Covid License

A technology Product to integrate technology to all purpose of common livelyhood especially the medical field and collaborative workspace

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Inspiration

The difficulties Faced during covid -19

What it does

it facilitates every known problems with a innovative and multi level of Thinking capacity beholding Awesome Real time data transaction and sensational startle to Technology improvements

How I built it

We used the Google Cloud Platform along with Firebase for this project. Firebase User Authentication Realtime Database Store Vital Data and User Information Store Information if the user is a Doctor Cloud Functions Other GCP Google Maps API Bootstrap HTML/CSS/JS Bootstrap Framework Use of JQuery Use of SmartForm for Contact Frontend Framework GitHub File Management Hosting Node Js Firebase Modules Alan - A Smart Assistant that we have built for users to ask simple questions. See the video demo for more.

Unity as our main tools for making the application. Using C# Language. ARFoundation to provide the Augmented Reality technology. Postman public API. Wolfram Alpha API as Intelligent Assistant. echoAR to provide a flexibility for 3D object and real-time info update.

, we have two sides of this project i.e. app and the website and all the technical details are mentioned below: Tech stack used: -Java: the android app has been written in the Java language with the help of Android Studio. -Django: the website is written using Django as the backend framework. -Bootstrap: the frontend of the website is written using bootstrap. -Firebase: Firebase has been used as the cloud backend service for the app. -PostgreSQL: the database management system of the website.

The frontend is built with HTML, CSS, JS, and Bootstrap. We use Javascript's Fetch API to communicate with the backend. The frontend is hosted on Firebase at apply-ai.online. The backend is a Ubuntu server configured with Nginx serving files to api.apply-ai.online. It is built using python and flask, which then communicates with a Firebase Realtime Database and a Google Cloud Storage Bucket to store resumes. This project also has a cronjob running on our Ubuntu server. This cronjob is constantly indexing many job sites to pull as much useful info as it can, and continues to train our ML model as it goes. This ensures that even with a limited number of jobs on the market, our algorithms will always be able to provide you with the best job recommendations out there.

Frontend Stack NextJS Typescript Styled Components Backend stack Express + Typescript Jest + Supertest PostgreSQL + TypeORM Deployment Docker

We used glide app to build and demo the multiple features included in our app. Because we want to bridge the gap between medical officials who are highly educated, but often skew older and lack technological skills, and the average US resident, who has less education but is more technologically savvy. The app uses an intuitive design with easy to understand menus for medical professionals with tech struggles, and simple menus and descriptions to give context for clinical research studies.

I have implemented multiple APIs throughout the project, which is compatible with Google's original Firebase which gives the user a seamless experience during this epidemic. I have integrated many APIs with geolocation services (Google Cloud Map API + Here Map API) and real-time notification applications (PushBot) to provide more flexibility to the user.

We pull live data provided by the Johns Hopkins University Center for Systems Science and Engineering. To perform infection probability estimates, we use a modified compartmentalized SIR model. The stages are: Susceptible Infected (asymptomatic) Infected (symptomatic) Removed (death, recovery, immunity) Based on interpersonal proximity measures and duration of time per activity, we use stochastic models to estimate the risk at each decision node and update conditional probabilities and expectations: E[X] = E[X | susceptible] + E[X | infected, asymptomatic] + E[X | infected, symptomatic], where E[X] is the expected cumulative number of deaths indirectly ultimately caused by the individual's actions. The app is powered by R, Python and Plotly Dash.

Challenges I ran into

Google cloud integration EchoAR Route sub-connections Firebase Database issues Limited Google Cloud Experience Limited amount of Google Cloud credits Difficulties merging frontend with the backend.

Our app is in continued development! For each of us in the team, this was a first dive into large-scale programming and new data visualization tools. Global communications and time zone differences posed a challenge in this short time period.

Integrating the Google Maps API that included icons into our web app

There were many challenges we ran into, but that's what programming's all about. One of the difficult challenges we ran into was making sure the UI worked. Another challenge was figuring out how to to extract information from the JSON file to the website.

We had challenges learning how to come together to do our tasks when we are so far apart. However, we made sure to communicate effectively on how we will carry out our tasks, which allowed us to get our finished product! Another huge issue we ran into on the development side was the abstraction of student and teacher user classes, initially we had attempted the solution that allows generic functions for either student or teacher, but due to time and energy needing to be spent in other areas, went with a solution of implementing both non-abstractly.

Connecting the frontend to the backend took a long time. We, rather naively, began connecting the two at about 2 am on the day of submissions. Integrating the two was not successful until 10 am on the day of submissions. This meant our entire Devpost is being written in an extremely sleep-deprived state, so we hope you like it! Besides the constant lack of sleep, another challenge was parsing the resume. Unfortunately, pdf resumes don't have a standardized format. This meant in many cases we needed to apply ML instead of an algorithm. Even still, these ML models are by no means perfect. If you run our demo and happen to get weird data in your pre-filled forms, or no data at all, that is likely a result of our model being unable to properly scrape your resume. If you would be willing, we would love to receive your resume so we could work on upgrading the model in the future to support it.

The challenge we ran into was narrowing our application's focus and quickly transitioning our app. We wanted to start the app with a focus on genetic research, but that style of complex personal care is difficult to integrate technologically without diverse datasets and requires vast amounts of funding. However, by shifting our focus to broad clinical trials, we were able to combat the lack of diversity in many tech settings and address an issue with a simple technological response.

Our app is in continued development! For each of us in the team, this was a first dive into large-scale programming and new data visualization tools. Global communications and time zone differences posed a challenge in this short time period.

There were many challenges we ran into, but that's what programming's all about. One of the difficult challenges we ran into was making sure the UI worked. Another challenge was figuring out how to to extract information from the JSON file to the website.

Accomplishments that I'm proud of

What I learned

What's next for Covid License

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