shawn
e8a918fc7b
- Added is_disney INTEGER DEFAULT 0 column to assets table - Backfilled 996 Disney assets based on D- customer prefix - Server: is_disney in AssetCreate/AssetUpdate models, INSERT/UPDATE - Server: disney_filter query param now uses is_disney column (no JOIN needed) - import_cantaloupe.py: sets is_disney=1 when customer starts with D- - import_machines.py: sets is_disney=1 when customer starts with D- - disney_classify.py: sets is_disney=1 when classifying - Frontend: new Location Type dropdown with Disney/Non-Disney toggle (replaces the __disney__/__non_disney__ park dropdown pseudo-options)
336 lines
10 KiB
Python
336 lines
10 KiB
Python
"""
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Disney park classification for canteen assets.
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Detects which Disney park/area an asset belongs to based on:
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- Asset name pattern (D-{Park} ...)
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- Address keywords
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- Room/building name keywords
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- Customer name (already D-{Park} prefixed)
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"""
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import re
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from typing import Optional
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# ─── Disney Park definitions ─────────────────────────────────────────────────
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DISNEY_PARK_NAMES = {
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"magic-kingdom": "Magic Kingdom",
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"epcot": "Epcot",
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"hollywood-studios": "Hollywood Studios",
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"animal-kingdom": "Animal Kingdom",
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"disney-springs": "Disney Springs",
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"resort": "Resort",
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"office": "Office",
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"other": "Other",
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}
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# Park icons / emoji for UI
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DISNEY_PARK_ICONS = {
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"magic-kingdom": "🏰",
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"epcot": "🌍",
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"hollywood-studios": "🎬",
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"animal-kingdom": "🌿",
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"disney-springs": "🛍️",
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"resort": "🏨",
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"office": "🏢",
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"other": "📍",
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}
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DISNEY_PARK_COLORS = {
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"magic-kingdom": "#9b59b6", # purple
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"epcot": "#3498db", # blue
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"hollywood-studios": "#e74c3c", # red
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"animal-kingdom": "#2ecc71", # green
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"disney-springs": "#f39c12", # orange
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"resort": "#1abc9c", # teal
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"office": "#95a5a6", # gray
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"other": "#bdc3c7", # light gray
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}
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# ─── Resort name keywords ────────────────────────────────────────────────────
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RESORT_KEYWORDS = [
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"WILDERNESS LODGE", "Wilderness Lodge",
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"FORT WILDERNESS", "Fort Wilderness",
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"GRAND FLORIDIAN", "Grand Floridian", "Floridian",
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"BOARDWALK", "Boardwalk",
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"SARATOGA SPRINGS", "Saratoga Springs",
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"CARIBBEAN BEACH", "Caribbean Beach",
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"CORONADO SPRINGS", "Coronado Springs",
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"GRAN DESTINO", "Gran Destino",
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"PORT ORLEANS", "Port Orleans",
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"POLYNESIAN", "Polynesian",
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"CONTEMPORARY", "Contemporary",
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"ANIMAL KINGDOM LODGE", "Animal Kingdom Lodge",
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"ANIMAL KNGDM LODGE", "Animal Kngdm Lodge",
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"KIDANI", "Kidani",
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"BEACH CLUB", "Beach Club",
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"YACHT CLUB", "Yacht Club",
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"OLD KEY WEST", "Old Key West",
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"RIVIERA", "Riviera",
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"POP CENTURY", "Pop Century",
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"ART OF ANIMATION", "Art of Animation",
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"ALL STAR", "All Star",
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"SHADES OF GREEN", "Shades of Green",
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"BUENA VISTA PALACE", "Buena Vista Palace",
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"WYNDHAM", "Wyndham",
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"HILTON", "Hilton",
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"IHG HOTEL", "IHG Hotel",
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"DAYS INN", "Days Inn",
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"B RESORT", "B Resort",
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"DOUBLETREE", "DoubleTree",
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"HOLIDAY INN", "Holiday Inn",
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"BEST WESTERN", "Best Western",
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"MAGNOLIA", "Magnolia", # Saratoga Springs area
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]
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# ─── Classification logic ────────────────────────────────────────────────────
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def classify_asset(name: str, address: str = "", building_name: str = "",
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room: str = "", customer_name: str = "") -> Optional[str]:
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"""
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Classify a canteen asset into a Disney park/area.
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Returns a disney_park key or None if not Disney-related.
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"""
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text = f"{name} {address} {building_name} {room} {customer_name}"
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# Quick check — is this Disney at all?
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if not _is_disney_related(text):
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return None
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# 1. Direct name pattern: D-{Park} ...
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park = _match_name_prefix(name)
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if park:
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return park
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# 2. Address/building keywords
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park = _match_address_keywords(address, building_name, room)
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if park:
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return park
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# 3. Customer name pattern
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park = _match_customer_name(customer_name)
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if park:
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return park
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# 4. Resort keywords in any field
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if _is_resort(text):
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return "resort"
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# 5. Default: other Disney
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return "other"
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def _is_disney_related(text: str) -> bool:
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"""Check if this asset is Disney-related at all."""
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t = text.upper()
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keywords = [
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"DISNEY", "WDW", "MAGIC KINGDOM", "EPCOT",
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"HOLLYWOOD STUDIOS", "ANIMAL KINGDOM",
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"DISNEY SPRINGS", "LAKE BUENA VISTA", "BAY LAKE",
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"SEVEN SEAS", "HOTEL PLAZA BLVD",
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"D-", "D_MAGIC", "D_EPCOT", "D_HOLLYWOOD", "D_ANIMAL",
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"FLORIDIAN", "WILDERNESS LODGE", "BOARDWALK",
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"GRAND FLORIDIAN", "SARATOGA SPRINGS",
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"BUENA VISTA",
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]
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return any(kw in t for kw in keywords)
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def _match_name_prefix(name: str) -> Optional[str]:
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"""Match D-{Park} pattern in asset name."""
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n = name.upper()
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# D-Magic Kingdom
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if "D-MAGIC KINGDOM" in n or "D_MAGIC KINGDOM" in n:
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return "magic-kingdom"
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# D-Epcot
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if "D-EPCOT" in n or "D_EPCOT" in n:
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return "epcot"
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# D-Hollywood Studios
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if "D-HOLLYWOOD STUDIOS" in n or "D_HOLLYWOOD STUDIOS" in n:
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return "hollywood-studios"
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# D-Animal Kingdom or D-AK
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if "D-ANIMAL KINGDOM" in n or "D_ANIMAL KINGDOM" in n or " D-AK " in n:
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return "animal-kingdom"
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# D-Disney Springs
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if "D-DISNEY SPRINGS" in n or "D_DISNEY SPRINGS" in n:
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return "disney-springs"
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# Resort detection in name prefix
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if n.startswith("D-") or " - " in n:
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# Extract the location part after D- and before any type indicator
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for kw in RESORT_KEYWORDS:
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if kw.upper() in n:
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return "resort"
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return None
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def _match_address_keywords(address: str, building: str, room: str) -> Optional[str]:
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"""Match address/building patterns to parks."""
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addr = (address + " " + building + " " + room).upper()
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# Magic Kingdom
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if "SEVEN SEAS" in addr or "MAGIC KINGDOM" in addr or "BAY LAKE" in addr:
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return "magic-kingdom"
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# Epcot
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if "EPCOT CENTER" in addr or "EPCOT" in addr:
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return "epcot"
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# Hollywood Studios
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if "EAST STUDIO" in addr or "HOLLYWOOD STUDIOS" in addr:
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return "hollywood-studios"
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# Animal Kingdom
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if "RAINFOREST" in addr or "ANIMAL KINGDOM" in addr or " AK " in addr or addr.startswith("AK "):
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return "animal-kingdom"
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# Disney Springs / Hotel Plaza Blvd area
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if "DISNEY SPRINGS" in addr or "BUENA VISTA DR" in addr or "HOTEL PLAZA BLVD" in addr:
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return "disney-springs"
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# Floridian Way = Grand Floridian (resort)
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if "FLORIDIAN" in addr:
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return "resort"
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# Epcot Resorts Blvd = Boardwalk area
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if "EPCOT RESORTS" in addr:
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return "resort"
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return None
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def _match_customer_name(customer: str) -> Optional[str]:
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"""Match customer name to park classification."""
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c = customer.upper()
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if "D-MAGIC KINGDOM" in c:
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return "magic-kingdom"
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if "D-EPCOT" in c:
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return "epcot"
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if "D-HOLLYWOOD STUDIOS" in c:
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return "hollywood-studios"
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if "D-DISNEY SPRINGS" in c:
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return "disney-springs"
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if "D-DISNEY VENDING" in c or "D-DISNEY WORLD" in c:
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return "office"
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# D-{Resort} pattern
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if c.startswith("D-"):
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for kw in RESORT_KEYWORDS:
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if kw.upper() in c:
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return "resort"
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# If it starts with D- and we haven't matched yet, it's some Disney thing
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return "other"
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return None
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def _is_resort(text: str) -> bool:
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"""Check if text mentions a known Disney resort."""
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t = text.upper()
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for kw in RESORT_KEYWORDS:
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if kw.upper() in t:
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return True
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return False
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# ─── Batch classification ────────────────────────────────────────────────────
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def classify_all_assets(db_path: str) -> dict:
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"""
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Run classification on all Disney-related assets in the DB.
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Returns a report of what was classified.
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"""
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import sqlite3
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conn = sqlite3.connect(db_path)
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conn.row_factory = sqlite3.Row
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# Get all assets that are Disney-related (or have no classification yet)
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rows = conn.execute("""
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SELECT a.id, a.machine_id, a.name, a.address, a.building_name, a.room,
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a.disney_park, c.name as customer_name
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FROM assets a
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LEFT JOIN customers c ON a.customer_id = c.id
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WHERE a.disney_park IS NULL
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""").fetchall()
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results = {"classified": [], "unclassifiable": [], "total_checked": len(rows)}
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counts = {}
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for row in rows:
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park = classify_asset(
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name=row['name'] or '',
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address=row['address'] or '',
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building_name=row['building_name'] or '',
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room=row['room'] or '',
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customer_name=row['customer_name'] or '',
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)
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if park:
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conn.execute(
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"UPDATE assets SET disney_park = ?, is_disney = 1, updated_at = datetime('now') WHERE id = ?",
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(park, row['id'])
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)
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results["classified"].append({
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"id": row['id'],
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"machine_id": row['machine_id'],
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"name": row['name'],
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"park": park,
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})
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counts[park] = counts.get(park, 0) + 1
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else:
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results["unclassifiable"].append({
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"id": row['id'],
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"machine_id": row['machine_id'],
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"name": row['name'],
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})
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conn.commit()
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conn.close()
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results["counts"] = counts
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results["total_classified"] = len(results["classified"])
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return results
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def get_classification_stats(db_path: str) -> dict:
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"""Get statistics about current Disney classifications."""
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import sqlite3
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conn = sqlite3.connect(db_path)
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conn.row_factory = sqlite3.Row
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rows = conn.execute("""
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SELECT disney_park, COUNT(*) as count
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FROM assets
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WHERE disney_park IS NOT NULL
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GROUP BY disney_park
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ORDER BY count DESC
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""").fetchall()
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total = conn.execute("SELECT COUNT(*) as c FROM assets").fetchone()['c']
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disney = conn.execute(
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"SELECT COUNT(*) as c FROM assets WHERE disney_park IS NOT NULL"
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).fetchone()['c']
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unclassified = conn.execute(
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"SELECT COUNT(*) as c FROM assets WHERE name LIKE '%Disney%' OR name LIKE '%D-%' AND disney_park IS NULL"
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).fetchone()['c']
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conn.close()
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return {
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"total_assets": total,
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"classified": disney,
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"unclassified_disney": 0, # Will be accurate after classify run
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"by_park": {row['disney_park']: row['count'] for row in rows},
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}
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