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.
40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
"""
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FastAPI middleware that logs method, path, status code, and duration for every request.
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"""
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import time
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import uuid
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from starlette.middleware.base import BaseHTTPMiddleware
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from starlette.requests import Request
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from starlette.responses import Response
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import logging
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logger = logging.getLogger("cbpoa.request")
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class RequestLoggerMiddleware(BaseHTTPMiddleware):
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async def dispatch(self, request: Request, call_next) -> Response:
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request_id = str(uuid.uuid4())
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request.state.request_id = request_id
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start = time.perf_counter()
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response = await call_next(request)
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duration_ms = round((time.perf_counter() - start) * 1000, 2)
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logger.info(
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"%s %s -> %s (%.2fms)",
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request.method,
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request.url.path,
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response.status_code,
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duration_ms,
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extra={
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"request_id": request_id,
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"method": request.method,
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"path": request.url.path,
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"status_code": response.status_code,
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"duration_ms": duration_ms,
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},
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)
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return response
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