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>
50 lines
1.4 KiB
Python
50 lines
1.4 KiB
Python
import time
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import uuid
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import logging
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from dataclasses import dataclass, field
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logger = logging.getLogger(__name__)
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# In-memory job store — safe for single Cloud Run instance
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_jobs: dict[str, "AnalysisJob"] = {}
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JOB_TTL_SECONDS = 1800 # 30 minutes
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@dataclass
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class AnalysisJob:
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job_id: str
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status: str # "pending" | "running" | "complete" | "error"
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created_at: float
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agents_started: list[str] = field(default_factory=list)
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agents_completed: dict[str, dict] = field(default_factory=dict)
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model_fallback: bool = False
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result: dict | None = None
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proof_id: str | None = None
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version_id: str | None = None
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pdf_pages: list[dict] | None = None
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is_identical_file: bool = False
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error_message: str | None = None
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def _cleanup_old_jobs() -> None:
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"""Remove jobs older than JOB_TTL_SECONDS."""
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cutoff = time.time() - JOB_TTL_SECONDS
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stale = [jid for jid, job in _jobs.items() if job.created_at < cutoff]
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for jid in stale:
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del _jobs[jid]
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if stale:
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logger.info(f"[JOB_STORE] Cleaned up {len(stale)} stale jobs")
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def create_job() -> AnalysisJob:
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_cleanup_old_jobs()
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job_id = str(uuid.uuid4())
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job = AnalysisJob(job_id=job_id, status="pending", created_at=time.time())
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_jobs[job_id] = job
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logger.info(f"[JOB_STORE] Created job {job_id}")
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return job
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def get_job(job_id: str) -> AnalysisJob | None:
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return _jobs.get(job_id)
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