CV Extractor

Upload PDF and DOCX resumes, extract name/email/phone from each, and download a structured Excel workbook.

Features

• Batch upload — select multiple files at once via drag-and-drop or file picker
• PDF & DOCX support — extracts text from both formats
• Smart extraction — regex-based parsing for names, email addresses, and phone numbers (international formats supported)
• Excel export — results compiled into a single .xlsx workbook
• Dark mode — respects system prefers-color-scheme
• Accessible — keyboard-navigable, ARIA labels, prefers-reduced-motion support
• CSRF-protected — form submissions secured with Flask-WTF

Tech Stack

Layer	Technology
Backend	Flask 3.x + Gunicorn
PDF	pypdf
DOCX	python-docx
Excel	openpyxl
Security	Flask-WTF (CSRF protection)
Frontend	Vanilla HTML/CSS/JS (no framework)
CI/CD	GitHub Actions (ruff + pytest + Docker → GHCR)

Prerequisites

• Python 3.10+
• pip

Quick Start

Local Development

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
python run.py

Open http://localhost:5000.

Docker

docker compose up

Open http://localhost:8000.

Configuration

Environment variables (.env file):

Variable	Default	Description
SECRET_KEY	auto-generated	Flask session signing key
MAX_CONTENT_LENGTH	16777216	Max upload size (bytes)
UPLOAD_FOLDER	uploads	Directory for uploaded files

SECRET_KEY is auto-generated via secrets.token_hex(32) if not set.

Project Structure

├── app/
│   ├── __init__.py        # App factory, CSRF init
│   ├── config.py          # Env-based configuration
│   ├── extractor.py       # Text extraction + regex parsing
│   ├── models.py          # CvData dataclass
│   ├── routes.py          # GET / and POST /extract
│   ├── shell.py           # Flask shell helpers
│   ├── workbook.py        # Excel workbook builder
│   ├── static/
│   │   ├── css/main.css   # Full-featured stylesheet
│   │   └── js/main.js     # Drag-drop, validation, spinner
│   └── templates/
│       └── index.html     # Upload form
├── tests/
│   ├── conftest.py        # pytest fixtures
│   ├── test_extractor.py  # Extraction logic tests
│   └── test_routes.py     # Route tests
├── run.py                 # Dev server entry point
├── wsgi.py                # Production entry point
├── pyproject.toml         # Build config + ruff + pytest
├── Dockerfile             # Multi-stage Docker build (OCI labels, healthcheck)
├── docker-compose.yml     # Single-service compose (supports GHCR image)
└── .github/workflows/
    ├── ci.yml             # Lint + test + build on PR/push
    └── deploy.yml         # Push Docker image to GHCR on main/tags

Development

Running Tests

pip install -r requirements-dev.txt
pytest

Linting

pip install ruff
ruff check .

Uses ruff with rulesets: E, F, W, I, N, UP, B, SIM.

Pre-commit Hooks

pip install pre-commit
pre-commit install

Runs ruff lint + format on commit.

API

POST /extract

Upload one or more CV files.

• Content-Type: multipart/form-data
• Field name: files[]
• Accepted formats: .pdf, .docx
• Response: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet (Excel download)

Returns 400 with error message on empty upload or extraction failure.

Deployment

Pre-built Image (GHCR)

Every push to main publishes a Docker image to the GitHub Container Registry:

docker pull ghcr.io/instax-dutta/cv-extractor-webapp:latest
docker run -p 8000:8000 ghcr.io/instax-dutta/cv-extractor-webapp:latest

Or with docker-compose (pulls the published image automatically):

cp .env.example .env
docker compose up

Build Locally

docker build -t cv-extractor .
docker run -p 8000:8000 cv-extractor

The Docker image uses gunicorn with a multi-stage build for a slim production image, and includes a built-in health check.

License

MIT