Create new services: inventory, recipes, suppliers
This commit is contained in:
Urtzi Alfaro
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services/inventory/app/api/classification.py
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231
services/inventory/app/api/classification.py
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# services/inventory/app/api/classification.py
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"""
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Product Classification API Endpoints
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AI-powered product classification for onboarding automation
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"""
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from fastapi import APIRouter, Depends, HTTPException, Path
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from typing import List, Dict, Any
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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.product_classifier import ProductClassifierService, get_product_classifier
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from shared.auth.decorators import get_current_user_dep, get_current_tenant_id_dep
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router = APIRouter(tags=["classification"])
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logger = structlog.get_logger()
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class ProductClassificationRequest(BaseModel):
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"""Request for single product classification"""
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product_name: str = Field(..., description="Product name to classify")
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sales_volume: float = Field(None, description="Total sales volume for context")
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sales_data: Dict[str, Any] = Field(default_factory=dict, description="Additional sales context")
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class BatchClassificationRequest(BaseModel):
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"""Request for batch product classification"""
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products: List[ProductClassificationRequest] = Field(..., description="Products to classify")
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class ProductSuggestionResponse(BaseModel):
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"""Response with product classification suggestion"""
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suggestion_id: str
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original_name: str
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suggested_name: str
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product_type: str
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category: str
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unit_of_measure: str
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confidence_score: float
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estimated_shelf_life_days: int = None
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requires_refrigeration: bool = False
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requires_freezing: bool = False
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is_seasonal: bool = False
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suggested_supplier: str = None
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notes: str = None
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class BusinessModelAnalysisResponse(BaseModel):
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"""Response with business model analysis"""
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model: str # production, retail, hybrid
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confidence: float
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ingredient_count: int
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finished_product_count: int
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ingredient_ratio: float
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recommendations: List[str]
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class BatchClassificationResponse(BaseModel):
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"""Response for batch classification"""
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suggestions: List[ProductSuggestionResponse]
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business_model_analysis: BusinessModelAnalysisResponse
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total_products: int
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high_confidence_count: int
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low_confidence_count: int
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@router.post("/tenants/{tenant_id}/inventory/classify-product", response_model=ProductSuggestionResponse)
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async def classify_single_product(
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request: ProductClassificationRequest,
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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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classifier: ProductClassifierService = Depends(get_product_classifier)
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):
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"""Classify a single product for inventory creation"""
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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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# Classify the product
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suggestion = classifier.classify_product(
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request.product_name,
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request.sales_volume
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)
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# Convert to response format
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response = ProductSuggestionResponse(
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suggestion_id=str(UUID.uuid4()), # Generate unique ID for tracking
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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.value,
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category=suggestion.category,
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unit_of_measure=suggestion.unit_of_measure.value,
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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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)
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logger.info("Classified single product",
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product=request.product_name,
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classification=suggestion.product_type.value,
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confidence=suggestion.confidence_score,
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tenant_id=tenant_id)
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return response
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except Exception as e:
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logger.error("Failed to classify product",
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error=str(e), product=request.product_name, tenant_id=tenant_id)
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raise HTTPException(status_code=500, detail=f"Classification failed: {str(e)}")
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@router.post("/tenants/{tenant_id}/inventory/classify-products-batch", response_model=BatchClassificationResponse)
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async def classify_products_batch(
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request: BatchClassificationRequest,
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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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classifier: ProductClassifierService = Depends(get_product_classifier)
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):
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"""Classify multiple products for onboarding automation"""
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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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if not request.products:
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raise HTTPException(status_code=400, detail="No products provided for classification")
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# Extract product names and volumes
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product_names = [p.product_name for p in request.products]
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sales_volumes = {p.product_name: p.sales_volume for p in request.products if p.sales_volume}
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# Classify products in batch
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suggestions = classifier.classify_products_batch(product_names, sales_volumes)
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# Convert suggestions to response format
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suggestion_responses = []
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for suggestion in suggestions:
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suggestion_responses.append(ProductSuggestionResponse(
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suggestion_id=str(UUID.uuid4()),
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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.value,
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category=suggestion.category,
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unit_of_measure=suggestion.unit_of_measure.value,
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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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))
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# Analyze business model
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ingredient_count = sum(1 for s in suggestions if s.product_type.value == 'ingredient')
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finished_count = sum(1 for s in suggestions if s.product_type.value == 'finished_product')
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total = len(suggestions)
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ingredient_ratio = ingredient_count / total if total > 0 else 0
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# Determine business model
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if ingredient_ratio >= 0.7:
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model = 'production'
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elif ingredient_ratio <= 0.3:
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model = 'retail'
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else:
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model = 'hybrid'
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confidence = max(abs(ingredient_ratio - 0.5) * 2, 0.1)
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recommendations = {
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'production': [
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'Focus on ingredient inventory management',
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'Set up recipe cost calculation',
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'Configure supplier relationships',
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'Enable production planning features'
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],
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'retail': [
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'Configure central baker relationships',
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'Set up delivery schedule tracking',
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'Enable finished product freshness monitoring',
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'Focus on sales forecasting'
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],
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'hybrid': [
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'Configure both ingredient and finished product management',
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'Set up flexible inventory categories',
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'Enable both production and retail features'
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]
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}
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business_model_analysis = BusinessModelAnalysisResponse(
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model=model,
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confidence=confidence,
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ingredient_count=ingredient_count,
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finished_product_count=finished_count,
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ingredient_ratio=ingredient_ratio,
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recommendations=recommendations.get(model, [])
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)
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# Count confidence levels
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high_confidence_count = sum(1 for s in suggestions if s.confidence_score >= 0.7)
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low_confidence_count = sum(1 for s in suggestions if s.confidence_score < 0.6)
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response = BatchClassificationResponse(
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suggestions=suggestion_responses,
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business_model_analysis=business_model_analysis,
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total_products=len(suggestions),
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high_confidence_count=high_confidence_count,
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low_confidence_count=low_confidence_count
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)
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logger.info("Batch classification complete",
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total_products=len(suggestions),
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business_model=model,
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high_confidence=high_confidence_count,
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low_confidence=low_confidence_count,
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tenant_id=tenant_id)
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return response
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except Exception as e:
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logger.error("Failed batch classification",
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error=str(e), products_count=len(request.products), tenant_id=tenant_id)
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raise HTTPException(status_code=500, detail=f"Batch classification failed: {str(e)}")
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