## 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:

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

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

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

## 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
Track: Social Good

[Disaster Relief]
[Best Data Visualization Hack]
[Best Social Impact Hack]
[Best Hack for Building Community]

## Inspiration

Do you ever worry about what is in your water? While some of us live in the luxury of getting clean water for granted, some of us do not. In Flint, MI, another main water pipeline broke. Again. Under water boil advisory, citizens are subject to government inaction and lack of communication. Our goal is to empower communities with what is in their water using data analytics and crowd sourced reports of pollutants in tap water found in people's homes.

## What it does

Water Crusader is an app that uses two categories of data to give communities an informed assessment of their water quality: publicly available government data and crowd sourced tap water assessments taken from people's homes. Firstly, it takes available government data of blood lead levels tested in children, records of home age to indicate the age of utilities and materials used in pipes, and income levels as an indicator of maintenance in a home. With the programs we have built, governments can expand our models by giving us more data to use. Secondly, users are supplied with a cost-effective, open source IOT sensor that uploads water quality measurements to the app. This empowers citizens to participate in knowing the water quality of their communities. As a network of sensors is deployed, real-time, crowd-sourced data can greatly improve our risk assessment models. By fusing critical information from these two components, we use regression models to give our users a risk measurement of lead poisoning from their water pipelines. Armed with this knowledge, citizens are empowered with being able to make more informed health decisions and call their governments to act on the problem of lead in drinking water.

## How we built it

Hardware:
To simulate the sensor system with the available hardware materials at HackMIT, we used a ESP32 and DHT11 Temperature and Humidity Sensor. The ESP32 takes temperature and humidity data read from DHT11. Data is received in Nose-RED json by specifying HTTP request and the sending actual post in the Arduino IDE.

Data Analytics:
Using IBM's Watson Studio and AI development tools, data from government sources were cleaned and used to model lead poisoning risk. Blood lead levels tested in children from the Center for Disease Control was used as the feature we wanted to predict. House age and poverty levels taken from the U.S. Census were used to predict blood lead levels tested in children from the Center for Disease Control.

## Challenges we ran into

1. We are limited by the amount of hardware available. We tried our best to create the best simulation of the sensor system as possible.
2. It was hard to retrieve and clean government data, especially with the need to make data requests.
3. Not all of us are familiar with Node-RED js and there was a lot to learn!

## Accomplishments that we're proud of

1.Learning new software!

1. Working in a super diverse team!!

## What's next for Water Crusader

We will continue to do research for different water sensors that can be added to our system. From our research, we learned that there is a lead sensor in development.
## Inspiration

Our inspiration for this project came from OpenAI's recent release of Swarms, which introduced us to the concept of using multiple AI agents to collaboratively accomplish tasks. We wanted to explore how AI agents could be applied in a practical, user-friendly way, particularly for tasks that require coordination and planning.

As college students, we often find ourselves excited about future plans, like traveling or exploring new places. However, with our busy schedules, these ideas often get pushed aside and forgotten. This is where Bucket List AI steps in. It’s designed to tackle this common problem by not only remembering the trips we want to take but actively helping us plan them. Using AI-driven recommendations and personalized itineraries, Bucket List AI simplifies the process of turning our aspirations into reality, ensuring we don't let those ideas wither away.

## What it does

Bucket List AI helps users turn their travel dreams into reality by intelligently planning trips based on their interests. First, it analyzes the items the user adds to their "bucket" list, such as destinations or activities. Then, it scans for flights from the user’s location and identifies options that are cheaper than usual.

Once potential flights are found, our AI agents step in to evaluate the quality of the trip, considering factors like whether the user's bucket list items will be fulfilled. After this evaluation, the information is passed to another agent that generates a detailed itinerary, which is then sent back to the user. This seamless process ensures that users not only discover affordable travel opportunities but also receive well-planned itineraries tailored to their personal goals.

## How we built it

We built Bucket List AI using the FaRM stack, which stands for FastAPI, React, and MongoDB. Our backend was developed in Python with FastAPI, providing a fast and scalable framework for handling requests and managing user data. For data storage, we utilized MongoDB, allowing us to store and manage complex, unstructured data like bucket list items and trip plans efficiently.

Our frontend was created with React for a dynamic and user-friendly interface, enabling users to easily interact with their bucket lists and receive travel recommendations. We styled the frontend using CSS to ensure a visually appealing and smooth user experience.

Additionally, we integrated Fetch.ai's agents, which allowed us to harness the power of decentralized AI. These agents work collaboratively to scan for flights, evaluate trip quality, and generate personalized itineraries. By leveraging Fetch.ai’s agent framework, we enabled seamless, automated task completion, ensuring that users receive high-quality, curated trip plans.

## Challenges we ran into

One of the key challenges we faced was implementing a multi-agent system with Fetch.ai. We encountered difficulties in establishing clear communication between the agents, despite following the guidelines and studying the documentation. The process of getting the agents to effectively collaborate and share data proved to be more complex than anticipated, and we weren’t able to fully implement the agents as we originally intended.

## Accomplishments that we're proud of

We’re proud of several accomplishments in building Bucket List AI. First, successfully integrating the FaRM stack—FastAPI, React, and MongoDB—allowed us to create a full-stack application that delivers a smooth user experience. Despite not being able to fully use the Agents through Fetch.ai, we learned a lot about how these agents work and how to implement them. The fact that we were able to generate trip recommendations based on user interests and flight prices is a major win as well as being able to use real time flight data to make trips.

## What we learned

Throughout the development of Bucket List AI, we gained valuable experience in working with AI agents and understanding the complexities of multi-agent systems. We deepened our knowledge of the FaRM stack, honing our skills in FastAPI, React, and MongoDB, while also improving our ability to handle database connectivity issues and optimize API performance. We learned the importance of collaborative problem-solving when overcoming technical hurdles and gained a better understanding of how AI-driven automation can enhance user experiences in real-world applications.

## What's next for BucketList AI

Following CalHacks, our team is excited to continue collaborating and perfecting our project. We aim to address the challenges we faced with Fetch.ai, enhancing and adding to the product to make it refined, impactful, and ready for users around the world to enjoy.