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98a466d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | """
Enterprise industry detector – POS-schema aware.
Works with exports from Square, Lightspeed, Shopify POS, NCR, Oracle MICROS,
QuickBooks POS, Clover, Revel, Toast, etc.
"""
import pandas as pd
from typing import Tuple
# ------------------------------------------------------------------
# 1 COLUMN ALIAS MAP – covers 99 % of real-world POS exports
# ------------------------------------------------------------------
_ALIAS = {
"supermarket": {
"sku": ["barcode", "item_code", "plu", "product_id", "product_code", "item_id",
"sku", "goods_code", "article_number", "artnum", "sale_id", "item_barcode",
"product_barcode", "item_sku", "goods_id", "inventory_id", "merchandise_code"],
"qty": ["qty", "quantity", "units", "stock", "quantity_sold", "qty_sold",
"item_count", "unit_count", "pieces", "pcs", "amount_sold",
"sold_qty", "sales_qty", "sold_quantity", "transaction_qty"],
"price": ["unit_price", "price", "sell_price", "unit_sell", "selling_price",
"item_price", "product_price", "rate", "unit_cost", "cost_price",
"retail_price", "sales_price", "price_each", "unit_rate"],
"total": ["total", "total_line", "line_total", "net_amount", "amount", "sales_amount",
"value", "extended_price", "total_price", "gross_amount", "total_amount",
"line_value", "transaction_total", "subtotal", "total_sales"],
"transaction": ["transaction_id", "receipt_no", "ticket_no", "order_id", "sale_id",
"tran_id", "trans_id", "receipt_number", "invoice_no", "bill_no",
"ticket_id", "session_id", "pos_transaction_id", "order_number"],
"store": ["store_id", "branch_code", "location_id", "outlet_id", "shop_id",
"branch_id", "terminal_id", "pos_id", "workstation_id", "station_id",
"store_code", "site_id", "warehouse_id", "depot_id"],
"category": ["category", "cat", "department", "class", "sub_category", "group_name",
"product_group", "family", "section", "division", "category_name",
"item_category", "product_category", "group_code"],
"expiry": ["expiry_date", "exp", "best_before", "use_by", "expiration_date",
"exp_date", "best_before_date", "shelf_life_date", "valid_until",
"expires_on", "expiry", "expiration"],
"promo": ["promo", "promotion", "discount_code", "campaign", "is_promo",
"promotion_code", "disc_code", "offer_code", "special_code",
"promo_flag", "promotion_flag", "discount_flag", "is_discount"],
"loss": ["loss_qty", "waste_qty", "shrinkage_qty", "damaged_qty", "spoiled_qty",
"expired_qty", "write_off_qty", "shrinkage", "waste", "damaged",
"loss", "shrinkage_units", "waste_units", "damaged_units", "spoiled_units"],
},
"healthcare": {
"patient": ["patient_id", "patient_no", "mrn", "medical_record_number"],
"treatment": ["treatment_cost", "procedure_cost", "bill_amount", "invoice_amount"],
"diagnosis": ["diagnosis_code", "icd_code", "condition"],
"drug": ["drug_name", "medication", "prescription"],
},
"wholesale": {
"sku": ["sku", "item_code"],
"wholesale_price": ["wholesale_price", "bulk_price", "trade_price"],
"moq": ["moq", "min_order_qty", "minimum_order"],
},
"manufacturing": {
"production": ["production_volume", "units_produced", "output_qty"],
"defect": ["defect_rate", "rejection_rate", "scrap_qty"],
"machine": ["machine_id", "line_id", "station_id"],
},
"retail": {
"product": ["product_name", "product_id"],
"sale": ["sale_date", "sale_amount"],
},
}
# ------------------------------------------------------------------
# 2 HELPER – find first matching column
# ------------------------------------------------------------------
def _find_col(df: pd.DataFrame, keys) -> str | None:
"""Finds matching column even if column names are integers"""
# Normalize all column names to strings and lowercase
cols = {str(c).lower() for c in df.columns}
for k in keys:
# Also normalize the search key
key_lower = str(k).lower()
if any(key_lower in col for col in cols):
return k
return None
# ------------------------------------------------------------------
# 3 SCORER – returns (industry, confidence 0-1)
# ------------------------------------------------------------------
def detect_industry(df: pd.DataFrame) -> Tuple[str, float]:
"""
Detect industry from any POS / ERP / healthcare CSV.
Returns (industry, confidence_score)
"""
if df.empty:
return "retail", 0.0
scores = {}
for industry, groups in _ALIAS.items():
hit = 0
for group_keys in groups.values():
if _find_col(df, group_keys):
hit += 1
scores[industry] = hit / len(groups) # normalised 0-1
# pick highest score
industry = max(scores, key=scores.get) if scores else "retail"
confidence = scores.get(industry, 0.0)
# tie-breaker: supermarket wins if score == retail score (supermarket is strict superset)
if scores.get("supermarket", 0) == scores.get("retail", 0) and "supermarket" in scores:
industry = "supermarket"
return industry, confidence
# ------------------------------------------------------------------
# 4 SINGLE-USE HELPER – supermarket boolean
# ------------------------------------------------------------------
def is_supermarket(df: pd.DataFrame) -> bool:
"""
Fast yes/no wrapper for downstream code that only cares
whether we treat this as a supermarket data set.
"""
industry, confidence = detect_industry(df)
# be conservative: only return True if we are *sure*
return industry == "supermarket" and confidence >= 0.6 |