a6fc149788
POST /api/analyze submits an analysis job and returns job_id instantly.
GET /api/analyze/{job_id} returns progress + result; frontend polls every 2s.
Analysis runs as asyncio.create_task in the background — each HTTP request
completes in milliseconds, well within the 30s GCP Load Balancer limit.
- Add backend/app/services/job_store.py: in-memory AnalysisJob store with
30-min TTL cleanup
- Add backend/app/api/analysis_routes.py: POST + GET /api/analyze endpoints
with full analysis pipeline (hash check, DB persistence, PDF pages, etc.)
- Remove backend/app/websocket/: handlers.py, manager.py, __init__.py
- Update backend/app/main.py: wire analysis_router, store analysis_service
in app.state, drop all WebSocket imports and endpoint
- Update frontend/services/geminiService.ts: replace WS with fetch+poll;
function signatures unchanged so App.tsx / WIPReviewer.tsx need no edits
- Remove VITE_BACKEND_WS_URL from vite.config.ts, deploy.sh, .env.deploy.example
- Update cloudrun.yaml: remove WebSocket-specific session affinity annotation
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
|
||
|---|---|---|
| .. | ||
| components | ||
| contexts | ||
| hooks | ||
| public | ||
| services | ||
| utils | ||
| App.tsx | ||
| constants.ts | ||
| index.html | ||
| index.tsx | ||
| metadata.json | ||
| package-lock.json | ||
| package.json | ||
| README.md | ||
| tsconfig.json | ||
| types.ts | ||
| vite.config.ts | ||
Run and deploy your AI Studio app
This contains everything you need to run your app locally.
View your app in AI Studio: https://ai.studio/apps/drive/1vH-R-vj0Xkk_g2ZFdHtLxNc12sFTOl2L
Run Locally
Prerequisites: Node.js
- Install dependencies:
npm install - Set the
GEMINI_API_KEYin .env.local to your Gemini API key - Run the app:
npm run dev