63 lines
1.9 KiB
TypeScript
63 lines
1.9 KiB
TypeScript
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import { NextResponse } from "next/server";
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import { PrismaClient } from "@prisma/client";
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import fs from "fs/promises";
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import path from "path";
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import { parse } from "csv-parse/sync";
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// Define the path to your CSV file.
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// Place your earthquakes.csv in your project root or `public` directory
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const csvFilePath = path.resolve(process.cwd(), "public/earthquakes.csv");
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const prisma = new PrismaClient();
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type CsvRow = {
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Date: string;
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Magnitude: string;
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Latitude: string;
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Longitude: string;
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Location: string;
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Depth: string;
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};
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export async function POST() {
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try {
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// 1. Read the CSV file
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const fileContent = await fs.readFile(csvFilePath, "utf8");
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// 2. Parse the CSV
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const records: CsvRow[] = parse(fileContent, {
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columns: true,
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skip_empty_lines: true
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});
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// 3. Transform each CSV row to Earthquake model
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// Since your prisma model expects: name, date (DateTime), location, magnitude (float), depth (float). We'll fill casualties/creatorId as zero/null for now.
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const earthquakes = records.map(row => {
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// You may want to add better parsing & validation depending on your actual data
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return {
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date: new Date(row.Date),
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location: row.Location,
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magnitude: parseFloat(row.Magnitude),
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latitude: row.Latitude,
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longitude: row.Longitude,
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depth: parseFloat(row.Depth.replace(" km", "")),
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// todo add creatorId
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creatorId: null
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};
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});
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// 4. Bulk create earthquakes in database:
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// Consider chunking if your CSV is large!
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await prisma.earthquake.createMany({
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data: earthquakes,
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skipDuplicates: true, // in case the route is called twice
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});
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return NextResponse.json({ success: true, count: earthquakes.length });
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} catch (error: any) {
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console.error(error);
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return NextResponse.json({ success: false, error: error.message }, { status: 500 });
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} finally {
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await prisma.$disconnect();
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}
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}
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