Please Fridge with Rp & pico
I created content with UI pentahcodes and it appeared in a news article.
WIZnet - W5100S-EVB-Pico
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The Raspberry Pi is obviously one of the most deliciously named boards of our time, but this Raspberry Pi project might be the tastiest use case we’ve ever seen. It was created by a maker and developer known as Simon over at Hackster. Using our favorite SBC, he’s developed a system that scans his refrigerator for food and recommends recipes based on what’s inside.
According to Simon, he trained his project using YOLOv5 to recognize potential ingredients from the contents of his fridge. The food is scanned using a simple camera module and processed using image recognition. The Pi is programmed to organize the information and research for possible recipes and cooking videos related to its findings.
The recipes are selected using ChatGPT with Open AI. A script sends a list of detected food with a prompt to suggest recipes using the ingredients. Once a list of possible recipes is put together, the Pi then notifies the user using Telegram. These messages include recipes and tutorial videos related to the recipes it’s gathered—saving you tons of time spent manually researching for a possible dinner menu.
There are actually two Raspberry Pis used in this project. A Raspberry Pi 4B is the main board handling most of the processing but a WIZnet W5100S-EVB-Pico is thrown into the mix, as well, connecting to the Pi 4 via UART. Simon is using a Raspberry Pi Camera Module but any camera should work fine in its stead.
The software for this project involves a few layers of software, all of which is detailed in the project page at Hackster for anyone who wants to check it out for themselves. OpenAI handles the ChatGPT portion while some Python helps integrate the Telegram API. MicroPython is used to program the WIZnet W5100S-EVB-Pico board.
Visit the Raspberry Pi fridge scanner project page at Hackster to get a closer look at how this Raspberry Pi project goes together and be sure to follow Simon for future updates.
Based on the feedback from UI Curation, I thought about how to utilize pico even more. Then I thought that I could create the environment itself with Fullstack and let pico handle it, and I re-curated the above project with Fullstack.
I called the project Let's Eat U, which literally means let the eater eat free. And it's a personalized service that provides recommendations based on various layers and forms.
In the next pentacode, I will try to API this webserver so that I can implement the project well.