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.
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>
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>
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
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
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>
- 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>
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>
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>
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>
Notiert an 3 Stellen:
1. insights.py: TODO-Kommentar im Code
2. ROADMAP.md: Deliverable bei M0.2 (lokal, nicht im Git)
3. Gitea Issue #28: Kommentar mit Spezifikation
Zukünftig:
- GET /api/insights/run/{slug}?quality_level=quality
- 4 Stufen: all, quality, very_good, excellent
- Frontend: Dropdown wie in History.jsx
- Pipeline-Configs können Standard-Level haben
User-Request: Quality-Level-Auswahl für KI-Analysen
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Problem: Import failed with "invalid literal for int() with base 10: '37.95'"
because Apple Health exports HRV and other vitals with decimal values.
Root cause: Code used int() directly on string values with decimals.
Fix:
- Added safe_int(): parses decimals as float first, then rounds to int
- Added safe_float(): robust float parsing with error handling
- Applied to all vital value parsing: RHR, HRV, VO2 Max, SpO2, resp rate
Example: '37.95' → float(37.95) → int(38) ✓
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>
Problem: Import expected English column names, but German Apple Health/Omron
exports use German names with units.
Fixed:
- Apple Health: Support both English and German column names
- "Start" OR "Datum/Uhrzeit"
- "Resting Heart Rate" OR "Ruhepuls (count/min)"
- "Heart Rate Variability" OR "Herzfrequenzvariabilität (ms)"
- "VO2 Max" OR "VO2 max (ml/(kg·min))"
- "Oxygen Saturation" OR "Blutsauerstoffsättigung (%)"
- "Respiratory Rate" OR "Atemfrequenz (count/min)"
- Omron: Support column names with/without units
- "Systolisch (mmHg)" OR "Systolisch"
- "Diastolisch (mmHg)" OR "Diastolisch"
- "Puls (bpm)" OR "Puls"
- "Unregelmäßiger Herzschlag festgestellt" OR "Unregelmäßiger Herzschlag"
- "Mögliches AFib" OR "Vorhofflimmern"
Added debug logging for both imports to show detected columns.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Logs:
- CSV column names from first row
- Rows skipped due to missing date
- Rows skipped due to no vitals data
- Shows which fields were found/missing
Helps diagnose CSV format mismatches.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Problem: Import reported all entries as "updated" even when skipped
due to WHERE clause (source != 'manual')
Root cause: RETURNING returns NULL when WHERE clause prevents update,
but code counted NULL as "updated" instead of "skipped"
Fix:
- Check if result is None → skipped (WHERE prevented update)
- Check if xmax = 0 → inserted (new row)
- Otherwise → updated (existing row modified)
Affects:
- vitals_baseline.py: Apple Health import
- blood_pressure.py: Omron import
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
**Backend (insights.py):**
- Extended _get_profile_data() to fetch sleep, rest_days, vitals
- Added template variables for Sleep Module:
{{sleep_summary}}, {{sleep_detail}}, {{sleep_avg_duration}}, {{sleep_avg_quality}}
- Added template variables for Rest Days:
{{rest_days_summary}}, {{rest_days_count}}, {{rest_days_types}}
- Added template variables for Vitals:
{{vitals_summary}}, {{vitals_detail}}, {{vitals_avg_hr}}, {{vitals_avg_hrv}},
{{vitals_avg_bp}}, {{vitals_vo2_max}}
**Frontend (Analysis.jsx):**
- Added 12 new template variables to VARS list in PromptEditor
- Enables AI prompt creation for Sleep, Rest Days, and Vitals analysis
All modules now have AI evaluation support for future prompt creation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Import endpoints for Omron blood pressure CSV (German date format)
- Import endpoints for Apple Health vitals CSV
- Import UI tab in VitalsPage with drag & drop for both sources
- German month mapping for Omron date parsing ("13 März 2026")
- Upsert logic preserves manual entries (source != 'manual')
- Import result feedback (inserted/updated/skipped/errors)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Avg blood pressure (systolic/diastolic) 7d and 30d
- Latest VO2 Max value
- Avg SpO2 7d and 30d
- Backend now provides all metrics expected by frontend
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Migration 014:
- blood_pressure_systolic/diastolic (mmHg)
- pulse (bpm) - during BP measurement
- vo2_max (ml/kg/min) - from Apple Watch
- spo2 (%) - blood oxygen saturation
- respiratory_rate (breaths/min)
- irregular_heartbeat, possible_afib (boolean flags from Omron)
- Added 'omron' to source enum
Backend:
- Updated Pydantic models (VitalsEntry, VitalsUpdate)
- Updated all SELECT queries to include new fields
- Updated INSERT/UPDATE with COALESCE for partial updates
- Validation: at least one vital must be provided
Preparation for Omron + Apple Health imports
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Backend:
- New router: vitals.py with CRUD endpoints
- GET /api/vitals (list)
- GET /api/vitals/by-date/{date}
- POST /api/vitals (upsert)
- PUT /api/vitals/{id}
- DELETE /api/vitals/{id}
- GET /api/vitals/stats (7d/30d averages, trends)
- Registered in main.py
Frontend:
- VitalsPage.jsx with manual entry form
- List with inline editing
- Stats overview (averages, trend indicators)
- Added to CaptureHub (❤️ icon)
- Route /vitals in App.jsx
API:
- Added vitals methods to api.js
v9d Phase 2d - Vitals tracking complete
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Admin endpoints for profile configuration:
- Extended TrainingTypeCreate/Update models with profile field
- Added profile column to all SELECT queries
- Profile templates for Running, Meditation, Strength Training
- Template endpoints: list, get, apply
- Profile stats endpoint (configured/unconfigured count)
New file: profile_templates.py
- TEMPLATE_RUNNING: Endurance-focused with HR zones
- TEMPLATE_MEDITATION: Mental-focused (low HR ≤ instead of ≥)
- TEMPLATE_STRENGTH: Strength-focused
API Endpoints:
- GET /api/admin/training-types/profiles/templates
- GET /api/admin/training-types/profiles/templates/{key}
- POST /api/admin/training-types/{id}/profile/apply-template
- GET /api/admin/training-types/profiles/stats
Next: Frontend Admin-UI (ProfileEditor component)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Problem: Backend crashed on startup due to evaluation import failure
Solution: Wrap evaluation_helper import in try/except
Changes:
- Import evaluation_helper with error handling
- Add EVALUATION_AVAILABLE flag
- All evaluation calls now check flag before executing
- System remains functional even if evaluation system unavailable
This prevents backend crashes if:
- Migrations haven't run yet
- Dependencies are missing
- Import errors occur
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Automatic evaluation on activity INSERT/UPDATE:
- create_activity(): Evaluate after manual creation
- update_activity(): Re-evaluate after manual update
- import_activity_csv(): Evaluate after CSV import (INSERT + UPDATE)
- bulk_categorize_activities(): Evaluate after bulk training type assignment
All evaluation calls wrapped in try/except to prevent activity operations
from failing if evaluation encounters an error. Only activities with
training_type_id assigned are evaluated.
Phase 1.2 complete ✅
## Next Steps (Phase 2):
Admin-UI for training type profile configuration
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Problem: Creating new training types via Admin UI resulted in
'Internal Server Error' because abilities dict was passed directly
to PostgreSQL JSONB column without Json() wrapper.
Solution:
- Import Json from psycopg2.extras
- Wrap abilities_json with Json() in INSERT
- Wrap data.abilities with Json() in UPDATE
Same issue as rest_days JSONB fix (commit 7d627cf).
Closes#13
Problem: User can create multiple rest days of same type per date
(e.g., 2x Mental Rest on 2026-03-23) - makes no sense.
Solution: UNIQUE constraint on (profile_id, date, focus)
## Migration 012:
- Add focus column (extracted from rest_config JSONB)
- Populate from existing data
- Add NOT NULL constraint
- Add CHECK constraint (valid focus values)
- Add UNIQUE constraint (profile_id, date, focus)
- Add index for performance
## Backend:
- Insert focus column alongside rest_config
- Handle UniqueViolation gracefully
- User-friendly error: "Du hast bereits einen Ruhetag 'Muskelregeneration' für 23.03."
## Benefits:
- DB-level enforcement (clean)
- Fast queries (no JSONB scan)
- Clear error messages
- Prevents: 2x muscle_recovery same day
- Allows: muscle_recovery + mental_rest same day ✓
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
