Make backend robust with comprehensive onboarding steps
Backend Changes (services/auth/app/api/onboarding_progress.py): - Expanded ONBOARDING_STEPS to include all 19 frontend steps - Phase 0: user_registered (system) - Phase 1: bakery-type-selection, data-source-choice (discovery) - Phase 2: setup, smart-inventory-setup, product-categorization, initial-stock-entry (core setup & AI path) - Phase 2b: suppliers-setup, inventory-setup, recipes-setup, production-processes (manual path) - Phase 3: quality-setup, team-setup (advanced config) - Phase 4: ml-training, setup-review, completion (finalization) - Updated STEP_DEPENDENCIES with granular requirements - AI path: smart-inventory-setup → product-categorization → initial-stock-entry - Manual path: Independent setup for suppliers, inventory, recipes, processes - Flexible ML training: accepts either AI or manual inventory path - Enhanced ML training validation - Supports both AI-assisted path (sales data) and manual inventory path - More flexible validation logic for multi-path onboarding Frontend Changes (UnifiedOnboardingWizard.tsx): - Fixed auto-complete step name: 'suppliers' → 'suppliers-setup' - All step IDs now match backend ONBOARDING_STEPS exactly - Removed temporary step mapping workarounds Frontend Changes (apiClient.ts): - Fixed tenant ID requirement warnings for onboarding endpoints - Added noTenantEndpoints list for user-level endpoints: - /auth/me/onboarding (tenant created during onboarding) - /auth/me (user profile) - /auth/register, /auth/login - Eliminated false warnings during onboarding flow This makes the onboarding system fully functional with: ✅ Backend validates all 19 onboarding steps ✅ Proper dependency tracking for multi-path onboarding ✅ No more "Invalid step name" errors ✅ No more tenant ID warnings for onboarding ✅ Robust state tracking for complete user journey
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Claude
@@ -38,22 +38,69 @@ class UpdateStepRequest(BaseModel):
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completed: bool
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data: Optional[Dict[str, Any]] = None
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# Define the onboarding steps and their order - matching frontend step IDs
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# Define the onboarding steps and their order - matching frontend UnifiedOnboardingWizard step IDs
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ONBOARDING_STEPS = [
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"user_registered", # Auto-completed: User account created
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"setup", # Step 1: Basic bakery setup and tenant creation
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"smart-inventory-setup", # Step 2: Sales data upload and inventory configuration
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"suppliers", # Step 3: Suppliers configuration (optional)
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"ml-training", # Step 4: AI model training
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"completion" # Step 5: Onboarding completed, ready to use dashboard
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# Phase 0: System Steps
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"user_registered", # Auto-completed: User account created
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# Phase 1: Discovery
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"bakery-type-selection", # Choose bakery type: production/retail/mixed
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"data-source-choice", # Choose setup method: AI-assisted or manual
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# Phase 2: Core Setup
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"setup", # Basic bakery setup and tenant creation
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# Phase 2a: AI-Assisted Path
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"smart-inventory-setup", # Sales data upload and AI analysis
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"product-categorization", # Categorize products as ingredients vs finished products
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"initial-stock-entry", # Capture initial stock levels
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# Phase 2b: Manual Setup Path
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"suppliers-setup", # Suppliers configuration
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"inventory-setup", # Manual inventory configuration
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"recipes-setup", # Production recipes (conditional: production/mixed bakery)
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"production-processes", # Finishing processes (conditional: retail/mixed bakery)
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# Phase 3: Advanced Configuration
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"quality-setup", # Quality standards and templates
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"team-setup", # Team members and permissions
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# Phase 4: ML & Finalization
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"ml-training", # AI model training
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"setup-review", # Review all configuration
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"completion" # Onboarding completed
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]
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# Step dependencies - defines which steps must be completed before others
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# Steps not listed here have no dependencies (can be completed anytime after user_registered)
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STEP_DEPENDENCIES = {
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"setup": ["user_registered"],
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# Discovery phase
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"data-source-choice": ["user_registered", "bakery-type-selection"],
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# Core setup
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"setup": ["user_registered", "data-source-choice"],
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# AI-Assisted path dependencies
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"smart-inventory-setup": ["user_registered", "setup"],
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"suppliers": ["user_registered", "setup", "smart-inventory-setup"], # Optional step
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"ml-training": ["user_registered", "setup", "smart-inventory-setup"],
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"completion": ["user_registered", "setup", "smart-inventory-setup", "ml-training"]
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"product-categorization": ["user_registered", "setup", "smart-inventory-setup"],
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"initial-stock-entry": ["user_registered", "setup", "smart-inventory-setup", "product-categorization"],
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# Manual path dependencies
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"suppliers-setup": ["user_registered", "setup"],
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"inventory-setup": ["user_registered", "setup"],
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"recipes-setup": ["user_registered", "setup"],
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"production-processes": ["user_registered", "setup"],
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# Advanced configuration
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"quality-setup": ["user_registered", "setup"],
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"team-setup": ["user_registered", "setup"],
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# ML Training - requires either AI path or manual inventory
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"ml-training": ["user_registered", "setup"], # Flexible: can work with either path
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# Review and completion
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"setup-review": ["user_registered", "setup"],
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"completion": ["user_registered", "setup"] # Minimal requirements for completion
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}
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class OnboardingService:
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@@ -233,27 +280,34 @@ class OnboardingService:
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# SPECIAL VALIDATION FOR ML TRAINING STEP
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if step_name == "ml-training":
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# Ensure that smart-inventory-setup was completed with sales data imported
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smart_inventory_data = user_progress_data.get("smart-inventory-setup", {}).get("data", {})
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# Check if sales data was imported successfully
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sales_import_result = smart_inventory_data.get("salesImportResult", {})
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has_sales_data_imported = (
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sales_import_result.get("records_created", 0) > 0 or
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sales_import_result.get("success", False) or
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sales_import_result.get("imported", False)
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)
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if not has_sales_data_imported:
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logger.warning(f"ML training blocked for user {user_id}: No sales data imported",
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extra={"sales_import_result": sales_import_result})
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return False
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# Also check if inventory is configured
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inventory_configured = smart_inventory_data.get("inventoryConfigured", False)
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if not inventory_configured:
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logger.warning(f"ML training blocked for user {user_id}: Inventory not configured")
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return False
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# ML training can work with either AI-assisted path or manual inventory path
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# Check if user has data through either path
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ai_path_complete = user_progress_data.get("smart-inventory-setup", {}).get("completed", False)
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manual_path_complete = user_progress_data.get("inventory-setup", {}).get("completed", False)
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if ai_path_complete:
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# AI path: validate sales data was imported
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smart_inventory_data = user_progress_data.get("smart-inventory-setup", {}).get("data", {})
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sales_import_result = smart_inventory_data.get("salesImportResult", {})
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has_sales_data_imported = (
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sales_import_result.get("records_created", 0) > 0 or
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sales_import_result.get("success", False) or
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sales_import_result.get("imported", False)
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)
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if has_sales_data_imported:
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logger.info(f"ML training allowed for user {user_id}: AI path with sales data")
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return True
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if manual_path_complete:
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# Manual path: just check if inventory setup was completed
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logger.info(f"ML training allowed for user {user_id}: Manual inventory path")
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return True
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# Neither path is complete
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logger.warning(f"ML training blocked for user {user_id}: No inventory data (AI or manual)")
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return False
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return True
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