Sofia

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  'Prediction' Page, our application outputs a seven dish meal plan based off user needs

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  'Sofia' Landing Page, users are prompted to input metrics

Inspiration

Almost half of Americans are diagnosed with obesity, making it a leading contributor to chronic health conditions. This concerning statistic motivated us to develop a solution that tackles this national health epidemic.

What it does

User Input: Users provide information such as health goals, dietary restrictions, and food preferences through user-friendly forms. Data Processing: The application formats and transmits this user data securely to the AI agent API. API Interaction: The application interacts with the AI agent API, potentially including anonymized user data and health information (subject to user consent) to further personalize the meal plans. Data Reception: The application receives the generated meal plan data from the AI agent API. Output: The application presents a user-friendly, personalized meal plan tailored to the user's needs and preferences.

How we built it

We utilized Vertex AI Gemini MedLM, Python, Flask for backend development, and Jupyter Notebook for data analysis and experimentation.

Challenges we ran into

Initially, deploying the React frontend proved challenging. However, we persevered and successfully implemented the user interface.

Accomplishments that we're proud of

We are proud of establishing a functional backend and successfully deploying our web application. This achievement demonstrates the effectiveness of our approach to tackling the issue of diabetes management through personalized meal plans.

What we learned

The application of AI in personalized dietary planning for diabetic users. Effective backend development using Python and Flask. The challenges and rewards of deploying a full-stack web application.

Built With

• flask
• javascript
• python