AI-Resume-Analyzer

AI Resume Analyzer

Analyze your resume. Discover your strengths. Improve your career.

A full-stack portfolio project that analyzes resumes (PDF/DOCX), estimates ATS-style compatibility, detects skills, compares against job descriptions, and presents insights in a professional SaaS-style dashboard.

Scores are estimated for learning and portfolio use. They are not real ATS vendor scores and do not guarantee interviews or job offers.

Problem statement

Job seekers struggle to understand how resumes look to automated screening and how well they match a role. This project demonstrates a practical AI/ML + full-stack architecture that:

Extracts resume text
Runs structured analysis (skills, sections, experience, projects)
Computes transparent estimated scores
Optionally matches a job description
Produces actionable recommendations and a downloadable PDF report

Features

Landing page, upload flow, results dashboard
PDF & DOCX text extraction
Modular skill catalog (easy to extend)
Estimated ATS Compatibility Score (0–100) with breakdown
Section scores with progress bars
Detected / strong / missing skills
Optional job-match percentage and keyword overlap
Summary, project, and experience analysis
AI-assisted recommendations (rule-based fallback today)
PDF report download
Works without an OpenAI API key
LLM-ready ai_service architecture for future integration

Technology stack

Layer	Technology
Frontend	React, Vite, JavaScript, CSS3, Lucide React, React Router
Backend	Python, FastAPI, Uvicorn
Parsing	pypdf, python-docx
Reports	reportlab
Persistence	SQLite (modular; swappable for PostgreSQL)
AI layer	Rule-based fallback + hooks for future LLM APIs

Architecture

Browser (React)
    │  REST JSON
    ▼
FastAPI routes  →  resume_parser  →  ai_service
                         │               │
                         │         ┌─────┴──────┐
                         │         │ fallback   │ (future LLM)
                         │         └─────┬──────┘
                         │               ▼
                         │         analyzer + job_matcher
                         ▼
                   SQLite storage → PDF report

Key idea for interviews: routes stay stable; swap analysis providers inside ai_service.py.

Project structure

ai-resume-analyzer/
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   ├── services/api.js
│   │   ├── context/
│   │   ├── utils/
│   │   ├── App.jsx
│   │   └── main.jsx
│   ├── package.json
│   └── .env.example
├── backend/
│   ├── app/
│   │   ├── main.py
│   │   ├── config.py
│   │   ├── routes/
│   │   ├── services/
│   │   ├── models/
│   │   └── utils/
│   ├── requirements.txt
│   └── .env.example
├── uploads/
├── README.md
└── .gitignore

Installation

Prerequisites

Python 3.10+
Node.js 18+ and npm (https://nodejs.org)

Backend setup

cd backend
python -m venv venv

# Windows PowerShell
.\venv\Scripts\Activate.ps1

# macOS / Linux
# source venv/bin/activate

pip install -r requirements.txt
copy .env.example .env
# On macOS/Linux: cp .env.example .env

Frontend setup

cd frontend
npm install
copy .env.example .env
# On macOS/Linux: cp .env.example .env

Environment variables

Frontend (`frontend/.env`)

VITE_API_URL=http://localhost:8000

Backend (`backend/.env`)

HOST=0.0.0.0
PORT=8000
UPLOAD_DIR=../uploads
MAX_FILE_SIZE_MB=5
ALLOWED_EXTENSIONS=.pdf,.docx
AI_PROVIDER=fallback
OPENAI_API_KEY=
OPENAI_MODEL=gpt-4o-mini

Leave OPENAI_API_KEY empty to use the rule-based analyzer.

How to run

Open two terminals.

Start the backend

cd backend
.\venv\Scripts\Activate.ps1
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

API docs: http://localhost:8000/docs
Health: http://localhost:8000/api/health

Start the frontend

cd frontend
npm run dev

App: http://localhost:5173

API endpoints

Method	Path	Description
GET	`/api/health`	Health check
POST	`/api/upload`	Upload PDF/DOCX resume
POST	`/api/analyze`	Analyze resume (+ optional job description)
POST	`/api/job-match`	Standalone job match
GET	`/api/report/{id}`	Download PDF report

Screenshots

Add screenshots here after running locally:

Landing page

Upload page

Results dashboard

Job match section

Future improvements

Connect OpenAI / other LLM providers in ai_service.py
PostgreSQL instead of SQLite
User accounts and analysis history UI
Better OCR for scanned PDFs
Export to DOCX / shareable links
Automated tests (pytest + React Testing Library)

Learning outcomes

Building a production-shaped FastAPI + React app
File upload validation and document parsing
Designing modular AI services with safe fallbacks
Transparent scoring systems you can defend in interviews
Clean frontend architecture (central API service, context, routes)

Author

Built as a portfolio project for AI/ML, Python, GenAI, and software development roles.

License

MIT — feel free to adapt for your own portfolio (do not claim real ATS certification).