TalentFlow AI

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\n\n \n \n \n \n\n \n `;\n}\n\nexport default async function(req: Request) {\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 action = url.searchParams.get(\"action\");\n const sourceUrl = `https://${url.hostname}${url.pathname}`;\n const openai = new OpenAI();\n const MAX_TEXT_ANALYZE = 30000;\n const jsonResponse = (body: object, status: number) =>\n new Response(JSON.stringify(body), { status, headers: { ...CORS_HEADERS, \"Content-Type\": \"application/json\" } });\n\n async function extractPdfText(data: ArrayBuffer): Promise {\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 return text?.trim() ? text : null;\n } catch (error) {\n console.error(\"PDF Extraction Error:\", error);\n return null;\n }\n }\n\n async function callAI(prompt: string, userMessage: string, isJson: boolean = true) {\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"system\", content: prompt }, { role: \"user\", content: userMessage }],\n temperature: 0.1,\n response_format: isJson ? { type: \"json_object\" } : undefined,\n });\n const content = completion.choices[0].message.content;\n if (!content) throw new Error(\"AI returned empty content.\");\n return isJson ? JSON.parse(content) : content;\n }\n\n if (req.method === \"POST\") {\n try {\n switch (action) {\n case \"analyzeCandidate\": {\n const formData = await req.formData();\n const resumeFile = formData.get(\"resumeFile\") as File;\n const jobDescription = formData.get(\"jobDescription\") as string;\n if (!resumeFile || !jobDescription) return jsonResponse({ error: \"Missing required form fields.\" }, 400);\n\n const fileBuffer = await resumeFile.arrayBuffer();\n const resumeText = await extractPdfText(fileBuffer);\n if (!resumeText) return jsonResponse({ error: \"Failed to extract text from resume.\" }, 500);\n\n const textToAnalyze = resumeText.length > MAX_TEXT_ANALYZE\n ? resumeText.substring(0, MAX_TEXT_ANALYZE)\n : resumeText;\n let prompt = RECRUITER_AI_ANALYSIS_SYSTEM_PROMPT_TEMPLATE.replace(\"%%JOB_DESCRIPTION%%\", jobDescription);\n const analysisResult = await callAI(prompt, textToAnalyze);\n\n return jsonResponse({ resumeText, analysis: analysisResult }, 200);\n }\n\n case \"analyzeJobDescription\": {\n const { jobDescription } = await req.json();\n const result = await callAI(JOB_DESCRIPTION_ANALYSIS_PROMPT, `Job Description: ${jobDescription}`);\n return jsonResponse(result, 200);\n }\n\n case \"prioritizeCandidates\": {\n const { candidates } = await req.json();\n const result = await callAI(CANDIDATE_PRIORITIZATION_PROMPT, JSON.stringify(candidates));\n return jsonResponse(result, 200);\n }\n\n case \"generateInterviewKit\": {\n const { candidateName, resumeText, jobTitle, jobDescription } = await req.json();\n let prompt = INTERVIEW_KIT_PROMPT.replace(\"{{jobTitle}}\", jobTitle)\n .replace(\"{{jobDescription}}\", jobDescription)\n .replace(\"{{candidateName}}\", candidateName)\n .replace(\"{{resumeText}}\", resumeText);\n const result = await callAI(prompt, \"Generate the kit based on the system prompt context.\", true);\n return jsonResponse(result, 200);\n }\n\n case \"generateCommunication\": {\n const { type, candidateName, jobTitle, notes } = await req.json();\n let prompt = COMMUNICATION_DRAFT_PROMPT.replace(\"{{type}}\", type)\n .replace(\"{{candidateName}}\", candidateName)\n .replace(\"{{jobTitle}}\", jobTitle)\n .replace(\"{{notes}}\", notes || \"\");\n const emailBody = await callAI(prompt, \"Generate the communication text.\", false);\n return jsonResponse({ emailBody }, 200);\n }\n\n case \"chat\": {\n const { messages, candidates, jobOpenings } = await req.json();\n const systemMessage = { role: \"system\", content: CHAT_PROMPT };\n let conversation = [...messages];\n if (candidates && jobOpenings) {\n const contextMessage = {\n role: \"user\",\n content: `Context:\\nJobs:\\n${JSON.stringify(jobOpenings)}\\nCandidates:\\n${\n JSON.stringify(candidates)\n }\\n\\n(Now, respond to my last message.)`,\n };\n conversation.unshift(contextMessage);\n }\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [systemMessage, ...conversation],\n });\n return jsonResponse({ response: completion.choices[0].message }, 200);\n }\n\n default:\n return jsonResponse({ error: \"Invalid action specified.\" }, 400);\n }\n } catch (e) {\n return jsonResponse({ error: \"Server error during POST action.\", details: e.message }, 500);\n }\n }\n\n const html = generateHtmlShell(sourceUrl);\n return new Response(html, { status: 200, headers: { \"Content-Type\": \"text/html\" } 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