# better.me, AI Journaling

## Project Description

better.me is an AI journaling tool that helps you analyze your emotions and provide you with smart recommendations for your well being. We used NLP emotion analytics to process text data and incorporated a suicidal prevention algorithm that will help you make better informed decisions about your mental health.

## Motivation

Poor mental health is a growing pandemic that is still being stigmatized. Even after spending $5 Billion in federal investments for mental health, 1.3 million adults attempted suicide and 1.1 million plans to commit suicide.

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> Our mission is to provide a private environment to help people analyze their emotions and receive mental health support.
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## MVP Product Features Overview

| Features | Description |
| --- | --- |
| Personal Journal | Better Me is a personal AI-powered journal where users can write daily notes reflecting on their life's progress. |
| NLP Emotion Analytics | With the help of natural language process, Better Me will classify the user's emotional situation and keep a track of the data. |
| Smart Recommendations | It uses this monitored data to suggest appropriate mental health resources to the users and also provides them with suitable data analytics. |
| Suicide Prevention | In order to take a step forward towards suicide prevention, it also incorporates a suicidal text detection algorithm that triggers a preventive measure . |

## How we built it

We used Google T5 NLP model for emotional recognition and categorizing emotions. We trained data set with deep learning to develop a fine tuned BERT model to prevent suicide. We also implemented our own algorithm to make resource recommendations to users based on their emotional changes, and also did some data analytics. Due to time restraints and a member's absence, we had to change from React.js plus Firebase stack to Streamlit, a python library design framework.

## Challenges

Initially, we tried creating a dashboard using full-stack web development, however, it proved to be quite a challenging task with the little amount of time we had. We decided to shift our focus to quickly prototyping using a lightweight tool, and streamlit was the ideal choice for our needs. While deploying our suicide prevention algorithm on Google Cloud Function, we had trouble deploying due to memory availability constraints.

## Accomplishments

We are proud that we came up with such a novel idea that could be useful to innumerable people suffering from mental health issues, or those who are like to stay reserved with themselves or in a confused state about their mental well-being, just by writing about their daily lives. We are also proud of incorporating a suicide prevention algorithm, which could be life-saving for many.

## Roadmap

| Future Implementations | Description |
| --- | --- |
| Firebase Back End Architecture | We hope to design a scalable backend which accommodates for the users needs. |
| AI Mental Health Chat bot | Provide on the spot, mental health support using Dialogflow AI chat bot. |
| Connect with Therapists | Elevate data analytical features to connect and report to personal therapists. |
| Scaling Up | Fund our project and develop this project with scalable front and back end. |
| Languages Support | Support multiple languages, including French, Mandarin, and Spanish. |
## Inspiration

1 in 2 Canadians will personally experience a mental health issue by age 40, with minority communities at a greater risk. As the mental health epidemic surges and support at its capacity, we sought to build something to connect trained volunteer companions with people in distress in several ways for convenience.

## What it does

Vulnerable individuals are able to call or text any available trained volunteers during a crisis. If needed, they are also able to schedule an in-person meet-up for additional assistance. A 24/7 chatbot is also available to assist through appropriate conversation. You are able to do this anonymously, anywhere, on any device to increase accessibility and comfort.

## How I built it

Using Figma, we designed the front end and exported the frame into Reacts using Acovode for back end development.

## Challenges I ran into

Setting up the firebase to connect to the front end react app.

## Accomplishments that I'm proud of

Proud of the final look of the app/site with its clean, minimalistic design.

## What I learned

The need for mental health accessibility is essential but unmet still with all the recent efforts. Using Figma, firebase and trying out many open-source platforms to build apps.

## What's next for HearMeOut

We hope to increase chatbot’s support and teach it to diagnose mental disorders using publicly accessible data. We also hope to develop a modeled approach with specific guidelines and rules in a variety of languages.
## Inspiration

Whether you’re thriving in life or really going through it, research shows that writing down your thoughts throughout the day has many benefits. We wanted to add a social element to this valuable habit and build a sense of community through sharing and acknowledging each other’s feelings. However, even on the internet, we've noticed that it is difficult for people to be vulnerable for fear of judgement, criticism, or rejection.

Thus, we centred our problem around this challenge and asked the question: How might we create a sense of community and connection among journalers without compromising their sense of safety and authenticity when sharing their thoughts?

## What it does

With Yapyap, you can write daily journal entries and share them anonymously with the public. Before posting, our AI model analyzes your written entry and provides you with an emotion, helping to label and acknowledge your feelings.

Once your thoughts are out in the world, you can see how other people's days are going too and offer mutual support and encouragement through post reactions.

Then, the next day comes, and the cycle repeats.

## How we built it

After careful consideration, we recognized that most users of our app would favour a mobile version as it is more versatile and accessible throughout the day. We used Figma to create an interesting and interactive design before implementing it in React Native. On the backend, we created an API using AWS Lambda and API Gateway to read and modify our MongoDB database. As a bonus, we prepared a sentimental analyzer using Tensorflow that could predict the overall mood of the written entry.

## Challenges we ran into

Learning new technologies and figuring out how to deploy our app so that they could all communicate were huge challenges for us.

## Accomplishments that we're proud of

Being able to apply what we learned about the new technologies in an efficient and collaborative way. We're also proud of getting a Bidirectional RNN for sentiment analysis ready in a few hours!

## What we learned

How to easily deal with merge conflicts, what it's like developing software as a group, and overall just knowing how to have fun even when you're pulling an all-nighter!

## What's next for yapyap

More personable AI Chatbots, and more emotions available for analysis!