Lebensmittel oder Rezept wählbar; gemappte Zutaten werden übernommen, offene erscheinen in der Liste. Dazu Fettgehalt-Suche, EPA-Stoffe, Rezept-CRUD und Wechsel bestehender Zuordnungen.
Co-authored-by: Cursor <cursoragent@cursor.com>
Mapping bleibt erhalten, auch wenn der Nährwert-Rebuild länger dauert. Offene Liste führt 1 ml/2 ml Olivenöl als ein Lebensmittel. Dazu JSON-Sicherung, eigener Katalogeintrag und Gramm-pro-Einheit.
Co-authored-by: Cursor <cursoragent@cursor.com>
Tagebuchzeilen eigener Rezepte werden über den Listen-Import in Zutaten zerlegt. Zuordnen erfolgt im Namens-Popup statt per BLS-Code.
Co-authored-by: Cursor <cursoragent@cursor.com>
Katalog, lernendes Mapping ohne KI, optionale Items und Import-Policy.
Playwright-Smoke und Issue-Audit um Ernährung/Zuordnen/API ergänzt.
Co-authored-by: Cursor <cursoragent@cursor.com>
- Introduced new API endpoints for managing report definitions, including listing, creating, and updating reports.
- Updated the frontend to include a dedicated section for configuring reports, enhancing user navigation and experience.
- Modified existing components to link to the new report settings, ensuring seamless access to report functionalities.
- Improved the report catalog API to support multiple definitions per profile and added validation for report limits.
- Updated documentation and tests to reflect the new features and ensure proper functionality.
- Introduced the `report_export` widget to the dashboard, allowing users to generate structured PDF reports.
- Updated widget configuration to include `report_export` in the allowed widgets and added validation for its configuration.
- Enhanced the widget catalog with details for the new `report_export` entry.
- Implemented API endpoints for managing report profiles and generating PDFs.
- Added frontend components for configuring and displaying report settings.
- Updated tests to ensure proper validation and functionality of the new report generation features.
- Bumped application version to reflect the addition of the new widget and related functionalities.
- Introduced `c_arm_relaxed` to the CircumferenceEntry model for tracking relaxed arm measurements.
- Updated database schema to include `c_arm_relaxed` in the circumference_log table.
- Implemented calculation for 28-day relaxed arm circumference change with `calculate_arm_relaxed_28d_delta`.
- Enhanced placeholder resolver and registration to support new relaxed arm measurement.
- Updated frontend components to accommodate the new measurement, including forms and CSV exports.
- Improved documentation and guide data to reflect the addition of relaxed arm measurements.
- Added `taken_at` timestamp to the photos table for improved photo metadata.
- Updated the photo upload API to support optional EXIF data extraction and file last modified timestamp.
- Enhanced the photo upload process to allow skipping EXIF data, defaulting to today's date if no other date is provided.
- Improved the photo display in various components to utilize a unified caption format.
- Refactored photo sorting and grouping logic for better organization in the UI.
Reverts cd29c7d..026c51b per git revert. Alle zwischenliegenden Commits bleiben in Gitea sichtbar; der Arbeitsbaum entspricht wieder dem Stand von ca8cee9.
Made-with: Cursor
- Updated the `ACTIVITY_SESSION_METRICS_EAV_AGENT_GUIDE` with new details on CSV import processes and EAV handling, improving documentation clarity.
- Refactored the `_import_activity` function to utilize `apply_activity_mapped_column_aliases`, ensuring proper mapping of training parameters and reducing redundancy.
- Introduced validation for numeric bounds in the `activity_csv_registry_updates_from_mapped` function, enhancing data integrity during CSV imports.
- Added new utility functions to manage column aliasing and streamline the upsert process for session metrics, preventing duplicate entries.
- Implemented unit tests to validate the new aliasing logic and ensure correct behavior during session metrics updates.
- Updated the README to include new activity production architecture and phases, improving clarity on the development roadmap.
- Enhanced the `ACTIVITY_SESSION_METRICS_EAV_AGENT_GUIDE` with details on the target architecture and phase plan for production readiness.
- Introduced a new function `merge_column_backed_and_eav_metrics` to streamline the merging of metrics from column-backed and EAV sources, ensuring data integrity and reducing duplication.
- Refactored session metrics handling to eliminate deprecated synchronization methods, improving the overall efficiency of data processing.
- Added unit tests for the new merging logic, ensuring robust validation of metrics handling.
- Enhanced the docstring for `upsert_session_metrics_from_csv_mapped` to clarify the handling of schema parameters and EAV logic.
- Modified the condition for skipping updates based on `source_field` to ensure only patchable columns are processed, improving data integrity during session metrics upsert operations.
- Introduced new endpoints for updating training category and type parameters in the backend.
- Added corresponding update functions in the frontend API utility.
- Enhanced the Admin Activity Attribute Profiles page to support editing and saving changes for category and type parameters.
- Implemented state management for editing parameters and improved error handling during updates.
- Introduced Activity Session Metrics for enhanced tracking of session data.
- Updated backend to support new API endpoints for managing session metrics.
- Added new Pydantic models for activity metrics and replaced metrics functionality.
- Enhanced data layer to include session metrics in recent training session data.
- Updated documentation to reflect changes in session metrics handling.
- Increased precision for `kcal_active`, `kcal_resting`, `hr_avg`, and `hr_max` fields in the activity log schema.
- Added a new function `_activity_hr_bpm` to validate heart rate values during CSV import, ensuring they fall within plausible ranges.
- Updated the CSV parser to utilize the new heart rate validation function for improved data integrity.
- Enhanced the type converter to accommodate additional aliases for energy fields in CSV imports.
- Added a test to verify conversion of active energy from kJ to kcal, ensuring accurate data handling.
- Modified `_resolve_training_type_for_activity` to accept a database cursor, improving efficiency and avoiding potential deadlocks during CSV imports.
- Introduced `get_training_type_for_activity_with_cursor` to handle training type resolution with an existing cursor, streamlining database interactions.
- Updated related calls in the activity import logic to utilize the new function, ensuring consistent behavior across the application.
- Updated migration scripts to ensure idempotent behavior for the source CHECK constraint, allowing for consistent application even if previous migrations were partially successful.
- Enhanced SQL logic to drop existing constraints safely and re-add them, ensuring compatibility with the universal CSV import.
- Clarified comments for better understanding of migration context and functionality.
- Updated the CSV import logic to include SAVEPOINT management, allowing for better error handling during the vitals baseline import process.
- Enhanced the SQL migration script to drop existing CHECK constraints related to the 'source' field, ensuring compatibility with the new universal CSV import.
- Incremented DB_SCHEMA_VERSION to "20260409c" to reflect these changes and improve the import process reliability.
- Incremented DB_SCHEMA_VERSION to "20260409b" to reflect changes related to the vitals_baseline.source CSV migration.
- Updated comments to clarify the migration context for better maintainability.
- Updated the nutrition import logic to utilize a new row processing specification, improving data aggregation and validation.
- Refactored the template rendering process in the workflow executor to use Jinja2's Environment with ChainableUndefined for better handling of missing attributes.
- Added backward-compatible shortcuts for accessing decision signals in node contexts, enhancing flexibility in template usage.
- Introduced import row processing options in CSV templates, allowing for more customizable data handling during imports.
- Changed source unit representation from "kJ" to "kj" for consistency across CSV templates and migrations.
- Updated CI workflow to enhance testing conditions, ensuring tests run in the correct environment based on deployment context.
- Improved job steps for backend testing and syntax checking by utilizing deployed application directories, streamlining the CI process.
- Integrated date parsing improvements using dateutil for better handling of various date formats in sleep data.
- Added total sleep hours to the nights dictionary for comprehensive sleep analysis.
- Updated the import logic to handle cases where sleep duration is zero, providing appropriate warnings.
- Enhanced the CSV import interface to detect Apple sleep CSV format and provide user feedback on template selection.
- Improved the admin CSV template editor to accommodate new sleep import requirements and clarify usage instructions.
- Added support for new CSV import modules: sleep and vitals_baseline, expanding the import capabilities.
- Implemented backend logic for handling CSV imports related to sleep and vitals baseline, including error handling and data processing.
- Updated frontend components to include new modules in the CSV import interface, improving user experience.
- Introduced unit tests for the new import functionalities to ensure reliability and correctness.
- Enhanced existing CSV analysis features to accommodate the new modules, ensuring consistent behavior across the application.
- Changed feature ID from 'csv_import' to 'data_import' in the features table.
- Updated foreign key references in tier_limits, user_feature_restrictions, user_feature_usage, and widget_feature_requirements.
- Removed the old 'csv_import' feature entry after ensuring all references are updated.
- Simplified the migration process by using an INSERT with ON CONFLICT for the new feature entry.
- Added permissions for editing and deleting CSV field mappings.
- Created type converter for CSV cells to handle various data types.
- Implemented database migrations for CSV field mappings and import logs.
- Seeded initial system templates for nutrition and activity data imports.
- Developed admin endpoints for managing system CSV templates.
- Introduced user endpoints for CSV import analysis and mapping retrieval.
- Added tests for core CSV parser functionalities, including delimiter detection and value conversion.
- Added new API endpoints for listing and updating widget-feature assignments, allowing for custom feature requirements.
- Introduced a new admin page for managing widget-feature assignments, enhancing the admin interface.
- Updated navigation to include a link to the new widget-feature assignments page.
- Refactored widget access logic to support AND-based feature requirements for widgets.
- Bumped app_dashboard version to 1.11.0 to reflect these changes and improvements.
- Added new API endpoints for managing the product dashboard standard, including retrieval, update, and deletion functionalities.
- Enhanced the DashboardConfigurePage to support admin mode for configuring the product dashboard standard.
- Updated the admin navigation to include a link for the product dashboard standard configuration.
- Refactored the dashboard layout logic to utilize the new product standard management features.
- Bumped app_dashboard version to 1.10.0 to reflect these enhancements and changes.
- 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.
- 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.
- 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.
**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)
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>
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! 🎯
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
