food_bar

This is a somewhat lousy attempt to incorporate the classic CS "foo bar" placeholders as puns. This repository is a website that showcases all the restaurants I have tried in various regions and perform analysis on my ratings for each restaurant.

let bar = "bar";
let foo = function(bar) {
  return "foo" + "d" + bar;
}
console.log(foo(bar));

Instructions

To play around with my repository, follow these steps to get started and install the necessary libraries and packages:

• git clone https://github.com/kylewong975/food_bar.git to clone my git repo
• cd food_bar to go to the root directory of the repo
• npm install to install the npm packages
• pip install -r requirements.txt to properly install/configure the Django frameworks with the specified versions
• ./node_modules/.bin/webpack --config webpack.config.js to configure webpack
• python3 manage.py runserver to start the server

In your browser of choice, go to http://127.0.0.1:8000/ which is the local server hosting the website, which should be indicated when you ran the runserver command above

Motivation

As a "foodie" I seek out new types of food (cultural, fusion, innovative) food. As a math-oriented person, I love numbers so I quantitatively describe my experience with the food at a particular restaurant by assigning ratings with my criteria. As a CS-oriented person, I would love to store these ratings in a database, parse data into a JSON object, and make a web application that showcases my restaurant reviews!

The first part of the name of the project is relevant because I really love food! Unfortunately, it will be approximately another two years from this time of writing (December 2017) before I can go to any bars. But I will robustly include bar ratings once I become of legal age to go to them!

Project Features

Project started December 2, 2017

Completed

• null

TODO

• Landing page
  • Sections to preview what features are available
  • Animations!
• Review page
  • List of restaurants rendered
  • Overall stats (average restaurant rating, number of restaurant reviews posted, etc)
  • Split reviews into pages, default 10
  • Show 10 per, 25 per, 100 per page through a dropdown
  • Set up database parsing and storing date, location, rating (cost, presentation, quality, overall) and review
  • Search functionality
    • Considering restaurant name, location, etc
    • Render "search based on <category>" in a sidebar
  • Add categorization via parsing (location, priciness, rating)
  • Robustness of application (if I want to add a new category or feature, it should be relatively easy)
• Analysis page
  • D3 charts

General Tech Stack

Frontend

• HTML
• CSS
• JavaScript
• React.js

Backend

• Django
• MongoDB

Restaurants

Los Angeles