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| """ | |
| 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 |