EatSafely

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Inspiration

Are you tired of losing money due to spoiled food and food waste? According to the Centers for Disease Control and Prevention, the economic cost of foodborne illness in the United States is around $15.6 billion per year. One of the major contributors to this cost is spoiled food. When food is spoiled, it can contain harmful bacteria, viruses, and other pathogens that can cause foodborne illness. With the price of medical care, it is important to not spend valuable money on situations that could have been avoided with the right tools. Another factor that contributes to this wasting money is food waste due to misidentification of food as spoiled. Oftentimes, food that is still safe to eat is thrown away because it may look or smell different from when it was first purchased.

This prompted us to create EatSafely : the perfect app to check if your food is spoiled or not.

What it does

Introducing EatSafely, the revolutionary web app that helps you to stay safe from foodborne illness and identify safe to eat foods. With the integration of machine learning and a comprehensive dataset, EatSafely is able to identify and determine the safety of any fruit you upload to the app.

We created a custom trained machine learning model based on a dataset from Kaggle. We used MobileNetV2 as the baseline mode and achieved a final accuracy of 97%.

No more guessing whether that apple or banana is safe to eat. Simply upload a photo of the fruit in question, and EatSafely will give you a clear and accurate assessment of whether or not it is safe to eat. So why risk it? Start using EatSafely today, and enjoy peace of mind knowing that the fruits you're eating are safe and healthy. Say goodbye to spoiled fruits and hello to EatSafely.

EatSafely is a project submission for the Treasure Hacks hackathon 3.0 made by 4 tech enthusiasts.

How we built it

• React
• Flask
• CSS
• Tensorflow
• Keras
• Hop.io

Challenges we ran into

We ran into a number of challenges while developing, integrating HTML, CSS, JS, React, and ML model but we successfully overcame it.

Accomplishments that we're proud of

We are proud that we could successfully build EatSafely with all the necessary functions. The integration of model with the frontend posed a challenge but we successfully overcame it.

What we learned

We learnt the about cooperation. Teamwork, motivation and on the code aspect we learnt more on machine learning, learned a ton about libraries, packages, implementation in various languages, machine learning.

What's next for EatSafely

We hope to make EatSafely more advance in algorithms for proper recognition of each fruits.

Built With

• css
• flask
• html
• javascript
• keras
• material-ui
• react
• tensorflow