TalentFlow AI - Recruiter Copilot

Skill Area: AI & Machine Learning
Projected Need: +40% increase in roles requiring Python & TensorFlow
Timeline: Next 36 months

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\n\n\n`;\n}\n\n// --- SERVER-SIDE LOGIC ---\nexport default async function(req: Request) {\n const { OpenAI } = await import(\"https://esm.town/v/std/openai\");\n const { PDFExtract } = await import(\"npm:pdf.js-extract\");\n\n const CORS_HEADERS = {\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"POST, GET, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type, Accept\",\n };\n if (req.method === \"OPTIONS\") return new Response(null, { status: 204, headers: CORS_HEADERS });\n\n const url = new URL(req.url);\n const format = url.searchParams.get(\"format\");\n const action = url.searchParams.get(\"action\");\n const sourceUrl = import.meta.url.replace(\"esm.town\", \"val.town\");\n const openai = new OpenAI();\n const MAX_TEXT_ANALYZE = 30000;\n\n async function extractPdfText(data: ArrayBuffer, fileName: string, log: LogEntry[]): Promise {\n log.push({ agent: \"PDF\", type: \"step\", message: `Processing: ${fileName}` });\n try {\n const pdfExtract = new PDFExtract();\n const pdfData = await pdfExtract.extractBuffer(data, {});\n const text = pdfData.pages.map(page => page.content.map(item => item.str).join(\" \")).join(\"\\n\\n\");\n if (!text?.trim()) {\n log.push({ agent: \"PDF\", type: \"error\", message: \"Extraction yielded empty text.\" });\n return null;\n }\n log.push({ agent: \"PDF\", type: \"info\", message: `Extracted ~${text.length} chars.` });\n return text;\n } catch (error) {\n log.push({ agent: \"PDF\", type: \"error\", message: `Extraction error: ${error.message}` });\n console.error(\"PDF Extraction Error:\", error);\n return null;\n }\n }\n\n async function callAI(\n systemPrompt: string,\n userMessage: string,\n log: LogEntry[],\n agentName: string,\n ): Promise