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Add the diet_guard package: a screen-locking meal-logging gate that fires on 4-hour slots (08/12/16/20) and records calories/macros, persisting an autocompleting food bank. - Trigger fix: the systemd timer fires at session start (Persistent=true) before lightdm has written ~/.Xauthority, so the gate crashed with a TclError instead of locking the screen. Add wait_for_display() / _display_is_ready() in _gatelock.py and wire it into _cli._cmd_gate so the gate retries on the next tick instead of crashing; add Environment=XAUTHORITY=%h/.Xauthority to the service as belt-and-suspenders. - Food-bank hardening: a transiently corrupt food_bank.json was warned about on every keystroke and then silently overwritten (data loss). _read_bank now quarantines it via _quarantine_corrupt_bank() (warn-once + timestamped backup) before starting fresh. - Multi-item meals: new _meal.py (MealItem, meal_total, MEAL_SOURCE), remember_meal() + _upsert() in _foodbank.py, and a "+ Add item" control in the gate that logs both the individual items and the composite meal. - Bundle resolve_nutrition's manual macros into a ManualMacros dataclass to stay within the argument-count limit. diet_guard at 100% branch coverage; full pre-commit suite passes. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
64 lines
2.4 KiB
Python
64 lines
2.4 KiB
Python
"""Shared typo-tolerant string matching for diet_guard.
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Two callers need the same similarity logic: the food bank (matching what the
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user typed against foods they have logged) and the portions table (matching a
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description like "apple" against the known staples). Both depend on the same
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key property -- a short typo must still match a long multi-word name -- so the
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scoring lives here once rather than being copied.
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The trick is to score *word by word* instead of whole-string to whole-string.
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"beast" scores near zero against "grilled chicken breast" as a whole (the
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length gap dominates) but ~0.91 against the single token "breast"; taking the
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best matching token per query word and averaging is what rescues the short
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typo. Built on :class:`difflib.SequenceMatcher` (stdlib, no dependency).
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"""
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from __future__ import annotations
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from difflib import SequenceMatcher
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def token_score(query: str, name: str) -> float:
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"""Score ``query`` against ``name`` word-by-word (length-penalty free).
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Each query word is matched against its best word in ``name`` and the
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per-word similarities are averaged, so a short typo matches the relevant
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word in a long multi-word name instead of being drowned out by length.
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Args:
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query: The normalized user query.
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name: The normalized candidate name.
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Returns:
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The mean best-per-word similarity in ``[0, 1]``.
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"""
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query_words = query.split()
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name_words = name.split()
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if not query_words or not name_words:
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return SequenceMatcher(None, query, name).ratio()
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total = 0.0
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for word in query_words:
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total += max(
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SequenceMatcher(None, word, target).ratio() for target in name_words
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)
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return total / len(query_words)
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def match_score(query: str, name: str) -> float:
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"""Score how well ``name`` matches ``query`` (higher is better).
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A substring hit scores at or above 1.0 (boosted by how much of the name the
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query covers, so the tightest containing name wins); otherwise fall back to
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the token-aware fuzzy score, which tolerates per-word typos.
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Args:
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query: The normalized user query.
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name: The normalized candidate name.
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Returns:
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A score; substring matches are ``>= 1.0``, fuzzy matches in ``[0, 1)``.
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"""
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if query and query in name:
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return 1.0 + len(query) / len(name)
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return token_score(query, name)
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