## Inspiration

We were working with 1and1's servers as a team for another app, but we realized that it was a pain to collaborate on editing the server.

## What it does

Hence we decided to make a chatbot that allows us to edit servers using messaging apps.

## How we built it

We used Facebook's messenger services to run our bot. We used python and 1and1's api for all the backend server work.

## Challenges we ran into

Our challenges revolved around maneuvering python3 which many of our team members had little experience with.

## Accomplishments that we're proud of

We are proud to have a completed chat bot.

## What we learned

We learned how to use python, API's, and servers.

## What's next for ChatterWorks

We plan to add more functionality to this wonderful product, and increase our NLP abilities.
## Inspiration

In the current media landscape, control over distribution has become almost as important as the actual creation of content, and that has given Facebook a huge amount of power. The impact that Facebook newsfeed has in the formation of opinions in the real world is so huge that it potentially affected the 2016 election decisions, however these newsfeed were not completely accurate. Our solution? FiB because With 1.5 Billion Users, Every Single Tweak in an Algorithm Can Make a Change, and we dont stop at just one.

## What it does

Our algorithm is two fold, as follows:

**Content-consumption**: Our chrome-extension goes through your facebook feed in real time as you browse it and verifies the authenticity of posts. These posts can be status updates, images or links. Our backend AI checks the facts within these posts and verifies them using image recognition, keyword extraction, and source verification and a twitter search to verify if a screenshot of a twitter update posted is authentic. The posts then are visually tagged on the top right corner in accordance with their trust score. If a post is found to be false, the AI tries to find the truth and shows it to you.

**Content-creation**: Each time a user posts/shares content, our chat bot uses a webhook to get a call. This chat bot then uses the same backend AI as content consumption to determine if the new post by the user contains any unverified information. If so, the user is notified and can choose to either take it down or let it exist.

## How we built it

Our chrome-extension is built using javascript that uses advanced web scraping techniques to extract links, posts, and images. This is then sent to an AI. The AI is a collection of API calls that we collectively process to produce a single "trust" factor. The APIs include Microsoft's cognitive services such as image analysis, text analysis, bing web search, Twitter's search API and Google's Safe Browsing API. The backend is written in Python and hosted on Heroku. The chatbot was built using Facebook's wit.ai

## Challenges we ran into

Web scraping Facebook was one of the earliest challenges we faced. Most DOM elements in Facebook have div ids that constantly change, making them difficult to keep track of. Another challenge was building an AI that knows the difference between a fact and an opinion so that we do not flag opinions as false, since only facts can be false. Lastly, integrating all these different services, in different languages together using a single web server was a huge challenge.

## Accomplishments that we're proud of

All of us were new to Javascript so we all picked up a new language this weekend. We are proud that we could successfully web scrape Facebook which uses a lot of techniques to prevent people from doing so. Finally, the flawless integration we were able to create between these different services really made us feel accomplished.

## What we learned

All concepts used here were new to us. Two people on our time are first-time hackathon-ers and learned completely new technologies in the span of 36hrs. We learned Javascript, Python, flask servers and AI services.

## What's next for FiB

Hopefully this can be better integrated with Facebook and then be adopted by other social media platforms to make sure we stop believing in lies.
## Inspiration

Over one-fourth of Canadians during their lifetimes will have to deal with water damage in their homes. This is an issue that causes many Canadians overwhelming stress from the sheer economical and residential implications.

As an effort to assist and solve these very core issues, we have designed a solution that will allow for future leaks to be avoidable. Our prototype system made, composed of software and hardware will ensure house leaks are a thing of the past!

## What is our planned solution?

To prevent leaks, we have designed a system of components that when functioning together, would allow the user to monitor the status of their plumbing systems.

Our system is comprised of:

>
> Two types of leak detection hardware
>
>
> * Acoustic leak detectors: monitors abnormal sounds of pipes.
> * Water detection probes: monitors the presence of water in unwanted areas.
>
>
>

Our hardware components will have the ability to send data to a local network, to then be stored in the cloud.

>
> Software components
>
>
> * Secure cloud to store vital information regarding pipe leakages.
> * Future planned app/website with the ability to receive such information
>
>
>

## Business Aspect of Leakio

Standalone, this solution is profitable through the aspect of selling the specific hardware to consumers. Although for insurance companies, this is a vital solution that has the potential to save millions of dollars.

It is far more economical to prevent a leak, rather than fix it when it already happens. The time of paying the average cost of $10,900 US dollars to fix water damage or a freezing claim is now avoidable!

In addition to saved funds, our planned system will be able to send information to insurance companies for specific data purposes such as which houses or areas have the most leaks, or individual risk assessment. This would allow insurance companies to more appropriately create better rates for the consumer, for the benefit of both consumer and insurance company.

### Software

Front End:
This includes our app design in Figma, which was crafted using knowledge on proper design and ratios. Specifically, we wanted to create an app design that looked simple but had all the complex features that would seem professional. This is something we are proud of, as we feel this component was successful.

Back End:
PHP, MySQL, Python

### Hardware

Electrical

* A custom PCB is designed from scratch using EAGLE
* Consists of USBC charging port, lithium battery charging circuit, ESP32, Water sensor connector, microphone connector
* The water sensor and microphone are extended from the PCB which is why they need a connector

3D-model

* Hub contains all the electronics and the sensors
* Easy to install design and places the microphone within the walls close to the pipes

## Challenges we ran into

Front-End:
There were many challenges we ran into, especially regarding some technical aspects of Figma. Although the most challenging aspect in this would’ve been the implementation of the design.

Back-End:
This is where most challenges were faced, which includes the making of the acoustic leak detector, proper sound recognition, cloud development, and data transfer.

It was the first time any of us had used MySQL, and we created it on the Google Cloud SQL platform. We also had to use both Python and PHP to retrieve and send data, two languages we are not super familiar with.

We also had no idea how to set up a neural network with PyTorch. Also finding the proper data that can be used to train was also very difficult.

## Accomplishments that we're proud of

Learning a lot of new things within a short period of time.

## What we learned

Google Cloud:
Creating a MySQL database and setting up a Deep Learning VM.

MySQL:
Using MySQL and syntaxes, learning PHP.

Machine Learning:
How to set up Pytorch.

PCB Design:
Learning how to use EAGLE to design PCBs.

Raspberry Pi:
Autorun Python scripts and splitting .wav files.

Others:
Not to leave the recording to the last hour. It is hard to cut to 3 minutes with an explanation and demo.

## What's next for Leakio

* Properly implement audio classification using PyTorch
* Possibly create a network of devices to use in a single home
* Find more economical components
* Code for ESP32 to PHP to Web Server
* Test on an ESP32