## Inspiration and What it does

We often go out with a lot of amazing friends for trips, restaurants, tourism, weekend expedition and what not. Every encounter has an associated messenger groupchat. We want a way to split money which is better than to discuss on the groupchat, ask people their public key/usernames and pay on a different platform. We've integrated them so that we can do transactions and chat at a single place.
We (our team) believe that **"The Future of money is Digital Currency "** (-Bill Gates), and so, we've integrated payment with Algorand's AlgoCoins with the chat. To make the process as simple as possible without being less robust, we extract payment information out of text as well as voice messages.

## How I built it

We used Google Cloud NLP and IBM Watson Natural Language Understanding apis to extract the relevant information. Voice messages are first converted to text using Rev.ai speech-to-text. We complete the payment using the blockchain set up with Algorand API. All scripts and database will be hosted on the AWS server

## Challenges I ran into

It turned out to be unexpectedly hard to accurately find out the payer and payee. Dealing with the blockchain part was a great learning experience.

## Accomplishments that I'm proud of

that we were able to make it work in less than 24 hours

## What I learned

A lot of different APIs

## What's next for Mess-Blockchain-enger

Different kinds of currencies, more messaging platforms
## Inspiration

We were inspired by the Interac API, because of how simple it made money requests. We all realized that one thing we struggle with sometimes is splitting the bill, as sometimes restaurants don't accommodate for larger parties.

## What it does

Our simple web app allows for you to upload your receipt, and digitally invoice your friends for their meals.

## How we built it

For processing the receipts, we used Google Cloud's Vision API, which is a machine learning application for recognizing and converting images of characters into digital text. We used HTML, CSS, JavaScript, and JQuery to create an easy-to-use and intuitive interface that makes splitting the bill as easy as ever. Behind the scenes, we used Flask and developed Python scripts to process the data entered by the users and to facilitate their movement through our interface. We used the Interac e-Transfer API to send payment requests to the user's contacts. These requests can be fulfilled and the payments will be automatically deposited into the user's bank account.

## Challenges we ran into

The Optical Character Recognition (OCR) API does not handle receipts format very well. The item names and cost are read in different orders, do not always come out in pairs, and have no characters that separate the items. Therefore we needed to develop an algorithm that can pick up the separate the words and recognize which characters were actually useful.
The INTERAC e-Transfer API example was given to us as an React app. Most of us have had no experience with React before. We needed to find a way to still be able to call the API and integrate the caller with the rest of the web app, which was build with HTML, CSS, and Javascript.
There has also been a few difficulties with passing data from the front end interface and the back end service routines.

## Accomplishments that we're proud of

It's the first hackathon for two of our team members, and it was a fresh experience for us to work on a project in 24 hours. We had little to no experience with full stack development and Google Cloud Platform tools. However, we figured out our way step by step, with help from the mentors and online resources. We managed to integrate a few APIs into this project and tied together the front end and back end designs into a functional web app.

## What we learned

How to call Google Cloud APIs
How to host a website on Google Cloud Platform
How to set up an HTTP request in various languages
How to make dynamically interactive web page
How to handle front end and back end requests

## What's next for shareceipt

We hope to take shareceipt to the next level by filling in all the places in which we did not have enough time to fully explore due to the nature of a hackathon. In the future, we could add mobile support, Facebook & other social media integration to expand our user-base and allow many more users to enjoy a simple way to dine out with friends.
## Inspiration

While caught in the the excitement of coming up with project ideas, we found ourselves forgetting to follow up on action items brought up in the discussion. We felt that it would come in handy to have our own virtual meeting assistant to keep track of our ideas. We moved on to integrate features like automating the process of creating JIRA issues and providing a full transcript for participants to view in retrospect.

## What it does

*Minutes Made* acts as your own personal team assistant during meetings. It takes meeting minutes, creates transcripts, finds key tags and features and automates the process of creating Jira tickets for you.

It works in multiple spoken languages, and uses voice biometrics to identify key speakers.

For security, the data is encrypted locally - and since it is serverless, no sensitive data is exposed.

## How we built it

Minutes Made leverages Azure Cognitive Services for to translate between languages, identify speakers from voice patterns, and convert speech to text. It then uses custom natural language processing to parse out key issues. Interactions with slack and Jira are done through STDLIB.

## Challenges we ran into

We originally used Python libraries to manually perform the natural language processing, but found they didn't quite meet our demands with accuracy and latency. We found that Azure Cognitive services worked better. However, we did end up developing our own natural language processing algorithms to handle some of the functionality as well (e.g. creating Jira issues) since Azure didn't have everything we wanted.

As the speech conversion is done in real-time, it was necessary for our solution to be extremely performant. We needed an efficient way to store and fetch the chat transcripts. This was a difficult demand to meet, but we managed to rectify our issue with a Redis caching layer to fetch the chat transcripts quickly and persist to disk between sessions.

## Accomplishments that we're proud of

This was the first time that we all worked together, and we're glad that we were able to get a solution that actually worked and that we would actually use in real life. We became proficient with technology that we've never seen before and used it to build a nice product and an experience we're all grateful for.

## What we learned

This was a great learning experience for understanding cloud biometrics, and speech recognition technologies. We familiarised ourselves with STDLIB, and working with Jira and Slack APIs. Basically, we learned a lot about the technology we used and a lot about each other ❤️!

## What's next for Minutes Made

Next we plan to add more integrations to translate more languages and creating Github issues, Salesforce tickets, etc. We could also improve the natural language processing to handle more functions and edge cases. As we're using fairly new tech, there's a lot of room for improvement in the future.