Resops

This simple resume optimizer (Resops) extracts keywords from a job description and then checks to see if the provided resume includes those keywords. The API is hosted on heroku and can be accessed by sending a POST request including a body with the target resume and job description (see example below). The Yake and Spacy packages are used for keyword searching and matching.

You can also make use of the resume rescue web app, which uses the resops package. This will allow you to enter a job description and your resume, and then provide you with feedback/allow you to edit your resume directly through the app.

Project hosting links

• API: https://dashboard.heroku.com/apps/resume-optimizer
• resume rescue site: https://nicole-ip.github.io/resume-rescue/

How it works

• Main endpoint used for frontend (job description parser and resume optimizer) is "/get_resume_feedback"
• Extracting keywords using YAKE and hard skills lists (stored in text files inside common.keyword_inputs)

Installing

This project is keeping track of dependencies using pipenv. To install all required packages and the correct versions, simply create a virtual environment and run the following inside it:

pipenv install

To run the FastAPI server on your local machine and be able to call the endpoints in main.py locally, run the following:

uvicorn main:app --reload

Sample Code

Python (API)

First, define some sample inputs:

data = {
    "job_title" : "Software Engineer",
    "job_description" : "Company A is looking for a Software Engineer to grow its...",
    "job_company" : "Company A",
    "raw_resume" : [{"title": "Software Engineer", "description": "Deployed machine learning models to production..."}]
}

Then, use the requests module to send a post request to the endpoint (locally or to the API):

import requests
resume_feedback_local = requests.post("http://127.0.0.1:8000/get_resume_feedback", json=data).text
resume_feedback_api = requests.post("https://resume-optimizer.herokuapp.com/get_resume_feedback", json=data).text

You'll get a dictionary back with the results which should look something like this:

resume_feedback = {
    "job_title": "Software Engineer",
    "all_keywords": ["Python", "SQL", "implement API", "experience with predictive analytics", ...],
    "skills": ["Python", "SQL", ...],
    "included_keywords": ["Python", "SQL"],
    "missing_keywords": ["implement API", "experience with predictive analytics", ...],
    "keyword_score": "2/n",
    "resume_lines_feedback": [{"job_title": "Software Engineer", "lines_with_keyword": [], "lines_without_keyword": []}, ...],
}

You can visit the API docs for more information.

Python (integration)

• TODO finish the code and write examples

TODO

• improve full-text resume parser
• ok there are tests now but they need to be better
• improve job_description class keyword extraction methods

Citations

• Yet Another Keyword Extractor (Yake)
  In-depth journal paper at Information Sciences Journal: Campos, R., Mangaravite, V., Pasquali, A., Jatowt, A., Jorge, A., Nunes, C. and Jatowt, A. (2020). YAKE! Keyword Extraction from Single Documents using Multiple Local Features. In Information Sciences Journal. Elsevier, Vol 509, pp 257-289. https://doi.org/10.1016/j.ins.2019.09.013
• spaCy: Industrial-strength NLP
  https://github.com/explosion/spaCy