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.
22 lines
423 B
Python
22 lines
423 B
Python
"""Pydantic models for authentication."""
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from pydantic import BaseModel, Field
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class UserCreate(BaseModel):
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username: str = Field(..., min_length=3, max_length=50)
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password: str = Field(..., min_length=6, max_length=128)
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class UserLogin(BaseModel):
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username: str
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password: str
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class Token(BaseModel):
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access_token: str
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token_type: str = "bearer"
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class UserOut(BaseModel):
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username: str
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