2c5e17c7c4
- 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
665 B
Text
23 lines
665 B
Text
FROM python:3.12-slim
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WORKDIR /app
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RUN apt-get update && apt-get install -y --no-install-recommends \
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libgl1 libglib2.0-0 curl git && \
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rm -rf /var/lib/apt/lists/*
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# Copy dependency spec first for layer caching
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COPY pyproject.toml .
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# Install all dependencies including ML stack
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RUN pip install --no-cache-dir ".[ml]" && \
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pip install --no-cache-dir "clip @ git+https://github.com/openai/CLIP.git" && \
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pip install --no-cache-dir "deepgaze-pytorch @ git+https://github.com/matthias-k/DeepGaze.git"
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# Copy application source
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COPY . .
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RUN mkdir -p data/uploads
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EXPOSE 8000
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CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
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