📄 CV Analyzer

An AI-powered CV analysis web application built with Flask, spaCy NLP, and pdfplumber. Upload a PDF resume and instantly extract key information, analyse skills, and visualise insights — all through a clean web interface. Fully containerised with Docker for easy deployment.

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✨ Features

• PDF Resume Parsing — Extracts raw text from uploaded PDF CVs using pdfplumber
• NLP-Powered Analysis — Processes resume content using spaCy's en_core_web_md model for named entity recognition and keyword extraction
• Skills & Entity Extraction — Identifies candidate skills, experience, education, and key entities from unstructured text
• Data Visualisation — Generates charts and visual summaries using matplotlib
• Web Interface — Clean HTML/CSS frontend served by Flask for uploading and viewing results
• Docker Support — Fully containerised for one-command deployment anywhere

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🛠️ Tech Stack

Layer	Technology
Backend	Python 3.10, Flask 2.0+
NLP Engine	spaCy (en_core_web_md)
PDF Parsing	pdfplumber
Visualisation	matplotlib
Frontend	HTML, CSS
Containerisation	Docker

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📁 Project Structure

CV-Analyzer/
├── app/                  # Flask application package
│   ├── __init__.py       # App factory (create_app)
│   ├── routes/           # URL routes & view logic
│   ├── templates/        # HTML templates
│   └── static/           # CSS & static assets
├── Dockerfile            # Docker container definition
├── req.txt               # Python dependencies
└── run.py                # Application entry point

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🚀 Getting Started

Option A — Run Locally

1. Clone the repository

git clone https://github.com/malikKanishka/CV-Analyzer.git
cd CV-Analyzer

2. Install dependencies

pip install -r req.txt
python -m spacy download en_core_web_md

3. Run the app

python run.py

Open your browser at:

http://localhost:5000

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Option B — Run with Docker

1. Build the image

docker build -t cv-analyzer .

2. Run the container

docker run -p 5000:5000 cv-analyzer

Open your browser at:

http://localhost:5000

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📦 Dependencies

Flask>=2.0
spacy>=3.0
pdfplumber>=0.5
matplotlib>=3.0

Install with:

pip install -r req.txt
python -m spacy download en_core_web_md

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🔄 How It Works

Upload PDF → pdfplumber extracts text → spaCy NLP analyses content
→ Skills & entities extracted → matplotlib generates charts → Results displayed in browser

1. User uploads a PDF resume via the web interface
2. pdfplumber reads and extracts raw text from the PDF
3. spaCy's en_core_web_md model processes the text and identifies entities, skills, and key information
4. matplotlib generates visual summaries of the extracted data
5. Results are rendered back to the user in the browser

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🐳 Docker Details

The Dockerfile:

• Uses python:3.10 as the base image
• Sets working directory to /ATS
• Installs all dependencies from req.txt
• Downloads the spaCy en_core_web_md language model
• Exposes port 5000
• Starts the app via python run.py

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