## Inspiration

Save plate is an app that focuses on narrowing the equity differences in society.It is made with the passion to solve the SDG goals such as zero hunger, Improving life on land, sustainable cities and communities and responsible consumption and production.

## What it does

It helps give a platform to food facilities to distribute their untouched meals to the shelter via the plate saver app. It asks the restaurant to provide the number of meals that are available and could be picked up by the shelters. It also gives the flexibility to provide any kind of food restriction to respect cultural and health restrictions for food.

## How we built it

* Jav
## Challenges we ran into
There were many challenges that I and my teammates ran into were learning new skills, teamwork and brainstorming.

## Accomplishments that we're proud of

Creating maps, working with

## What we learned

We believe our app is needed not only in one region but entire world, we all are taking steps towards building a safe community for everyone Therefore we see our app's potential to run in collaboration with UN and together we fight world hunger.
## Inspiration

Our inspiration comes from the favorable push towards food delivery for almost any event such as lethargic evenings, lunch with someone special or even catering for a birthday party. Rapid technological advances have allowed for a new generation of the food industry and although beneficial, we heedlessly brush over some important aspects. Everything arrives in an instant and we often exchange nutritional value and sustainable practices for time. Our application would address these issues and help engage users to uptake practices that would better serve our communities and environments. This is why we created **SusFood**.

## What it does

**SusFood** is an online ordering and delivery application focused on encouraging users to eat more sustainably without giving up on the convenience of ready-made food. The users can choose locations by type of cuisine, name, or location. Each eatery is assigned a score out of 100 which is its Sustainability Score. The Sustainability Score is computed as a function of the distance that a certain delivery needs to travel, use of sustainable and/or organic products in food preparation, materials used to package, prepare food, additional cutlery, bags etc.

Users are incentivized to pick more sustainable food options and adapt more environmentally friendly practices through rewards earned by collected points in proportion to the sustainability of their food choices. In addition, eateries are also incentivized to adopt more environmentally friendly practices given the rewards system which would boost business activites.

## How we built it

Information about the various restaurants, fast food eateries, cafes etc and their location, hours, contact information etc were extracted using a combination of OpenStreetMap API, OverPass API, and other standard python modules such as os, webbrowser, pandas, random, and json. The map was built using a combination of the Folium library and OpenStreetMap API. The Twilio python module and API were used to provide notifications and communication support for the user.

The area ID assisted in placing markers on the map to show the location of restaurants and cafes. The user location is live and based on the network IP address allowing the query to sync accordingly to find nearby food options. Distance metrics were calculated using average time traveled and route conditions. The color system goes as follows: green is most sustainable, followed by yellow and red being the least sustainable.

While connecting front-end to back-end we decided on a local environment due to unforeseen issues and time constraints to fully implement a back-end database. We took a CSV file containing the restaurants’ data and converted it to a JSON file that was parsed through with javascript and embedded within the application.

On the front-end a combination of python, javascript, HTML and CSS were used to implement **SusFood**. We developed a website that is user friendly and it features a navigation bar that has a search bar, cart, and profile page. The home screen offers users the choice of preferred food categories that have a sustainability rating associated with it and is within your vicinity.

There are two custom-made carousels: one for the food categories and the second for displaying the various restaurants. These restaurants are ranked by most sustainable to least sustainable. Each restaurant card contains the name of the restaurant and its Sustainability Score along with a tier badge. There are 4 badges: sprout, potted plant, bonsai, and golden stickers; as the user engages in healthier behaviors and uses the application more, they can build up points in which they would be able to redeem it at their favorite eating spots. The adorable pile of leaves indicates the accumulation of such rewards! Also there is a map system where users can visually explore their eating options around where they currently are.

When you select a restaurant, the standard menu and business information is displayed along with the Sustainability Score. The metrics that determine this score are distance, packaging, sourcing, and leftovers that restaurants may offer to customers the following business day. Each of these factors are weighted, with distance being the highest and the other categories being equally weighted. The restaurant page also features standard ratings, reviews, and additional information on the calculations for the Sustainability Score.

## Challenges we ran into

Lack of availability of extensive data about the sustainability and contact information for eateries made it challenging to tabulate appropriate search results for all queries. We encountered challenges with quality open sourced data. On the front end, we had to navigate through formatting and alignment issues with CSS and integrating different modules which we ended up not utilizing. Some applications that we would have liked to use include Open Route Service and Django, one of python’s database services.

## Accomplishments that we're proud of

One of our main accomplishments is extracting geospatial information, processing it efficiently, and displaying it on the map. We are also very proud of our UI design.

## What we learned

Geospatial programming, REST APIs, non-REST APIs, web-development with python, HTML, and CSS. Although we had a few major setbacks, overall we are extremely proud of what we were able to create with an idea that we were all excited to pursue.

## What's next for SusFood

* Build a more extensive database containing information concerning various facets of sustainability for each eatery
* Adding additional features to order and deliver food
* Improve and optimize the algorithm to compute the sustainability score
* Build a robust customer support system using Twilio
* Expand on the mapping feature to include road traffic conditions and additional variables
* A searching algorithm to cater to the user
* Authentication of a new user (Login / Sign Up)
* Building a database that would be fully implemented and used in conjunction with the front-end
* Partner with restaurants to give users a functioning rewards system to encourage them to have sustainable buying habits.
## Inspiration

Our team enjoys working on projects that bring about a positive social impact and connect with the world at large. In many communities worldwide, where food is either scarce or lacking in nutritional value, it is crucial for us to develop technology as a means to offer both nourishment to underserved communities, decrease greenhouse gas emissions, and preserve natural resources.

## What it does

Sustainabite is a system that connects restaurants with non-profit organizations, such as food banks and homeless shelters, in their area to deliver food left at the restaurant at the end of each day. After a participating restaurant's workday is over, they can log in specific items on their menu that are left over or unserved. Once they update this list, organizations within a 10-mile radius are notified of this update and are asked whether they accept the request to order the food. If the request is accepted, the restaurant confirms it, and a volunteer at the organization comes to pick it up within the hour.

## How we built it

For the User Interface, we used **React Native** and **Typescript**. For the backend, where we sent post and get requests to manage requests from the users to the database, we used **Node.js**, **Express.js**, and **Google Cloud** server. We included registration pages, login pages, restaurant menus, and a request page to handle food requests between restaurants and organizations. To store the registration tables for both organizations and restaurants, and restaurant menus, we used **Google Cloud SQL** for MySQL. Additionally, to read the text from the images taken by restaurants to upload a digitized version of their menu, we use **Google Cloud Vision API**. After these menus were digitized, we found a way to parse through the text, and separate menu items from their ingredients, as many menus contain ingredients and common allergens found in their food. To do this, we implemented **Google Cloud Natural Language API**, using entities such as `DISH`, `CONSUMER_FOOD`, and `INGREDIENT`, to assign menu items and their respective ingredients. Moreover, we used **Google Cloud Geocoding API** in order to geo-code addresses of restaurants to latitude and longitude coordinates. We used USDA Food Data Central API to derive nutritional facts about the dishes. Finally, we use ProCreate to design our logo.

## Challenges we ran into

We faced several challenges throughout our project. Connecting to Google Cloud and setting up a remote database was difficult, especially since it was our first experience with the service. We also struggled to incorporate an image upload feature into our app. Initially, we considered using Google Drive links, but we recognized that would be inconvenient and were determined to make the app as user-friendly as possible. To tackle this, we researched various methods for iOS image selection to provide a smoother experience for our users. Additionally, reading and parsing the menu images posed another challenge. We had to learn how to effectively combine our individual skill sets, as we had similar project ideas but needed to figure out how to implement them and leverage each other's strengths to complete the tasks at hand. These obstacles pushed us to innovate and develop creative solutions, ultimately making our project more robust and adaptable.

## Accomplishments that we're proud of

We are proud of our creative and aesthetically pleasing User Interface. Additionally, we accomplished a substantial portion of our stretch goals including leveraging a machine learning algorithm through the Google Cloud Natural Language API to analyze menu data, including nutritional information about the food we serve to individuals, which emphasizes their well-being. We have also integrated a map feature to provide a visual overview of restaurant locations nearby. Ultimately, we are very proud of ourselves for coming together from different parts of the world to create a product that will have a sustainable impact everywhere.

## What we learned

Everyone on the team had exposure to both front-end and back-end development. We were able to grow our collaboration skills because we all tackled one area of the project at a time. We also learned the best way to debug each other's code. It's often better to test-and-rewind, rather than spend time researching the error.

## What's next for SustainaBite

In addition to increasing the functionality of communication between the organization and restaurant through a live chat, we hope to incorporate more precision is measurements to better approximate nutritional value for the items we store. We also hope to make it easier for organizations to pick up the food by partnering with third party delivery services such as Uber or Lyft.