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'
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>
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>
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
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
Added "📋 Platzhalter exportieren" button in debug viewer:
- Exports all resolved placeholders with values
- Includes all available_variables
- For pipelines: exports per-stage placeholder data
- JSON format with timestamp and prompt metadata
- Filename: placeholders-{slug}-{date}.json
Use case: Development aid - see exactly what data is available
for prompt templates without null values.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
BREAKING: Analysis page switched from old /insights/run to new /prompts/execute
Changes:
- Backend: Added save=true parameter to /prompts/execute
- When enabled, saves final output to ai_insights table
- Extracts content from pipeline output (last stage)
- Frontend api.js: Added save parameter to executeUnifiedPrompt()
- Frontend Analysis.jsx: Switched from api.runInsight() to api.executeUnifiedPrompt()
- Transforms new result format to match InsightCard expectations
- Pipeline outputs properly extracted and displayed
Fixes: PIPELINE_MASTER responses (old template being sent to AI)
The old /insights/run endpoint used raw template field, which for the
legacy "pipeline" prompt was literally "PIPELINE_MASTER". The new
executor properly handles stages and data processing.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Removed conditional hiding of test button (prompt?.slug)
- Button now always visible with helpful tooltip
- handleTest already has save-check logic
Improves discoverability of test functionality.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Frontend: debug viewer now shows even when test fails
- Frontend: export button to download complete prompt config as JSON
- Backend: attach debug info to JSON validation errors
- Backend: include raw output and length in error details
Users can now debug failed prompts and export configs for analysis.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Backend: debug mode in prompt_executor with placeholder tracking
- Backend: show resolved/unresolved placeholders, final prompts, AI responses
- Frontend: test button in UnifiedPromptModal for saved prompts
- Frontend: debug output viewer with JSON preview
- Frontend: wider placeholder example fields in PlaceholderPicker
Resolves pipeline execution debugging issues.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- PlaceholderPicker: Example values in separate full-width row
- Analysis.jsx: Show only pipeline-type prompts
- Analysis.jsx: Remove base prompts and Prompts tab
- Cleanup: Remove PromptEditor component and unused imports
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Major improvements:
1. PlaceholderPicker component (new)
- Loads placeholders dynamically from backend catalog
- Grouped by categories: Profil, Körper, Ernährung, Training, etc.
- Search/filter functionality
- Shows live example values from user data
- Popup modal with expand/collapse categories
2. Replaced hardcoded placeholder chips
- 'Platzhalter einfügen' button opens picker
- Works in both base templates and pipeline inline templates
- Auto-closes after selection
3. Uses existing backend system
- GET /api/prompts/placeholders
- placeholder_resolver.py with PLACEHOLDER_MAP
- Dynamic, module-based placeholder system
- No manual updates needed when modules add new placeholders
Benefits:
- Scalable: New modules can add placeholders without frontend changes
- User-friendly: Search and categorization
- Context-aware: Shows real example values
- Future-proof: Backend-driven catalog
New features:
1. Placeholder chips now visible in pipeline inline templates
- Click to insert: weight_data, nutrition_data, activity_data, etc.
- Same UX as base prompts
2. Convert to Base Prompt button
- New icon (ArrowDownToLine) in actions column
- Only visible for 1-stage pipeline prompts
- Converts pipeline → base by extracting inline template
- Validates: must be 1-stage, 1-prompt, inline source
This allows migrated prompts to be properly categorized as base prompts
for reuse in other pipelines.
Fixes:
1. Template field in stages now full width (was too narrow)
2. Table horizontal scrollbar for mobile (overflow-x: auto)
3. Table min-width 900px to prevent icon clipping
4. Added clickable placeholder chips below base template
- Click to insert placeholders into template
- Shows: weight_data, nutrition_data, activity_data, sleep_data, etc.
UI now mobile-ready and more user-friendly.
Migration 018:
- Add display_name column to ai_prompts
- Migrate existing prompts from hardcoded SLUG_LABELS
- Fallback: name if display_name is NULL
Backend:
- PromptCreate/Update models with display_name field
- create/update/duplicate endpoints handle display_name
- Fallback: use name if display_name not provided
Frontend:
- PromptEditModal: display_name input field
- Placeholder picker: button + dropdown with all placeholders
- Shows example values, inserts {{placeholder}} on click
- Analysis.jsx: use display_name instead of SLUG_LABELS
User-facing changes:
- Prompts now show custom display names (e.g. '🍽️ Ernährung')
- Admin can edit display names instead of hardcoded labels
- Template editor has 'Platzhalter einfügen' button
- No more hardcoded SLUG_LABELS in frontend
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- PromptEditModal: all inputs/textareas now full-width
- Labels positioned above fields (not inline)
- Text left-aligned (was right-aligned)
- Added resize:vertical for textareas
- Side-by-side comparison with word-wrap
- Follows app-wide form design pattern
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Frontend components:
- PromptEditModal.jsx: Full editor with preview, generator, optimizer
- PromptGenerator.jsx: KI-assisted prompt creation from goal description
- Extended api.js with 10 new prompt endpoints
Navigation:
- Added /admin/prompts route to App.jsx
- Added KI-Prompts section to AdminPanel with navigation button
Features complete:
✅ Admin can create/edit/delete/duplicate prompts
✅ Category filtering and reordering
✅ Preview prompts with real user data
✅ KI generates prompts from goal + example data
✅ KI analyzes and optimizes existing prompts
✅ Side-by-side comparison original vs optimized
Ready for testing: http://dev.mitai.jinkendo.de/admin/prompts
Issue #28 Phase 2 complete - 13-18h estimated, ~14h actual
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Backend complete:
- Migration 017: Add category column to ai_prompts
- placeholder_resolver.py: 20+ placeholders with resolver functions
- Extended routers/prompts.py with CRUD endpoints:
* POST /api/prompts (create)
* PUT /api/prompts/:id (update)
* DELETE /api/prompts/:id (delete)
* POST /api/prompts/:id/duplicate
* PUT /api/prompts/reorder
* POST /api/prompts/preview
* GET /api/prompts/placeholders
* POST /api/prompts/generate (KI-assisted generation)
* POST /api/prompts/:id/optimize (KI analysis)
- Extended models.py with PromptCreate, PromptUpdate, PromptGenerateRequest
Frontend:
- AdminPromptsPage.jsx: Full CRUD UI with category filter, reordering
Meta-Features:
- KI generates prompts from goal description + example data
- KI analyzes and optimizes existing prompts
Next: PromptEditModal, PromptGenerator, api.js integration
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Removed local quality filter UI from History page since backend now
handles filtering globally. Activities are already filtered when loaded.
Changes:
- Removed qualityLevel local state
- Simplified filtA to only filter by period
- Replaced filter buttons with info banner showing active global filter
- Added 'Hier ändern →' link to Settings
User can now only change quality filter in Settings (global), not per
page. History shows which filter is active with link to change it.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The component was loading data from backend (which uses global filter)
but useEffect dependency didn't include quality_filter_level, so it
didn't reload when user changed the filter in Settings.
Added useProfile() context and activeProfile.quality_filter_level
to dependency array.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implemented global quality_filter_level in user profiles for consistent
data filtering across all views (Dashboard, History, Charts, KI-Pipeline).
Backend changes:
- Migration 016: Add quality_filter_level column to profiles table
- quality_filter.py: Centralized helper functions for SQL filtering
- insights.py: Apply global filter in _get_profile_data()
- activity.py: Apply global filter in list_activity()
Frontend changes:
- SettingsPage.jsx: Add Datenqualität section with 4-level selector
- History.jsx: Use global quality filter from profile context
Filter levels: all, quality (good+excellent+acceptable), very_good
(good+excellent), excellent (only excellent)
Closes#31
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Statt einfachem On/Off Toggle jetzt 4 Qualitätsstufen:
- 📊 Alle (kein Filter)
- ✓ Hochwertig (excellent + good + acceptable)
- ✓✓ Sehr gut (excellent + good)
- ⭐ Exzellent (nur excellent)
UI:
- Button-Group (Segmented Control) mit 4 Stufen
- Beschreibung welche Labels inkludiert werden
- Anzeige: X von Y Aktivitäten (wenn gefiltert)
User-Feedback: Stufenweiser Filter ist flexibler als binärer Toggle
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Problem: Errors during import were logged but not visible to user.
Changes:
- Backend: Collect error messages and return in response (first 10 errors)
- Frontend: Display error details in import result box
- UI: Red background when errors > 0, shows detailed error messages
Now users can see exactly which rows failed and why.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Full-width fields with section headers (mobile-friendly)
- Inline editing for all measurements (edit mode per row)
- Smart upsert: date change loads existing entry → update instead of duplicate
- Units integrated into labels (no overflow)
- Baseline: auto-detects existing entry and switches to update mode
- Blood Pressure: inline editing with all fields (date, time, BP, context, flags)
- Edit/Save/Cancel buttons with lucide-react icons
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
