TalentFlow AI - Recruiter Copilot

[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_152":153,"_154":155},"routes/_app.x.$handle.$name",{"_11":12,"_13":14,"_15":16,"_18":19,"_17":-7,"_115":-5,"_20":116,"_22":117,"_118":119,"_120":121,"_25":122,"_24":-5,"_123":-5,"_124":-5,"_125":53,"_126":127,"_128":44,"_129":-5,"_130":-5,"_131":132},"routes/_app.x.$handle.$name.branch.$branch.code.$",{"_11":12,"_13":14,"_15":16,"_17":-7,"_18":19,"_20":21,"_22":23,"_24":-7,"_25":26,"_27":-5,"_28":-5,"_29":-5,"_30":-5,"_31":-5},"handle","legal","name","talentflow","branchName","main","version","path","","project",{"_33":61,"_13":14,"_45":62,"_63":-5,"_64":53,"_65":66,"_67":66,"_68":-5,"_69":-5,"_70":71,"_72":37,"_73":44,"_74":44,"_75":-5,"_76":77,"_78":79,"_80":-5,"_81":-5,"_82":-5,"_83":-5,"_84":85,"_86":85,"_87":88,"_89":88,"_90":88,"_91":85,"_92":85,"_93":94,"_95":96,"_97":98,"_99":53},"tree",[56],"file","contents",[32],"upstream","pull","ahead","behind","envVars",{"_33":34,"_13":35,"_36":37,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_17":47,"_48":49,"_50":51,"_52":53,"_54":-5,"_18":55},"id","70fe2e7c-47ac-11f0-aa33-76b3cceeab13","main.tsx","fileId","bab8659e-47a9-11f0-afa4-76b3cceeab13","parentId","branchId","bab63918-47a9-11f0-afa4-76b3cceeab13","branchVersion",7,"latest",true,"createdAt",["D",1749746640401],6,"fileType","http","content","// --- PROMPT ENGINEERING: Redesigned for Recruitment ---\n\nconst recruiterAIAnalysisSystemPromptTemplate = `\nYou are an expert AI Talent Acquisition Analyst. Your goal is to analyze a candidate's resume against a provided job description and return a detailed, structured analysis as a single JSON object.\n\nJOB DESCRIPTION:\n================\n%%JOB_DESCRIPTION%%\n================\n\nCANDIDATE'S RESUME TEXT is provided in the user message.\n\nAnalyze the resume and produce a JSON object that strictly follows the schema below. Base all scores and summaries on how well the candidate's resume aligns with the specific job description provided.\n\nOutput JSON Schema:\n{\n \"candidate_summary\": {\n \"name\": \"string\", // Candidate's full name\n \"title\": \"string\", // The job title they are applying for or their most recent title\n \"summary\": \"string\" // A 2-3 sentence executive summary of the candidate's profile and their suitability for the role.\n },\n \"overall_match_score\": \"integer\", // A score from 0 to 100 representing the overall fit for the job.\n \"skill_assessment\": {\n \"match_score\": \"integer\", // A score from 0-100 for skill alignment.\n \"skills\": [\n { \"skill\": \"string\", \"proficiency\": \"string\" /* e.g., 'Expert', 'Proficient', 'Familiar' */, \"is_in_job_desc\": \"boolean\" }\n ],\n \"summary\": \"string\" // Brief analysis of skill strengths and weaknesses.\n },\n \"experience_evaluation\": {\n \"relevance_score\": \"integer\", // A score from 0-100 for experience relevance.\n \"experience\": [\n { \"job_title\": \"string\", \"company\": \"string\", \"duration\": \"string\", \"relevance\": \"string\" /* e.g., 'Highly Relevant', 'Relevant', 'Somewhat Relevant' */ }\n ],\n \"summary\": \"string\" // Brief analysis of career progression and experience relevance.\n },\n \"education_match\": {\n \"summary\": \"string\" // Summary of educational background and its relevance (e.g., degree, certifications).\n },\n \"cultural_fit_indicators\": {\n \"keywords\": [\"string\"], // Keywords from the resume suggesting cultural attributes (e.g., 'team player', 'fast-paced', 'leadership').\n \"summary\": \"string\" // A brief analysis of potential cultural fit based on language and phrasing.\n },\n \"execution_metadata\": {\n \"status\": \"string\", // e.g., \"Completed\"\n \"timestamp\": \"string\", // ISO 8601\n \"disclaimer\": \"AI-generated analysis. Please verify details and conduct a thorough interview.\"\n }\n}\n`;\n\n// --- TYPE DEFINITIONS (For Backend) ---\ninterface LogEntry {\n agent: string;\n message: string | object;\n type: \"step\" | \"result\" | \"error\" | \"input\" | \"final\" | \"info\";\n}\n\n// --- FRONTEND GENERATION ---\nfunction generateHtmlShell(initialQuery, sourceUrl) {\n const escapedQuery = initialQuery.replace(//g, \">\");\n return `\n\n\n\n\n\nTalentFlow AI - Recruiter Copilot\n\n\n\n

\n\n

\n${\n [\"dashboard\", \"analyze-candidate\", \"pipeline\", \"analytics\", \"strategic-growth\", \"settings\", \"candidate-detail\"].map(\n v => `

`,\n ).join(\"\")\n }\n

\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

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