Fix new services implementation 1
This commit is contained in:
Urtzi Alfaro
@@ -10,11 +10,12 @@ from uuid import UUID
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from pydantic import BaseModel, Field
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import structlog
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from app.services.onboarding_import_service import (
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OnboardingImportService,
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OnboardingImportResult,
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InventoryCreationRequest,
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get_onboarding_import_service
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from app.services.ai_onboarding_service import (
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AIOnboardingService,
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OnboardingValidationResult,
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ProductSuggestionsResult,
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OnboardingImportResult,
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get_ai_onboarding_service
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)
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from shared.auth.decorators import get_current_user_dep, get_current_tenant_id_dep
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@@ -22,16 +23,6 @@ router = APIRouter(tags=["onboarding"])
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logger = structlog.get_logger()
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class OnboardingAnalysisResponse(BaseModel):
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"""Response for onboarding analysis"""
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total_products_found: int
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inventory_suggestions: List[Dict[str, Any]]
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business_model_analysis: Dict[str, Any]
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import_job_id: str
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status: str
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processed_rows: int
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errors: List[str]
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warnings: List[str]
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class InventoryApprovalRequest(BaseModel):
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@@ -58,23 +49,22 @@ class SalesImportResponse(BaseModel):
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warnings: List[str]
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@router.post("/tenants/{tenant_id}/onboarding/analyze", response_model=OnboardingAnalysisResponse)
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async def analyze_onboarding_data(
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@router.post("/tenants/{tenant_id}/onboarding/validate-file", response_model=FileValidationResponse)
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async def validate_onboarding_file(
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file: UploadFile = File(..., description="Sales data CSV/Excel file"),
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tenant_id: UUID = Path(..., description="Tenant ID"),
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current_tenant: str = Depends(get_current_tenant_id_dep),
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current_user: Dict[str, Any] = Depends(get_current_user_dep),
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onboarding_service: OnboardingImportService = Depends(get_onboarding_import_service)
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onboarding_service: AIOnboardingService = Depends(get_ai_onboarding_service)
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):
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"""
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Step 1: Analyze uploaded sales data and suggest inventory items
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Step 1: Validate uploaded file and extract unique products
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This endpoint:
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1. Parses the uploaded sales file
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2. Extracts unique products and sales metrics
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3. Uses AI to classify products and suggest inventory items
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4. Analyzes business model (production vs retail)
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5. Returns suggestions for user review
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1. Validates the file format and content
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2. Checks for required columns (date, product, etc.)
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3. Extracts unique products from sales data
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4. Returns validation results and product list
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"""
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try:
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# Verify tenant access
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@@ -89,34 +79,42 @@ async def analyze_onboarding_data(
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if not any(file.filename.lower().endswith(ext) for ext in allowed_extensions):
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raise HTTPException(status_code=400, detail=f"Unsupported file format. Allowed: {allowed_extensions}")
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# Determine file format
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file_format = "csv" if file.filename.lower().endswith('.csv') else "excel"
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# Read file content
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file_content = await file.read()
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if not file_content:
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raise HTTPException(status_code=400, detail="File is empty")
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# Analyze the data
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result = await onboarding_service.analyze_sales_data_for_onboarding(
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file_content=file_content,
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filename=file.filename,
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tenant_id=tenant_id,
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user_id=UUID(current_user['user_id'])
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# Convert bytes to string for CSV
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if file_format == "csv":
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file_data = file_content.decode('utf-8')
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else:
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import base64
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file_data = base64.b64encode(file_content).decode('utf-8')
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# Validate and extract products
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result = await onboarding_service.validate_and_extract_products(
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file_data=file_data,
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file_format=file_format,
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tenant_id=tenant_id
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)
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response = OnboardingAnalysisResponse(
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total_products_found=result.total_products_found,
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inventory_suggestions=result.inventory_suggestions,
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business_model_analysis=result.business_model_analysis,
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import_job_id=str(result.import_job_id),
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status=result.status,
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processed_rows=result.processed_rows,
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errors=result.errors,
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warnings=result.warnings
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response = FileValidationResponse(
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is_valid=result.is_valid,
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total_records=result.total_records,
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unique_products=result.unique_products,
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product_list=result.product_list,
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validation_errors=result.validation_details.errors,
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validation_warnings=result.validation_details.warnings,
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summary=result.summary
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)
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logger.info("Onboarding analysis complete",
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logger.info("File validation complete",
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filename=file.filename,
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products_found=result.total_products_found,
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business_model=result.business_model_analysis.get('model'),
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is_valid=result.is_valid,
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unique_products=result.unique_products,
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tenant_id=tenant_id)
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return response
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@@ -124,9 +122,120 @@ async def analyze_onboarding_data(
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except HTTPException:
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raise
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except Exception as e:
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logger.error("Failed onboarding analysis",
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logger.error("Failed file validation",
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error=str(e), filename=file.filename if file else None, tenant_id=tenant_id)
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raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
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raise HTTPException(status_code=500, detail=f"Validation failed: {str(e)}")
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@router.post("/tenants/{tenant_id}/onboarding/generate-suggestions", response_model=ProductSuggestionsResponse)
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async def generate_inventory_suggestions(
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file: UploadFile = File(..., description="Same sales data file from step 1"),
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product_list: str = Form(..., description="JSON array of product names to classify"),
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tenant_id: UUID = Path(..., description="Tenant ID"),
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current_tenant: str = Depends(get_current_tenant_id_dep),
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current_user: Dict[str, Any] = Depends(get_current_user_dep),
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onboarding_service: AIOnboardingService = Depends(get_ai_onboarding_service)
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):
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"""
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Step 2: Generate AI-powered inventory suggestions
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This endpoint:
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1. Takes the validated file and product list from step 1
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2. Uses AI to classify products into inventory categories
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3. Analyzes business model (production vs retail)
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4. Returns detailed suggestions for user review
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"""
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try:
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# Verify tenant access
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if str(tenant_id) != current_tenant:
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raise HTTPException(status_code=403, detail="Access denied to this tenant")
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# Parse product list
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import json
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try:
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products = json.loads(product_list)
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except json.JSONDecodeError as e:
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raise HTTPException(status_code=400, detail=f"Invalid product list format: {str(e)}")
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if not products:
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raise HTTPException(status_code=400, detail="No products provided")
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# Determine file format
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file_format = "csv" if file.filename.lower().endswith('.csv') else "excel"
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# Read file content
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file_content = await file.read()
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if not file_content:
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raise HTTPException(status_code=400, detail="File is empty")
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# Convert bytes to string for CSV
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if file_format == "csv":
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file_data = file_content.decode('utf-8')
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else:
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import base64
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file_data = base64.b64encode(file_content).decode('utf-8')
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# Generate suggestions
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result = await onboarding_service.generate_inventory_suggestions(
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product_list=products,
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file_data=file_data,
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file_format=file_format,
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tenant_id=tenant_id
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)
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# Convert suggestions to dict format
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suggestions_dict = []
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for suggestion in result.suggestions:
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suggestion_dict = {
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"suggestion_id": suggestion.suggestion_id,
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"original_name": suggestion.original_name,
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"suggested_name": suggestion.suggested_name,
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"product_type": suggestion.product_type,
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"category": suggestion.category,
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"unit_of_measure": suggestion.unit_of_measure,
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"confidence_score": suggestion.confidence_score,
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"estimated_shelf_life_days": suggestion.estimated_shelf_life_days,
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"requires_refrigeration": suggestion.requires_refrigeration,
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"requires_freezing": suggestion.requires_freezing,
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"is_seasonal": suggestion.is_seasonal,
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"suggested_supplier": suggestion.suggested_supplier,
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"notes": suggestion.notes,
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"sales_data": suggestion.sales_data
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}
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suggestions_dict.append(suggestion_dict)
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business_model_dict = {
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"model": result.business_model_analysis.model,
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"confidence": result.business_model_analysis.confidence,
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"ingredient_count": result.business_model_analysis.ingredient_count,
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"finished_product_count": result.business_model_analysis.finished_product_count,
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"ingredient_ratio": result.business_model_analysis.ingredient_ratio,
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"recommendations": result.business_model_analysis.recommendations
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}
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response = ProductSuggestionsResponse(
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suggestions=suggestions_dict,
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business_model_analysis=business_model_dict,
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total_products=result.total_products,
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high_confidence_count=result.high_confidence_count,
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low_confidence_count=result.low_confidence_count,
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processing_time_seconds=result.processing_time_seconds
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)
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logger.info("AI suggestions generated",
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total_products=result.total_products,
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business_model=result.business_model_analysis.model,
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high_confidence=result.high_confidence_count,
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tenant_id=tenant_id)
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return response
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except HTTPException:
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raise
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except Exception as e:
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logger.error("Failed to generate suggestions",
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error=str(e), tenant_id=tenant_id)
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raise HTTPException(status_code=500, detail=f"Suggestion generation failed: {str(e)}")
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@router.post("/tenants/{tenant_id}/onboarding/create-inventory", response_model=InventoryCreationResponse)
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@@ -135,16 +244,16 @@ async def create_inventory_from_suggestions(
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tenant_id: UUID = Path(..., description="Tenant ID"),
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current_tenant: str = Depends(get_current_tenant_id_dep),
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current_user: Dict[str, Any] = Depends(get_current_user_dep),
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onboarding_service: OnboardingImportService = Depends(get_onboarding_import_service)
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onboarding_service: AIOnboardingService = Depends(get_ai_onboarding_service)
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):
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"""
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Step 2: Create inventory items from approved suggestions
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Step 3: Create inventory items from approved suggestions
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This endpoint:
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1. Takes user-approved inventory suggestions
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2. Applies any user modifications
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1. Takes user-approved inventory suggestions from step 2
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2. Applies any user modifications to suggestions
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3. Creates inventory items via inventory service
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4. Returns creation results
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4. Returns creation results for final import step
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"""
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try:
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# Verify tenant access
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@@ -154,18 +263,9 @@ async def create_inventory_from_suggestions(
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if not request.suggestions:
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raise HTTPException(status_code=400, detail="No suggestions provided")
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# Convert to internal format
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approval_requests = []
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for suggestion in request.suggestions:
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approval_requests.append(InventoryCreationRequest(
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suggestion_id=suggestion.get('suggestion_id'),
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approved=suggestion.get('approved', False),
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modifications=suggestion.get('modifications', {})
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))
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# Create inventory items
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# Create inventory items using new service
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result = await onboarding_service.create_inventory_from_suggestions(
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suggestions_approval=approval_requests,
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approved_suggestions=request.suggestions,
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tenant_id=tenant_id,
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user_id=UUID(current_user['user_id'])
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)
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@@ -199,16 +299,16 @@ async def import_sales_with_inventory(
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tenant_id: UUID = Path(..., description="Tenant ID"),
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current_tenant: str = Depends(get_current_tenant_id_dep),
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current_user: Dict[str, Any] = Depends(get_current_user_dep),
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onboarding_service: OnboardingImportService = Depends(get_onboarding_import_service)
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onboarding_service: AIOnboardingService = Depends(get_ai_onboarding_service)
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):
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"""
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Step 3: Import sales data using created inventory items
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Step 4: Final sales data import using created inventory items
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This endpoint:
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1. Takes the same sales file from step 1
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2. Uses the inventory mapping from step 2
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3. Imports sales records with proper inventory product references
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4. Returns import results
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1. Takes the same validated sales file from step 1
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2. Uses the inventory mapping from step 3
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3. Imports sales records using detailed processing from DataImportService
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4. Returns final import results - onboarding complete!
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"""
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try:
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# Verify tenant access
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@@ -223,41 +323,51 @@ async def import_sales_with_inventory(
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import json
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try:
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mapping = json.loads(inventory_mapping)
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# Convert string UUIDs to UUID objects
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inventory_mapping_uuids = {
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product_name: UUID(inventory_id)
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# Convert to string mapping for the new service
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inventory_mapping_dict = {
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product_name: str(inventory_id)
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for product_name, inventory_id in mapping.items()
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}
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except (json.JSONDecodeError, ValueError) as e:
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except json.JSONDecodeError as e:
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raise HTTPException(status_code=400, detail=f"Invalid inventory mapping format: {str(e)}")
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# Determine file format
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file_format = "csv" if file.filename.lower().endswith('.csv') else "excel"
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# Read file content
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file_content = await file.read()
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if not file_content:
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raise HTTPException(status_code=400, detail="File is empty")
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# Import sales data
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# Convert bytes to string for CSV
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if file_format == "csv":
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file_data = file_content.decode('utf-8')
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else:
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import base64
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file_data = base64.b64encode(file_content).decode('utf-8')
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# Import sales data using new service
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result = await onboarding_service.import_sales_data_with_inventory(
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file_content=file_content,
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filename=file.filename,
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file_data=file_data,
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file_format=file_format,
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inventory_mapping=inventory_mapping_dict,
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tenant_id=tenant_id,
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user_id=UUID(current_user['user_id']),
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inventory_mapping=inventory_mapping_uuids
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filename=file.filename
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)
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response = SalesImportResponse(
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import_job_id=str(result.import_job_id),
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status=result.status,
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processed_rows=result.processed_rows,
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successful_imports=result.successful_imports,
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failed_imports=result.failed_imports,
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errors=result.errors,
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warnings=result.warnings
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import_job_id="onboarding-" + str(tenant_id), # Generate a simple job ID
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status="completed" if result.success else "failed",
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processed_rows=result.import_details.records_processed,
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successful_imports=result.import_details.records_created,
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failed_imports=result.import_details.records_failed,
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errors=[error.get("message", str(error)) for error in result.import_details.errors],
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warnings=[warning.get("message", str(warning)) for warning in result.import_details.warnings]
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)
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logger.info("Sales import complete",
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successful=result.successful_imports,
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failed=result.failed_imports,
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successful=result.import_details.records_created,
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failed=result.import_details.records_failed,
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filename=file.filename,
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tenant_id=tenant_id)
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