🤖 AI Resume Screening System

Automatically analyze, score, and rank candidates against any job description using semantic NLP — built with Python, Sentence Transformers, and Streamlit.

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🚧 v2 In Progress

This project is currently being rebuilt as a full-stack SaaS application with a Next.js frontend and FastAPI backend. The new version will include a polished dashboard UI, multi-resume batch processing, and a live public demo.

Current version: Streamlit prototype (fully functional)
v2 stack: Next.js · FastAPI · PostgreSQL · Docker · Deployed on Vercel + Render

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What It Does

Recruiters spend hours manually reviewing resumes. This system automates that process — upload multiple resumes, paste a job description, and get an objective ranked list of candidates with detailed scoring explanations in seconds.

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Features

• Batch resume upload — PDF and DOCX support, multiple files at once
• Semantic similarity matching — uses all-MiniLM-L6-v2 to understand meaning, not just keywords
• Multi-factor scoring — skill match, experience relevance, education level, and semantic fit
• Missing skills detection — shows exactly what each candidate lacks for the role
• Score explainability — human-readable breakdown of why each candidate ranked where they did
• Visual ranking — charts and score cards for quick comparison

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How It Works

Resume (PDF/DOCX) ──┐
                    ├──► Parser ──► Feature Extractor ──► Scorer ──► Ranked Output
Job Description ────┘

The scorer uses cosine similarity between sentence embeddings to measure semantic relevance between resume content and job requirements — going beyond simple keyword matching to understand context and meaning.

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Tech Stack

Layer	Technology
NLP Model	sentence-transformers/all-MiniLM-L6-v2
Similarity	Cosine similarity via scikit-learn
Resume Parsing	pdfplumber + python-docx
Data Processing	pandas
UI (v1)	Streamlit
UI (v2 — in progress)	Next.js + Tailwind CSS
API (v2 — in progress)	FastAPI

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Run Locally

# 1. Clone the repo
git clone https://github.com/Yusufcommit/ai-resume-screening-system.git
cd ai-resume-screening-system

# 2. Install dependencies
pip install -r requirements.txt

# 3. Run the app
streamlit run app.py

Open http://localhost:8501 in your browser.

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Project Structure

ai-resume-screening-system/
├── app.py              # Streamlit UI and app entry point
├── parser.py           # PDF/DOCX resume text extraction
├── ranking.py          # Scoring and candidate ranking logic
├── explainer.py        # Human-readable score explanations
├── utils.py            # Shared helpers
├── requirements.txt
└── screenshots/        # UI screenshots

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Screenshots

Full dashboard screenshots coming with v2. Current Streamlit UI:

Main Interface	Candidate Ranking	Score Visualization

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Roadmap

• Resume parsing (PDF + DOCX)
• Semantic similarity scoring
• Multi-factor ranking
• Missing skills detection
• Score explainability
• Rebuild frontend in Next.js with polished dashboard UI
• FastAPI backend with REST endpoints
• PostgreSQL integration for storing screening sessions
• Docker + CI/CD pipeline
• Live public demo deployment
• Bias detection in candidate evaluation

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Built by Yusuf

Yusuf Abdirashid — AI Full Stack Developer
Building polished AI-powered tools for hiring and job applications.