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## Architectural Changes **1. Remove Manual Entry Path** - Deleted data-source-choice step (DataSourceChoiceStep) - Removed manual inventory-setup step (InventorySetupStep) - Removed all manual path conditions from wizard flow - Set dataSource to 'ai-assisted' by default in WizardContext Files modified: - frontend/src/components/domain/onboarding/UnifiedOnboardingWizard.tsx:11-28,61-162 - frontend/src/components/domain/onboarding/context/WizardContext.tsx:64 **2. Add Inventory Lots UI to AI Inventory Step** Added full stock lot management with expiration tracking to UploadSalesDataStep: **Features Added:** - Inline stock lot entry form after each AI-suggested ingredient - Multi-lot support - add multiple lots per ingredient with different expiration dates - Fields: quantity*, expiration date, supplier, batch/lot number - Visual list of added lots with expiration dates - Delete individual lots before completing - Smart validation with expiration date warnings - FIFO help text - Auto-select supplier if only one exists **Technical Implementation:** - Added useAddStock and useSuppliers hooks (lines 5,7,102-103) - Added stock state management (lines 106-114) - Stock handler functions (lines 336-428): - handleAddStockClick - Opens stock form - handleCancelStock - Closes and resets form - validateStockForm - Validates quantity and expiration - handleSaveStockLot - Saves to local state, supports "Add Another Lot" - handleDeleteStockLot - Removes from list - Modified handleNext to create stock lots after ingredients (lines 490-533) - Added stock lots UI section in ingredient rendering (lines 679-830) **UI Flow:** 1. User uploads sales data 2. AI suggests ingredients 3. User reviews/edits ingredients 4. **NEW**: User can optionally add stock lots with expiration dates 5. Click "Next" creates both ingredients AND stock lots 6. FIFO tracking enabled from day one **Benefits:** - Addresses JTBD: waste prevention, expiration tracking from onboarding - Progressive disclosure - optional but encouraged - Maintains simplicity of AI-assisted path - Enables inventory best practices from the start Files modified: - frontend/src/components/domain/onboarding/steps/UploadSalesDataStep.tsx:1-12,90-114,335-533,679-830 **Build Status:** ✓ Successful in 20.78s
🍞 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
-
Clone the repository:
git clone <repository-url> cd bakery-ia -
Set up environment variables:
cp .env.example .env # Edit .env with your specific configuration -
Run with Docker Compose:
docker-compose up --build -
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:
- Set up your clouding.io Kubernetes cluster
- Update image references to your container registry
- Configure production-specific values
- Deploy using the production kustomization:
kubectl apply -k infrastructure/kubernetes/environments/production/
🤝 Contributing
- Fork the repository
- Create a feature branch
- Make your changes
- Submit a pull request
📄 License
This project is licensed under the MIT License.