## Inspiration

Around 40% of the lakes in America are too polluted for aquatic life, swimming or fishing.Although children make up 10% of the world’s population, over 40% of the global burden of disease falls on them. Environmental factors contribute to more than 3 million children under age five dying every year. Pollution kills over 1 million seabirds and 100 million mammals annually. Recycling and composting alone have avoided 85 million tons of waste to be dumped in 2010. Currently in the world there are over 500 million cars, by 2030 the number will rise to 1 billion, therefore doubling pollution levels. High traffic roads possess more concentrated levels of air pollution therefore people living close to these areas have an increased risk of heart disease, cancer, asthma and bronchitis. Inhaling Air pollution takes away at least 1-2 years of a typical human life. 25% deaths in India and 65% of the deaths in Asia are resultant of air pollution. Over 80 billion aluminium cans are used every year around the world. If you throw away aluminium cans, they can stay in that can form for up to 500 years or more. People aren’t recycling as much as they should, as a result the rainforests are be cut down by approximately 100 acres per minute
On top of this, I being near the Great Lakes and Neeral being in the Bay area, we have both seen not only tremendous amounts of air pollution, but marine pollution as well as pollution in the great freshwater lakes around us. As a result, this inspired us to create this project.

## What it does

For the react native app, it connects with the Website Neeral made in order to create a comprehensive solution to this problem.

There are five main sections in the react native app:

The first section is an area where users can collaborate by creating posts in order to reach out to others to meet up and organize events in order to reduce pollution. One example of this could be a passionate environmentalist who is organizing a beach trash pick up and wishes to bring along more people. With the help of this feature, more people would be able to learn about this and participate.

The second section is a petitions section where users have the ability to support local groups or sign a petition in order to enforce change. These petitions include placing pressure on large corporations to reduce carbon emissions and so forth. This allows users to take action effectively.

The third section is the forecasts tab where the users are able to retrieve data regarding various data points in pollution. This includes the ability for the user to obtain heat maps regarding the amount of air quality, pollution and pollen in the air and retrieve recommended procedures for not only the general public but for special case scenarios using apis.

The fourth section is a tips and procedures tab for users to be able to respond to certain situations. They are able to consult this guide and find the situation that matches them in order to find the appropriate action to take. This helps the end user stay calm during situations as such happening in California with dangerously high levels of carbon.

The fifth section is an area where users are able to use Machine Learning in order to figure out whether where they are is in a place of trouble. In many instances, not many know exactly where they are especially when travelling or going somewhere unknown. With the help of Machine Learning, the user is able to place certain information regarding their surroundings and the Algorithm is able to decide whether they are in trouble. The algorithm has 90% accuracy and is quite efficient.

## How I built it

For the react native part of the application, I will break it down section by section.

For the first section, I simply used Firebase as a backend which allowed a simple, easy and fast way of retrieving and pushing data to the cloud storage. This allowed me to spend time on other features, and due to my ever growing experience with firebase, this did not take too much time. I simply added a form which pushed data to firebase and when you go to the home page it refreshes and see that the cloud was updated in real time

For the second section, I used native base in order to create my UI and found an assortment of petitions which I then linked and added images from their website in order to create the petitions tab. I then used expo-web-browser, to deep link the website in opening safari to open the link within the app.
For the third section, I used breezometer.com’s pollution api, air quality api, pollen api and heat map apis in order to create an assortment of data points, health recommendations and visual graphics to represent pollution in several ways. The apis also provided me information such as the most common pollutant and protocols for different age groups and people with certain conditions should follow. With this extensive api, there were many endpoints I wanted to add in, but not all were added due to lack of time.

For the fourth section, it is very much similar to the second section as it is an assortment of links, proofread and verified to be truthful sources, in order for the end user to have a procedure to go to for extreme emergencies. As we see horrible things happen, such as the wildfires in California, air quality becomes a serious concern for many and as a result these procedures help the user stay calm and knowledgeable.

For the fifth section, Neeral please write this one since you are the one who created it.

## Challenges I ran into

API query bugs was a big issue in formatting back the query and how to map the data back into the UI. It took some time and made us run until the end but we were still able to complete our project and goals.

## What's next for PRE-LUTE

We hope to use this in areas where there is commonly much suffering due to the extravagantly large amount of pollution, such as in Delhi where seeing is practically hard due to the amount of pollution. We hope to create a finished product and release it to the app and play stores respectively.
We created this app in light of the recent wildfires that have raged across the west coast. As California Natives ourselves, we have witnessed the devastating effects of these fires first-hand. Not only do these wildfires pose a danger to those living around the evacuation area, but even for those residing tens to hundreds of miles away, the after-effects are lingering.

For many with sensitive respiratory systems, the wildfire smoke has created difficulty breathing and dizziness as well. One of the reasons we like technology is its ability to impact our lives in novel and meaningful ways. This is extremely helpful for people highly sensitive to airborne pollutants, such as some of our family members that suffer from asthma, and those who also own pets to find healthy outdoor spaces. Our app greatly simplifies the process of finding a location with healthier air quality amidst the wildfires and ensures that those who need essential exercise are able to do so.

We wanted to develop a web app that could help these who are particularly sensitive to smoke and ash to find a temporary respite from the harmful air quality in their area. With our app air.ly, users can navigate across North America to identify areas where the air quality is substantially better. Each dot color indicates a different air quality level ranging from healthy to hazardous. By clicking on a dot, users will be shown a list of outdoor recreation areas, parks, and landmarks they can visit to take a breather at.

We utilized a few different APIs in order to build our web app. The first step was to implement the Google Maps API using JavaScript. Next, we scraped location and air quality index data for each city within North America. After we were able to source real-time data from the World Air Quality Index API, we used the location information to connect to our Google Maps API implementation. Our code took in longitude and latitude data to place a dot on the location of each city within our map. This dot was color-coded based on its city AQI value.

At the same time, the longitude and latitude data was passed into our Yelp Fusion API implementation to find parks, hiking areas, and outdoor recreation local to the city. We processed the Yelp city and location data using Python and Flask integrations. The city-specific AQI value, as well as our local Yelp recommendations, were coded in HTML and CSS to display an info box upon clicking on a dot to help a user act on the real-time data. As a final touch, we also included a legend that indicated the AQI values with their corresponding dot colors to allow ease with user experience.

We really embraced the hacker resilience mindset to create a user-focused product that values itself on providing safe and healthy exploration during the current wildfire season. Thank you :)
## Inspiration

We wanted a low-anxiety tool to boost our public speaking skills. With an ever-accelerating shift of communication away from face-to-face and towards pretty much just memes, it's becoming difficult for younger generations to express themselves or articulate an argument without a screen as a proxy.

## What does it do?

DebateABot is a web-app that allows the user to pick a topic and make their point, while arguing against our chat bot.

## How did we build it?

Our website is boot-strapped with Javascript/JQuery and HTML5. The user can talk to our web app which used NLP to convert speech to text, and sends the text to our server, which was built with PHP and background written in python. We perform key-word matching and search result ranking using the indico API, after which we run Sentiment Analysis on the text. The counter-argument, as a string, is sent back to the web app and is read aloud to the user using the Mozilla Web Speech API

## Some challenges we ran into

First off, trying to use the Watson APIs and the Azure APIs lead to a lot of initial difficulties trying to get set up and get access. Early on we also wanted to use our Amazon Echo that we have, but reached a point where it wasn't realistic to use AWS and Alexa skills for what we wanted to do. A common theme amongst other challenges has simply been sleep deprivation; staying up past 3am is a sure-fire way to exponentiate your rate of errors and bugs. The last significant difficulty is the bane of most software projects, and ours is no exception- integration.

## Accomplishments that we're proud of

The first time that we got our voice input to print out on the screen, in our own program, was a big moment. We also kicked ass as a team! This was the first hackathon EVER for two of our team members, and everyone had a role to play, and was able to be fully involved in developing our hack. Also, we just had a lot of fun together. Spirits were kept high throughout the 36 hours, and we lasted a whole day before swearing at our chat bot. To our surprise, instead of echoing out our exclaimed profanity, the Web Speech API read aloud "eff-asterix-asterix-asterix you, chat bot!" It took 5 minutes of straight laughing before we could get back to work.

## What we learned

The Mozilla Web Speech API does not swear! So don't get any ideas when you're talking to our innocent chat bot...

## What's next for DebateABot?

While DebateABot isn't likely to evolve into the singularity, it definitely has the potential to become a lot smarter. The immediate next step is to port the project over to be usable with Amazon Echo or Google Home, which eliminates the need for a screen, making the conversation more realistic. After that, it's a question of taking DebateABot and applying it to something important to YOU. Whether that's a way to practice for a Model UN or practice your thesis defence, it's just a matter of collecting more data.

<https://www.youtube.com/watch?v=klXpGybSi3A>