Claude 2ced1ec670 feat: Complete JTBD-aligned bakery dashboard redesign
Implements comprehensive dashboard redesign based on Jobs To Be Done methodology
focused on answering: "What requires my attention right now?"

## Backend Implementation

### Dashboard Service (NEW)
- Health status calculation (green/yellow/red traffic light)
- Action queue prioritization (critical/important/normal)
- Orchestration summary with narrative format
- Production timeline transformation
- Insights calculation and consequence prediction

### API Endpoints (NEW)
- GET /dashboard/health-status - Overall bakery health indicator
- GET /dashboard/orchestration-summary - What system did automatically
- GET /dashboard/action-queue - Prioritized tasks requiring attention
- GET /dashboard/production-timeline - Today's production schedule
- GET /dashboard/insights - Key metrics (savings, inventory, waste, deliveries)

### Enhanced Models
- PurchaseOrder: Added reasoning, consequence, reasoning_data fields
- ProductionBatch: Added reasoning, reasoning_data fields
- Enables transparency into automation decisions

## Frontend Implementation

### API Hooks (NEW)
- useBakeryHealthStatus() - Real-time health monitoring
- useOrchestrationSummary() - System transparency
- useActionQueue() - Prioritized action management
- useProductionTimeline() - Production tracking
- useInsights() - Glanceable metrics

### Dashboard Components (NEW)
- HealthStatusCard: Traffic light indicator with checklist
- ActionQueueCard: Prioritized actions with reasoning/consequences
- OrchestrationSummaryCard: Narrative of what system did
- ProductionTimelineCard: Chronological production view
- InsightsGrid: 2x2 grid of key metrics

### Main Dashboard Page (REPLACED)
- Complete rewrite with mobile-first design
- All sections integrated with error handling
- Real-time refresh and quick action links
- Old dashboard backed up as DashboardPage.legacy.tsx

## Key Features

### Automation-First
- Shows what orchestrator did overnight
- Builds trust through transparency
- Explains reasoning for all automated decisions

### Action-Oriented
- Prioritizes tasks over information display
- Clear consequences for each action
- Large touch-friendly buttons

### Progressive Disclosure
- Shows 20% of info that matters 80% of time
- Expandable details when needed
- No overwhelming metrics

### Mobile-First
- One-handed operation
- Large touch targets (min 44px)
- Responsive grid layouts

### Trust-Building
- Narrative format ("I planned your day")
- Reasoning inputs transparency
- Clear status indicators

## User Segments Supported

1. Solo Bakery Owner (Primary)
   - Simple health indicator
   - Action checklist (max 3-5 items)
   - Mobile-optimized

2. Multi-Location Owner
   - Multi-tenant support (existing)
   - Comparison capabilities
   - Delegation ready

3. Enterprise/Central Bakery (Future)
   - Network topology support
   - Advanced analytics ready

## JTBD Analysis Delivered

Main Job: "Help me quickly understand bakery status and know what needs my intervention"

Emotional Jobs Addressed:
- Feel in control despite automation
- Reduce daily anxiety
- Feel competent with technology
- Trust system as safety net

Social Jobs Addressed:
- Demonstrate professional management
- Avoid being bottleneck
- Show sustainability

## Technical Stack

Backend: Python, FastAPI, SQLAlchemy, PostgreSQL
Frontend: React, TypeScript, TanStack Query, Tailwind CSS
Architecture: Microservices with circuit breakers

## Breaking Changes

- Complete dashboard page rewrite (old version backed up)
- New API endpoints require orchestrator service deployment
- Database migrations needed for reasoning fields

## Migration Required

Run migrations to add new model fields:
- purchase_orders: reasoning, consequence, reasoning_data
- production_batches: reasoning, reasoning_data

## Documentation

See DASHBOARD_REDESIGN_SUMMARY.md for complete implementation details,
JTBD analysis, success metrics, and deployment guide.

BREAKING CHANGE: Dashboard page completely redesigned with new data structures
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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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