[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_217":218,"_219":220},"routes/_app.x.$handle.$name",{"_95":94,"_31":44,"_103":85,"_26":104,"_13":14,"_105":-5,"_11":106,"_107":108,"_109":110,"_111":112,"_113":114,"_20":-5,"_115":-5,"_116":-5,"_117":24,"_118":119,"_120":24,"_121":-5,"_122":-5,"_123":124},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_42":43,"_31":44,"_37":45,"_46":-5,"_47":24,"_48":49,"_50":49,"_51":-5,"_52":-5,"_18":53,"_54":55,"_56":57,"_58":57,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":24},"version",6,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":-7,"_23":24,"_25":24,"_26":27,"_28":27},"file",{"_29":30,"_31":32,"_33":34,"_35":36,"_37":38,"_39":14,"_40":41,"_23":24},"previous","deleted",false,"deletedHere","path","backend/scorer.ts","latestPath","fileId","45887ee2-316c-11f0-bc4b-569c3dd06744","name","scorer.ts","fileType","script","content","import { OpenAI } from \"https://esm.town/v/std/openai\";\nimport type { Resume, JobRequirement, ScoringResult, ParsedResumeData } from \"../shared/types\";\nimport { calculateSimilarity } from \"../shared/utils\";\n\nconst openai = new OpenAI();\n\n/**\n * Scores a resume against job requirements\n */\nexport function scoreResume(resume: Resume, jobRequirement: JobRequirement): ScoringResult {\n if (!resume.parsedData) {\n throw new Error(\"Resume must be parsed before scoring\");\n }\n\n const { parsedData } = resume;\n const { requiredSkills, preferredSkills, minimumExperience } = jobRequirement;\n \n // Calculate skill match score\n const skillMatches = calculateSkillMatches(parsedData.skills, requiredSkills, preferredSkills);\n \n // Calculate experience relevance\n const experienceRelevance = calculateExperienceRelevance(\n parsedData.experience, \n jobRequirement.title, \n jobRequirement.description\n );\n \n // Calculate education relevance\n const educationRelevance = calculateEducationRelevance(parsedData.education);\n \n // Calculate overall score (weighted average)\n const skillWeight = 0.5;\n const experienceWeight = 0.35;\n const educationWeight = 0.15;\n \n const overallScore = (\n skillMatches.score * skillWeight +\n experienceRelevance * experienceWeight +\n educationRelevance * educationWeight\n ) * 100; // Convert to percentage\n \n return {\n resumeId: resume.id!,\n candidateName: resume.candidateName,\n score: Math.round(overallScore * 10) / 10, // Round to 1 decimal place\n skillMatches: {\n matched: skillMatches.matched,\n missing: skillMatches.missing\n },\n experienceRelevance,\n educationRelevance,\n highlights: parsedData.highlights || []\n };\n}\n\n/**\n * Calculates skill matches between resume skills and job requirements\n */\nfunction calculateSkillMatches(\n candidateSkills: string[],\n requiredSkills: string[],\n preferredSkills: string[]\n) {\n const normalizedCandidateSkills = candidateSkills.map(s => s.toLowerCase());\n const normalizedRequiredSkills = requiredSkills.map(s => s.toLowerCase());\n const normalizedPreferredSkills = preferredSkills.map(s => s.toLowerCase());\n \n // Find matching skills\n const matchedRequired = normalizedRequiredSkills.filter(skill => \n normalizedCandidateSkills.some(candidateSkill => \n candidateSkill.includes(skill) || skill.includes(candidateSkill)\n )\n );\n \n const matchedPreferred = normalizedPreferredSkills.filter(skill => \n normalizedCandidateSkills.some(candidateSkill => \n candidateSkill.includes(skill) || skill.includes(candidateSkill)\n )\n );\n \n // Calculate score\n const requiredWeight = 0.7;\n const preferredWeight = 0.3;\n \n const requiredScore = requiredSkills.length > 0 \n ? (matchedRequired.length / requiredSkills.length) * requiredWeight \n : 0;\n \n const preferredScore = preferredSkills.length > 0 \n ? (matchedPreferred.length / preferredSkills.length) * preferredWeight \n : 0;\n \n const totalScore = requiredScore + preferredScore;\n \n // Find missing required skills\n const missingRequired = normalizedRequiredSkills.filter(skill => \n !normalizedCandidateSkills.some(candidateSkill => \n candidateSkill.includes(skill) || skill.includes(candidateSkill)\n )\n );\n \n return {\n matched: [...new Set([...matchedRequired, ...matchedPreferred])],\n missing: missingRequired,\n score: totalScore\n };\n}\n\n/**\n * Calculates experience relevance based on job title and description\n */\nfunction calculateExperienceRelevance(\n experiences: { company: string; title: string; description: string }[],\n jobTitle: string,\n jobDescription: string\n): number {\n if (experiences.length === 0) {\n return 0;\n }\n \n // Calculate relevance for each experience\n const relevanceScores = experiences.map(exp => {\n const titleSimilarity = calculateSimilarity(exp.title, jobTitle);\n const descriptionSimilarity = calculateSimilarity(exp.description, jobDescription);\n \n // Weight title similarity more heavily\n return titleSimilarity * 0.6 + descriptionSimilarity * 0.4;\n });\n \n // Use the highest relevance score (most relevant experience)\n return Math.max(...relevanceScores);\n}\n\n/**\n * Calculates education relevance (simplified)\n */\nfunction calculateEducationRelevance(\n education: { institution: string; degree: string; field?: string }[]\n): number {\n if (education.length === 0) {\n return 0;\n }\n \n // Simple scoring based on degree level\n const degreeScores = education.map(edu => {\n const degree = edu.degree.toLowerCase();\n \n if (degree.includes(\"phd\") || degree.includes(\"doctorate\")) {\n return 1.0;\n } else if (degree.includes(\"master\") || degree.includes(\"mba\")) {\n return 0.8;\n } else if (degree.includes(\"bachelor\") || degree.includes(\"bs\") || degree.includes(\"ba\")) {\n return 0.6;\n } else if (degree.includes(\"associate\") || degree.includes(\"certificate\")) {\n return 0.4;\n } else {\n return 0.2;\n }\n });\n \n // Use the highest education score\n return Math.max(...degreeScores);\n}\n\n/**\n * Uses AI to generate personalized feedback for a candidate\n */\nexport async function generateCandidateFeedback(\n resume: Resume,\n jobRequirement: JobRequirement,\n scoringResult: ScoringResult\n): Promise {\n try {\n const prompt = `\n You are an AI-powered resume screening assistant. Generate constructive feedback for a candidate\n based on their resume and the job requirements. Be professional, helpful, and specific.\n \n Job Title: ${jobRequirement.title}\n Job Description: ${jobRequirement.description}\n \n Required Skills: ${jobRequirement.requiredSkills.join(\", \")}\n Preferred Skills: ${jobRequirement.preferredSkills.join(\", \")}\n \n Candidate's Skills: ${resume.parsedData?.skills.join(\", \") || \"None listed\"}\n \n Matching Skills: ${scoringResult.skillMatches.matched.join(\", \") || \"None\"}\n Missing Required Skills: ${scoringResult.skillMatches.missing.join(\", \") || \"None\"}\n \n Overall Score: ${scoringResult.score}/100\n \n Provide specific feedback on:\n 1. Strengths of the candidate's profile\n 2. Areas for improvement\n 3. Suggestions for how they could better position themselves for this role\n \n Keep the feedback concise (maximum 250 words) and constructive.\n `;\n\n const completion = await openai.chat.completions.create({\n messages: [{ role: \"user\", content: prompt }],\n model: \"gpt-4o-mini\",\n max_tokens: 500,\n temperature: 0.7,\n });\n\n return completion.choices[0]?.message?.content || \n \"Unable to generate feedback at this time.\";\n } catch (error) {\n console.error(\"Error generating feedback:\", error);\n return \"Unable to generate feedback at this time.\";\n }\n}","createdAt",["D",1747300154122],"branchVersion","branchId","feeba1da-316b-11f0-bbde-569c3dd06744","id","feeb4794-316b-11f0-bbde-569c3dd06744","Akash",["D",1747300035655],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","imageUrl","7cf973e0-f526-11ef-a409-85fb41e7ab54","pinnedFileId","5b6019e6-316c-11f0-9b15-569c3dd06744","pinPreview",true,"pinMigrated","screenshotVersion","likeCount",0,"orgId","d88a0c8d-5a02-479a-8fe1-02a12d784bd7","tursoName","tursoHostname","tursoMeasuredAt","pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_95":94,"_102":98},"org",{"_95":94,"_96":-5,"_97":98,"_18":53,"_99":100,"_101":24},"branch",{"_13":84,"_31":85,"_42":41,"_86":43,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":44,"_93":94},"isOwned",17,"main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","Akashashn","handle","displayName","avatar","https://img.clerk.com/eyJ0eXBlIjoiZGVmYXVsdCIsImlpZCI6Imluc18yTm10cGhQUklwU0tqZHNiOHNnNGd2ZjJURFUiLCJyaWQiOiJ1c2VyXzJ0ZERXOGZzV0tuS0RBWklTTHg4eFBSNG4ydCJ9","tier","free","canRestrictVals","profileImageUrl","branchName","","adoptOrgId",{"_42":43,"_31":44,"_37":213,"_46":-5,"_47":24,"_48":49,"_50":49,"_51":-5,"_52":-5,"_18":53,"_54":55,"_56":57,"_58":57,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":214,"_79":215,"_81":216,"_83":24},"tree",[161,165,168],"files",[161,162,163,164,165,166,167,168],"counts",{"_151":61,"_152":132,"_153":61,"_154":155,"_156":157},"contents",[125,126,127],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},{"_42":145,"_31":146,"_29":147,"_131":-5,"_40":41,"_39":148,"_133":57,"_37":149,"_13":61,"_33":143,"_35":-5,"_23":24,"_136":-5,"_26":150},{"_42":138,"_31":139,"_29":140,"_131":-5,"_40":41,"_39":141,"_133":57,"_37":142,"_13":61,"_33":143,"_35":-5,"_23":24,"_136":-5,"_26":144},{"_42":128,"_31":129,"_29":130,"_131":-5,"_40":41,"_39":132,"_133":57,"_37":134,"_13":61,"_33":20,"_35":135,"_23":24,"_136":-5,"_26":137},"1562d028-316c-11f0-a9ad-569c3dd06744","README.md","1562d0d2-316c-11f0-a9ad-569c3dd06744","parentId",1,"latest",["D",1747300073314],"# AI-Based Resume Screening System\n\nThis project implements an AI-based resume screening system using NLP and machine learning techniques. The system analyzes resume text, extracts key information, and ranks candidates based on job requirements.\n\n## Features\n\n- Resume text analysis using OpenAI's GPT models\n- Keyword extraction and skills matching\n- Candidate scoring and ranking\n- Simple web interface for uploading resumes and viewing results\n\n## Project Structure\n\n```\n├── backend/\n│ ├── index.ts # Main API entry point with Hono\n│ ├── parser.ts # Resume parsing logic\n│ ├── scorer.ts # Candidate scoring algorithms\n│ └── database.ts # SQLite database operations\n├── frontend/\n│ ├── index.html # Main HTML template\n│ ├── app.tsx # Frontend React application\n│ └── components/ # UI components\n│ ├── ResumeUpload.tsx # Resume upload component\n│ ├── JobForm.tsx # Job requirements input form\n│ └── Results.tsx # Results display component\n└── shared/\n ├── types.ts # Shared type definitions\n └── utils.ts # Utility functions\n```\n\n## How It Works\n\n1. Users upload resume text or paste it directly\n2. Users define job requirements and skills\n3. The system processes the resume using NLP techniques\n4. Candidates are scored based on matching with job requirements\n5. Results are displayed with highlighted matching skills and experience\n\n## Technologies Used\n\n- OpenAI API for natural language processing\n- SQLite for data storage\n- Hono for backend API\n- React for frontend UI\n- TailwindCSS for styling","label",[129],"199ae69e-316c-11f0-8adb-569c3dd06744","shared","f568ab83-0c46-4080-9f0d-87776cf5cd2b",2,["D",1747300080419],"directory",[139],"2ab1a6f2-316c-11f0-9caa-569c3dd06744","backend","9934e3a7-9096-4288-9aec-48ac6f40309b",4,["D",1747300109087],[146],"pulls","branches","forks","envVars",{"_158":61,"_160":61},"connections",{"_158":61,"_159":61},"count","unfilledCount","emptyCount",{"_42":145,"_31":146,"_29":147,"_131":-5,"_40":41,"_39":148,"_133":57,"_37":210,"_13":61,"_33":143,"_35":-5,"_23":24,"_136":-5,"_170":211,"_26":212},{"_42":203,"_31":204,"_29":205,"_131":147,"_40":41,"_39":148,"_133":57,"_37":206,"_13":61,"_33":34,"_35":207,"_23":24,"_136":-5,"_170":208,"_26":209},{"_42":195,"_31":196,"_29":197,"_131":147,"_40":41,"_39":198,"_133":57,"_37":199,"_13":61,"_33":34,"_35":200,"_23":24,"_136":-5,"_170":201,"_26":202},{"_42":191,"_31":32,"_29":30,"_131":147,"_40":41,"_39":14,"_133":57,"_37":192,"_13":61,"_33":34,"_35":36,"_23":24,"_136":-5,"_170":193,"_26":194},{"_42":138,"_31":139,"_29":140,"_131":-5,"_40":41,"_39":141,"_133":57,"_37":188,"_13":61,"_33":143,"_35":-5,"_23":24,"_136":-5,"_170":189,"_26":190},{"_42":181,"_31":182,"_29":183,"_131":140,"_40":41,"_39":141,"_133":57,"_37":184,"_13":61,"_33":34,"_35":185,"_23":24,"_136":-5,"_170":186,"_26":187},{"_42":173,"_31":174,"_29":175,"_131":140,"_40":41,"_39":176,"_133":57,"_37":177,"_13":61,"_33":34,"_35":178,"_23":24,"_136":-5,"_170":179,"_26":180},{"_42":128,"_31":129,"_29":130,"_131":-5,"_40":41,"_39":132,"_133":57,"_37":169,"_13":61,"_33":20,"_35":135,"_23":24,"_136":-5,"_170":171,"_26":172},["D",1747300073314],"children",[],[129],"1f3a5eb8-316c-11f0-b149-569c3dd06744","utils.ts","1f3a5fa8-316c-11f0-b149-569c3dd06744",3,["D",1747300089857],"// Shared utility functions for both frontend and backend\n\n/**\n * Calculates the similarity score between two strings\n * Simple implementation for demonstration purposes\n */\nexport function calculateSimilarity(str1: string, str2: string): number {\n const s1 = str1.toLowerCase();\n const s2 = str2.toLowerCase();\n \n // Count matching words\n const words1 = new Set(s1.split(/\\s+/).filter(w => w.length > 2));\n const words2 = new Set(s2.split(/\\s+/).filter(w => w.length > 2));\n \n let matchCount = 0;\n for (const word of words1) {\n if (words2.has(word)) {\n matchCount++;\n }\n }\n \n const totalUniqueWords = new Set([...words1, ...words2]).size;\n return totalUniqueWords > 0 ? matchCount / totalUniqueWords : 0;\n}\n\n/**\n * Formats a date string to a readable format\n */\nexport function formatDate(dateString: string): string {\n if (!dateString) return '';\n \n try {\n const date = new Date(dateString);\n return date.toLocaleDateString('en-US', {\n year: 'numeric',\n month: 'short',\n day: 'numeric'\n });\n } catch (e) {\n return dateString;\n }\n}\n\n/**\n * Validates an email address\n */\nexport function isValidEmail(email: string): boolean {\n const emailRegex = /^[^\\s@]+@[^\\s@]+\\.[^\\s@]+$/;\n return emailRegex.test(email);\n}\n\n/**\n * Extracts skills from text based on common skill keywords\n * This is a simple implementation - in production, you'd use NLP\n */\nexport function extractSkills(text: string, skillsList: string[]): string[] {\n const textLower = text.toLowerCase();\n return skillsList.filter(skill => \n textLower.includes(skill.toLowerCase())\n );\n}\n\n/**\n * Truncates text to a specified length\n */\nexport function truncateText(text: string, maxLength: number): string {\n if (text.length <= maxLength) return text;\n return text.substring(0, maxLength) + '...';\n}",[],[139,174],"199ac77c-316c-11f0-8adb-569c3dd06744","types.ts","199ac844-316c-11f0-8adb-569c3dd06744",["D",1747300080419],"// Shared type definitions for the resume screening system\n\nexport interface Resume {\n id?: number;\n candidateName: string;\n email: string;\n phone?: string;\n resumeText: string;\n parsedData?: ParsedResumeData;\n score?: number;\n createdAt: string;\n}\n\nexport interface ParsedResumeData {\n skills: string[];\n experience: Experience[];\n education: Education[];\n summary?: string;\n highlights?: string[];\n}\n\nexport interface Experience {\n company: string;\n title: string;\n startDate?: string;\n endDate?: string;\n description: string;\n highlights?: string[];\n}\n\nexport interface Education {\n institution: string;\n degree: string;\n field?: string;\n graduationDate?: string;\n}\n\nexport interface JobRequirement {\n id?: number;\n title: string;\n description: string;\n requiredSkills: string[];\n preferredSkills: string[];\n minimumExperience?: number;\n createdAt: string;\n}\n\nexport interface ScoringResult {\n resumeId: number;\n candidateName: string;\n score: number;\n skillMatches: {\n matched: string[];\n missing: string[];\n };\n experienceRelevance: number;\n educationRelevance: number;\n highlights: string[];\n}\n\nexport interface ApiResponse {\n success: boolean;\n data?: T;\n error?: string;\n}",[],[139,182],["D",1747300080419],[166,167],[139],"45887e1a-316c-11f0-bc4b-569c3dd06744",["D",1747300154122],[],[146,32],"34aa2616-316c-11f0-bdd8-569c3dd06744","parser.ts","34aa26b6-316c-11f0-bdd8-569c3dd06744",5,["D",1747300125822],"import { OpenAI } from \"https://esm.town/v/std/openai\";\nimport type { ParsedResumeData } from \"../shared/types\";\n\nconst openai = new OpenAI();\n\n/**\n * Parses resume text using OpenAI to extract structured information\n */\nexport async function parseResume(resumeText: string): Promise {\n try {\n const prompt = `\n Extract the following information from this resume:\n 1. Skills (as a list of individual skills)\n 2. Work experience (including company, title, dates, and description)\n 3. Education (including institution, degree, field, and graduation date)\n 4. A brief professional summary\n 5. Key highlights or achievements\n\n Format the response as a valid JSON object with the following structure:\n {\n \"skills\": [\"skill1\", \"skill2\", ...],\n \"experience\": [\n {\n \"company\": \"Company Name\",\n \"title\": \"Job Title\",\n \"startDate\": \"YYYY-MM\",\n \"endDate\": \"YYYY-MM or Present\",\n \"description\": \"Job description\",\n \"highlights\": [\"achievement1\", \"achievement2\", ...]\n },\n ...\n ],\n \"education\": [\n {\n \"institution\": \"University Name\",\n \"degree\": \"Degree Type\",\n \"field\": \"Field of Study\",\n \"graduationDate\": \"YYYY-MM\"\n },\n ...\n ],\n \"summary\": \"Professional summary\",\n \"highlights\": [\"highlight1\", \"highlight2\", ...]\n }\n\n Resume:\n ${resumeText}\n `;\n\n const completion = await openai.chat.completions.create({\n messages: [{ role: \"user\", content: prompt }],\n model: \"gpt-4o-mini\",\n response_format: { type: \"json_object\" },\n temperature: 0.2,\n });\n\n const content = completion.choices[0]?.message?.content;\n if (!content) {\n throw new Error(\"Failed to get a response from OpenAI\");\n }\n\n // Parse the JSON response\n const parsedData = JSON.parse(content) as ParsedResumeData;\n \n // Validate and ensure the structure is correct\n return {\n skills: Array.isArray(parsedData.skills) ? parsedData.skills : [],\n experience: Array.isArray(parsedData.experience) ? parsedData.experience : [],\n education: Array.isArray(parsedData.education) ? parsedData.education : [],\n summary: parsedData.summary || \"\",\n highlights: Array.isArray(parsedData.highlights) ? parsedData.highlights : []\n };\n } catch (error) {\n console.error(\"Error parsing resume:\", error);\n // Return a minimal valid structure if parsing fails\n return {\n skills: [],\n experience: [],\n education: []\n };\n }\n}\n\n/**\n * Extracts contact information from resume text\n */\nexport async function extractContactInfo(resumeText: string): Promise<{ name: string; email: string; phone?: string }> {\n try {\n const prompt = `\n Extract only the following contact information from this resume:\n 1. Full name\n 2. Email address\n 3. Phone number (if available)\n\n Format the response as a valid JSON object with the following structure:\n {\n \"name\": \"Full Name\",\n \"email\": \"email@example.com\",\n \"phone\": \"phone number or null if not found\"\n }\n\n Resume:\n ${resumeText.substring(0, 1000)} // Only use the beginning of the resume for contact info\n `;\n\n const completion = await openai.chat.completions.create({\n messages: [{ role: \"user\", content: prompt }],\n model: \"gpt-4o-mini\",\n response_format: { type: \"json_object\" },\n temperature: 0.2,\n });\n\n const content = completion.choices[0]?.message?.content;\n if (!content) {\n throw new Error(\"Failed to get a response from OpenAI\");\n }\n\n // Parse the JSON response\n const contactInfo = JSON.parse(content);\n \n return {\n name: contactInfo.name || \"Unknown\",\n email: contactInfo.email || \"unknown@example.com\",\n phone: contactInfo.phone !== \"null\" ? contactInfo.phone : undefined\n };\n } catch (error) {\n console.error(\"Error extracting contact info:\", error);\n return {\n name: \"Unknown\",\n email: \"unknown@example.com\"\n };\n }\n}",[],[146,196],"2ab17ede-316c-11f0-9caa-569c3dd06744","database.ts","2ab17f88-316c-11f0-9caa-569c3dd06744",["D",1747300109087],"import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\nimport type { Resume, JobRequirement, ParsedResumeData } from \"../shared/types\";\n\n// Table names - use versioning for schema changes\nconst RESUMES_TABLE = \"resume_screening_resumes_v1\";\nconst JOB_REQUIREMENTS_TABLE = \"resume_screening_jobs_v1\";\n\n// Initialize database tables\nexport async function initDatabase() {\n // Create resumes table\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${RESUMES_TABLE} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n candidateName TEXT NOT NULL,\n email TEXT NOT NULL,\n phone TEXT,\n resumeText TEXT NOT NULL,\n parsedData TEXT,\n score REAL,\n createdAt TEXT NOT NULL\n )\n `);\n\n // Create job requirements table\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS ${JOB_REQUIREMENTS_TABLE} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n title TEXT NOT NULL,\n description TEXT NOT NULL,\n requiredSkills TEXT NOT NULL,\n preferredSkills TEXT NOT NULL,\n minimumExperience INTEGER,\n createdAt TEXT NOT NULL\n )\n `);\n}\n\n// Resume operations\nexport async function saveResume(resume: Resume): Promise {\n const { candidateName, email, phone, resumeText, parsedData, score, createdAt } = resume;\n \n const result = await sqlite.execute(\n `INSERT INTO ${RESUMES_TABLE} \n (candidateName, email, phone, resumeText, parsedData, score, createdAt) \n VALUES (?, ?, ?, ?, ?, ?, ?)\n RETURNING id`,\n [\n candidateName,\n email,\n phone || null,\n resumeText,\n parsedData ? JSON.stringify(parsedData) : null,\n score || null,\n createdAt\n ]\n );\n \n return result.rows[0].id as number;\n}\n\nexport async function getResume(id: number): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${RESUMES_TABLE} WHERE id = ?`,\n [id]\n );\n \n if (result.rows.length === 0) {\n return null;\n }\n \n const row = result.rows[0];\n return {\n id: row.id,\n candidateName: row.candidateName,\n email: row.email,\n phone: row.phone,\n resumeText: row.resumeText,\n parsedData: row.parsedData ? JSON.parse(row.parsedData) : undefined,\n score: row.score,\n createdAt: row.createdAt\n };\n}\n\nexport async function getAllResumes(): Promise {\n const result = await sqlite.execute(`SELECT * FROM ${RESUMES_TABLE} ORDER BY createdAt DESC`);\n \n return result.rows.map(row => ({\n id: row.id,\n candidateName: row.candidateName,\n email: row.email,\n phone: row.phone,\n resumeText: row.resumeText,\n parsedData: row.parsedData ? JSON.parse(row.parsedData) : undefined,\n score: row.score,\n createdAt: row.createdAt\n }));\n}\n\nexport async function updateResumeScore(id: number, score: number): Promise {\n await sqlite.execute(\n `UPDATE ${RESUMES_TABLE} SET score = ? WHERE id = ?`,\n [score, id]\n );\n}\n\nexport async function updateParsedData(id: number, parsedData: ParsedResumeData): Promise {\n await sqlite.execute(\n `UPDATE ${RESUMES_TABLE} SET parsedData = ? WHERE id = ?`,\n [JSON.stringify(parsedData), id]\n );\n}\n\n// Job requirement operations\nexport async function saveJobRequirement(job: JobRequirement): Promise {\n const { title, description, requiredSkills, preferredSkills, minimumExperience, createdAt } = job;\n \n const result = await sqlite.execute(\n `INSERT INTO ${JOB_REQUIREMENTS_TABLE} \n (title, description, requiredSkills, preferredSkills, minimumExperience, createdAt) \n VALUES (?, ?, ?, ?, ?, ?)\n RETURNING id`,\n [\n title,\n description,\n JSON.stringify(requiredSkills),\n JSON.stringify(preferredSkills),\n minimumExperience || null,\n createdAt\n ]\n );\n \n return result.rows[0].id as number;\n}\n\nexport async function getJobRequirement(id: number): Promise {\n const result = await sqlite.execute(\n `SELECT * FROM ${JOB_REQUIREMENTS_TABLE} WHERE id = ?`,\n [id]\n );\n \n if (result.rows.length === 0) {\n return null;\n }\n \n const row = result.rows[0];\n return {\n id: row.id,\n title: row.title,\n description: row.description,\n requiredSkills: JSON.parse(row.requiredSkills),\n preferredSkills: JSON.parse(row.preferredSkills),\n minimumExperience: row.minimumExperience,\n createdAt: row.createdAt\n };\n}\n\nexport async function getAllJobRequirements(): Promise {\n const result = await sqlite.execute(`SELECT * FROM ${JOB_REQUIREMENTS_TABLE} ORDER BY createdAt DESC`);\n \n return result.rows.map(row => ({\n id: row.id,\n title: row.title,\n description: row.description,\n requiredSkills: JSON.parse(row.requiredSkills),\n preferredSkills: JSON.parse(row.preferredSkills),\n minimumExperience: row.minimumExperience,\n createdAt: row.createdAt\n }));\n}",[],[146,204],["D",1747300109087],[162,163,164],[146],["D",1747300035655],{"_95":94,"_102":98},{"_95":94,"_96":-5,"_97":98,"_18":53,"_99":100,"_101":24},{"_13":84,"_31":85,"_42":41,"_86":43,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":44,"_93":94},"ctx",{"_77":-5,"_122":250,"_251":252,"_253":254,"_255":24,"_256":24,"_257":24,"_258":57,"_259":-5,"_260":-7,"_261":-7,"_262":61,"_263":24,"_264":24,"_265":-5,"_266":24,"_267":24,"_268":24,"_269":-5},"ENV",{"_221":222,"_223":224,"_225":85,"_226":227,"_228":229,"_230":231,"_232":233,"_234":235,"_236":237,"_238":239,"_240":241,"_242":243,"_244":245,"_246":247,"_248":249},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","81405aa0531e90dfbb71a5bf473aaa0cf4c8702f","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v6