Add weekly-shop-to-inventory import (on-demand, exclusions) + structured basket save
- scripts/shop_basket.py: normalize + save structured weekly basket to last-shop.json (git-ignored live data) - SKILL.md: weekly job now persists last-shop.json; new on-demand rule 'add everything from Monday's shop except X' - .gitignore: ignore last-shop.json in both live skill and repo - Weekly cron prompt updated to save basket (still never auto-modifies inventory)
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meal-agent
2
.gitignore
vendored
2
.gitignore
vendored
@ -4,6 +4,8 @@ inventory.json
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meal-history.json
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# Weekly cuisine pick (regenerated by scripts/pick_cuisine.py; not source).
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cuisine-rotation.json
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# Last generated weekly shop (regenerated by the weekly job; not the inventory).
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last-shop.json
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# Python caches
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__pycache__/
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scripts/__pycache__/
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23
SKILL.md
23
SKILL.md
@ -259,6 +259,29 @@ If the user lists ingredients in chat, add them to the inventory and optionally
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give an immediate 2-meal suggestion. If they ask "what should I eat today?", read
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the inventory and suggest now (and offer to email it).
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### Import a weekly shop into inventory (exclusions supported)
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The weekly job does **NOT** auto-add the shop to inventory — `last-shop.json` is
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only a record of what the Monday email proposed. To bring it in, the user says
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e.g. *"add everything from Monday's shop to the inventory, except milk and
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vitamin D3"*. Procedure:
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1) Read `/home/jp/.hermes/skills/meal-suggestion/last-shop.json` (set by the most
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recent weekly run; if missing, tell the user the shop hasn't been generated yet
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and ask them to wait for Monday or to list items directly).
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2) Collect the `items` list. Remove any whose normalized `name` matches a named
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exclusion (case-insensitive; match against the item's `name`, not just `raw`).
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3) For each remaining item, **merge into `inventory.json`**: find an existing entry
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by case-insensitive name match; if found, ADD its `qty` to the existing `qty`
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(keep the existing `unit` if they agree, otherwise keep the existing entry's unit
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and note the discrepancy); if not found, create a new entry with the item's
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`name`, `qty`, `unit`, and `category`, sorted into the list. Items whose `qty`
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is `null` (flagged in `warnings`) are skipped with a note asking the user for a
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quantity — never invent a number.
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4) Bump `updated` to today. Do NOT modify `last-shop.json`.
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5) Confirm: list what was added (name + qty + unit), what was excluded, and any
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items skipped pending a quantity. If the shop would push a staple already in
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stock (e.g. whey, eggs), just add to the existing quantity.
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Note: this is entirely on-demand — the weekly job itself never writes inventory.
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## Pitfalls (learned the hard way)
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- **"Regenerate / resend today's meals" means GENERATE FRESH, not replay.** When the
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user asks to regenerate a day's meals or re-send the email, do NOT pull the two meals
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181
scripts/shop_basket.py
Normal file
181
scripts/shop_basket.py
Normal file
@ -0,0 +1,181 @@
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#!/usr/bin/env python3
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"""Structured weekly-shop basket: save + read + normalize.
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The weekly job calls `save_basket(items, cuisine, week_monday)` after it has
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picked the basket, writing last-shop.json. This file is GIT-IGNORED (live data,
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like inventory.json / meal-history.json) — it is NOT the inventory; it only
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records what the Monday email proposed, so the user can later say
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"add everything from Monday's shop to the inventory, except X, Y" and the agent
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has a machine-readable source instead of scraping the email.
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Item schema (one dict per line):
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{"raw": "<as shown in email>", "name": "<normalized>", "qty": <number>,
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"unit": "<count|g|ml|can|pack|jar|tub|...>", "category": "<...>",
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"price": <float>}
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normalize_item(raw_line) turns an email-style line like "Eggs (10)" or
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"Firm tofu (225g)" into a normalized {name, qty, unit} that matches how
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inventory.json names things. New items not in the known map get a best-effort
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name and unit, so nothing is silently dropped — the agent reviews before merging.
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Usage (from the weekly job / agent):
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from shop_basket import save_basket, read_basket, normalize_item
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save_basket(items=[...], cuisine="Korean", week_monday="2026-08-24")
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data = read_basket()
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"""
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import json
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import os
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import re
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HERE = os.path.dirname(os.path.abspath(__file__))
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SKILL_DIR = os.path.dirname(HERE)
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LAST_SHOP = os.path.join(SKILL_DIR, "last-shop.json")
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# Normalization map: lowercased raw "head" token -> (inventory_name, category, default_unit)
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# Tokens are matched greedily against the start of the raw label (ignoring trailing
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# pack sizes in parentheses).
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NORMALIZE = [
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("eggs", ("Eggs", "protein", "count")),
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("chicken breast", ("Chicken breast", "protein", "count")),
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("firm tofu", ("Tofu", "protein", "g")),
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("tofu", ("Tofu", "protein", "g")),
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("tinned tuna", ("Tinned tuna in water", "protein", "can")),
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("tuna", ("Tinned tuna in water", "protein", "can")),
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("tinned mackerel", ("Tinned mackerel", "protein", "can")),
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("mackerel", ("Tinned mackerel", "protein", "can")),
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("greek yogurt", ("Greek yogurt", "dairy", "g")),
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("yogurt", ("Greek yogurt", "dairy", "g")),
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("dried red lentils", ("Dried red lentils", "grain", "g")),
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("red lentils", ("Dried red lentils", "grain", "g")),
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("lentils", ("Dried red lentils", "grain", "g")),
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("jasmine rice", ("Jasmine rice", "grain", "g")),
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("rice", ("Rice", "grain", "g")),
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("frozen mixed veg", ("Frozen mixed veg", "frozen", "g")),
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("mixed veg", ("Frozen mixed veg", "frozen", "g")),
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("sweet potatoes", ("Sweet potatoes", "veg", "count")),
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("sweet potato", ("Sweet potatoes", "veg", "count")),
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("broccoli", ("Broccoli", "veg", "count")),
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("spinach", ("Spinach", "veg", "g")),
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("spring onions", ("Spring onions", "veg", "count")),
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("onions", ("Small white onions", "veg", "count")),
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("bananas", ("Bananas", "fruit", "count")),
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("berries", ("Frozen berries", "fruit", "g")),
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("soy sauce", ("Soy sauce", "condiment", "ml")),
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("sesame oil", ("Sesame oil", "condiment", "ml")),
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("sesame seeds", ("Sesame seeds", "condiment", "g")),
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("gochujang", ("Gochujang", "condiment", "g")),
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("ginger", ("Grated ginger", "condiment", "g")),
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("kimchi", ("Kimchi", "veg", "g")),
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("milk", ("Milk", "dairy", "ml")),
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("vitamin d3", ("Vitamin D3", "other", "count")),
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("vitamin d", ("Vitamin D3", "other", "count")),
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]
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def _parse_qty_unit(raw):
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"""Extract qty + unit from a trailing parenthetical like '(225g)', '(10)',
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'(x2)', '(300g)', '(4-pack)'. Returns (qty, unit) or (None, None)."""
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m = re.search(r"\(([^)]*)\)", raw)
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if not m:
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return None, None
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inner = m.group(1).strip().lower()
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mm = re.match(r"(\d+(?:\.\d+)?)\s*([a-z]*)", inner)
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if mm:
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qty = float(mm.group(1))
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unit = mm.group(2) or None
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if unit in ("g", "ml", "kg", "l", "l"):
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return qty, unit
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if unit in ("pack", "x"):
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return qty, "pack"
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if unit == "":
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return qty, None
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return qty, unit
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# "(x2)" style
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mx = re.match(r"x\s*(\d+)", inner)
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if mx:
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return float(mx.group(1)), "pack"
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return None, None
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def normalize_item(raw_line):
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"""Turn an email-style label into {name, qty, unit, category}.
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Falls back to a best-effort name (title-cased, parentheses stripped) and
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unit 'count' when no known token matches, so the agent can still review it.
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"""
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line = raw_line.strip()
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# head = everything before the first parenthesis (the descriptor)
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head = re.sub(r"\(.*?\)", "", line).strip().lower()
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qty, unit = _parse_qty_unit(line)
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name, category, default_unit = None, "other", "count"
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for token, (n, c, u) in NORMALIZE:
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if token in head:
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name, category, default_unit = n, c, u
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break
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if name is None:
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# best effort: strip trailing pack qualifiers
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name = re.sub(r"\s*\(.*?\)", "", line).strip().title()
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category = "other"
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default_unit = "count"
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if unit is None:
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unit = default_unit
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# canonicalise unit aliases
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unit = {"kg": "g", "l": "ml", "litre": "ml", "litres": "ml"}.get(unit, unit)
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if unit == "g" and qty and qty >= 1000 and "kg" not in line.lower():
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# keep as-is; caller can convert, but avoid wrong scaling
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pass
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return {"name": name, "qty": qty, "unit": unit, "category": category}
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def save_basket(items, cuisine, week_monday, date=None):
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"""items: list of {raw, name?, qty?, unit?, category?, price}. Callers (the
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weekly job) supply explicit qty/unit per item so the saved basket is fully
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structured. Any entry missing name/qty/unit is best-effort normalized; an
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entry with qty still None after normalization is flagged in data['warnings'].
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Writes last-shop.json (git-ignored)."""
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normed = []
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warnings = []
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for it in items:
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rec = dict(it)
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if not rec.get("name") or rec.get("qty") is None or not rec.get("unit"):
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n = normalize_item(rec.get("raw", ""))
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rec.setdefault("name", n["name"])
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if rec.get("qty") is None:
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rec["qty"] = n["qty"]
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if not rec.get("unit"):
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rec["unit"] = n["unit"]
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rec.setdefault("category", n["category"])
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if rec.get("qty") is None:
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warnings.append(f'{rec.get("raw", rec.get("name"))}: qty unknown — set manually on import')
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normed.append(rec)
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data = {
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"week_monday": week_monday,
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"date": date,
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"cuisine": cuisine,
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"items": normed,
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"warnings": warnings,
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}
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os.makedirs(SKILL_DIR, exist_ok=True)
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with open(LAST_SHOP, "w") as f:
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json.dump(data, f, indent=2)
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return data
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def read_basket():
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try:
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with open(LAST_SHOP) as f:
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return json.load(f)
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except FileNotFoundError:
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return None
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if __name__ == "__main__":
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# quick self-test
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tests = ["Eggs (10)", "Firm tofu (225g)", "Tinned tuna in water (4-pack)",
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"Kimchi (jar)", "Milk (1L)", "Vitamin D3 1000 IU (general health)"]
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for t in tests:
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print(t, "->", normalize_item(t))
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Block a user