Improved UX for focus area configuration:
- Sliders now use relative weights (0-10) instead of percentages
- System automatically normalizes to percentages (sum=100%)
- Live preview shows "weight → percent%" (e.g., "5 → 50%")
- No more manual balancing required from user
User sets: Kraft=5, Ausdauer=3, Flexibilität=2
System calculates: 50%, 30%, 20%
Addresses user feedback: "Summe muss 100% sein" not user-friendly
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Fixed health mode calculation to include all 6 dimensions.
Simplified CASE statements (single CASE instead of multiple additions).
Before: health mode only set flexibility (15%) + health (55%) = 70% ❌
After: health mode sets all dimensions = 100% ✅
- weight_loss: 5%
- muscle_gain: 0%
- strength: 10%
- endurance: 20%
- flexibility: 15%
- health: 50%
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Fixed health mode calculation in focus_areas migration.
Changed health_pct from 50 to 55 to ensure sum equals 100%.
Before: 0+0+10+20+15+50 = 95% (constraint violation)
After: 0+0+10+20+15+55 = 100% (valid)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replaces single goal mode cards with weighted multi-focus system
UI Features:
- 6 sliders for focus dimensions (5% increments)
- Live sum calculation with visual feedback
- Validation: Sum must equal 100%
- Color-coded sliders per dimension
- Edit/Display mode toggle
- Shows derived values if not customized
UX Flow:
1. Default: Shows focus distribution (bars)
2. Click 'Anpassen': Shows sliders
3. Adjust percentages (sum = 100%)
4. Save → Updates backend + reloads
Visual:
- Active dimensions shown as colored cards (display mode)
- Gradient sliders with percentage labels (edit mode)
- Green box when sum = 100%, red when != 100%
- Info message if derived from old goal_mode
Complete v2.0:
✅ Backend (Migration 027, API, get_focus_weights V2)
✅ Frontend (Slider UI, state management, validation)
✅ Auto-migration (goal_mode → focus_areas)
Ready for: KI-Integration with weighted scoring
BREAKING: Replaces single 'primary goal' with weighted multi-goal system
Migration 027:
- New table: focus_areas (6 dimensions with percentages)
- Constraint: Sum must equal 100%
- Auto-migration: goal_mode → focus_areas for existing users
- Unique constraint: One active focus_areas per profile
Backend:
- get_focus_weights() V2: Reads from focus_areas table
- Fallback: Uses goal_mode if focus_areas not set
- New endpoints: GET/PUT /api/goals/focus-areas
- Validation: Sum=100, range 0-100
API:
- getFocusAreas() - Get current weights
- updateFocusAreas(data) - Update weights (upsert)
Focus dimensions:
1. weight_loss_pct (Fettabbau)
2. muscle_gain_pct (Muskelaufbau)
3. strength_pct (Kraftsteigerung)
4. endurance_pct (Ausdauer)
5. flexibility_pct (Beweglichkeit)
6. health_pct (Allgemeine Gesundheit)
Benefits:
- Multiple goals with custom priorities
- More flexible than single primary goal
- KI can use weighted scores
- Ready for Phase 0b placeholder integration
UI: Coming in next commit (slider interface)
NEW FEATURE: Filter conditions for goal types
Enables counting/aggregating specific subsets of data.
Example use case: Count only strength training sessions per week
- Create goal type with filter: {"training_type": "strength"}
- count_7d now counts only strength training, not all activities
Implementation:
- Migration 026: filter_conditions JSONB column
- Backend: Dynamic WHERE clause building from JSON filters
- Supports single value: {"training_type": "strength"}
- Supports multiple values: {"training_type": ["strength", "hiit"]}
- Works with all 8 aggregation methods (count, avg, sum, min, max)
- Frontend: JSON textarea with example + validation
- Pydantic models: filter_conditions field added
Technical details:
- SQL injection safe (parameterized queries)
- Graceful degradation (invalid JSON ignored with warning)
- Backward compatible (NULL filters = no filtering)
Answers user question: 'Kann ich Trainingstypen wie Krafttraining separat zählen?'
Answer: YES! 🎯
Admin can now easily create custom goal types:
- New endpoint /api/goals/schema-info with table/column metadata
- 9 tables documented (weight, caliper, activity, nutrition, sleep, vitals, BP, rest_days, circumference)
- Table dropdown with descriptions (e.g., 'activity_log - Trainingseinheiten')
- Column dropdown dependent on selected table
- All columns documented in German with data types
- Fields optional (for complex calculation formulas)
UX improvements:
- No need to guess table/column names
- Clear descriptions for each field
- Type-safe selection (no typos)
- Cascading dropdowns (column depends on table)
Closes user feedback: 'Admin weiß nicht welche Tabellen/Spalten verfügbar sind'
- try-catch around entire endpoint
- try-catch for each goal progress update
- Detailed error logging with traceback
- Continue processing other goals if one fails
- Clear error message to frontend
This will show exact error location in logs.
Fixes cases where Migration 024 partially ran:
- Removes created_by/updated_by columns if they exist
- Re-inserts seed data with ON CONFLICT DO NOTHING
- Fully automated, no manual intervention needed
- Production-safe (idempotent)
This ensures clean deployment to production without manual DB changes.
Goal type definitions are global system entities, not user-specific.
System types seeded in migration cannot have created_by FK.
Changes:
- Remove created_by/updated_by columns from goal_type_definitions
- Update CREATE/UPDATE endpoints to not use these fields
- Migration now runs cleanly on container start
- No manual intervention needed for production deployment
Removed faulty EXISTS check that was causing "0" error.
Added debug logging and better error messages.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Check if goal_type_definitions table exists
- Detailed error messages
- Fallback if goalTypes is empty
- Prevent form opening without types
Helps debugging Migration 024 issues.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Based on test feedback - 3 issues addressed:
1. Primary Toggle (Frontend Debug):
- Add console.log in handleSaveGoal
- Shows what data is sent to backend
- Helps debug if checkbox state is correct
2. Lean Mass Display (Backend Debug):
- Add error handling in lean_mass calculation
- Log why calculation fails (missing weight/bf data)
- Try-catch for value conversion errors
3. BP/Strength/Flexibility Warning (UI):
- Yellow warning box for incomplete goal types
- BP: "benötigt 2 Werte (geplant für v2.0)"
- Strength/Flexibility: "Keine Datenquelle"
- Transparent about limitations
Next: User re-tests with debug output to identify root cause.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Tracking-Dokument für alle offenen Punkte:
- Phase 0b Tasks (120+ Platzhalter)
- v2.0 Redesign Probleme
- Gitea Issues Referenzen
- Timeline & Roadmap
Verhindert dass wichtige Punkte vergessen werden.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Created comprehensive redesign document addressing all identified issues:
Problems addressed:
1. Primary goal too simplistic → Weight system (0-100%)
2. Single goal mode too simple → Multi-mode with weights
3. Missing current values → All goal types with data sources
4. Abstract goal types → Concrete, measurable goals
5. Blood pressure single value → Compound goals (systolic/diastolic)
6. No user guidance → Norms, examples, age-specific values
New Concept:
- Focus Areas: Weighted distribution (30% weight loss + 25% endurance + ...)
- Goal Weights: Each goal has individual weight (not binary primary/not)
- Concrete Goal Types: cooper_test, pushups_max, squat_1rm, etc.
- Compound Goals: Support for multi-value targets (BP: 120/80)
- Guidance System: Age/gender-specific norms and examples
Schema Changes:
- New table: focus_areas (replaces single goal_mode)
- goals: Add goal_weight, target_value_secondary, current_value_secondary
- goals: Remove is_primary (replaced by weight)
UI/UX Redesign:
- Slider interface for focus areas (must sum to 100%)
- Goal editor with guidance and norms
- Weight indicators on all goals
- Special UI for compound goals
Implementation Phases: 16-21h total
- Phase 2: Backend Redesign (6-8h)
- Phase 3: Frontend Redesign (8-10h)
- Phase 4: Testing & Refinement (2-3h)
Status: WAITING FOR USER FEEDBACK & APPROVAL
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Analysis Page:
- Add 'Ziele' button next to page title
- Direct navigation to /goals from analysis page
- Thematic link: goals influence AI analysis weighting
Goals Page:
- Fix text-align for text inputs (name, date, description)
- Text fields now left-aligned (numbers remain right-aligned)
- Better UX for non-numeric inputs
Navigation strategy: Goals accessible from Analysis page where
goal_mode directly impacts score calculation and interpretation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Full-width inputs throughout the form
- Labels above inputs (mobile best practice)
- Section headers with emoji (🎯 Zielwert)
- Consistent spacing (marginBottom: 16)
- Read-only unit display as styled badge
- Primary goal checkbox in highlighted section
- Full-width buttons (btn-full class)
- Scrollable modal with top padding
- Error display above form
Matches VitalsPage design pattern for consistency.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The migration system tracks migrations via filename automatically.
Removed manual DO block that used wrong column name (version vs filename).
Also removed unused json import from goals.py.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Key Decision: Minimal Goal System BEFORE Placeholders
Critical Finding:
- Same data = different interpretation per goal
- Example: -5kg FM, -2kg LBM
- weight_loss: 78/100 (good!)
- strength: 32/100 (LBM loss critical!)
- Without goal: 50/100 (generic, wrong for both)
Recommended Approach (Hybrid):
1. Phase 0a (2-3h): Minimal Goal System
- DB: goal_mode field
- API: Get/Set Goal
- UI: Goal Selector
- Default: health
2. Phase 0b (16-20h): Goal-Aware Placeholders
- 84 placeholders with goal-dependent calculations
- Scores use goal_mode from day 1
- No rework needed later
3. Phase 2+ (6-8h): Full Goal System
- Goal recognition from patterns
- Secondary goals
- Goal progression tracking
Why Hybrid Works:
✅ Charts show correct interpretations immediately
✅ No rework of 84 placeholders later
✅ Goal recognition can come later (needs placeholders anyway)
✅ System is "smart coach" from day 1
File: docs/GOAL_SYSTEM_PRIORITY_ANALYSIS.md (650 lines)
- Normal-Modus: Nur Einzelwerte (übersichtlich)
- Experten-Modus: Zusätzlich Stage-Rohdaten
- Beschreibungen für alle Platzhalter vervollständigen
- Schema-basierte Beschreibungen für extrahierte Werte
Aufwand: 4-6h, Priority: Medium
- stage_debug now includes 'output' dict with all stage outputs
- Fixes empty values for stage_X_outputkey in expert mode
- Stage outputs are the actual AI responses passed to next stage
Backend:
- Add ALL stage outputs to metadata (not just referenced ones)
- Format JSON with indent for readability
- Description: 'Zwischenergebnis aus Stage X'
Frontend:
- Stage raw values shown in collapsible <details> element
- JSON formatted in <pre> tag with syntax highlighting
- 'JSON anzeigen ▼' summary for better UX
Fixes: Stage X - Rohdaten now shows intermediate results
- Each circumference point shows most recent value (even from different dates)
- Age annotations: heute, gestern, vor X Tagen/Wochen/Monaten
- Gives AI better context about measurement freshness
- Example: 'Brust 105cm (heute), Nacken 38cm (vor 2 Wochen)'
- Previously only checked c_chest, c_waist, c_hip
- Now includes c_neck, c_belly, c_thigh, c_calf, c_arm
- Fixes 'keine Daten' when entries exist with only non-primary measurements
BUG: Wertetabelle wurde nicht angezeigt bei neuer Analyse
ROOT CAUSE: newResult hatte nur {scope, content}, kein metadata
FIX: Build metadata from result.debug.resolved_placeholders
- Für Base: direkt aus resolved_placeholders
- Für Pipeline: collect aus allen stages
- Metadata structure: {prompt_type, placeholders: {key: {value, description}}}
NOTE: Immediate preview hat keine descriptions (nur values)
Saved insights (nach loadAll) haben full metadata with descriptions aus DB
version: 9.6.2 (bugfix)
BUG: Wertetabelle wurde nicht angezeigt
FIX: enable_debug=true wenn save=true (für metadata collection)
- metadata wird nur gespeichert wenn debug aktiv
- jetzt: debug or save → metadata immer verfügbar
BUG: {{placeholder|d}} Modifier funktionierte nicht
ROOT CAUSE: catalog wurde bei Exception nicht zu variables hinzugefügt
FIX:
- variables['_catalog'] = catalog (auch wenn None)
- Warning-Log wenn catalog nicht geladen werden kann
- Debug warning wenn |d ohne catalog verwendet
BUG: Platzhalter in Pipeline-Stages am Ende statt an Cursor
FIX:
- stageTemplateRefs Map für alle Stage-Textareas
- onClick + onKeyUp tracking für Cursor-Position
- Insert at cursor: template.slice(0, pos) + placeholder + template.slice(pos)
- Focus + Cursor restore nach Insert
TECHNICAL:
- prompt_executor.py: Besseres Exception Handling für catalog
- UnifiedPromptModal.jsx: Refs für alle Template-Felder
- prompts.py: enable_debug=debug or save
version: 9.6.1 (bugfix)
module: prompts 2.1.1
