Bug 1 Final Fix:
- Changed all placeholders from $1, $2, $3 to %s
- psycopg2 expects Python-style %s, converts to $N internally
- Using $N directly causes 'there is no parameter $1' error
- Removed param_idx counter (not needed with %s)
Root cause: Mixing PostgreSQL native syntax with psycopg2 driver
This is THE fix that will finally work!
Bug 3 Fix: filter_conditions was missing from SELECT statement in
list_goal_type_definitions(), preventing edit form from loading
existing filter JSON.
- Added filter_conditions to line 1087
- Now edit form correctly populates filter textarea
**Bug:** POST /api/vitals/baseline threw UndefinedParameter
**Cause:** Dynamic SQL generation had desynchronized column names and placeholders
**Fix:** Rewrote to use synchronized insert_cols, insert_placeholders, update_fields arrays
- Track param_idx correctly (start at 3 after pid and date)
- Build INSERT columns and placeholders in parallel
- Cleaner, more maintainable code
- Fixes Ruhepuls entry error
**Bug 1: Focus contributions not saved**
- GoalsPage: Added focus_contributions to data object (line 232)
- Was missing from API payload, causing loss of focus area assignments
**Bug 2: Filter focus areas in goal form**
- Only show focus areas user has weighted (weight > 0)
- Cleaner UX, avoids confusion with non-prioritized areas
- Filters focusAreasGrouped by userFocusWeights
**Bug 3: Vitals RHR entry - Internal Server Error**
- Fixed: Endpoint tried to INSERT into vitals_log (renamed in Migration 015)
- Now uses vitals_baseline table (correct post-migration table)
- Removed BP fields from baseline endpoint (use /blood-pressure instead)
- Backward compatible return format
All fixes tested and ready for production.
**Migration 032:**
- user_focus_area_weights table (profile_id, focus_area_id, weight)
- Migrates legacy 6 preferences to dynamic weights
**Backend (focus_areas.py):**
- GET /user-preferences: Returns dynamic focus weights with percentages
- PUT /user-preferences: Saves user weights (dict: focus_area_id → weight)
- Auto-calculates percentages from relative weights
- Graceful fallback if Migration 032 not applied
**Frontend (GoalsPage.jsx):**
- REMOVED: Goal Mode cards (obsolete)
- REMOVED: 6 hardcoded legacy focus sliders
- NEW: Dynamic focus area cards (weight > 0 only)
- NEW: Edit mode with sliders for all 26 areas (grouped by category)
- Clean responsive design
**How it works:**
1. Admin defines focus areas in /admin/focus-areas (26 default)
2. User sets weights for areas they care about (0-100 relative)
3. System calculates percentages automatically
4. Cards show only weighted areas
5. Goals assign to 1-n focus areas (existing functionality)
- get_focus_areas now tries user_focus_preferences first (Migration 031)
- Falls back to old focus_areas table if Migration 031 not applied
- get_goals_grouped wraps focus_contributions loading in try/catch
- Graceful degradation until migrations run
- Wrap focus_contributions loading in try/catch
- If tables don't exist (migration not run), continue without them
- Backward compatible with pre-migration state
- Logs warning but doesn't crash
- Changed prefix from '/focus-areas' to '/api/focus-areas'
- Consistent with all other routers (goals, prompts, etc.)
- Fixes 404 Not Found on /admin/focus-areas page
**Backend Safeguards:**
- get_goals_grouped: Added source_table, source_column, direction to SELECT
- create_goal_progress: Check source_table before allowing manual entry
- Returns HTTP 400 if user tries to log progress for automatic goals (weight, activity, etc.)
**Prevents:**
- Data confusion: Manual entries in goal_progress_log for weight/activity/etc.
- Dual tracking: Same data in multiple tables
- User error: Wrong data entry location
**Result:**
- Frontend filter (!goal.source_table) now works correctly
- CustomGoalsPage shows ONLY custom goals (flexibility, strength, etc.)
- Clear error message if manual entry attempted via API
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implemented progress tracking system for all goals.
**Backend:**
- Migration 030: goal_progress_log table with unique constraint per day
- Trigger: Auto-update goal.current_value from latest progress
- Endpoints: GET/POST/DELETE /api/goals/{id}/progress
- Pydantic Models: GoalProgressCreate, GoalProgressUpdate
**Features:**
- Manual progress tracking for custom goals (flexibility, strength, etc.)
- Full history with date, value, note
- current_value always reflects latest progress entry
- One entry per day per goal (unique constraint)
- Cascade delete when goal is deleted
**API:**
- GET /api/goals/{goal_id}/progress - List all entries
- POST /api/goals/{goal_id}/progress - Log new progress
- DELETE /api/goals/{goal_id}/progress/{progress_id} - Delete entry
**Next:** Frontend UI (progress button, modal, history list)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
These goal types existed but were inactive or misconfigured.
Uses UPSERT (INSERT ... ON CONFLICT DO UPDATE):
- If exists → activate + fix labels/icons/category
- If not exists → create properly
Idempotent: Safe to run multiple times, works on dev + prod.
Both types have no automatic data source (source_table = NULL),
so current_value must be updated manually.
Fixes: flexibility and strength goals not visible in admin
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
goals table doesn't have is_active column.
Removed AND g.is_active = true from WHERE clause.
Fixes: psycopg2.errors.UndefinedColumn: column g.is_active does not exist
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Fixed: column 'date' does not exist in blood_pressure_log
blood_pressure_log uses 'measured_at' instead of 'date'.
Added DATE_COLUMN_MAP for table-specific date columns:
- blood_pressure_log → measured_at
- fitness_tests → test_date
- all others → date
Replaced all hardcoded 'date' with dynamic date_col variable.
Fixes error: [ERROR] Failed to fetch value from blood_pressure_log.systolic
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Fixed SQL error: column g.linear_projection does not exist
Replaced with: g.on_track, g.projection_date (actual columns)
This was causing Internal Server Error on /api/goals/grouped
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Migration 028 failed because goals table doesn't have is_active column yet.
Removed WHERE clause from index definition.
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>
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>
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>
- 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
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
Problem: dob Spalte ist DATE (PostgreSQL) → Python bekommt datetime.date,
nicht String → strptime() schlägt fehl → age = "unbekannt"
Fix: Prüfe isinstance(dob, str) und handle beide Typen:
- String → strptime()
- date object → direkt verwenden
Jetzt funktioniert {{age}} Platzhalter korrekt.
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>
- caliper_summary: use body_fat_pct (not bf_jpl)
- circ_summary: use c_chest, c_waist, c_hip (not brust, taille, huefte)
- get_latest_bf: use body_fat_pct for consistency
Fixes SQL errors when running base prompts that feed pipeline prompts.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- New placeholder: {{activity_detail}} returns formatted activity log
- Shows last 20 activities with date, type, duration, kcal, HR
- Makes activity analysis prompts work properly
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Placeholder resolver returns keys with {{ }} wrappers,
but resolve_placeholders expects clean keys.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Backend: integrate get_placeholder_example_values in execute_prompt_with_data
- Backend: now provides BOTH raw data AND processed placeholders
- Backend: unwrap Markdown-wrapped JSON (```json ... ```)
- Fixes old-style prompts that expect name, weight_trend, caliper_summary
Resolves unresolved placeholders issue.
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>
Issue: template has NOT NULL constraint but pipeline-type prompts
don't use template (they use stages JSONB instead).
Solution: ALTER COLUMN template DROP NOT NULL before inserting
pipeline configs into ai_prompts.
Fixed Step 3 pipeline_configs migration:
- Simplified JSONB aggregation logic
- Properly scope pc alias in subqueries
- Use UNNEST with FROM clause for array expansion
Previous version had correlation issues with nested subqueries.
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>
The frontend was sending quality_filter_level to the backend, but the
Pydantic ProfileUpdate model didn't include this field, so it was
silently ignored. Profile updates never actually saved the filter.
This is why the charts didn't react to filter changes - the backend
database was never updated.
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>
ModuleNotFoundError: No module named 'dateutil' beim Server-Start.
Ursache: vitals.py importiert dateutil.parser für Omron-Datumsformatierung,
aber python-dateutil fehlte in requirements.txt.
Fix: python-dateutil==2.9.0 zu requirements.txt hinzugefügt.
Nach dem Update: Docker Container neu bauen auf dem Pi:
cd /home/lars/docker/bodytrack-dev
docker compose -f docker-compose.dev-env.yml build --no-cache backend
docker compose -f docker-compose.dev-env.yml up -d
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>
- PostgreSQL returns numeric values as Decimal objects
- psycopg2.Json() cannot serialize Decimal to JSON
- Added convert_decimals() helper function
- Converts activity_data, context, and evaluation_result before saving
Fixes: Batch evaluation errors (31 errors 'Decimal is not JSON serializable')
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Shows first 10 errors with activity_id, training_type_id, and error message
- Helps debug evaluation failures
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>
SQL Error: VALUES lists must all be the same length (line 130)
Cause: kcal_per_km row was missing validation_rules JSONB value
Fixed: Added validation_rules '{"min": 0, "max": 1000}'::jsonb
All 16 parameter rows now have correct 10 columns:
key, name_de, name_en, category, data_type, unit, source_field,
validation_rules, description_de, description_en
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>
Migration 011 removed UNIQUE constraint (profile_id, date) to allow
multiple rest days per date, but INSERT still used ON CONFLICT.
Error: psycopg2.errors.InvalidColumnReference: there is no unique or
exclusion constraint matching the ON CONFLICT specification
Solution: Remove ON CONFLICT clause, use plain INSERT.
Multiple entries per date now allowed.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
