The Aubergine Index

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  Kokkari Estiatorio - Greek restaurant in SF

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  Monseiur Benjamin. - French restaurant in SF

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  Kokkari Estiatorio - Greek restaurant in SF

Problem

Difficult to create feedback loops with restaurants to push for more sustainability on the menu

What does it do?

The aubergine index provides a ranking of local restaurants based on data from SU-EATABLE LIFE database to measure the carbon footprint & restaurant reviews to grab the sentiment of the specific eating experiences

What we built

We built an app that allows the user to enter their preferences and find restaurants that match those preferences.

Accomplishments that we're proud of

-creating carbon footprint score restaurants (the aubergine index) -create a direct method to measure friendliness with respect to a user preference (e.g. gluten free)

What we learned

-Discovered food climate database -how difficult it is to scrape menus -how OpenAI can be rather whimsical with its findings, but a lot of fun as well.

What's next for The Aubergine Index

-add a component where users can submit reviews & findings -continue to develop the scoring methodology to include taste & dining experience -Elaborate on the prompts to the OpenAI engine to help better align with the given user preferences -build a social aspect: connect folks to drive more interaction with sustainable -launch a brand campaign to make this as coveted a rating as michelin stars

Built With

• openai
• python
• serpapi