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| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 | 8x 8x 8x 8x 8x 8x 8x 8x 8x 8x 8x | import { config } from '../config/config';
import path from 'path';
import fs from 'fs';
import axios from 'axios';
import { chatWithAI, streamChatWithAI } from './chat_api_service';
import mammoth from 'mammoth';
import pdfParse from 'pdf-parse';
import AdmZip from 'adm-zip';
import { DOMParser, XMLSerializer } from 'xmldom';
export interface ParsedResume {
aboutMe: string;
skills: string[];
roleMatch: string;
experience:string[];
education?: string[];
}
const SYSTEM_TEMPLATE = `You are a very experienced ATS (Application Tracking System) bot with a deep understanding named Bob the Resume builder.
You will review resumes with or without job descriptions.
You are an expert in resume evaluation and provide constructive feedback with dynamic evaluation.
You should also provide an improvement table, taking into account:
- Content (Medium priority)
- Keyword matching (High priority)
- Hard skills (High priority)
- Soft skills (High priority)
- Overall presentation (Low priority)`;
const feedbackTemplate = (resumeText: string, jdText: string) => `
Resume Feedback Report
Here is the resume you provided:
${resumeText}
And the job description:
${jdText}
Create the Improvement Table in relevance to the resume and give the consideration and suggestion for each section strictly following
the pattern as below and don't just out this guided pattern :
| Area | Consideration | Status | Suggestions |
| ------------- | --------------------------------------------------------------- | ------ | ----------- |
| Content | Measurable Results: At least 5 specific achievements or impact. | Low | |
| | Words to avoid: Negative phrases or clichés. | | |
| Keywords | Hard Skills: Presence and frequency of hard skills. | High | |
| | Soft Skills: Presence and frequency of soft skills. | | |
| Presentation | Education Match: Does the resume list a degree that matches the job requirements? | High | |
Strengths:
List the strengths of the resume here.
Detailed Feedback:
Provide detailed feedback on the resume's content, structure, grammar, and relevance to the job description.
Suggestions:
Provide actionable suggestions for improvement, including specific keywords to include and skills to highlight.
Based on your analysis, provide a numerical score between 0-100 that represents the overall quality and match of the resume.
The score should be provided at the end of your response in the format: "SCORE: X" where X is the numerical score.
`;
const FEEDBACK_ERROR_MESSAGE = 'The Chat AI feature is turned off. Could not score your resume.';
const parseDocument = async (filePath: string): Promise<string> => {
const ext = path.extname(filePath).toLowerCase();
try {
switch (ext) {
case '.pdf':
return await parsePdf(filePath);
case '.docx':
case '.doc':
return await parseWord(filePath);
case '.txt':
case '.text':
return fs.readFileSync(filePath, 'utf-8');
default:
throw new Error(`Unsupported file format: ${ext}`);
}
} catch (error: any) {
console.error(`Error parsing document ${filePath}:`, error);
throw new Error(`Failed to parse document: ${error.message}`);
}
};
const parsePdf = async (filePath: string): Promise<string> => {
try {
const dataBuffer = fs.readFileSync(filePath);
const data = await pdfParse(dataBuffer);
return data.text;
} catch (error: any) {
console.error('Error parsing PDF:', error);
throw new Error('Failed to parse PDF document');
}
};
const parseWord = async (filePath: string): Promise<string> => {
try {
const result = await mammoth.extractRawText({ path: filePath });
return result.value;
} catch (error: any) {
console.error('Error parsing Word document:', error);
throw new Error('Failed to parse Word document');
}
};
const scoreResume = async (resumePath: string, jobDescription?: string): Promise<{ score: number; feedback: string }> => {
try {
const resumeText = await parseDocument(resumePath);
if (resumeText.trim() == '') {
throw new TypeError('Could not parse the resume file');
}
const prompt = feedbackTemplate(resumeText, jobDescription || 'No job description provided.');
let feedback = FEEDBACK_ERROR_MESSAGE;
if (config.chatAi.turned_on()) {
// Get feedback from the AI
feedback = await chatWithAI(SYSTEM_TEMPLATE, [prompt]);
}
// Extract the score from the feedback
const scoreMatch = feedback.match(/SCORE: (\d+)/);
const score = scoreMatch ? parseInt(scoreMatch[1]) : 0;
return { score, feedback };
} catch (error: any) {
if (error instanceof TypeError) {
console.error('TypeError while scoring resume:', error);
throw error;
} else {
console.error('Unexpected error while scoring resume:', error);
throw new Error('Failed to score resume');
}
}
};
const streamScoreResume = async (
resumePath: string,
jobDescription: string | undefined,
onChunk: (chunk: string) => void
): Promise<number> => {
try {
const resumeText = await parseDocument(resumePath);
if (resumeText.trim() == '') {
throw new TypeError('Could not parse the resume file');
}
const prompt = feedbackTemplate(resumeText, jobDescription || 'No job description provided.');
let fullResponse = '';
let finalScore = 0;
if (config.chatAi.turned_on()) {
await streamChatWithAI(
SYSTEM_TEMPLATE,
[prompt],
(chunk) => {
fullResponse += chunk;
onChunk(chunk);
// Try to extract score from the accumulated response
const scoreMatch = fullResponse.match(/SCORE: (\d+)/);
if (scoreMatch) {
finalScore = parseInt(scoreMatch[1]);
}
}
);
} else {
onChunk(FEEDBACK_ERROR_MESSAGE);
}
return finalScore;
} catch (error: any) {
if (error instanceof TypeError) {
console.error('TypeError while streaming resume score:', error);
throw error;
} else {
console.error('Unexpected error while streaming resume score:', error);
throw new Error('Failed to stream resume score');
}
}
};
const getResumeTemplates = async (): Promise<{ name: string; content: string; type: string }[]> => {
try {
const templatesDir = config.assets.resumeTemplatesDirectoryPath();
if (!fs.existsSync(templatesDir)) {
return [];
}
const files = fs.readdirSync(templatesDir);
const templates = await Promise.all(
files
.filter(file => {
const ext = path.extname(file).toLowerCase();
return ['.pdf', '.doc', '.docx'].includes(ext);
})
.map(async file => {
const filePath = path.join(templatesDir, file);
const content = fs.readFileSync(filePath);
const base64Content = content.toString('base64');
const mimeType = {
'.pdf': 'application/pdf',
'.doc': 'application/msword',
'.docx': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document'
}[path.extname(file).toLowerCase()] || 'application/octet-stream';
return {
name: path.basename(file),
content: base64Content,
type: mimeType
};
})
);
return templates;
} catch (error) {
console.error('Error reading resume templates:', error);
return [];
}
};
// Helper to split a string into N parts
function splitString(str: string, parts: number): string[] {
const len = Math.ceil(str.length / parts);
return Array.from({ length: parts }, (_, i) => str.slice(i * len, (i + 1) * len));
}
const generateImprovedResume = async (
feedback: string,
jobDescription: string,
templateName: string
): Promise<{ content: string; type: string }> => {
try {
const templatesDir = config.assets.resumeTemplatesDirectoryPath();
const templatePath = path.join(templatesDir, templateName);
if (!fs.existsSync(templatePath)) {
throw new Error(`Template ${templateName} not found`);
}
// Only handle DOCX files for now
if (!templateName.toLowerCase().endsWith('.docx')) {
throw new Error('Only DOCX templates are currently supported');
}
// Read and unzip the DOCX template
const zip = new AdmZip(templatePath);
const documentXml = zip.getEntry('word/document.xml');
if (!documentXml) {
throw new Error('Could not find document.xml in the template');
}
// Parse the XML content
const parser = new DOMParser();
const xmlDoc = parser.parseFromString(documentXml.getData().toString(), 'text/xml');
// Extract the content structure with full XML context
const paragraphs = xmlDoc.getElementsByTagName('w:p');
const contentStructure = [];
for (let i = 0; i < paragraphs.length; i++) {
const paragraph = paragraphs[i];
const runs = paragraph.getElementsByTagName('w:r');
const paragraphContent = [];
// Get paragraph properties
const pPr = paragraph.getElementsByTagName('w:pPr')[0];
const paragraphStyle = pPr ? pPr.toString().replace(/"/g, '\\"') : '';
for (let j = 0; j < runs.length; j++) {
const run = runs[j];
const text = run.getElementsByTagName('w:t')[0];
if (text) {
// Get run properties
const rPr = run.getElementsByTagName('w:rPr')[0];
const runStyle = rPr ? rPr.toString().replace(/"/g, '\\"') : '';
paragraphContent.push({
text: text.textContent,
style: runStyle
});
}
}
if (paragraphContent.length > 0) {
contentStructure.push({
type: 'paragraph',
content: paragraphContent,
style: paragraphStyle
});
}
}
// Convert structure to readable text for AI while preserving context
const readableContent = contentStructure.map((para, index) => {
const content = para.content.map(run => run.text).join('');
return `[Paragraph ${index + 1}]
Content: ${content}`;
}).join('\n\n');
// Prepare the prompt for AI to modify the content
const prompt = `You are a resume expert. Please modify the following resume content based on the feedback and job description.
Current Resume Content:
${readableContent}
Feedback:
${feedback}
Job Description:
${jobDescription}
IMPORTANT: You must return your response in the following EXACT JSON format. Do not include any other text or explanation:
[
{
"paragraphIndex": 0,
"text": "First paragraph content here"
}
]
Rules:
1. Return ONLY the JSON array, nothing else
2. Each paragraph must maintain its original structure
3. The text content should be updated based on the feedback while preserving formatting
4. Maintain the same number of paragraphs as the original
5. Do not include any markdown, formatting, or additional text`;
// Get the modified content from AI
const modifiedContent = await chatWithAI(SYSTEM_TEMPLATE, [prompt]);
console.log('AI Response:', modifiedContent); // Debug log
let modifiedParagraphs;
try {
// Clean the response to ensure it's valid JSON
const cleanedResponse = modifiedContent.trim()
.replace(/^```json\s*/, '')
.replace(/```\s*$/, '')
.replace(/^\[/, '[')
.replace(/\]$/, ']')
.replace(/\n/g, ' ') // Remove newlines that might break JSON
.replace(/\r/g, '') // Remove carriage returns
.replace(/\t/g, ' ') // Replace tabs with spaces
.replace(/\s+/g, ' '); // Normalize whitespace
modifiedParagraphs = JSON.parse(cleanedResponse);
// Validate the structure
if (!Array.isArray(modifiedParagraphs)) {
throw new Error('Response is not an array');
}
for (const para of modifiedParagraphs) {
if (!para.text || typeof para.text !== 'string') {
throw new Error('Invalid paragraph structure: missing or invalid text property');
}
}
} catch (error: any) {
console.error('Error parsing AI response:', error);
console.error('Raw AI response:', modifiedContent);
throw new Error(`Failed to parse AI response: ${error.message}`);
}
// Update the document with modified content while preserving structure
for (let i = 0; i < paragraphs.length && i < modifiedParagraphs.length; i++) {
const paragraph = paragraphs[i];
const modifiedParagraph = modifiedParagraphs[i];
const runs = paragraph.getElementsByTagName('w:r');
// Update the first run's text content while preserving its style
if (runs.length > 0) {
const firstRun = runs[0];
const text = firstRun.getElementsByTagName('w:t')[0];
if (text) {
text.textContent = modifiedParagraph.text;
}
}
}
// Serialize the modified XML
const serializer = new XMLSerializer();
const modifiedXml = serializer.serializeToString(xmlDoc);
// Update the document.xml in the zip
zip.updateFile('word/document.xml', Buffer.from(modifiedXml));
// Get the modified DOCX as a buffer
const modifiedDocxBuffer = zip.toBuffer();
return {
content: modifiedDocxBuffer.toString('base64'),
type: 'application/vnd.openxmlformats-officedocument.wordprocessingml.document'
};
} catch (error: any) {
console.error('Error generating improved resume:', error);
throw error;
}
};
/**
* Extracts raw text from the uploaded resume buffer,
* prompts the AI to return { aboutMe, skills[], roleMatch, experience[] } as JSON.
*/
const parseResumeFields = async (
fileBuffer: Buffer,
originalName: string
): Promise<ParsedResume> => {
// 1) Extract text
const ext = path.extname(originalName).toLowerCase();
let text: string;
if (ext === '.pdf') {
const data = await pdfParse(fileBuffer);
text = data.text;
} else {
// mammoth supports buffer input
const { value } = await mammoth.extractRawText({ buffer: fileBuffer });
text = value;
}
// 2) Build the extraction prompt
const prompt = `
Extract from this resume the following fields as JSON:
• "aboutMe": a 1–2 sentence self-summary.
• "skills": an array of technical skills.
• "roleMatch": one-sentence best-fit role suggestion.
• "experience": an array of 3–5 bullet points of key achievements.
Resume text:
---
${text}
---
Respond with a single JSON object and nothing else. The json object should begin directly with parentheses and have the following structure: {"a": "value", "b": "value", ...}
`;
// 3) Call your Chat AI
const aiResponse = await chatWithAI(
SYSTEM_TEMPLATE, // you can reuse your existing SYSTEM_TEMPLATE or define a new one
[prompt]
);
// 4) Parse & return
const parsed = JSON.parse(aiResponse.trim().replace("```json", "").replace("```", "")) as ParsedResume;
return parsed;
};
export { scoreResume, streamScoreResume, getResumeTemplates, generateImprovedResume, parseResumeFields }; |