Create new services: inventory, recipes, suppliers
This commit is contained in:
Urtzi Alfaro
467
services/inventory/app/services/product_classifier.py
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467
services/inventory/app/services/product_classifier.py
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# services/inventory/app/services/product_classifier.py
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"""
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AI Product Classification Service
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Automatically classifies products from sales data during onboarding
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"""
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import re
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import structlog
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from typing import Dict, Any, List, Optional, Tuple
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from enum import Enum
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from dataclasses import dataclass
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from app.models.inventory import ProductType, IngredientCategory, ProductCategory, UnitOfMeasure
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logger = structlog.get_logger()
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@dataclass
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class ProductSuggestion:
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"""Suggested inventory item from sales data analysis"""
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original_name: str
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suggested_name: str
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product_type: ProductType
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category: str # ingredient_category or product_category
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unit_of_measure: UnitOfMeasure
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confidence_score: float # 0.0 to 1.0
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estimated_shelf_life_days: Optional[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: Optional[str] = None
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notes: Optional[str] = None
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class ProductClassifierService:
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"""AI-powered product classification for onboarding automation"""
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def __init__(self):
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self._load_classification_rules()
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def _load_classification_rules(self):
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"""Load classification patterns and rules"""
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# Ingredient patterns with high confidence
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self.ingredient_patterns = {
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IngredientCategory.FLOUR: {
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'patterns': [
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r'harina', r'flour', r'trigo', r'wheat', r'integral', r'whole.*wheat',
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r'centeno', r'rye', r'avena', r'oat', r'maiz', r'corn'
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],
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'unit': UnitOfMeasure.KILOGRAMS,
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'shelf_life': 365,
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'supplier_hints': ['molinos', 'harinera', 'mill']
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},
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IngredientCategory.YEAST: {
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'patterns': [
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r'levadura', r'yeast', r'fermento', r'baker.*yeast', r'instant.*yeast'
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],
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'unit': UnitOfMeasure.GRAMS,
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'shelf_life': 730,
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'refrigeration': True
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},
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IngredientCategory.DAIRY: {
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'patterns': [
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r'leche', r'milk', r'nata', r'cream', r'mantequilla', r'butter',
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r'queso', r'cheese', r'yogur', r'yogurt'
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],
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'unit': UnitOfMeasure.LITERS,
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'shelf_life': 7,
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'refrigeration': True
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},
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IngredientCategory.EGGS: {
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'patterns': [
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r'huevo', r'egg', r'clara', r'white', r'yema', r'yolk'
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],
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'unit': UnitOfMeasure.UNITS,
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'shelf_life': 28,
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'refrigeration': True
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},
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IngredientCategory.SUGAR: {
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'patterns': [
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r'azucar', r'sugar', r'edulcorante', r'sweetener', r'miel', r'honey',
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r'jarabe', r'syrup', r'mascabado', r'brown.*sugar'
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],
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'unit': UnitOfMeasure.KILOGRAMS,
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'shelf_life': 730
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},
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IngredientCategory.FATS: {
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'patterns': [
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r'aceite', r'oil', r'grasa', r'fat', r'margarina', r'margarine',
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r'manteca', r'lard', r'oliva', r'olive'
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],
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'unit': UnitOfMeasure.LITERS,
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'shelf_life': 365
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},
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IngredientCategory.SALT: {
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'patterns': [
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r'sal', r'salt', r'sodium', r'sodio'
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],
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'unit': UnitOfMeasure.KILOGRAMS,
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'shelf_life': 1825 # 5 years
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},
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IngredientCategory.SPICES: {
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'patterns': [
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r'canela', r'cinnamon', r'vainilla', r'vanilla', r'cacao', r'cocoa',
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r'chocolate', r'anis', r'anise', r'cardamomo', r'cardamom',
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r'jengibre', r'ginger', r'nuez.*moscada', r'nutmeg'
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],
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'unit': UnitOfMeasure.GRAMS,
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'shelf_life': 730
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},
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IngredientCategory.ADDITIVES: {
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'patterns': [
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r'polvo.*hornear', r'baking.*powder', r'bicarbonato', r'soda',
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r'cremor.*tartaro', r'cream.*tartar', r'lecitina', r'lecithin',
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r'conservante', r'preservative', r'emulsificante', r'emulsifier'
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],
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'unit': UnitOfMeasure.GRAMS,
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'shelf_life': 730
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},
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IngredientCategory.PACKAGING: {
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'patterns': [
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r'bolsa', r'bag', r'envase', r'container', r'papel', r'paper',
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r'plastico', r'plastic', r'carton', r'cardboard'
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],
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'unit': UnitOfMeasure.UNITS,
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'shelf_life': 1825
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}
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}
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# Finished product patterns
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self.product_patterns = {
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ProductCategory.BREAD: {
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'patterns': [
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r'pan\b', r'bread', r'baguette', r'hogaza', r'loaf', r'molde',
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r'integral', r'whole.*grain', r'centeno', r'rye.*bread'
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],
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'unit': UnitOfMeasure.UNITS,
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'shelf_life': 3,
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'display_life': 24 # hours
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},
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ProductCategory.CROISSANTS: {
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'patterns': [
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r'croissant', r'cruasan', r'napolitana', r'palmera', r'palmier'
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],
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'unit': UnitOfMeasure.UNITS,
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'shelf_life': 2,
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'display_life': 12
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},
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ProductCategory.PASTRIES: {
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'patterns': [
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r'pastel', r'pastry', r'hojaldre', r'puff.*pastry', r'empanada',
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r'milhojas', r'napoleon', r'eclair', r'profiterol'
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],
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'unit': UnitOfMeasure.UNITS,
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'shelf_life': 2,
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'display_life': 24,
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'refrigeration': True
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},
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ProductCategory.CAKES: {
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'patterns': [
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r'tarta', r'cake', r'bizcocho', r'sponge', r'cheesecake',
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r'tiramisu', r'mousse', r'torta'
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],
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'unit': UnitOfMeasure.UNITS,
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'shelf_life': 3,
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'refrigeration': True
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},
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ProductCategory.COOKIES: {
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'patterns': [
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r'galleta', r'cookie', r'biscuit', r'mantecada', r'madeleine'
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],
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'unit': UnitOfMeasure.UNITS,
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'shelf_life': 14
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},
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ProductCategory.MUFFINS: {
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'patterns': [
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r'muffin', r'magdalena', r'cupcake', r'fairy.*cake'
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],
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'unit': UnitOfMeasure.UNITS,
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'shelf_life': 3
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},
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ProductCategory.SANDWICHES: {
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'patterns': [
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r'sandwich', r'bocadillo', r'tostada', r'toast', r'bagel'
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],
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'unit': UnitOfMeasure.UNITS,
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'shelf_life': 1,
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'display_life': 6,
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'refrigeration': True
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},
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ProductCategory.BEVERAGES: {
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'patterns': [
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r'cafe', r'coffee', r'te\b', r'tea', r'chocolate.*caliente',
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r'hot.*chocolate', r'zumo', r'juice', r'batido', r'smoothie'
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],
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'unit': UnitOfMeasure.UNITS,
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'shelf_life': 1
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}
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}
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# Seasonal indicators
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self.seasonal_patterns = {
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'christmas': [r'navidad', r'christmas', r'turron', r'polvoron', r'roscon'],
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'easter': [r'pascua', r'easter', r'mona', r'torrija'],
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'summer': [r'helado', r'ice.*cream', r'granizado', r'sorbete']
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}
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def classify_product(self, product_name: str, sales_volume: Optional[float] = None) -> ProductSuggestion:
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"""Classify a single product name into inventory suggestion"""
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# Normalize product name for analysis
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normalized_name = self._normalize_name(product_name)
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# Try to classify as ingredient first
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ingredient_result = self._classify_as_ingredient(normalized_name, product_name)
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if ingredient_result and ingredient_result.confidence_score >= 0.7:
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return ingredient_result
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# Try to classify as finished product
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product_result = self._classify_as_finished_product(normalized_name, product_name)
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if product_result:
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return product_result
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# Fallback: create generic finished product with low confidence
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return self._create_fallback_suggestion(product_name, normalized_name)
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def classify_products_batch(self, product_names: List[str],
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sales_volumes: Optional[Dict[str, float]] = None) -> List[ProductSuggestion]:
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"""Classify multiple products and detect business model"""
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suggestions = []
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for name in product_names:
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volume = sales_volumes.get(name) if sales_volumes else None
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suggestion = self.classify_product(name, volume)
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suggestions.append(suggestion)
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# Analyze business model based on classification results
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self._analyze_business_model(suggestions)
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return suggestions
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def _normalize_name(self, name: str) -> str:
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"""Normalize product name for pattern matching"""
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if not name:
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return ""
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# Convert to lowercase
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normalized = name.lower().strip()
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# Remove common prefixes/suffixes
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prefixes_to_remove = ['el ', 'la ', 'los ', 'las ', 'un ', 'una ']
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for prefix in prefixes_to_remove:
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if normalized.startswith(prefix):
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normalized = normalized[len(prefix):]
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# Remove special characters but keep spaces and accents
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normalized = re.sub(r'[^\w\sáéíóúñü]', ' ', normalized)
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# Normalize multiple spaces
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normalized = re.sub(r'\s+', ' ', normalized).strip()
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return normalized
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def _classify_as_ingredient(self, normalized_name: str, original_name: str) -> Optional[ProductSuggestion]:
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"""Try to classify as ingredient"""
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best_match = None
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best_score = 0.0
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for category, config in self.ingredient_patterns.items():
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for pattern in config['patterns']:
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if re.search(pattern, normalized_name, re.IGNORECASE):
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# Calculate confidence based on pattern specificity
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score = self._calculate_confidence_score(pattern, normalized_name)
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if score > best_score:
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best_score = score
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best_match = (category, config)
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if best_match and best_score >= 0.6:
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category, config = best_match
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return ProductSuggestion(
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original_name=original_name,
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suggested_name=self._suggest_clean_name(original_name, normalized_name),
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product_type=ProductType.INGREDIENT,
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category=category.value,
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unit_of_measure=config['unit'],
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confidence_score=best_score,
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estimated_shelf_life_days=config.get('shelf_life'),
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requires_refrigeration=config.get('refrigeration', False),
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requires_freezing=config.get('freezing', False),
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suggested_supplier=self._suggest_supplier(normalized_name, config.get('supplier_hints', [])),
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notes=f"Auto-classified as {category.value} ingredient"
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)
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return None
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def _classify_as_finished_product(self, normalized_name: str, original_name: str) -> Optional[ProductSuggestion]:
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"""Try to classify as finished product"""
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best_match = None
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best_score = 0.0
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for category, config in self.product_patterns.items():
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for pattern in config['patterns']:
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if re.search(pattern, normalized_name, re.IGNORECASE):
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score = self._calculate_confidence_score(pattern, normalized_name)
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if score > best_score:
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best_score = score
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best_match = (category, config)
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if best_match:
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category, config = best_match
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# Check if seasonal
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is_seasonal = self._is_seasonal_product(normalized_name)
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return ProductSuggestion(
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original_name=original_name,
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suggested_name=self._suggest_clean_name(original_name, normalized_name),
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product_type=ProductType.FINISHED_PRODUCT,
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category=category.value,
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unit_of_measure=config['unit'],
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confidence_score=best_score,
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estimated_shelf_life_days=config.get('shelf_life'),
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requires_refrigeration=config.get('refrigeration', False),
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requires_freezing=config.get('freezing', False),
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is_seasonal=is_seasonal,
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notes=f"Auto-classified as {category.value}"
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)
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return None
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def _create_fallback_suggestion(self, original_name: str, normalized_name: str) -> ProductSuggestion:
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"""Create a fallback suggestion for unclassified products"""
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return ProductSuggestion(
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original_name=original_name,
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suggested_name=self._suggest_clean_name(original_name, normalized_name),
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product_type=ProductType.FINISHED_PRODUCT,
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category=ProductCategory.OTHER_PRODUCTS.value,
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unit_of_measure=UnitOfMeasure.UNITS,
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confidence_score=0.3,
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estimated_shelf_life_days=3,
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notes="Needs manual classification - defaulted to finished product"
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)
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def _calculate_confidence_score(self, pattern: str, normalized_name: str) -> float:
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"""Calculate confidence score for pattern match"""
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# Base score for match
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base_score = 0.8
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# Boost score for exact matches
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if pattern.lower() == normalized_name:
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return 0.95
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# Boost score for word boundary matches
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if re.search(r'\b' + pattern + r'\b', normalized_name, re.IGNORECASE):
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base_score += 0.1
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# Reduce score for partial matches
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if len(pattern) < len(normalized_name) / 2:
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base_score -= 0.2
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return min(0.95, max(0.3, base_score))
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def _suggest_clean_name(self, original_name: str, normalized_name: str) -> str:
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"""Suggest a cleaned version of the product name"""
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# Capitalize properly
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words = original_name.split()
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cleaned = []
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for word in words:
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if len(word) > 0:
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# Keep original casing for abbreviations
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if word.isupper() and len(word) <= 3:
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cleaned.append(word)
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else:
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cleaned.append(word.capitalize())
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return ' '.join(cleaned)
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def _suggest_supplier(self, normalized_name: str, supplier_hints: List[str]) -> Optional[str]:
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"""Suggest potential supplier based on product type"""
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for hint in supplier_hints:
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if hint in normalized_name:
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return f"Suggested: {hint.title()}"
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return None
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def _is_seasonal_product(self, normalized_name: str) -> bool:
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"""Check if product appears to be seasonal"""
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for season, patterns in self.seasonal_patterns.items():
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for pattern in patterns:
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if re.search(pattern, normalized_name, re.IGNORECASE):
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return True
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return False
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def _analyze_business_model(self, suggestions: List[ProductSuggestion]) -> Dict[str, Any]:
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"""Analyze business model based on product classifications"""
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ingredient_count = sum(1 for s in suggestions if s.product_type == ProductType.INGREDIENT)
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finished_count = sum(1 for s in suggestions if s.product_type == ProductType.FINISHED_PRODUCT)
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total = len(suggestions)
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if total == 0:
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return {"model": "unknown", "confidence": 0.0}
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ingredient_ratio = ingredient_count / total
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if ingredient_ratio >= 0.7:
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model = "production" # Production bakery
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elif ingredient_ratio <= 0.3:
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model = "retail" # Retail/Distribution bakery
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else:
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model = "hybrid" # Mixed model
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confidence = max(abs(ingredient_ratio - 0.5) * 2, 0.1)
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logger.info("Business model analysis",
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model=model, confidence=confidence,
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ingredient_count=ingredient_count,
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finished_count=finished_count)
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return {
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"model": model,
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"confidence": confidence,
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"ingredient_ratio": ingredient_ratio,
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"recommendations": self._get_model_recommendations(model)
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}
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def _get_model_recommendations(self, model: str) -> List[str]:
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"""Get recommendations based on detected business model"""
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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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return recommendations.get(model, [])
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# Dependency injection
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def get_product_classifier() -> ProductClassifierService:
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"""Get product classifier service instance"""
|
||||
return ProductClassifierService()
|
||||
Reference in New Issue
Block a user