## Inspiration

After learning about the current shortcomings of disaster response platforms, we wanted to build a modernized emergency services system to assist relief organizations and local governments in responding faster and appropriately.

## What it does

safeFront is a cross between next-generation 911 and disaster response management. Our primary users are local governments and relief organizations. The safeFront platform provides organizations and governments with the crucial information that is required for response, relief, and recovery by organizing and leveraging incoming disaster related data.

## How we built it

safeFront was built using React for the web dashboard and a Flask service housing the image classification and natural language processing models to process the incoming mobile data.

## Challenges we ran into

Ranking urgency of natural disasters and their severity by reconciling image recognition, language processing, and sentiment analysis on mobile data and reported it through a web dashboard. Most of the team didn't have a firm grasp on React components, so building the site was how we learned React.

## Accomplishments that we're proud of

Built a full stack web application and a functioning prototype from scratch.

## What we learned

Stepping outside of our comfort zone is, by nature, uncomfortable. However, we learned that we grow the most when we cross that line.

## What's next for SafeFront

We'd like to expand our platform for medical data, local transportation delays, local river level changes, and many more ideas. We were able to build a fraction of our ideas this weekend, but we hope to build additional features in the future.
**Made by Ella Smith (ella#4637) & Akram Hannoufa (ak\_hannou#7596) -- Team #15**

*Domain: <https://www.birtha.online/>*

## Inspiration

Conversations with friends and family about the difficulty of finding the right birth control pill on the first try.

## What it does

Determines the brand of hormonal contraceptive pill most likely to work for you using data gathered from drugs.com. Data includes: User Reviews, Drug Interactions, and Drug Effectiveness.

## How we built it

The front-end was built using HTML, CSS, JS, and Bootstrap. The data was scraped from drugs.com using Beautiful Soup web-scraper.

## Challenges we ran into

Having no experience in web-dev made this a particularly interesting learning experience. Determining how we would connect the scraped data to the front-end was challenging, as well as building a fully functional multi-page form proved to be difficult.

## Accomplishments that we're proud of

We are proud of the UI design, given it is our first attempt at web development. We are also proud of setting up a logic system that provides variability in the generated results. Additionally, figuring out how to web scrape was very rewarding.

## What we learned

We learned how to use version control software, specifically Git and GitHub. We also learned the basics of Bootstrap and developing a functional front-end using HTML, CSS, and JS.

## What's next for birtha

Giving more detailed and accurate results to the user by further parsing and analyzing the written user reviews. We would also like to add some more data sources to give even more complete results to the user.
## Inspiration

Our inspiration for the disaster management project came from living in the Bay Area, where earthquakes and wildfires are constant threats. Last semester, we experienced a 5.1 magnitude earthquake during class, which left us feeling vulnerable and unprepared. This incident made us realize the lack of a comprehensive disaster management plan for our school and community. We decided to take action and develop a project on disaster management to better prepare ourselves for future disasters.

## What it does

Our application serves as a valuable tool to help manage chaos during disasters such as earthquakes and fire. With features such as family search, location sharing, searching for family members, an AI chatbot for first aid, and the ability to donate to affected individuals and communities, our app can be a lifeline for those affected by a crisis.

## How we built it

Our disaster management application was built with Flutter for the Android UI, Dialogflow for the AI chat assistant, and Firebase for the database. The image face similarity API was implemented using OpenCV in Django REST.

## Challenges we ran into

We are proud of the fact that, as first-time participants in a hackathon, we were able to learn and implement a range of new technologies within a 36-hour time frame.

## Accomplishments that we're proud of

* Our disaster management application has a valuable feature that allows users to search for their family members during a crisis. By using an image similarity algorithm API (OpenCV), users can enter the name of a family member and get information about their recent location. This helps to ensure the safety of loved ones during a disaster, and can help identify people who are injured or unconscious in hospitals. The image is uploaded to Firebase, and the algorithm searches the entire database for a match. We're proud of this feature, and will continue to refine it and add new technologies to the application.

## What we learned

We were not able to implement the live location sharing feature due to time constraints, but we hope to add it in the future as we believe it could be valuable in emergency situations.

## What's next for -

We plan to improve our AI chatbot, implement an adaptive UI for responders, and add text alerts to the application in the future.