import { NextResponse } from "next/server";
import { PrismaClient } from "@prisma/client";
import fs from "fs/promises";
import path from "path";
import { parse } from "csv-parse/sync";

// Define the path to your CSV file.
// Place your earthquakes.csv in your project root or `public` directory
const csvFilePath = path.resolve(process.cwd(), "public/earthquakes.csv");

const prisma = new PrismaClient();

type CsvRow = {
  Date: string;
  Magnitude: string;
  Latitude: string;
  Longitude: string;
  Location: string;
  Depth: string;
};

export async function POST() {
  try {
    // 1. Read the CSV file
    const fileContent = await fs.readFile(csvFilePath, "utf8");

    // 2. Parse the CSV
    const records: CsvRow[] = parse(fileContent, {
      columns: true,
      skip_empty_lines: true
    });

    // 3. Transform each CSV row to Earthquake model
    // Since your prisma model expects: name, date (DateTime), location, magnitude (float), depth (float). We'll fill casualties/creatorId as zero/null for now.
    const earthquakes = records.map(row => {
      // You may want to add better parsing & validation depending on your actual data
      return {
        date: new Date(row.Date),
        location: row.Location,
        magnitude: parseFloat(row.Magnitude),
        latitude: row.Latitude,
        longitude: row.Longitude,
        depth: parseFloat(row.Depth.replace(" km", "")),
        // todo add creatorId
        creatorId: null
      };
    });

    // 4. Bulk create earthquakes in database:
    // Consider chunking if your CSV is large!
    await prisma.earthquake.createMany({
      data: earthquakes,
      skipDuplicates: true, // in case the route is called twice
    });

    return NextResponse.json({ success: true, count: earthquakes.length });
  } catch (error: any) {
    console.error(error);
    return NextResponse.json({ success: false, error: error.message }, { status: 500 });
  } finally {
    await prisma.$disconnect();
  }
}