Mental health is often an overlooked and stigmatized issue in many developing countries, in spite of its prevalence. According to a World Health Organisation (WHO) Report, major or minor mental illnesses requiring expert intervention is 7.5 per cent of India’s population. Also, estimates show that of roughly 60 million people with mental illnesses, about 90 per cent are currently not receiving treatment in India. The Covid-19 pandemic has highlighted the urgent need for reinvestment in mental health, as altered daily routines, financial pressures, extensive social isolation and information overload continues to take its toll on mental health.

Studies do show, however, that the population has begun to take mental health seriously, with 90% of survey respondents agreeing that mental health should be given as much consideration as physical health. Nonetheless, several physical and social infrastructural weaknesses in the system exist, resulting in:

1. One in seven Indians were affected by mental disorders of varying severity. The proportional contribution of mental disorders to the total disease burden in India has almost doubled since 1990. Substantial variations exist between states in the burden from different mental disorders and in their trends over time.

2. Many mental health issues ending up getting detected and diagnosed too late, leading to poorer outcomes and increased mortality, and

3. Patients who do end up seeking professional help end up with extremely infrequent appointments, due to India’s severe shortage of licensed psychiatrists (1 per 334,000 people). This leads to patients having difficulty recounting what has happened since their last appointments, and overworked providers who may have difficulty keeping track of patient records.

We witnessed a huge gap with the needs and the existing solutions hence, created How You Doin'?

Our solution has two parts: Web - application Android application

We plan to develop these applications to create a seamless ecosystem for our users. These platforms allow the users to describe their feelings and with the help of our custom-built algorithm, we analyze their emotions from the description provided and calculate a mood index. Based on this mood index, we allow them to chat on our platform with people who have similar problems apart from this we also suggest relevant playlists, external audio rooms(Twitter spaces), etc.

We also provide a graphical representation of the monthly/annual moods of the user in the form of graphs and pie charts.

• Integrating the firebase database and the Authentication Function into a single web solution and a complimentary flutter app. • Designing a scalable architecture without compromising much in security and still maintaining fast deliverability. • To deploy the flask app to the server was a cumbersome process as it was the first time that we accomplished it as a team. • Lastly, maintaining a development process simultaneously from UI design, front-end & back-end for the Web app and the mobile flutter app was a major management problem to overcome.

• Python Libraries
• Machine Learning Libraries
• Flask
• Flutter
• Firebase
• React
• NextJS
• Styled components
• HTML/CSS/JavaScript
• Material UI
• SCSS
• Figma

• https://how-you-doin-app.herokuapp.com/
• https://emo-track.vercel.app/
• https://www.figma.com/file/NFm2TYmKCx2fDbmInxzozH/How-You-Doin%27%3F?node-id=0%3A1

• https://www.youtube.com/watch?v=XVVm1w_qiUY
• https://www.youtube.com/watch?v=n38mKC6mtcE

from flask import Flask, request, render_template from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer import nltk from string import punctuation import re from nltk.corpus import stopwords

• Flask
• Sklearn
• vaderSentiment
• NLTK
• Next.js - Docs
• Firebase Docs
• Material UI - Docs
• Flutter and Dart Docs