Futuristic food assessment and delivery
- Makes user take Questionnaire to assess which cuisine/restaurant they'd like
- Decides which food to order using an Natural Language Processing Bot that answers and sorts Multiple Choice Questions
- Generates a bill by adding the food and then running the order through an offer finder.
- Allows you to divide the payment by guessing which of you comes the closest to the price. The tip, and the amount of money used will be a function of the amounts guessed
- Sets the ordering location by taking your location from Google coordinates
- Places the order on a connected phone
The goal of this section is to assess a person's current cravings through an innoccuous Questionnaire of easy to answer choices
An array of cuisine choices is created and answers to each question determine the number of tally-marks added to or removed from each possible cuisine.
Here's an example of one of the questions:
Do you feel like visiting the seaside?
1.) Never
2.) Occassionally but only when forced to
3.) I'd like to.
4.) The sea is my one true passion. Dry land is death
The more a person visits the seaside, the more likely they are to like seafood. Thus, the tally marks would be added to Sushi and any other cuisine that does involve fish.
This is the implementation of a simple bot to answer multiple choice question. The idea is pretty simple - design a function that takes as input a question, and a list of choices (number of choices is not fixed), and that returns the index of the choice believed to be the right/ most popular one.
Do you feel like visiting the seaside?
1.) Never
2.) Occassionally but only when forced to
3.) I'd like to.
4.) The sea is my one true passion. Dry land is death
This particular bot focusses on semantic analysis for NLP to answer pseudo open field questions. It looks through google results and uses the information it receives to make the best choice. This is taken from an open source API on github. There is a simple API that lets you make some research in google, which has been wrapped in python, Google-Search-API. Here's the syntax of the python statements I've used to search for stuff on Google.
from google import google
my_search = google.search('Search terms')
This Google API software uses screen scraping to retreive search results from google.com.
my_search will contain a list of GoogleResult objects. To this, I add the name of the user so as to search specifically within their social media, their postings etc.
GoogleResult:
self.name # The title of the link
self.link # The link url
self.description # The description of the link
self.thumb # The link to a thumbnail of the website (not implemented yet)
self.cached # A link to the cached version of the page
self.page # What page this result was on (When searching more than one page)
self.index # What index on this page it was on
After cleaning the text extracted from the search, I concatenate all the text together as one really long string that represents all the text available on first pages of google. I then score each of my search results in order to determine which one would be the best.
In order to find the answer, I use n-grams. For example, if the search term is 'How often visit sea FName LName', then,
1-grams = ["How", "often", "visit", "sea", "FName", "LName"]
2-grams = ["How often", "visit sea", "FName LName"]
etc.
Rather that counting in the google search result the entire choice string (preprocessed), we look for the 1-grams, 2-grams and the full string (preprocessed as method 1). The score is thus the sum of these sub scores, each sub score having a multiplier:
total_score = 1*(1-grams occurences) + 3*(2-grams occurences) + 10*(full string occurences)
In that way, if only one word of the choice occurs, it only adds 1 point to the total score, if a 2-gram is found is found, adds 3 points, and if the full string is found, it adds 10 points.
The counter measures the number of entries (i,e the one with the highest score) and chooses the relevant answer. In the case of Kylie Jenner, the answer would be 3 since her social media contains multiple references to the seaside. This would select the cuisine and then add in food from the restaurant and add it into the checkout using the same principles.
The food selected by the MCQBot is put into the cart. We then use the Honey-Coupon-API to run a series of discount checks on the cart. The final price, along with taxes but excluding the tip is calculated and sent to the DividingPayment function to help distribute and figure out the bill (for groups of people).
To make the process of ordering food even more entertaining, we thought it was necessary to implement a way to choose who gets to pay.
We felt it was necessary to make everyone pay at least a percentage of the meal, so we devised a clever (possibly evil) game to decide how the meal will be paid for.
1) The computer will ask how many people are ordering food.
Example Input/Output Using 4 persons:
How many people are there?
4
2) Given the price, the computer will then ask each person to input a value that THEY think is the price of the total meal (including taxes, not including tips).
Example Input/Output:
Person 1, enter your guess:
50.50
Person 2, enter your guess:
73.40
Person 3, enter your guess:
45.60
Person 4, enter your guess:
50.60
3) Then the difference between each of the guesses and the actual price will be calculated. The person with the largest price difference will be chosen to pay that difference as the tip. The remaining people will then split the bill evenly.
Example Output:
Person 3, you must pay the difference of $14.90 as the tip.
The rest of you must pay $20.17 each, for the total meal price of $60.50
The person who had the largest absolute difference connects their phone to the computer. The computer remotely controls the phone and opens the SkipTheDishes App, fills in the information blanks one by one with the data it receives from the MCQBot; and then places the order for the food.
In order to do this, we created a ClickerBot that uses the scrcpy software. We determine the precise screen coordinates where we must click in order to perform the function we need it to perform and enter in the information into the search bar.
klick(550, 600);
pastee(600, 350, "Seafood");
klick(600, 350);
scrolle();
klick(600, 820);
klick(600, 820);
klick(600, 820);
pastee(740,720,"10");
Our bot performs these functions as we have very specifically defined for it. For instance, the klick function looks like this
public static void klick(int x, int y) {
bot.mouseMove(x, y);
bot.delay(5);
bot.mousePress(MouseEvent.BUTTON1_MASK);
bot.mouseRelease(MouseEvent.BUTTON1_MASK);
bot.delay(10000);
}
The order is then placed using the Google Pay card on that person's cellphone.
Our Program involves the use of-
- a Natural Language Bot,
- an intelligent Questionnaire based off of Buzzfeed Algorithms (Which our MCQBot fills in)
- A roster of cuisines and menu items selected by screen scraping from the SkipTheDishes App
- A Discount API
- A Price is Right / Russian Roulette Style game to determine who pays for the food
- A ClickerBot that makes the computer use the phone to place an order
Besides being a long process for the computer, our project is futuristic. Far from a world of Flying Cars and Teleportation, our Application focuses on the more real/ dystopian aspects of human nature and society that we expect to see more of later.
It reflects on a capitalistic system inherently overengineered yet functioning. Much like taxes, we first calculate how much you owe the app. However, instead of just giving you the price, we make you guess how much you owe; and if you guess wrong, we pull an IRS and take more of your money. Our application also uses two extremely sophisticated bots to talk to each other and agree that they both know information that they both received to begin with. In a world with a future filled with Red Tape and Beaureaucrats, we can expect nothing better.
At the same time, our application does what it's meant to - Identify and Order Food that you crave with the touch of a single button.
Also our project name has a mountain pun.