FlowerAlts

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  Home screen with an example of a flower

Inspiration

This app was inspired by the growing urgency to conserve water in times of climate change and drought in Arizona. Many people in Arizona, including us, rarely pay attention to the water consumption by their flowers. To reduce the usage of water and preserve the beauty of outdoors, we have come up with a solution that suggests similar, but water-efficient flowers.

What it does

FlowerAlts allows users to select a flower from a list of 100 common Arizona flowers and they can view details about the selected flower's water consumption. The app's main feature shows the users more water-efficient alternatives to the user's flower selection while maintaining a similar look.

How we built it

Firstly, the app loads a CSV file containing the flower names, their water requirements, and alternative flowers. We have added a Streamlit select box with all 100 of the flower options. Next, the app retrieves the flower's water needs and compares them with alternatives using simple division and percentage change calculations. Lastly, it displays alternatives that are more water-efficient or equally efficient.

Challenges we ran into

Initially when installing the Streamlit package to develop the front end, we ran into the issue where the package would install but the error "PackageNotFoundError: Package not found at..." would come up. We were able to fix this by correcting the location of the Python interpreter in environment variables. Finding Flower Alternatives that were not only more water efficient, but also looked similar to the flower proved tedious as we had to scroll through hundreds of websites and google images for the data and proof of visual similarity respectively.

Accomplishments that we're proud of

The biggest accomplishment throughout this project was being able to accurately calculate and present the efficiency percentages of the user's selected flower and the alternate flower suggestions. We needed to apply the Percentage Change Formula to the efficiency rations to accurately calculate the percentages.

What we learned

As a team, we learned what it took to go through the debugging process and look for various sources to help us overcome problems or errors we were facing. We learned to try different solutions to see which gave us the most efficient results while maintaining simplicity throughout our code.

What's next for FlowerAlts

Our goals for the future are to add more flowers to the datasets, expand our flower selections to other regions, and add an interactive map for visualizing water-efficient zones. We plan to present our app to our local communities to help people make better choices with their gardening. Happy Planting🪴!

Built With

• css
• html
• pandas
• python
• streamlit