HireLens

Hire Smarter. Screen Faster.
Automatically rank candidates, surface key strengths & gaps, and generate fit recommendations using an editorial AI-powered interface.

What It Does • Tech Stack • Getting Started • How to Use • Connect

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Demo

demo.mp4

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What It Does

HireLens is a fully automated AI resume screening pipeline. HR teams upload a Job Description + up to 10 resumes and receive:

Output	Description
Match Score (0–100)	Holistic LLM-evaluated fit score
Candidate Ranking	Sorted from best to worst fit
Key Strengths (2–3)	What the candidate does well
Key Gaps (2–3)	What the candidate is missing
Recommendation	Strong Fit / Moderate Fit / Not Fit
Recruiter Summary	2-sentence AI-generated note

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Premium UI/UX Features

• Mobile First & Adaptive: Pure vanilla CSS responsiveness that adjusts grid layouts, font sizes, and paddings for a seamless experience on any device.
• Sticky Actions: Important call-to-actions (like the "View Resume" button) are pinned to ensure they are always accessible, even with heavy content.
• Orchestrated Animations: Smooth transitions for FAQs, staggered list entries, and dynamic score rings powered by motion/react.
• Glassmorphic Design: A modern, high-end "frosted glass" aesthetic with dark mode optimization and sharp typography.

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Tech Stack

Layer	Technology
Framework	Next.js 14 (App Router)
Authentication	Clerk
AI / LLM Engine	Groq API – llama-3.3-70b-versatile
Database	Supabase (PostgreSQL)
File Storage	Cloudinary
Animations	Motion
Styling	Vanilla CSS (dark glassmorphism)

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Project Structure

HireLens/
├── app/                     # Next.js App Router pages & API routes
│   ├── (auth)/              # Sign-in / Sign-up (Clerk)
│   ├── screen/              # Upload JD + Resumes
│   ├── results/[sessionId]/ # Results Dashboard
│   └── dashboard/           # Session History
├── components/              # Reusable UI components
├── lib/                     # Core modules (Groq, Supabase, Cloudinary, parseFile)
├── sample_data/             # 1 JD + 8 realistic resume profiles
├── Docs/                    # Architecture documentation
│   ├── HLD.md               # High Level Design
│   └── LLD.md               # Low Level Design
├── .env.local               # Environment variables (never commit!)
├── LICENSE
└── README.md

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Architecture & Design Docs

Document	Description
High Level Design (HLD)	System architecture, data flow, component overview, deployment model
Low Level Design (LLD)	API specs, module interfaces, DB schema, component breakdown

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Getting Started

Prerequisites

• Node.js 18+
• Accounts on: Clerk, Supabase, Cloudinary, Groq

1. Clone & Install

git clone https://github.com/TuShArBhArDwA/HireLens.git
cd HireLens
npm install

2. Configure Environment

Copy and fill in your API keys:

cp .env.example .env.local

Edit .env.local with your keys for Clerk, Supabase, Groq, and Cloudinary.

3. Set Up Supabase

Run these SQL commands in your Supabase SQL editor:

CREATE TABLE screening_sessions (
  id         UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  user_id    TEXT NOT NULL,
  jd_title   TEXT NOT NULL,
  jd_text    TEXT NOT NULL,
  created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE candidates (
  id             UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  session_id     UUID REFERENCES screening_sessions(id) ON DELETE CASCADE,
  name           TEXT NOT NULL,
  file_url       TEXT,
  resume_text    TEXT,
  score          INTEGER NOT NULL,
  rank           INTEGER NOT NULL,
  strengths      JSONB NOT NULL DEFAULT '[]',
  gaps           JSONB NOT NULL DEFAULT '[]',
  recommendation TEXT NOT NULL,
  summary        TEXT,
  created_at     TIMESTAMPTZ DEFAULT NOW()
);

4. Run Locally

npm run dev

Visit http://localhost:3000

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How to Use

1. Sign in with Clerk
2. Go to Screen Candidates → upload your JD and resumes (PDF/DOCX/TXT)
3. Or click "Load Sample Data" to use the built-in demo
4. Click "Screen Candidates" → Groq analyzes each resume
5. View the ranked results dashboard with scores, strengths, gaps, and recommendations
6. Review past sessions in the Dashboard

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Sample Data

Located in sample_data/:

• jd_senior_data_engineer.txt – Realistic JD for a Senior Data Engineer role
• resumes/ – 8 diverse candidate profiles covering Strong Fit, Moderate Fit, and Not Fit scenarios

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Security

• All app routes are protected by Clerk authentication middleware
• API routes validate the authenticated user before processing
• No API keys are ever exposed to the client
• .env.local is gitignored

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Connect with me

If you’d like to connect, feel free to reach out — Click here

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