README.md

Stories - HackCBS3.0

Stories from the true you!

---

A Preview of What Stories Is:

Landing page:

Tag Selection

Chat Window

Toxicity Censoring

Banning Reported Users

The problem Stories solves

The importance of mental health has been in its rightful spotlight for a while now but many people don't have access to the costly form of therapy. It is well known that people are likely to share their experiences with a stranger rather than an acquaintance due to the fear of being judged. It is a common practice to seek help via online chatrooms. The issue is that these chatrooms are not monitored properly. The rare ones that do, can be easily used against their purpose and push the seeker in a worse place.

This is where our product stories come in. We provide a simplistic chat room that does much more for people who want to provide help and get helped.

Challenges we ran into

Implementing a chat room management system along with a banning middleware in Redis was rather difficult. Moreover, implementing authentication with JWT in Socket.io events was a second challenge as far as the web application goes. For the Tensorflow model, we had to fine-tune the model as per our applications, which meant fiddling with the confidence threshold of the output categories to get an optimum solution. It was also pretty tricky to pair the seeker & supporter based on their tags selection. After pairing them, getting a score based 'compatibility' for the same. And pushing them into the same chatroom based on the score.

Features offered by Stories

• Google Authentication

  • Using G-Auth as the first layer of authenticity, allowing only verified users on our portal.

• Anonymous Identities

  • The identities of the seeker & the supporter are kept hidden from each other to eliminate biases.

• Tags-Selection based pairing

  • We are maintaining a score between the supporters and seekers, representing the total number of tags matched. The pair with the highest score is connected to the chatroom.

• Censoring toxic messages

  • We run the Tensorflow.js toxicity classifier on every message sent by the supporter to give us the probability of it being toxic. If it is deemed hurtful, the message is censored with a warning on the seeker's end.

• Real-time anaysis of data

• To ensure real-time analysis of chat data, we use the popular memcache database Redis to store the chat room data and client management along with the list of banned users.

• Reporting and banning of users

  • The seeker can report the supporter under the circumstances the supporter is a troll. Having enough reports file against them, the supporter's email and IP will be banned from our portal.

Technology Stack and Dependencies

• APIs
  • Tensorflow.js
  • Node.js
  • Express in TypeScript
  • Socket.IO
  • Redis
  • Google OAuth
• Front-end
  • React.js in TypeScript-XML
  • Tailwind CSS
  • Styled Components
  • Socket.IO - Client

Thank You!

Contributors

Gita Alekhya Paul

Snehil

Paras Rawat

Yashvardhan Jagnani