5a84be83d6
This commit addresses all identified bugs and issues in the training code path: ## Critical Fixes: - Add get_start_time() method to TrainingLogRepository and fix non-existent method call - Remove duplicate training.started event from API endpoint (trainer publishes the accurate one) - Add missing progress events for 80-100% range (85%, 92%, 94%) to eliminate progress "dead zone" ## High Priority Fixes: - Fix division by zero risk in time estimation with double-check and max() safety - Remove unreachable exception handler in training_operations.py - Simplify WebSocket token refresh logic to only reconnect on actual user session changes ## Medium Priority Fixes: - Fix auto-start training effect with useRef to prevent duplicate starts - Add HTTP polling debounce delay (5s) to prevent race conditions with WebSocket - Extract all magic numbers to centralized constants files: - Backend: services/training/app/core/training_constants.py - Frontend: frontend/src/constants/training.ts - Standardize error logging with exc_info=True on critical errors ## Code Quality Improvements: - All progress percentages now use named constants - All timeouts and intervals now use named constants - Improved code maintainability and readability - Better separation of concerns ## Files Changed: - Backend: training_service.py, trainer.py, training_events.py, progress_tracker.py - Backend: training_operations.py, training_log_repository.py, training_constants.py (new) - Frontend: training.ts (hooks), MLTrainingStep.tsx, training.ts (constants, new) All training progress events now properly flow from 0% to 100% with no gaps.
🍞 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.