102 lines
3.2 KiB
TypeScript
102 lines
3.2 KiB
TypeScript
import { NextRequest, NextResponse } from "next/server";
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import { createServerSupabaseClient } from "@/lib/supabase";
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import { generateEmbedding } from "@/lib/embeddings";
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import { PDFParse } from "pdf-parse";
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import { extractCandidateProfile } from "@/lib/gemini";
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export async function POST(request: NextRequest) {
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try {
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const formData = await request.formData();
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const file = formData.get("file") as File | null;
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const jobId = formData.get("jobId") as string | null;
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if (!file) {
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return NextResponse.json({ error: "No file uploaded" }, { status: 400 });
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}
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const arrayBuffer = await file.arrayBuffer();
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const buffer = Buffer.from(arrayBuffer);
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// Extract text from PDF using PDFParse v2 API
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const parser = new PDFParse({ data: buffer });
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const pdfData = await parser.getText();
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const text = pdfData.text;
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await parser.destroy();
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if (!text) {
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return NextResponse.json({ error: "Failed to extract text from PDF" }, { status: 400 });
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}
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// Clean text
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const cleanText = text.replace(/\s+/g, " ").trim();
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// Extract professional profile using Gemini 1.5 Flash
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const profile = await extractCandidateProfile(cleanText);
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// Generate candidate embedding
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const embedding = await generateEmbedding(cleanText);
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const isTest = formData.get("isTest") === "true";
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let candidateId = "00000000-0000-0000-0000-000000000000";
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let candidateName = profile.candidateName;
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if (!isTest) {
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// Initialize Supabase admin client
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const supabase = createServerSupabaseClient();
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// Insert candidate with extracted details (including skills, summary, and cv_text)
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const { data: candidate, error: candidateError } = await supabase
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.from("candidates")
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.insert({
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name: profile.candidateName,
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contact_info: {
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email: profile.email,
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phone: profile.phone,
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skills: profile.skills || [],
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summary: profile.summary || "",
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cv_text: cleanText,
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},
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embedding,
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})
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.select("*")
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.single();
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if (candidateError || !candidate) {
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return NextResponse.json(
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{ error: candidateError?.message || "Failed to insert candidate" },
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{ status: 500 }
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);
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}
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candidateId = candidate.id;
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candidateName = candidate.name;
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// Create an initial interview record if jobId is provided (but do not trigger n8n evaluate webhook yet)
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if (jobId) {
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const { error: interviewError } = await supabase
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.from("interviews")
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.insert({
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candidate_id: candidate.id,
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job_id: jobId,
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interview_date: new Date().toISOString(),
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stage: "Screening",
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});
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if (interviewError) {
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console.error("Failed to insert interview:", interviewError.message);
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}
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}
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}
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return NextResponse.json({
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success: true,
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candidateId,
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candidateName,
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profile,
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});
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} catch (error: unknown) {
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console.error("Error in parse-cv route:", error);
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const errorMessage = error instanceof Error ? error.message : "Internal server error";
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return NextResponse.json({ error: errorMessage }, { status: 500 });
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}
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}
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