsModal.tsx:53**
   - Before: ['kg', 'g', 'L', 'ml', 'units', 'dozen']
   - After: ['kg', 'g', 'l', 'ml', 'units', 'pcs', 'pkg', 'bags', 'boxes']

3. **QuickAddIngredientModal.tsx:69**
   - Before: ['kg', 'g', 'L', 'ml', 'units', 'dozen']
   - After: ['kg', 'g', 'l', 'ml', 'units', 'pcs', 'pkg', 'bags', 'boxes']

4. **inventory/index.ts:51-62**
   - Removed: 'piece', 'package', 'bag', 'box', 'dozen', 'cup', 'tbsp', 'tsp'
   - Added: 'units', 'pcs', 'pkg', 'bags', 'boxes'
   - Added comment: "must match backend UnitOfMeasure enum exactly"

5. **ingredientHelpers.ts:168**
   - Eggs unit changed from 'dozen' → 'units'

6. **utils/constants.ts:77-87**
   - Removed volume units: cup, tbsp, tsp
   - Removed count units: piece, dozen, package, bag, box
   - Added: units, pcs, pkg, bags, boxes
   - Now matches backend enum exactly

**Also Fixed:**
- Changed 'L' to lowercase 'l' for consistency with backend

**Impact:**
 All ingredient creation now uses valid backend units
 No more 422 validation errors
 Frontend/backend unit enums synchronized

**Build Status:** ✓ Successful in 22.23s
2025-11-07 08:36:35 +00:00
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🍞 Bakery IA - Multi-Service Architecture

Welcome to Bakery IA, 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

📄 License

This project is licensed under the MIT License.

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