- Updated dashboard layout schema to introduce separate default layouts for product and lab dashboards.
- Added new functions for managing product and lab default layouts, improving user customization options.
- Updated app_dashboard version to 1.9.0 to reflect the introduction of product vs lab layout defaults and new API fields for dashboard configuration.
- Enhanced tests to validate new layout functionalities and ensure proper widget visibility based on user settings.
- Added new "Dashboard-Lab-Widgets" entry to the documentation for better guidance on widget configuration.
- Updated the app_dashboard version to 1.8.0 to reflect the introduction of widget catalog features and layout entitlements.
- Enhanced widget catalog entries to include optional feature requirements for better visibility and access control.
- Improved the DashboardLabPage to manage widget visibility based on feature entitlements, ensuring a more tailored user experience.
- Added functions to handle Apple Health sleep data in both segment and summary formats.
- Implemented robust error handling for date parsing and data conversion.
- Updated documentation to reflect new CSV format support and data aggregation logic.
- Bumped version in version.py to reflect the changes in the activity module.
- Integrated a new API endpoint for fetching the widget catalog in the Dashboard-Lab.
- Updated the dashboard layout schema to utilize the widget catalog for dynamic widget management.
- Refactored DashboardLabPage and PilotVizPage to leverage the new widget rendering system.
- Removed deprecated widget metadata from the frontend, streamlining the widget management process.
- Bumped app_dashboard version to 1.1.0 to reflect the new features and improvements.
- Added new routes and API endpoints for the Dashboard-Lab layout in the app.
- Updated main.py to include the app_dashboard router for backend integration.
- Enhanced App.jsx to include a route for the DashboardLabPage.
- Modified SettingsPage to add a link to the new Dashboard-Lab layout, improving user access to dashboard features.
- Updated version.py to reflect the new app_dashboard module version.
- Bumped version of reference_values module to 1.3.0.
- Added new imports and functionality for reference values in the backend, enhancing data retrieval.
- Introduced a new PilotVizPage in the frontend for visualizing data, linked from the SettingsPage for easy access.
- Updated routing in App.jsx to include the new pilot visualization route.
- Introduced a new API endpoint for fetching a summary of profile reference values, providing the latest and previous entries for each reference type.
- Updated ProfileReferenceValuesPage to display summary tiles with trend indicators for better user insights.
- Enhanced CSS for responsive layout of reference value tiles, improving the overall user experience on different screen sizes.
- Implemented trend calculation logic to visually represent changes between the latest and previous reference values.
- Added a new API endpoint for reordering reference value types based on user-defined order.
- Updated the AdminReferenceValueTypesPage to allow users to reorder types using up/down buttons.
- Introduced a consistent confidence level sorting mechanism across the application.
- Refactored related components to remove unused sort order fields and improve user experience.
- Updated the backend to include new fields for validation rules and metadata in reference value types.
- Enhanced the AdminReferenceValueTypesPage to support new validation rules for different data types.
- Improved the ProfileReferenceValuesPage to handle validation and metadata for profile reference values.
- Added API endpoint for fetching reference value metadata enums to support frontend validation.
- Refactored frontend forms to incorporate new fields and validation logic for a better user experience.
- Added new routes and API endpoints for managing reference value types in the admin section.
- Updated the frontend to include navigation and components for reference value types management.
- Enhanced the backend to support the new reference value types in the data layer and versioning.
- Introduced new routes and API endpoints for managing personal reference values.
- Updated the SettingsPage to include a section for reference values with navigation to manage them.
- Enhanced the backend to support reference values in the data layer and versioning.
- Added necessary imports and UI components for a seamless user experience.
- Added endpoints for listing and updating focus area usage types in the backend.
- Enhanced the AdminFocusAreasPage to display and manage allowed usage types for focus areas.
- Introduced a new state for usage types catalog and integrated it into the focus area editing process.
- Updated API utility functions to support new usage types operations.
- Enhanced the caliper listing and export functionalities to include enriched data from weight logs.
- Updated the upsert and update operations to utilize new composition functions for body composition calculations.
- Refactored the CaliperScreen component to streamline payload construction by removing unnecessary parameters.
Critical Backend Bug:
- Frontend calls api.getPrompt(id) → GET /api/prompts/{uuid}
- Backend had NO endpoint for single prompt retrieval by ID
- Result: 405 Method Not Allowed
Backend Endpoints Before:
✓ GET /api/prompts - List all
✓ POST /api/prompts - Create
✓ PUT /api/prompts/{id} - Update
✗ GET /api/prompts/{id} - MISSING!
Backend Endpoints After:
✓ GET /api/prompts - List all
✓ GET /api/prompts/{id} - Get single (NEW)
✓ POST /api/prompts - Create
✓ PUT /api/prompts/{id} - Update
Implementation:
- Added get_prompt(prompt_id: str) function
- Returns single prompt by UUID
- 404 if not found
- Requires auth (admin or user)
This fixes:
- Workflow loading after save (loadWorkflow calls getPrompt)
- Workflow editing from admin list (Edit button calls getPrompt)
- All 405 Method Not Allowed errors
Root Cause: Backend was incomplete, missing basic CRUD read-by-id endpoint
Error: NameError TimeWindow not defined
Fix: Graceful degradation if old metadata enums not available
Gap report now optional (empty if old system unavailable)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
NameError: name 'Header' is not defined
Added Header to fastapi imports for export endpoints auth fix.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Charts router had no prefix, causing 404 errors.
Fixed:
- Added prefix="/api/charts" to APIRouter()
- Changed all endpoint paths from "/charts/..." to "/..."
(prefix already includes /api/charts)
Now endpoints resolve correctly:
/api/charts/energy-balance
/api/charts/recovery-score
etc.
All 23 chart endpoints now accessible.
Root cause: listGoalsGrouped() SELECT was missing g.start_date and g.reached_date
Result: Frontend used grouped goals for editing, so start_date was undefined
This is why target_date worked (it was in SELECT) but start_date didn't.
- Added serialize_dates() helper to convert date objects to strings
- Applied to list_goals and get_goals_grouped endpoints
- Fixes issue where start_date was saved but not visible in frontend
- Python datetime.date objects need explicit .isoformat() conversion
Root cause: FastAPI doesn't auto-serialize all date types consistently
- Log UPDATE SQL and parameters
- Verify saved values after UPDATE
- Show date types in list_goals response
- Track down why start_date not visible in UI
- Rewrote update logic to determine final_start_date/start_value first
- Then append to updates/params arrays (ensures alignment)
- Fixes bug where only start_value was saved but not start_date
User feedback: start_value correctly calculated but start_date not persisted
**User Feedback:** "Macht es nicht Sinn, den nächsten verfügbaren Wert
am oder nach dem Startdatum automatisch zu ermitteln und auch das
Startdatum dann automatisch auf den Wert zu setzen?"
**New Logic:**
1. User sets start_date: 2026-01-01
2. System finds FIRST measurement >= 2026-01-01 (e.g., 2026-01-15: 88 kg)
3. System auto-adjusts:
- start_date → 2026-01-15
- start_value → 88 kg
4. User sees: "Start: 88 kg (15.01.26)" ✓
**Benefits:**
- User doesn't need to know exact date of first measurement
- More user-friendly UX
- Automatically finds closest available data
**Implementation:**
- Changed query from "BETWEEN date ±7 days" to "WHERE date >= target_date"
- Returns dict with {'value': float, 'date': date}
- Both create_goal() and update_goal() now adjust start_date automatically
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
**Error:**
function pg_catalog.extract(unknown, integer) does not exist
HINT: No function matches the given name and argument types.
**Problem:**
In PostgreSQL, date - date returns INTEGER (days), not INTERVAL.
EXTRACT(EPOCH FROM integer) fails because EPOCH expects timestamp/interval.
**Solution:**
Changed from:
ORDER BY ABS(EXTRACT(EPOCH FROM (date - '2026-01-01')))
To:
ORDER BY ABS(date - '2026-01-01'::date)
This directly uses the day difference (integer) for sorting,
which is exactly what we need to find the closest date.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
**Problem:** Goals created today had start_value = current_value,
showing 0% progress even after months of tracking.
**Solution:**
1. Added start_date and start_value to GoalCreate/GoalUpdate models
2. New function _get_historical_value_for_goal_type():
- Queries source table for value on specific date
- ±7 day window for closest match
- Works with all goal types via goal_type_definitions
3. create_goal() logic:
- If start_date < today → auto-populate from historical data
- If start_date = today → use current value
- User can override start_value manually
4. update_goal() logic:
- Changing start_date recalculates start_value
- Can manually override start_value
**Example:**
- Goal created today with start_date = 3 months ago
- System finds weight on that date (88 kg)
- Current weight: 85.2 kg, Target: 82 kg
- Progress: (85.2 - 88) / (82 - 88) = 47% ✓
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This file was replaced by the refactored vitals system:
- vitals_baseline.py (morning measurements)
- blood_pressure.py (BP tracking with context)
Migration 015 completed the split in v9d Phase 2d.
File was no longer imported in main.py.
Cleanup result: -684 lines of dead code
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!
