Voice search (microphone button) now uses the AI analysis pipeline
instead of USDA text search, enabling natural language food descriptions
like "a medium bowl of spicy ramen and a side of gyoza". Text-typed
searches continue using USDA/OpenFoodFacts as before.

Changes:
- SearchBar: Add mic button with voice search callback
- SearchRouter: Add analyzeFoodByDescription() routing through AI providers
- SearchViewModel: Add performVoiceSearch() async method
- EntryPoint: Wire VoiceSearchView sheet to AI analysis pipeline

Replace the separate mic button with automatic natural language detection.
When the user dictates into the search field via iOS keyboard dictation,
the text is analyzed: short queries (1-3 words like "apple") use USDA,
while longer descriptive phrases (4+ words like "a medium bowl of spicy
ramen and a side of gyoza") automatically route to the AI analysis path.

Changes:
- SearchBar: Remove mic button and onVoiceSearchTapped parameter
- SearchViewModel: Add isNaturalLanguageQuery() heuristic, route detected
  natural language through performVoiceSearch in performFoodSearch
- EntryPoint: Remove voice search sheet, wire onGenerativeSearchResult
  callback to handleAIFoodAnalysis

The Python script created group definitions but didn't properly attach
all of them to their parent groups. Fixes:
- Services group → now child of Loop app root (was orphaned)
- Resources group → now child of Loop app root (was orphaned)
- Documentation group → now child of project root (was orphaned)
- ViewModels/FoodFinder → moved from Loop root to View Models group
- Tests/FoodFinder → moved from project root to LoopTests group

…, analysis history

- Fix triple barcode fire by consuming scan result immediately in Combine sink
- Replace AsyncImage with pre-downloaded thumbnail to avoid SwiftUI rebuild issues
- Use smallest OFF thumbnail (100px) with static food icon fallback for slow servers
- Add secure Keychain storage for AI provider API keys
- Add analysis history tracking with FoodFinder_AnalysisRecord
- Consolidate AI provider settings and remove BYOTestConfig

- Remove barcode connectivity pre-check that added 3+ seconds latency per scan
- Add NSCache to ImageDownloader for thumbnail deduplication (50 items, 10MB)
- Remove artificial minimumSearchDuration delay from search and error paths
- Merge duplicate Combine observers into single combineLatest for AI recomputation
- Decode image_thumb_url from OpenFoodFacts API for smallest available thumbnail
- Wrap 369 bare print() calls in #if DEBUG across 8 FoodFinder files

…eaders

File consolidations (6 files removed, 2 new files created):

1. FoodFinder_ScanResult.swift + FoodFinder_VoiceResult.swift
   → FoodFinder_InputResults.swift

2. FoodFinder_FavoriteDetailView.swift + FoodFinder_FavoriteEditView.swift
   + FoodFinder_FavoritesView.swift → FoodFinder_FavoritesHelpers.swift

3. FoodFinder_AISettingsManager.swift
   → absorbed into FoodFinder_AIProviderConfig.swift

4. FoodFinder_FavoritesViewModel.swift
   → absorbed into FoodFinder_SearchViewModel.swift

Other changes:
- Fix long analysis titles overflowing the screen by programmatically
  truncating picker row names and constraining food type to 20 chars
- Improve AI prompts for menu/recipe/text image analysis
- Add text-only AI analysis path in AIServiceManager
- Increase AI token budget for multi-item responses
- Standardize all 26 FoodFinder file headers with consistent format

- Add originalAICarbs and aiConfidencePercent fields to
  FoodFinder_AnalysisRecord for tracking AI estimate accuracy
- Add Notification.Name.foodFinderMealLogged for real-time
  meal event observation
- Add MealDataProvider protocol with date-range query interface
  and AnalysisHistoryStore conformance
- Add "Last 30 days" retention option to Analysis History settings

- Add originalAICarbs and aiConfidencePercent fields to
  FoodFinder_AnalysisRecord for tracking AI estimate accuracy
- Add Notification.Name.foodFinderMealLogged for real-time
  meal event observation
- Add MealDataProvider protocol with date-range query interface
  and AnalysisHistoryStore conformance
- Add "Last 30 days" retention option to Analysis History settings

…inder

- Absorption time model: conservative adjustments anchored to Loop's
  3-hour default. FPU adds +0/+0.5/+1.0 hr (was +1/+2.5/+4), fiber
  +0/+0.25/+0.5 (was +0/+1/+2), meal size +0/+0.25/+0.5 (was +0/+1/+2).
  Cap reduced from 8 to 5 hours. Updated AI prompt and 3 examples.
- OCR routing fix: raised menu detection threshold from 1 to 5 significant
  lines and always include image on menu path to prevent food photo
  misclassification (fixes "Unidentifiable Food Item" on food photos).
- Inline "Why X hrs?" pill on Absorption Time row replaces standalone
  DisclosureGroup row. Purple centered pill with fixed width, expands
  reasoning on tap. Uses AIAbsorptionTimePickerRow when AI-generated.

Add LoopInsights feature: an AI-driven therapy settings advisor that analyzes
glucose, insulin, and carb data to suggest adjustments to Carb Ratios, Insulin
Sensitivity Factors, and Basal Rates.

Core components:
- Dashboard with therapy settings overview, pattern detection, and AI suggestions
- Configurable AI provider (OpenAI, Anthropic, Gemini, Grok, self-hosted)
- Data aggregation pipeline with test data fixtures from Tidepool
- Suggestion lifecycle: pending → applied/dismissed with full history
- AI personality settings (Supportive Coach, Clinical Expert, Dry Wit, Tough Love)
- Developer mode with auto-apply and test data toggles
- Secure API key storage via Keychain
- Safety guardrails: max 20% change per adjustment, one setting at a time
- Unit tests for models, data aggregation, and suggestion store

22 new files, 4 modified files across Views, View Models, Models, Services,
Managers, Resources, and Tests.

…ng, and UI refinements

- Wire real therapy settings writes via LoopInsightsSettingsWriter closure
- Schedule splitting: insert new entries when AI suggests times not in user's schedule
- Revert feature: restore pre-apply settings from suggestion history
- Settings Score (0-100) with TIR, Safety, Stability, GMI breakdown
- Clinical reasoning framework: AI now understands AID-specific patterns
  (corrections/day, basal/bolus ratio, time-of-day analysis, cross-setting interactions)
- All three settings visible in every AI prompt for cross-setting reasoning
- Pre-computed red flags injected into prompt (algorithm workload, basal % alerts)
- Stale-data guard: excludes manually reverted changes from recent context
- Suggestion merge: consolidates split AI responses into single cards
- Pre-Fill Editor: editable proposed values before applying
- Auto-applied notification banner
- Debug log with Copy Full Log for troubleshooting AI behavior
- Temperature forced to 0.0 for deterministic analysis

… advisor UI

Add Ask LoopInsights chat with AI advisor powered by therapy context and glucose data.
Background monitoring with configurable frequency and notification banners. New Trends
& Insights view with Daily/Weekly/Monthly/Stats/Advisor tabs. Dark gradient styling
for chat and trends views. Banner now includes Ask button to open chat directly.

…ports

Add clinical goal tracking (TIR, A1C, below-range, custom) with progress bars,
AI-powered 30-day pattern discovery with sick day and negative basal detection,
timestamped reflection journal with mood tags, and HTML-to-PDF report generation
with share sheet. Goals & Patterns accessible from the Dashboard navigation section.

… analysis

Add HealthKit biometric data (heart rate, HRV, steps, sleep, active energy, weight)
to the AI analysis and chat pipelines. Biometrics are read-only, independently
authorized, and gracefully degrade when individual types are unavailable.

New file: LoopInsights_HealthKitManager.swift
Modified: Models, DataAggregator, AIAnalysis, ChatViewModel, Coordinator,
FeatureFlags, SettingsView, DashboardView, pbxproj, Localizable.xcstrings

…nsights, Nightscout import

- Ambulatory Glucose Profile (AGP) chart with percentile bands and median line
- Clarity-style dashboard redesign: Glucose card, Time in Range 5-zone stacked bar,
  capsule period picker with exact Clarity colors (#C14F0C, #F0CA4C, #74A52E, #D36265, #7F0302)
- Caffeine tracker with half-life decay modeling and glucose correlation
- Meal insights with food response analysis and per-meal glucose impact
- Nightscout data import support
- Advanced analyzers for pattern detection
- 5-zone TIR breakdown (Very High/High/In Range/Low/Very Low) replacing 3-zone model
- Compact list section spacing for tighter dashboard layout
- Chat view UI refinements

…card fixes

P1: Parallel HealthKit queries via async let (6 concurrent fetches)
P2: Single-pass TIR zone counting (5-zone) replacing multiple filter passes
P3: Pre-fetch raw data in DataAggregator, cache for cross-component reuse
P4: Binary search for glucose lookups in FoodResponseAnalyzer
P5: Pre-sorted glucose samples with binary search in AdvancedAnalyzers
P6: Pre-compute AGP data in ViewModel instead of SwiftUI view body
P7: Static DateFormatter in LoopInsightsTimeBlock.formatTime
P8: Pre-sort schedule items before dose loops, pre-sort in ViewModel
P9: Pre-convert glucose to parallel arrays avoiding repeated doubleValue calls
P10: Pass precomputed hourly averages to circadian profile builder

Also: enhanced step/activity data in AI prompts with time-of-day breakdowns
and activity-glucose correlation analysis (2h lag), and meal card layout cleanup.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

…y fixes

Glucose chart now operates in two modes: standard Ambulatory Glucose Profile
(24-hour overlay with percentile bands) for 14-day lookback, and Glucose Profile
(multi-day time series) for all other periods. Both modes include an info button
explaining the visualization. HealthKit glucose data supplements Loop store for
longer analysis periods. Chart data clears on period change to prevent stale labels.

Additional fixes across 22 files: improved HealthKit data pipeline reliability,
enhanced test data provider, refined food response analysis, and minor bug fixes
in background monitor, coordinator, caffeine tracker, and goals/trends views.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

…y fixes

Glucose chart now operates in two modes: standard Ambulatory Glucose Profile
(24-hour overlay with percentile bands) for 14-day lookback, and Glucose Profile
(multi-day time series) for all other periods. Both modes include an info button
explaining the visualization. HealthKit glucose data supplements Loop store for
longer analysis periods. Chart data clears on period change to prevent stale labels.

Additional fixes across 22 files: improved HealthKit data pipeline reliability,
enhanced test data provider, refined food response analysis, and minor bug fixes
in background monitor, coordinator, caffeine tracker, and goals/trends views.

…pInsights

Bump all body text, headers, and stat values to full white for readability
on dark backgrounds. Replace .toolbarColorScheme (iOS 16+) with manual
toolbar principal title for compatibility. Restore UINavigationBarAppearance
approach in ChatView.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Added steps for creating and using test data in developer mode for demos and feature functionality testing.

Safety guardrails (3 layers of defense against dangerous therapy values):
- LoopInsights_SafetyGuardrails struct with clinical bounds mirroring LoopKit
  (CR 4-28 recommended/2-150 absolute, ISF 16-400/10-500, Basal 0.05-10/0.05-30)
- Post-parse validation rejects values outside absolute bounds and >25% changes
- AI prompt now includes absolute bounds with clamping instructions
- confirmApply() hard-blocks absolute violations
- applyEditedSuggestion() validates edited blocks against absolute bounds
- autoApplySuggestion() blocks anything outside recommended range (stricter)
- SuggestionDetailView shows orange warning banner and color-coded values
- DashboardView alert changes to "Safety Warning" with specific warnings
- Suggestion cards show orange triangle badge for guardrail warnings

Data-first AI prompts (all 4 AI interaction points):
- Chat, Analysis, Goals/Patterns, and Trends prompts now require every
  answer to cite the user's specific numbers — no generic diabetes advice
- Added "#1 RULE" blocks emphasizing real data over textbook answers

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

computeGlucoseStats was writing to self.lastGlucoseForAGP from within
an async let concurrent context, causing heap corruption on newer Swift
runtimes ("freed pointer was not the last allocation"). Returns AGP
data via struct instead of mutating self during concurrent execution.

…ncy window

Two bugs caused Walking to confirm on insufficient activity:
1. After reset, stale pedometer callbacks from the old subscription immediately
   re-triggered the step threshold. Fixed with generation counter.
2. Recency limit inflated with iOS timer delay (30s + delay), letting step data
   from 2+ minutes ago pass. Fixed to a flat 60s window.

…oning panel

Add multilineTextAlignment and fixedSize to servingSizeDisplay text
so long food item lists wrap instead of clipping off-screen.

…tes spacing

- Fix ScrollView horizontal overflow by constraining content width via GeometryReader
- Remove "cal" unit from inside Calories nutrition circle (label below is sufficient)
- Deduplicate analysis history records by name on save
- Add red trash button to clear all recent AI analyses
- Reduce Food Type max length from 50 to 25 chars to prevent row overflow
- Add "Things to watch out for" subheader above revert warnings in reasoning panel
- Move "Choose Recent:" tap target to the label, show last analysis name instead of "None"
- Add spacing above FAVORITE FOODS section

- LoopInsightsChatMessage + Role now Codable
- New LoopInsightsChatTranscript model (id, startedAt, messages, preview)
- LoopInsights_ChatHistoryStore: JSON persistence to applicationSupport/LoopInsights/,
  10-session cap, 30-day retention, prune-on-save
- LoopInsights_ChatHistoryView: conversation list with swipe-to-delete and clear-all;
  LoopInsights_ChatTranscriptView: read-only dark-theme replay of a saved session
- ChatViewModel.saveSessionIfNeeded() called on chat dismiss and on clear
- ChatView: clock history button in toolbar alongside existing trash button
- Localizable.xcstrings: 6 new keys registered by Xcode

# Conflicts:
#	Loop/Localizable.xcstrings

…d steps

Switch averageDailySteps calculation from HKSampleQuery to
HKStatisticsCollectionQuery(.cumulativeSum), which uses HealthKit's own
source-deduplication engine — matching what the Health app displays.
Raw HKSampleQuery sums every source independently, inflating totals on
multi-device setups. Hourly distribution retains the raw query since
relative shape (peak hour, glucose correlation) is unaffected by
uniform bias across sources.

…he key

- LocationService: strengthen prompt instruction so AI always opens
  diabetes_considerations with a visible location callout (restaurant
  name, data source, and correction hint) rather than optionally
  mentioning it somewhere in the response
- AIAnalysis: include location name in image cache key so a cached
  result without location context is never served when location has
  since resolved

- Reframe LoopInsights prompts: algorithm corrections are normal AID behavior,
  focus on glucose outcomes instead of algorithm workload
- FoodFinder default temperature 0.3 → 0.0 for deterministic analysis
- Clarify HealthKitManager step count hourly bucketing comment

- New FoodFinder_ImageCropView: interactive crop with draggable handles,
  dimmed overlay, rule-of-thirds grid, crop & analyze or skip
- Camera flow now: capture → crop → analyze (crop is optional)
- AI prompt: new identification priority order — GPS+menu lookup first,
  visual branding second, regional cuisine third, image-only as fallback
- Location service now captures city + country for regional cuisine context
- Location tagging defaults to ON (privacy-safe one-shot GPS)
- FoodFinder temperature set to 0.0 for deterministic analysis

- Title appends " - Venue Name" when geo-locate identifies a restaurant
- Description capped at 140 chars with Show More/Less toggle
- Title line limit increased to 2 for restaurant name to fit

…f-increments

- Temperature 0.0 caused deterministic extreme nutrition values; 0.05
  is nearly deterministic without locking into hallucination paths
- Servings stepper now increments/decrements by 0.5 instead of 0.25

…splay

- Move RECENT AI ANALYSES from Add Carb Entry into FoodFinder Settings
- Re-use button auto-navigates to Add Carb Entry with data pre-filled
- Fix bug: selecting None now clears carb entry fields (was showing 0)
- Add ±Xg confidence range next to confidence badge based on CV data

- Add FoodFinder_ImageCropView.swift to Xcode project (was missing from pbxproj)
- Fix carbsValue out-of-scope in confidence range by recomputing at usage site

…mbnail

- Defer restoredAnalysisResult assignment to onAppear so FoodFinder_EntryPoint's
  .onChange observer is active and triggers full UI restore (circles, thumbnail, etc.)
- Move checkForPendingReUse() from init to CarbEntryView.onAppear

…ttings

Selecting an item shows its details and Re-use button below the picker.

Tap 'Recent Analyses' to expand a scrollable list with food thumbnails.
Select an item to see details and Re-use button. List collapses on selection.

One tap to re-use — no intermediate selection step needed.

Vision API requests (especially Anthropic) regularly take 30-45s for the
API call alone. Combined with OCR + image encoding overhead, 60s was too
tight and caused consistent timeouts on Photos-sourced images.

Root cause: servings was computed as the SUM of per-item USDA serving
multipliers (e.g. salmon=2 + egg=1 + potatoes=3 = 6+ servings) instead
of representing 1 plate/meal. This inflated numberOfServings to 10+ and
scaled all nutrition values up accordingly.

Fix: AI analysis = 1 serving = 1 plate as photographed. The per-item
USDA multipliers are internal to the item-level breakdown, not the
user-facing serving count. originalServings is now always 1.0, and
convertAIResultToFoodProduct no longer divides by the aggregate USDA
serving count.

Implements three programmatic safeguards from Street (2026) "Frontier LLMs
require explicit clinical context to avoid training-data anchoring for
insulin pump settings" (https://www.diabettech.com/wp-content/uploads/2026/04/diabettech_preprint-3.pdf):

1. Bounds clamping (recommendation #2): Values exceeding the max change %
   by up to 1.5x are clamped to the limit instead of hard-rejected, with a
   logged validation note.

2. Citation verification (recommendation LoopKit#3): Glucose values cited in AI
   reasoning are cross-checked against the actual hourly averages sent to
   the model (±2 mg/dL tolerance). Misattribution rate is reported as a
   validation note.

3. Confidence adjustment by data availability: CR analysis with <5 meals or
   ISF analysis with <3 corrections is capped at Low confidence with an
   explicit warning about anchor substitution risk. Basal (the most data-
   driven setting per Street's anchoring spectrum) is not capped.

All changes are in the response parsing layer — no prompt modifications:
- LoopInsights_AIAnalysis.swift: parseResponse() + extractCitedGlucoseValues()
- LoopInsights_Models.swift: validationNotes field on LoopInsightsAnalysisResponse
- LoopInsights_DashboardView.swift: validation notes display in UI

Additional safeguards addressing Street (2026) findings that LLMs cannot
distinguish "I have evidence" from "I should generate something plausible":

1. Stronger refusal mandate in system prompt: Model MUST return empty
   suggestions when data is insufficient to validate a setting. Explicitly
   forbids echoing current values with medium/high confidence when no
   supporting data pattern exists.

2. Explicit AID system context in user prompt: States "Loop (oref-based)"
   with DIA prominently positioned and marked as DO NOT CHANGE. Prevents
   catastrophic DIA collapse to 4-5h textbook values (Street 2026 found
   100% of models defaulted to textbook DIA without this context).

3. Contradiction detection (validation pass LoopKit#4): Scans reasoning for
   admissions like "cannot be derived", "insufficient data", "no meal data"
   while confidence is medium/high. If found, suppresses the suggestion
   entirely — the model was filling gaps with training priors.

All changes in LoopInsights_AIAnalysis.swift (system prompt, user prompt,
and parseResponse validation layer).

Reference: Street T. "Frontier LLMs require explicit clinical context to
avoid training-data anchoring for insulin pump settings." April 2026.
https://www.diabettech.com/wp-content/uploads/2026/04/diabettech_preprint-3.pdf