- Create cloud_run/saliency: FastAPI service running DeepGaze I/IIE/III on Cloud Run (4 vCPU, 16GB RAM); pre-downloads model weights in Docker build to eliminate cold-start delays; returns saliency map + gaze sequence + hotspots + design scores - Create cloud_run/processing: lightweight FastAPI service for heatmap generation and gaze sequence visualization (2 vCPU, 4GB RAM) - Add cloud_run/deploy.sh for gcloud deployment to project optical-414516 in region europe-west2 - Refactor analysis pipeline to route via Cloud Run when CLOUD_RUN_SALIENCY_URL is set, with local fallback for dev mode - Add cloud_run_client.py with sync httpx wrappers for background tasks - Split pyproject.toml: base = API-only deps, [ml] = torch/deepgaze for local dev; production Dockerfile is now lightweight (~no PyTorch) - Preserve Dockerfile.full + docker-compose.dev.yml for local ML dev - Auth via X-Internal-Secret header (CLOUD_RUN_SECRET env var) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
23 lines
738 B
YAML
23 lines
738 B
YAML
services:
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backend:
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build:
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context: ./backend
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dockerfile: Dockerfile.full # Full image with PyTorch + DeepGaze for local dev
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command: uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
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environment:
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ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY:-}
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CLOUD_RUN_SALIENCY_URL: ${CLOUD_RUN_SALIENCY_URL:-}
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CLOUD_RUN_PROCESSING_URL: ${CLOUD_RUN_PROCESSING_URL:-}
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CLOUD_RUN_SECRET: ${CLOUD_RUN_SECRET:-}
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GOOGLE_CLOUD_PROJECT: ${GOOGLE_CLOUD_PROJECT:-optical-414516}
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volumes:
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- ./backend:/app
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- uploads:/app/data/uploads
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frontend:
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build:
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context: ./frontend
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command: npm run dev -- --host 0.0.0.0
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volumes:
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- ./frontend:/app
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- /app/node_modules
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