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.
27 lines
495 B
TypeScript
27 lines
495 B
TypeScript
import { defineConfig } from 'vite'
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import react from '@vitejs/plugin-react'
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import path from 'path'
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export default defineConfig({
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plugins: [react()],
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resolve: {
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alias: {
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'@': path.resolve(__dirname, './src'),
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},
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},
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server: {
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port: 3000,
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allowedHosts: ['alpha.hyh.ink'],
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proxy: {
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'/api': {
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target: 'http://localhost:8000',
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changeOrigin: true,
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},
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},
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},
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preview: {
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port: 3000,
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allowedHosts: ['alpha.hyh.ink'],
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},
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})
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