Inspiration
We had two main problems we wanted to tackle: the problem of farmers losing money selling their vegetables and these vegetables going to waste just because of deformities. As people who want these farmers to succeed, we wanted to develop an app for farmers to list their produce and customers to buy it at fairer prices compared to retailers. We also wanted these farmers to be able to sell deformed produce that would've ended up in landfills because of retailers, resulting in a lower waste of food.
What it does
Our app is extremely similar to how eBay and Mercari work. The seller (farmers) puts in an item they would sell, like 5 apples. The request is made and will become a post, where the buyer (consumers) can see these posts and choose to buy them with their preferred payment system.
How we built it
On the front end aspect, we used both React.js and Tailwind CSS to design the website, and posts, and communicate with the Firebase Database. On the backend aspect, we used a combination of Flask and Python to develop a machine-learning model that would predict prices based on the data given.
Challenges we ran into
The server would not run multiple times because of small errors Had trouble implementing a payment method for an hour Early problems with machine learning and how string data was not being converted Developing multiple pages with little to no experience in front-end development Choosing a correct model for our predictor
Accomplishments that we're proud of
We made a change in the farmer's industry that will help these farmers, and we went through with that change, creating everything from scratch, struggling throughout the way as a team rather than individually. What we are proud of is how we grew throughout this hackathon and how we tackled problems as a team.
What we learned
We learned how to be more collaborative as a team, splitting work towards our strengths rather than just assigning random work, understood how to build websites using more complex code such as React and Tailwind CSS, and understood how machine-learning works in our idea and how we implement it into real-world applications
What's next for Fresh For All
Implement rows that have categories like daily deals, hottest produce, and recently viewed Have a rating system where the sellers (farmers) can be given stars for the produce quality and delivery Coupons that can be applied for discounts and redeem free produce A reward system to access these coupons A subscription service like Amazon Prime that gives exclusive discounts to members
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