MediMeal

Inspiration

The inspiration for this idea was the fact that many people struggle with not knowing how their medications can do more harm than help if they don't watch what they eat or watch out for a harmful drug interaction. This project should help almost anyone regardless of age or medication because it could be as simple as an over-the-counter Tylenol to high-dose prescription medication. This website is intended to help educate everyone about the medication they take.

What it does

Our Smart Prescription & Nutrition Advisor gives you an overview of the medication you are taking, tells you the active ingredients in the medication, the foods you should avoid, any other medication you should avoid while consuming this, the average price of the medication, and lastly a graphic display of the success rate of the medication.

How we built it

We built our program using Streamlit for our frontend code and fastAPI for the backend. We gathered datasets from Kaggle, used preprocessors, and trained the data sets to make smarter predictions & recommendations, identifying patterns, and improving accuracy for the user.

Challenges we ran into

Datasets were a major challenge we ran into because the data models took a long time to train and it was difficult to work through the errors when connecting it to the backend.

Accomplishments that we're proud of

We are proud that, in the end, we were able to create a fast-working website that provides real-time information about prescription drugs and give a model of the success rate.

What we learned

We learned how to work with streamlit and fastapi, we also downloaded Amazon Q, which help make out code for efficient and decreased the time we spent writing the code itself.

What's next for MediMeal

There are so many ways we can revamp MediMeal; we would like to start by adding a virtual assistant, like a virtual doctor, that can help answer questions, and we also had plans to make it more personalized by adding fitness tracking so that its a complete health tracker!

Built With

• amazon-q
• css
• fastapi
• github
• javascript
• python
• streamlit
• vscode