## Inspiration

Originally, we wanted to think of various ways drone delivery could be used to solve problems, and decided to create an app for emergency medicine delivery as there are certainly situations in which someone might not be able to make it to the hospital or pharmacy. Drones could deliver life-saving products like insulin and inhalers.
Building upon that, I don't think many people enjoy having to drive to CVS or Walgreens to pick up a prescription medicine, so the drones could be used for all sorts of pharmaceutical deliveries as well.

## What it does

This is a user-facing web app that allows the user to pick a delivery location and follow the delivery through real-time tracking. The app also provides an ETA.

## How we built it

The app is built on CherryPy and the styling is done with HTML/CSS/SCSS/JS (jQuery). In terms of APIs, we used Mapbox to set and view location for tracking and ordering. This app was built off of DroneKit's API and drone delivery example, and we used DroneKit's drone simulator to test it.

## Challenges we ran into

We really wanted to add SMS notifications that would alert the user when the package had arrived but ran into issues implementing the Twilio API without a server using only jQuery, as most solutions utilized PHP. It was also our first time working with CherryPy, so that was a challenging learning experience in terms of picking up a new framework.

## Accomplishments that we're proud of

I'm proud of figuring out how to calculate ETA given coordinates, learning a lot more Python than I'd previously ever known, and integrating nice styling with the bare bones website. I'm also proud of the photoshopped Pusheen background.

## What we learned

I learned how to work with new APIs, since I hadn't had much prior experience using them. I also learned more Python in the context of web development and jQuery.

## What's next for Flight Aid

I really want to figure out how to add notifications so I can flesh out more features of the user-facing app. I would in the future want to build a supplier-facing app that would give the supplier analytics and alarms based on the drone's sensor data.
## Inspiration

We want people to make right decision at right time so that they can get returns on their investments and trading in stock market.

## What it does

It will enable to invest and trade in the stock by using HFT and technical analysis techniques. The product will have large scale applications in situations where the investor or trader does not has much time for individual analysis and for professionals that would like to deploy their money on auto pilot.

## How we built it

We have only the idea :)

## Challenges we ran into

How to implement the web app and mobile app for auto trading.

## What's next for TradeGO

We want to implement the app so that it can be used for auto trading.
As a response to the ongoing wildfires devastating vast areas of Australia, our team developed a web tool which provides wildfire data visualization, prediction, and logistics handling. We had two target audiences in mind: the general public and firefighters. The homepage of Phoenix is publicly accessible and anyone can learn information about the wildfires occurring globally, along with statistics regarding weather conditions, smoke levels, and safety warnings. We have a paid membership tier for firefighting organizations, where they have access to more in-depth information, such as wildfire spread prediction.

We deployed our web app using Microsoft Azure, and used Standard Library to incorporate Airtable, which enabled us to centralize the data we pulled from various sources. We also used it to create a notification system, where we send users a text whenever the air quality warrants action such as staying indoors or wearing a P2 mask.

We have taken many approaches to improving our platform’s scalability, as we anticipate spikes of traffic during wildfire events. Our codes scalability features include reusing connections to external resources whenever possible, using asynchronous programming, and processing API calls in batch. We used Azure’s functions in order to achieve this.

Azure Notebook and Cognitive Services were used to build various machine learning models using the information we collected from the NASA, EarthData, and VIIRS APIs. The neural network had a reasonable accuracy of 0.74, but did not generalize well to niche climates such as Siberia.

Our web-app was designed using React, Python, and d3js. We kept accessibility in mind by using a high-contrast navy-blue and white colour scheme paired with clearly legible, sans-serif fonts. Future work includes incorporating a text-to-speech feature to increase accessibility, and a color-blind mode. As this was a 24-hour hackathon, we ran into a time challenge and were unable to include this feature, however, we would hope to implement this in further stages of Phoenix.