Wegmans Recipe Helper

Wegmans Recipe Helper is designed to make home cooking easier! Simply input the URL of a recipe on the Web UI and a list of ingredients will be generated. After the customer confirms the ingredients (adding and deleting elements as neccessary), a convenient view is generated for every item–name, price, and location. With this tool, going from recipe to dinner table has never been easier!

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

Flask (python web environment): https://flask.palletsprojects.com/en/1.1.x/installation/
Google Cloud Natural Langage API: https://cloud.google.com/natural-language/docs/reference/libraries#client-libraries-install-python, https://cloud.google.com/natural-language/docs/analyzing-entities#language-entities-string-python
Wegmans API: https://dev.wegmans.io/product#product=hackathon (subscribe to the API and apply your own subscription key)
BeautifulSoup4 (python web scraping): https://pypi.org/project/beautifulsoup4/

Usage

To run the flask site on your local network, run these lines on the command line in the directory containing site.py:

set FLASK_APP=wegSite.py
python -m flask run

Then, go to the ip address indicated on the command line.

Enhancements

With more time or resources this project could be improved by:

Train our own neural network to properly identify food types
Identify and display amount of each ingredient neccessary for the recipe
Deploy to a live environment (web and mobile)
More substance to the web environment (links, store map, etc.)
Enhancements for the Wegmans API (more requests per minute, more efficient queries for common items, implementing store locator and recipe features)
Subscription key rotation / API call caching to reduce requests per minute

Deployment

Flask has several options and tutorials for deploying to a live environment: https://flask.palletsprojects.com/en/1.1.x/deploying/

Built With

Flask - The web framework used
Google Natural Language API - Entity analysis
Wegmans API - Used to determine product information
BeautifulSoup4 - Web scraping

Authors

Nick Gardner - Web Scraping
Ethan Howes - Entity Analysis
Nate Kushner - Flask Web Design
Albin Lang - Wegman's API Utilization

Built With

Submitted to

BrickHack 6

Created by

Used BeautifulSoup4 to create a generalized web scraper to collect the ingredients list for parsing.

Nick Gardner
Worked on natural language processing entity analysis with the google cloud NLP API to extract food items from text. Learned a lot about API's and NLP!

Ethan Howes
Worked on integrating and routing from Wegman's API, parsed queries ran from the API and returned it to the UI as useful information.Learned about caching and other dynammic programming techniques to deal with expensive applications.

Albin Liang
I did the UI and web design in python flask and HTML :) Thank you for your consideration ^^

nk8960 The Man