Inspiration

We aimed to best make use of our group's diverse skillsets while also creating an app or service that could serve as a useful tool. We all feel that we want to be more socially responsible about the food products we buy, but the information to make these decisions can be difficult to access and parse. After finding a dataset that provides information on the environmental impact of many of our country's largest companies, we created an app to quickly connect the average shopper to detailed information on food manufacturers' carbon footprints.

What it does

We envision our app used primarily by shoppers seeking to better understand the impact of their buying habits. The shopper would use the app to take a picture of an unknown food item and would receive a simple composite score out of 100 rating the ecological responsibility of both the item and its manufacturer. The user may then scan another item or select "learn more" to be directed to a webpage with information on the highest-scoring foods and companies in that specific category. Our app features a quick-responding UI and requires only 4 taps to access all relevant information.

How we built it

The work for this app was split evenly among the three of us. One member of your group focused on the server, database, and website; another member did the iOS Application, and a third member provided visual design work, CSS programing and research.

We first made parts of the application and server, focusing on achieving reliable communication between the two. We also experimented with Google Vision API and learned to send images in a post request. Our system automatically sends each user-taken photo to the Google Vision API; the response is sent to the server where it is used to query a database to relay the composite score of the food and parent company back to the app.

The user will be presented this score as well as the opportunity to learn about more environmentally-sustainable foods in the category. This "learn more" option directs the user to an embedded web page displaying three high-scoring foods.

Challenges we ran into

We struggled with some technical aspects of the project, including learning to parse JSON files in Swift and making asynchronous calls from javascript to update the webpage. We also aimed to scan brands that we saw around us and at the store, not necessarily the large conglomerates that owned the brands; for this reason we created a hypothetical dataset that provided scores for individual food products and connections with larger corporations. That way we would scan a bag of M&Ms, for example, and get true information about Mars, the parent company.

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