README.md

Skills Extractor

This codebase helps to extract skills of people from a CSV files and create tags of those skills. These tags are then used to create a skill taxonomy and assign these tags from this taxonomy to the emoployees mentioned in the CSV file.

Explanation of the files

1. cluster_skills.py - Consists of the parallelized clusterization algorithm to make the skill taxonomy broader. Low n_clusters means more generalization and vice versa to be more specific.
2. utils.py - Main file that consists the logic to generate skills_taxonomy.txt and individual_skills.csv.
3. app.py
4. individual_skills.csv - A dataframe consists of 2 columns Name and Skills for every employee.
5. skills_taxonomy.txt - List of Skills that were generated from the initial dataset after clusterization.
6. postprocessing.py - In case you need a more refined output, i.e., make the skill taxonomy more broader or more specific. It generates individual_skills_refined.csv and skills_taxonomy_refined.txt
7. individual_skills_refined.csv - It has the same format as individual_skills.csv after running postprocessing.py.
8. skills_taxonomy_refined.txt - It has the same format as skills_taxonomy.txt after running postprocessing.py.
9. logs.txt - Consists of the logs of an example run of utils.py

Instructions to run

1. Create a CSV that has a column called "Skill Sets" that consists of skills defined in natural language for employees.
2. Create a .env file and define OPENAI_API_KEY environment variable. (Number of API calls will be equal to the number of rows in your CSV.)
3. python3 -m venv venv
4. pip install -r requirements.txt
5. Run python3 utils.py > logs.txt 2>&1
6. To get a more refined output run python3 postprocessing.py --n_clusters 100

Streamlit application link

The application is deployed in this link: Skill Extractor UI