Inspiration
Our project is driven by a deep-seated commitment to address the escalating issues of hate speech and crime in the digital realm. We recognized that technology holds immense potential in playing a pivotal role in combating these societal challenges and nurturing a sense of community and safety.

## What It Does

Our platform serves as a beacon of hope, empowering users to report incidents of hate speech and crime. In doing so, we have created a vibrant community of individuals wholeheartedly devoted to eradicating such toxic behaviors. Users can not only report but also engage with the reported incidents through posts, reactions, and comments, thereby fostering awareness and strengthening the bonds of solidarity among its users. Furthermore, our platform features an AI chatbot that simplifies and enhances the reporting process, ensuring accessibility and ease of use.

## How We Built It

The foundation of our platform is a fusion of cutting-edge front-end and back-end technologies. The user interface came to life through MERN stack, ensuring an engaging and user-friendly experience. The backend infrastructure, meanwhile, was meticulously crafted using Node.js, providing robust support for our APIs and server-side operations. To house the wealth of user-generated content, we harnessed the prowess of MongoDB, a NoSQL database. Authentication and user data privacy were fortified through the seamless integration of Auth0, a rock-solid authentication solution.

## Challenges We Ran Into

Our journey was not without its trials. Securing the platform, effective content moderation, and the development of a user-friendly AI chatbot presented formidable challenges. However, with unwavering dedication and substantial effort, we overcame these obstacles, emerging stronger and more resilient, ready to tackle any adversity.

## Accomplishments That We're Proud Of

Our proudest accomplishment is the creation of a platform that emboldens individuals to stand up against hate speech and crime. Our achievement is rooted in the nurturing of a safe and supportive digital environment where users come together to share their experiences, ultimately challenging and combatting hatred head-on.

## What We Learned

The journey was not just about development; it was a profound learning experience. We gained valuable insights into the vast potential of technology as a force for social good. User privacy, effective content moderation, and the vital role of community-building have all come to the forefront of our understanding, enhancing our commitment to addressing these critical issues.

## What's Next for JustIT

The future holds exciting prospects for JustIT. We envision expanding our platform's reach and impact. Plans are underway to enhance the AI chatbot's capabilities, streamline the reporting process, and implement more robust content moderation techniques. Our ultimate aspiration is to create a digital space that is inclusive, empathetic, and, above all, safe for everyone.
## Inspiration

One thing the pandemic has taught us was the severity of mental health and how crucial it is to have someone to talk to during hardships. We believe in using technology to create a solution that addresses mental health, a global issue of utmost importance in today's world. As high school students, we constantly face stress and emotional challenges that significantly impact our lives. We often want help to get out of a hard situation, however; the help we receive, such as a therapist and a list of non-profit help lines, isn't usually the help that we want such as someone to talk to.

## What it does

My-AI is a chatbox that has a one-on-one conversation with the user in a way that is comforting and helpful by providing emotional support and advice. Maiya, the AI chatbot, assesses the user's messages and feelings to generate a human-like response so the user feels as if they are talking to another human, more so to a friend; a friend without bias and judgement. The entire conversation is not saved and so deleted once the user finishes the conversation.

## How we built it

We prototyped on Figma and then developed the front end with React. Our backend was created using Java Script to made the node server, which is responsible for calling the API.

## Challenges we ran into

Our team ran into many problems as it was one of the first hackathons that most of our team members attended. In terms of front-end development, none of our team members had experience in React so building a website within a time constraint was a learning curve. Other problems we faced in the backend were issues with the API as it would not respond sometimes, the accuracy was off, and wasn't doing what we needed it to do. Finally, we had trouble connecting the back end to the front end.

## Accomplishments that we're proud of

One thing our whole team is most proud of is our skillset improvement in React. All members experienced using React for the first time so navigating an issue for us helped us grasp a better understanding of it. We are also extremely proud of the fine-tuning of the API to meet our project's demands.

## What we learned

We mainly enhanced our skills in React and how to implement and fine-time an API.

## What's next for My-AI

The more Maya is trained with people and different experiences obtained through the chat box, the more accurate the support is.
## Inspiration

Over the Summer one of us was reading about climate change but then he realised that most of the news articles that he came across were very negative and affected his mental health to the point that it was hard to think about the world as a happy place. However one day he watched this one youtube video that was talking about the hope that exists in that sphere and realised the impact of this "goodNews" on his mental health. Our idea is fully inspired by the consumption of negative media and tries to combat it.

## What it does

We want to bring more positive news into people’s lives given that we’ve seen the tendency of people to only read negative news. Psychological studies have also shown that bringing positive news into our lives make us happier and significantly increases dopamine levels.

The idea is to maintain a score of how much negative content a user reads (detected using cohere) and once it reaches past a certain threshold (we store the scores using cockroach db) we show them a positive news related article in the same topic area that they were reading.
We do this by doing text analysis using a chrome extension front-end and flask, cockroach dp backend that uses cohere for natural language processing.

Since a lot of people also listen to news via video, we also created a part of our chrome extension to transcribe audio to text - so we included that into the start of our pipeline as well! At the end, if the “negativity threshold” is passed, the chrome extension tells the user that it’s time for some good news and suggests a relevant article.

## How we built it

**Frontend**
We used a chrome extension for the front end which included dealing with the user experience and making sure that our application actually gets the attention of the user while being useful. We used react js, HTML and CSS to handle this. There was also a lot of API calls because we needed to transcribe the audio from the chrome tabs and provide that information to the backend.

**Backend**

## Challenges we ran into

It was really hard to make the chrome extension work because of a lot of security constraints that websites have. We thought that making the basic chrome extension would be the easiest part but turned out to be the hardest. Also figuring out the overall structure and the flow of the program was a challenging task but we were able to achieve it.

## Accomplishments that we're proud of

1) (co:here) Finetuned a co:here model to semantically classify news articles based on emotional sentiment
2) (co:here) Developed a high-performing classification model to classify news articles by topic
3) Spun up a cockroach db node and client and used it to store all of our classification data
4) Added support for multiple users of the extension that can leverage the use of cockroach DB's relational schema.
5) Frontend: Implemented support for multimedia streaming and transcription from the browser, and used script injection into websites to scrape content.
6) Infrastructure: Deploying server code to the cloud and serving it using Nginx and port-forwarding.

## What we learned

1) We learned a lot about how to use cockroach DB in order to create a database of news articles and topics that also have multiple users
2) Script injection, cross-origin and cross-frame calls to handle multiple frontend elements. This was especially challenging for us as none of us had any frontend engineering experience.
3) Creating a data ingestion and machine learning inference pipeline that runs on the cloud, and finetuning the model using ensembles to get optimal results for our use case.

## What's next for goodNews

1) Currently, we push a notification to the user about negative pages viewed/a link to a positive article every time the user visits a negative page after the threshold has been crossed. The intended way to fix this would be to add a column to one of our existing cockroach db tables as a 'dirty bit' of sorts, which tracks whether a notification has been pushed to a user or not, since we don't want to notify them multiple times a day. After doing this, we can query the table to determine if we should push a notification to the user or not.
2) We also would like to finetune our machine learning more. For example, right now we classify articles by topic broadly (such as War, COVID, Sports etc) and show a related positive article in the same category. Given more time, we would want to provide more semantically similar positive article suggestions to those that the author is reading. We could use cohere or other large language models to potentially explore that.