413f652bbc
CRITICAL FIX: Dashboard was calling non-existent API endpoints
The Problem:
------------
The orchestrator dashboard service was calling API endpoints that don't exist:
1. Procurement: Expected dict {items: [...]} but API returns array [...]
2. Production: Called /production/production-batches/today - doesn't exist
3. Production: Called /production/production-batches - doesn't exist
Root Cause:
-----------
Created client methods without verifying actual backend API structure.
Made assumptions about response formats that didn't match reality.
The Fix:
--------
**1. Procurement Client (shared/clients/procurement_client.py)**
- Fixed get_pending_purchase_orders return type: Dict → List
- Procurement API returns: List[PurchaseOrderResponse] directly
- Changed: "Dict with {items: [...], total: n}" → "List of purchase order dicts"
**2. Production Client (shared/clients/production_client.py)**
- Fixed get_todays_batches endpoint:
OLD: "/production/production-batches/today" (doesn't exist)
NEW: "/production/batches?start_date=today&end_date=today"
- Fixed get_production_batches_by_status endpoint:
OLD: "/production/production-batches?status=X"
NEW: "/production/batches?status=X"
- Updated return type docs: {"items": [...]} → {"batches": [...], "total_count": n}
- Response structure: ProductionBatchListResponse (batches, total_count, page, page_size)
**3. Orchestrator Dashboard API (services/orchestrator/app/api/dashboard.py)**
- Fixed all po_data access patterns:
OLD: po_data.get("items", [])
NEW: direct list access or po_data if isinstance(po_data, list)
- Fixed production batch access:
OLD: prod_data.get("items", [])
NEW: prod_data.get("batches", [])
- Updated 6 locations:
* Line 206: health-status pending POs count
* Line 216: health-status production delays count
* Line 274-281: orchestration-summary PO summaries
* Line 328-329: action-queue pending POs
* Line 472-487: insights deliveries calculation
* Line 499-519: insights savings calculation
Verified Against:
-----------------
Frontend successfully calls these exact APIs:
- /tenants/{id}/procurement/purchase-orders (ProcurementPage.tsx)
- /tenants/{id}/production/batches (production hooks)
Both return arrays/objects as documented in their respective API files:
- services/procurement/app/api/purchase_orders.py: returns List[PurchaseOrderResponse]
- services/production/app/api/production_batches.py: returns ProductionBatchListResponse
Now dashboard calls match actual backend APIs! ✅
🍞 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.