Resumenate: AI-Powered Resume Optimizer

Resumenate is a web-based AI resume optimization platform that analyzes resumes against target job descriptions to improve ATS compatibility and professional presentation. The system extracts ATS-relevant keywords, evaluates resume-role alignment, and generates enhanced resumes using NLP pipelines and LLM-driven content enhancement.

The platform combines keyword extraction, structured resume enhancement workflows, automated PDF generation, and a modern Flask-based web interface to streamline resume tailoring for job applications.

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Getting Started

These instructions will help you set up Resumenate on your local machine for development and testing. Refer to the Deployment section for information on hosting the application in a production environment.

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Prerequisites

Requirements for running the project:

• Python 3.10 or higher
• pip (Python package manager)
• OpenRouter API key
• spaCy English language model (en_core_web_sm)

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Installing

Clone the repository:

git clone https://github.com/bavRaghu/Resumenate
cd resumenate

Create a virtual environment:

python -m venv venv
source venv/bin/activate
# Windows: venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Download the spaCy model:

python -m spacy download en_core_web_sm

Create a .env file in the project root:

OPENROUTER_API_KEY=your_api_key_here

Run the application:

python main.py

Open the application in a browser:

http://localhost:5000

Upload a resume, paste a target job description, and generate an ATS-optimized enhanced resume.

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Running the tests

Testing is performed through functional validation of the resume analysis and enhancement workflow.

Sample Tests

The system was tested using representative job descriptions and resumes across multiple domains:

• Software engineering resumes for ATS keyword optimization
• Data science job descriptions for technical keyword extraction
• Minimal resumes to test enhancement robustness
• Multi-page PDF resumes for extraction consistency
• DOCX resumes to validate cross-format compatibility

Example:

Input: Resume missing cloud and backend keywords
Expected: Extracted ATS keywords include missing technologies and generated resume integrates relevant terminology naturally

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Features

ATS Keyword Extraction

• Uses KeyBERT and spaCy-based NLP pipelines to extract role-specific ATS keywords from job descriptions
• Identifies technical skills, frameworks, tools, and domain terminology
• Applies rule-based filtering and keyword deduplication for cleaner extraction

Resume-Role Match Analysis

• Compares extracted ATS keywords against uploaded resumes
• Calculates a resume-role alignment percentage
• Highlights detected ATS keywords for transparency and analysis

AI-Powered Resume Enhancement

• Uses OpenRouter-hosted GPT models to professionally rewrite and optimize resumes
• Enhances wording, formatting consistency, and technical phrasing
• Expands weak bullet points into stronger professional descriptions
• Integrates ATS-relevant keywords naturally throughout the resume

Structured Resume Generation

• Uses controlled HTML templates for consistent formatting and layout
• Separates content generation from rendering logic for improved stability
• Maintains professional typography, spacing, and ATS-friendly formatting

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Built With

• Flask (Python web framework)
• Flask-CORS
• KeyBERT
• spaCy
• OpenRouter API
• GPT-based LLMs
• WeasyPrint
• pdfminer.six
• docx2txt
• HTML, CSS, JavaScript
• Microsoft Azure
• GitHub Actions

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Deployment

The application is designed for deployment as a Flask web service using Microsoft Azure App Service.

Deployment process:

• Code is pushed to GitHub
• Azure is integrated with the repository
• GitHub Actions automatically triggers deployment workflows
• Application updates are deployed on pushes to the main branch