- Add environment-aware CORS and secure cookie support:
* Add secureCookies config to env.ts based on SECURE_COOKIES env var or https detection
* Integrate @elysiajs/cors with credentials and origin configuration
* Update cookie helpers to use SameSite=None; Secure in production
- Wrap expert review update and insert in database transaction for atomicity:
* Ensures diagnosis status update and review insert succeed together
* Rolls back both operations if either fails
* Preserves behavior: only update if status is needs_review, return badRequest if no row updated
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Implement diagnosis persistence routes with image upload, classification,
and prediction storage. Add missing HTTP error helpers (unauthorized,
forbidden) and update uploader client to use configurable base URL.
- Create POST /api/v1/diagnoses to upload images and store classifications
- Create GET /api/v1/diagnoses to list user's diagnoses (30 most recent)
- Create GET /api/v1/diagnoses/:id to retrieve diagnosis with predictions
- Add loadDiagnosisRecord helper for consistent diagnosis data loading
- Update uploader-client.ts to use env.uploaderBaseUrl
- Add unauthorized and forbidden error helpers to http-errors.ts
- Register diagnosisRoutes in main app
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Extend shared types for image classification, including PredictionProbability, UploaderMetadata, and ImageClassificationRecord.
- Create image_classifications table in the database with necessary fields and foreign key constraints.
- Implement disease mappers to convert database rows to shared disease records.
- Develop uploader client to handle image uploads to external service.
- Create image model service to load and classify images using TensorFlow.js.
- Add API routes for image classification, including GET for history and POST for new classifications.
- Implement frontend components for image classification form and display results.
- Update dashboard to integrate image classification functionality and display results.
- Document implementation plan for backend image classification.
- Add disease detail page component with data fetching and error handling
- Create shared types for diseases and classifications
- Implement API routes for diseases, classifications, and dashboard summary
- Develop reusable components for risk badge and disease card
- Build catalog page with search and filter functionality
- Update dashboard page with data-backed summary and manual classification form
- Register new routes in the web application
- Ensure type safety and consistency across shared modules
MythEclipse