SELECTRA Documentation

Project Overview

SELECTRA is an AI-powered resume screening application designed to streamline the hiring process for HR teams. It uses advanced natural language processing to analyze resumes against job requirements, ranking candidates based on their suitability.

Why We Built SELECTRA

The traditional resume screening process is time-consuming and often subjective. SELECTRA addresses these challenges by:

  • Automating the initial screening of hundreds of resumes
  • Providing objective, consistent evaluation based on job requirements
  • Identifying top candidates with quantifiable match scores
  • Generating targeted interview questions based on candidate profiles
  • Saving HR teams countless hours in the hiring process

Development Team

SELECTRA is developed by the students of Software Engineering Department of SMIU.

Wasif Mehmood Ali

Wasif Mehmood Ali

Team Lead / Backend / AI

Led the team, developed the core application logic and Gemini AI Model integration.
Tajammul Abbasi

Shaharayar Khan

UI/UX Lead

Designed the modern, intuitive interface and user experience flows for SELECTRA.
Nasarullah

Nasarullah

Frontend Developer

Implemented the responsive frontend architecture and interactive components.
Shaharayar Khan

Tajammul Abbasi

UI & Frontend Contributor

Collaborated on UI design and assisted in frontend development for SELECTRA.
Zain Zaib

Zain Zaib

Research Lead

Conducted market research and created technical documentation for SELECTRA.

Technical Pipeline

The SELECTRA application follows a structured pipeline to process resumes and generate insights:

1

Resume Upload

Users upload resumes in various formats (PDF, DOCX, TXT) through a drag-and-drop interface or file browser. The application supports batch processing of multiple resumes simultaneously.

File API
PDF Support
DOCX Support
2

Text Extraction

The application extracts raw text from uploaded documents using format-specific libraries:

  • PDF files: Processed using PDF.js, a Mozilla-developed library that parses PDF documents in the browser without server-side dependencies.
  • DOCX files: Processed using Mammoth.js, which converts DOCX documents to HTML and then extracts the text content.
  • TXT files: Read directly using the FileReader API.
PDF.js
Mammoth.js
3

AI Analysis with Gemini

The extracted text is sent to Google's Gemini API with a carefully crafted prompt that includes:

  • The job requirements entered by the HR team
  • Instructions for analyzing the resume content
  • A specific response format for consistent parsing

The API returns structured data including candidate name (extracted from resume), match score (0-100), relevant experience, key skills, and education.

Gemini 2.0 Flash
4

Results Processing

The application processes the API response to:

  • Sort candidates by match score (descending)
  • Calculate aggregate metrics (average score, top candidates, etc.)
  • Generate visual indicators (score bars, color-coded ratings)
  • Prepare data for display in the results table
Data Processing
5

Interview Questions Generation

For the top candidates, SELECTRA generates targeted interview questions by:

  • Sending the job requirements and candidate profiles to Gemini
  • Requesting specific, relevant questions categorized by type (technical, behavioral, etc.)
  • Formatting the responses for clear display in the UI
Question Generation
6

Export Functionality

HR teams can export results in multiple formats:

  • PDF Export: Uses jsPDF to generate professional reports with candidate rankings and interview questions
  • Excel Export: Uses SheetJS to create spreadsheet-ready data for further analysis
jsPDF
SheetJS

Evaluation Methodology

SELECTRA evaluates candidates based on several factors extracted from their resumes:

Match Score Calculation

The AI evaluates each resume against the job requirements and assigns a score (0-100) based on:

  • Relevant Experience: Years and quality of experience matching the job requirements
  • Skills Match: Presence and proficiency of required skills
  • Education: Relevance and level of education
  • Keyword Analysis: Presence of important keywords from the job description

Score Categories

  • 90-100% Excellent match - Strong in all required areas
  • 80-89% Very good match - Meets most requirements
  • 70-79% Good match - Meets many requirements
  • 60-69% Fair match - Meets some requirements
  • Below 60% Weak match - Lacks key requirements

Interview Questions Generation

Questions are tailored based on:

  • Gaps between candidate qualifications and job requirements
  • Areas where more information is needed
  • Specific skills or experiences mentioned in the resume
  • Common interview questions for the role type

Technical Stack

SELECTRA is built with modern web technologies:

Core Technologies

HTML5
CSS3
JavaScript

Libraries & APIs

PDF.js
Mammoth.js
Gemini API
jsPDF
SheetJS