# Sharemuters

Web application built on NodeJS that uses the Mojio api to get user data. Finds other people that have similar trips as you so you can carpool with them and share the gas money!

Uses CockroachDB for storing user data.

## Built at nwhacks2017

### Sustainable Future

Reaching a sustainable future requires intermediate steps to achieve. Electric car adoption is currently under 1% in both the US and Canada. Most cities do not have an amazing transportation system like Vancouver; they rely on cars. Carpooling can reduce environmental impact of driving, and benefit users by saving money, meeting new people, and making commutes more enjoyable.

### Barriers

Transport Canada states the biggest barriers for carpooling are personal safety, flexibility, and effort to organize. Sharemuters targets all three of these inconveniences. Users are vetted through Mojio accounts, and the web app pairs drivers with similar commutes.

### Benefits

Carpooling is a large market; currently just under 10% of drivers in the US and Canada carpool, and 75% of those drivers carpool with family. In addition to reducing pollution, carpooling saves money on gas and parking, time spent commuting, and offers health benefits from reduced stress. Many companies also offer perks to employees that carpool.
## Inspiration

Care.ai was inspired by our self-conducted study involving 60 families and 23 smart devices, focusing on elderly healthcare. Over three months, despite various technologies, families preferred the simplicity of voice-activated assistants like Alexa. This preference led us to develop an intuitive, user-friendly AI healthcare chatbot tailored to everyday needs.

## What it does

Care.ai, an AI healthcare chatbot, leverages custom-trained Large Language Models (LLMs) and visual recognition technology hosted on the Intel Cloud for robust processing power. These models, refined and accessible via Hugging Face, underwent further fine-tuning through MonsterAPI, enhancing their accuracy and responsiveness to medical queries. The web application, powered by the Reflex library, provides a seamless and intuitive front-end experience, making it easy for users to interact with and benefit from the chatbot's capabilities. Care.ai supports real-time data analytics and critical care necessary for humans.

## How we built it

We built our AI healthcare chatbot by training LLMs and visual recognition systems on the Intel Cloud, then hosting and fine-tuning these models on Hugging Face with MonsterAPI. The chatbot's user-friendly web interface was developed using the Reflex library, creating a seamless user interaction platform.

For data collection,

* We researched datasets and performed literature review
* We used the pre-training data for developing and fine-tuning our LLM and visual models
* We collect live data readings using sensors to test against our trained models

We categorized our project into three parts:

* Interactive Language Models: We developed deep learning models on Intel Developer Cloud and fine-tuned our Hugging Face hosted models using MonsterAPI. We further used Reflex Library to be the face of Care.ai and create a seamless platform.
* Embedded Sensor Networks: Developed our IoT sensors to track the real-time data and test our LLVMs on the captured data readings.
* Compliance and Security Components: Intel Developer Cloud to extract emotions and de-identify patient's voice to be HIPAA

## Challenges we ran into

Integrating new technologies posed significant challenges, including optimizing model performance on the Intel Cloud, ensuring seamless model fine-tuning via MonsterAPI and achieving intuitive user interaction through the Reflex library. Balancing technical complexity with user-friendliness and maintaining data privacy and security were among the key hurdles we navigated.

## Accomplishments that we're proud of

We're proud of creating a user-centric AI healthcare chatbot that combines advanced LLMs and visual recognition hosted on the cutting-edge Intel Cloud. Successfully fine-tuning these models on Hugging Face and integrating them with a Reflex-powered interface showcases our technical achievement. Our commitment to privacy, security, and intuitive design has set a new standard in accessible home healthcare solutions.

## What we learned

We learned the importance of integrating advanced AI with user-friendly interfaces for healthcare. Balancing technical innovation with accessibility, the intricacies of cloud hosting, model fine-tuning, and ensuring data privacy were key lessons in developing an effective, secure, and intuitive AI healthcare chatbot.

## What's next for care.ai

Next, Care.ai is expanding its disease recognition capabilities, enhancing user interaction with natural language processing improvements, and exploring partnerships for broader deployment in healthcare systems to revolutionize home healthcare access and efficiency.
## Inspiration

Recognizing the disastrous effects of the auto industry on the environment, our team wanted to find a way to help the average consumer mitigate the effects of automobiles on global climate change. We felt that there was an untapped potential to create a tool that helps people visualize cars' eco-friendliness, and also helps them pick a vehicle that is right for them.

## What it does

CarChart is an eco-focused consumer tool which is designed to allow a consumer to make an informed decision when it comes to purchasing a car. However, this tool is also designed to measure the environmental impact that a consumer would incur as a result of purchasing a vehicle. With this tool, a customer can make an auto purhcase that both works for them, and the environment. This tool allows you to search by any combination of ranges including Year, Price, Seats, Engine Power, CO2 Emissions, Body type of the car, and fuel type of the car. In addition to this, it provides a nice visualization so that the consumer can compare the pros and cons of two different variables on a graph.

## How we built it

We started out by webscraping to gather and sanitize all of the datapoints needed for our visualization. This scraping was done in Python and we stored our data in a Google Cloud-hosted MySQL database. Our web app is built on the Django web framework, with Javascript and P5.js (along with CSS) powering the graphics. The Django site is also hosted in Google Cloud.

## Challenges we ran into

Collectively, the team ran into many problems throughout the weekend. Finding and scraping data proved to be much more difficult than expected since we could not find an appropriate API for our needs, and it took an extremely long time to correctly sanitize and save all of the data in our database, which also led to problems along the way.
Another large issue that we ran into was getting our App Engine to talk with our own database. Unfortunately, since our database requires a white-listed IP, and we were using Google's App Engine (which does not allow static IPs), we spent a lot of time with the Google Cloud engineers debugging our code.
The last challenge that we ran into was getting our front-end to play nicely with our backend code

## Accomplishments that we're proud of

We're proud of the fact that we were able to host a comprehensive database on the Google Cloud platform, in spite of the fact that no one in our group had Google Cloud experience. We are also proud of the fact that we were able to accomplish 90+% the goal we set out to do without the use of any APIs.

## What We learned

Our collaboration on this project necessitated a comprehensive review of git and the shared pain of having to integrate many moving parts into the same project. We learned how to utilize Google's App Engine and utilize Google's MySQL server.

## What's next for CarChart

We would like to expand the front-end to have even more functionality
Some of the features that we would like to include would be:

* Letting users pick lists of cars that they are interested and compare
* Displaying each datapoint with an image of the car
* Adding even more dimensions that the user is allowed to search by

## Check the Project out here!!

<https://pennapps-xx-252216.appspot.com/>