Resume Web Application

Creator: John Walsh

This application displays my credentials for employment in a fun and occassionally interactive manner. For developing the application locally, it is reccomended to use pyenv + pipenv to create the local dev environment. This application also contains a simple text classification model built using pySpark. The model and transformation pipeline have been serialized and stored as files in the src/ directory of the project. The source code used to build and train the models will be released shortly.

Installing the dev environment

In your favorite UNIX terminal run the following commands

git clone https://github.com/jrw34/JohnWalsh-Resume

cd JohnWalsh-Resume

pyenv local 3.13.0

pip install pipenv

pipenv install --dev

pipenv run pre-commit install

When changes are made to the codebase, be sure to run mypy and ruff and correct any errors before pushing to the repo

pipenv run ruff format
pipenv run ruff check
pipenv run mypy .

To locally run the application

pipenv run streamlit run src/resume_app.py

Note About Running Locally

When locally running the application, it is important that the proper version of Java is used. In order to ensure the Text Classification model properly configures the Spark environment, openjdk@17 should be used.

Ingredient Identifier

• CSV was downloaded from https://fdc.nal.usda.gov/download-datasets
• File downloaded was "Full Download of All Data Types"
• Size of File: approx. 3GB
• File is cleaned and parsed then pushed to a cloud hosted AIVEN PostgreSQL instance
• ETL steps are found in src/IngredientIdentifier/etl_tools/
• To locally develop this application, the database url+key is required as a local environment variable
  • Add the url+key as DB_URL to .bashrc
• If locally developing, an unzipped copy of the directory should be stored in the local repo
  • The location for the directory should be in src/IngredientIdentifier/