Three interrelated fixes for the Android Google sign-in flow:
1. API base URL mismatch (404 error):
- auth-form.tsx used 'window.location.origin || VITE_API_BASE_URL',
which fell back to 'http://tauri.localhost' in Android WebView
instead of the actual API server.
- Fix: import shared 'apiBaseUrl' from api-client.ts (already had
the correct fallback: 'https://zeavisedu.asepharyana.my.id').
- Added .env with VITE_API_BASE_URL for dev mode resilience.
2. Deep-link caused IPC callback errors:
- 'processDeepLinkUrl()' used window.location.href = target,
triggering a full page reload that orphaned pending Tauri IPC
promises, causing 'Cannot read properties of undefined (reading
'runCallback')' errors.
- Cold-start: keep get_current but use window.location.href (safe
at boot — no SPA state to lose).
- Warm-start: use sessionStorage + custom DOM event + React Router
navigate() via new <DeepLinkRouterHandler /> layout route,
avoiding any page reload.
3. SPA navigation did not trigger OAuth token handler:
- LoginPage's useEffect for ?token=xxx depended only on
[setUser, queryClient, navigate] — location.search changes
from a SPA navigate() call were ignored.
- Fix: added location.search and location to deps.
- Added visibilitychange + focus listeners so returning from the
Google auth browser always re-checks URL params.
Co-Authored-By: Claude <noreply@anthropic.com>
Root cause: dynamic import('@tauri-apps/plugin-opener') silently fails on
Android Tauri WebView because the module resolution path for @tauri-apps/api
(plugin dependency) differs from npm expectations in the bundled context.
Rewrote tauri.ts to use window.__TAURI_INTERNALS__.invoke() directly:
- openUrl() → invoke('plugin:opener|open_url', {url})
- setupDeepLinkHandler() → invoke('plugin:deep-link|get_current')
- Warm-start listener still uses import('@tauri-apps/api/event') for
deep-link://new-url events (bundled as separate chunk by Vite)
- Added @tauri-apps/api as direct dependency
Also kept withGlobalTauri: true (needed for __TAURI_INTERNALS__ injection)
but reverted APK frontendDist back to bundled React app (../web/dist)
since redirect-to-live-web approach was unreliable.
Co-Authored-By: Claude <noreply@anthropic.com>
APK architecture change: replaces bundled React SPA with minimal redirect
page that always loads live web content. No more APK rebuilds for web changes.
Root cause of OAuth failure on Android:
1. Cold-start deep links lost — APK's old bundled JS called onOpenUrl()
(warm-start listener only) but NOT getCurrent() which is required for
cold-start deep links. Fix: redirect page + setupDeepLinkHandler both
call getCurrent() before redirecting/navigating.
2. Session cookie was dropped — renderTauriDeepLinkPage returned a raw
new Response() which overwrote the Set-Cookie header set by the
callback handler. Fix: inject Set-Cookie into the Response.
3. tauri.conf.json frontendDist → "./web/dist-tauri" (redirect page)
4. Added @tauri-apps/plugin-deep-link and @tauri-apps/plugin-opener as
web app deps so live-web imports work in Tauri WebView.
Co-Authored-By: Claude <noreply@anthropic.com>
The intent:// URL in renderTauriDeepLinkPage was intent:/path (single slash),
producing zeavisedu:/login?token=xxx — a non-hierarchical URL that new URL()
cannot parse. Fixed to intent://login/path which produces a proper
hierarchical URI (zeavisedu://login/login?token=xxx).
Also hardened setupDeepLinkHandler to handle both double-slash (://) and
single-slash (:/) custom-scheme URLs as fallback.
Co-Authored-By: Claude <noreply@anthropic.com>
Login-page won't auto-process token on deep link return because
the WebView stays on the page it was on. Added setupDeepLinkHandler()
which listens for zeavisedu:// scheme URLs and navigates the WebView
to the correct path+query.
Co-Authored-By: Claude <noreply@anthropic.com>
Background: Google blocks OAuth in embedded WebView (403 disallowed_useragent).
Solution: open Google login in the Android system browser, then deep-link
back to the Tauri app via custom scheme after callback.
Changes:
- Tauri: add tauri-plugin-opener + tauri-plugin-deep-link to Cargo.toml
- Tauri: register plugins in lib.rs, add capabilities
- Web: auth-form.tsx Google button uses openUrl() via @tauri-apps/plugin-opener
on Tauri (opens in system browser), falls back to window.location.href
- Web: add lib/tauri.ts for isTauri() detection + lazy opens
- API: /auth/google accepts ?platform=tauri → encodes into OAuth state param
- API: /auth/google/callback decodes state → if tauri, renders HTML page
that deep-links back via zeavisedu:// scheme; if web, 302 redirect
- Android: patch script adds deep link intent filter for zeavisedu:// scheme
Co-Authored-By: Claude <noreply@anthropic.com>
Android WebView blocks third-party cookies by default. This patch:
- Returns session token in login/register response body
- Stores token in localStorage, sends via Authorization: Bearer header
- Backend getCurrentUser supports Authorization header fallback
Co-Authored-By: Claude <noreply@anthropic.com>
- Set VITE_API_BASE_URL default to production API URL in api-client.ts
so fetch() uses absolute URLs instead of relative paths that fail on Tauri
- Add tauri://localhost and https://tauri.localhost to API CORS allowed origins
- Also fix .env WEB_APP_URL from stale .tech to .my.id
Co-Authored-By: Claude <noreply@anthropic.com>
Extract toImageClassificationRecord helper function to eliminate duplicated
transformation code between getImageClassifications and createImageClassification.
Replace hardcoded fake disease object with explicit error when diagnosis.disease
is null, ensuring data integrity. Remove unnecessary type casts now that the
helper is properly typed.
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
