## Inspiration

Choose your own adventure books and table top rpgs

## What it does

Has google assistant act as a storyteller

## How we built it

We built it using firebase for the data storage, stblib for backend connection, google home for main front-end interface, dialogflow for information learning, and we implemented google sentiment analysis API to calculate sentiment store for different stories.

## Challenges we ran into

Hard to find database so we hardcoded it. It was hard to decided on a specific topic, before we were trying to do something healthcare related, data visualization, and an audio diary.

## Accomplishments that we're proud of

Comprehensive machine learning process, a fulfunctioning backend and how everyone was able to learn javascript from no background.

## What we learned

javascript, stdlib, dialogflow

## What's next for googleAdventure

That depends on you.
## Inspiration

Similar to how we cannot decide for ourselves on questions such as "Where should I get food?", or "What should I do for today?". Now we can outsource our decision-making to a smart home.

## What it does

Utilizing historical user input as a reference point, we have a profile on the user's likes and dislikes. When a command is issued to Google Home, tone is first analyzed with IBM Watson's NLP API, and based on the tone, a list of relevant words are used to scrape the news, with the Google News API. This is to get a current list of topics relevant to the user's preferences. Finally a decision is formulated by the multiple factors considered previously.

## How we built it

We tested our code for proof of concept in Python, then translated and executed them in Node-JS, because it was universally accepted for all the platforms we used. The whole project is operated from FireBase, all inputs are sent through Watson, and all decisions are sent to the neural network for improvement on the weightings.

## Challenges we ran into

Google Home had trouble connecting if it was more than 4 meters away from the WIFI router.
Syntax problems when translating from Python to JS.
Time complexity optimization issues for Watson API when it is not locally ran.
Trouble in conversion of categorical data when trying to train the NN.

## What we learned

Bring a sleeping bag.

## What's next for No Bored

IPO
## Inspiration

2 days before flying to Hack the North, Darryl forgot his keys and spent the better part of an afternoon retracing his steps to find it- But what if there was a personal assistant that remembered everything for you? Memories should be made easier with the technologies we have today.

## What it does

A camera records you as you go about your day to day life, storing "comic book strip" panels containing images and context of what you're doing as you go about your life. When you want to remember something you can ask out loud, and it'll use Open AI's API to search through its "memories" to bring up the location, time, and your action when you lost it. This can help with knowing where you placed your keys, if you locked your door/garage, and other day to day life.

## How we built it

The React-based UI interface records using your webcam, screenshotting every second, and stopping at the 9 second mark before creating a 3x3 comic image. This was done because having static images would not give enough context for certain scenarios, and we wanted to reduce the rate of API requests per image. After generating this image, it sends this to OpenAI's turbo vision model, which then gives contextualized info about the image. This info is then posted sent to our Express.JS service hosted on Vercel, which in turn parses this data and sends it to Cloud Firestore (stored in a Firebase database). To re-access this data, the browser's built in speech recognition is utilized by us along with the SpeechSynthesis API in order to communicate back and forth with the user. The user speaks, the dialogue is converted into text and processed by Open AI, which then classifies it as either a search for an action, or an object find. It then searches through the database and speaks out loud, giving information with a naturalized response.

## Challenges we ran into

We originally planned on using a VR headset, webcam, NEST camera, or anything external with a camera, which we could attach to our bodies somehow. Unfortunately the hardware lottery didn't go our way; to combat this, we decided to make use of MacOS's continuity feature, using our iPhone camera connected to our macbook as our primary input.

## Accomplishments that we're proud of

As a two person team, we're proud of how well we were able to work together and silo our tasks so they didn't interfere with each other. Also, this was Michelle's first time working with Express.JS and Firebase, so we're proud of how fast we were able to learn!

## What we learned

We learned about OpenAI's turbo vision API capabilities, how to work together as a team, how to sleep effectively on a couch and with very little sleep.

## What's next for ReCall: Memories done for you!

We originally had a vision for people with amnesia and memory loss problems, where there would be a catalogue for the people that they've met in the past to help them as they recover. We didn't have too much context on these health problems however, and limited scope, so in the future we would like to implement a face recognition feature to help people remember their friends and family.