Akiba So
fc468464b2
Context: Build a spatial risk assessment system correlating air quality data with children's respiratory disease incidence across Wuhan. Approach: FastAPI backend serving PostGIS spatial queries, React frontend with Deck.gl maps, and a PyTorch SpatialTemporalGCN pipeline for multi-day (1d/3d/7d) risk prediction. Changes: - backend/ — FastAPI API with auth (JWT), alerts, risk analysis, geocoded case data, grid statistics, and report endpoints - frontend/ — React dashboard with interactive risk maps, alert monitoring, district comparison charts, and timeline player - models/ — SpatialTemporalGCN model with trained weights and ONNX export for inference - scripts/ — ETL pipeline for weather + medical data, grid generation, feature engineering, training, and daily inference - deploy/ — Docker Compose configs for backend, frontend, and MLflow - docs/ — API docs, deployment guide, user guide, and code review Impact: Enables spatial risk visualization, alert monitoring, and ML-driven health risk forecasting for environmental health teams.
33 lines
826 B
Docker
33 lines
826 B
Docker
FROM python:3.11-slim
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# Install system dependencies
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RUN apt-get update && apt-get install -y --no-install-recommends \
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libpq-dev \
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&& rm -rf /var/lib/apt/lists/*
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# Create non-root user
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RUN groupadd --gid 1000 appgroup && \
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useradd --uid 1000 --gid appgroup --shell /bin/bash --create-home appuser
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WORKDIR /home/appuser
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# Copy requirements and install dependencies
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COPY --chown=appuser:appgroup requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy backend code
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COPY --chown=appuser:appgroup . .
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# Switch to non-root user
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USER appuser
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# Expose port
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EXPOSE 8000
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# Health check
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HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
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CMD curl -f http://localhost:8000/docs || exit 1
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# Run uvicorn
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
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