## Inspiration

What it does
Takes an input from user describing their symptoms and recommend over-the-counter medications if their symptoms are mild enough to not require a doctor visit

How we built it

Python, ChatGPT api to parse user input, React, TypeScript, Tailwind CSS, AWS to deploy backend

Challenges we ran into
deploying the backend to aws

## Accomplishments that we're proud of

## What we learned

What's next for PharmaBot
To include MongoDB for ease of database management
User authentication and personalization for data persistence and preferences
Incorporate drug interaction database for patients to enter their current medical conditions and current drugs, then recommend over-the-counter medications based on that information
Multilingual support for countries outside of the Anglophone world
Symptom severity scale to more promptly direct the user to help
## Inspiration

We were inspired to create our product because of one of our teammate's recent experience in our healthcare system. After a particularly bad bike accident, he went to the emergency department to be checked on but faced egregious wait times due to inefficiencies within the hospital. Unfortunately, the medical staff is so occupied with administrative work such as filling out forms, that their valuable time, which could be spent with patients, is drawn thin. We hoped to make something that could address this issue, which will both lower costs of operation and increase patient satisfaction.

## What it does

Our product is a web page that uses the language model GPT-3 to expedite the process of creating patient visit summaries. The UI prompts the user to simply enter a few words or phrases pertaining to the patient's situation - their initial incident, symptoms, and treatments - and the model, along with our back-end, works to synthesize it all into a summary.

## How we built it

Much of the beauty of this project lies in the UI, which streamlines the whole process. The web page was built using React with components from Google's Material UI to easily integrate front- and back-end. We also used OpenAI's GPT-3 playground to test various queries and eventually decide on the exact ones that would be used within the React framework.

## Challenges we ran into

Working with GPT-3 proved to be a trickier task than expected. The language model was often rather fickle, producing content that we found to be irrelevant or even incorrect. Even more confounding was formatting the results we got. We tried a variety of methods of generating the multi-paragraph structure that we wanted, yet all of them had some sort of inconsistency. Ultimately, we realized that the reliability we needed depended on more simplicity, and thus came up with simpler, but more streamlined queries that got the job done consistently.

## Accomplishments that we're proud of

We are proud of having built a product from scratch while implementing cutting-edge natural language technology. It was exciting to see the components of our site develop from its planning stages and then come together as an actual product that can feasibly be deployed for actual use.

## What we learned

Having been so entranced by ChatGPT recently, we learned how to integrate large language models into applications for ourselves. It turns out that it was much more difficult than just typing a question into ChatGPT, and designing the pipeline became a valuable learning experience.

## What's next for Untitled

Despite having such a niche application, our project has many possibilities for expansion. We can further optimize the process with better, perhaps more specifically trained language models that will be able to predict possible symptoms or treatments for an incident. Additionally, we can expand our concept and product to other similar administrative tasks that take up the valuable time of medical workers, helping to expedite many more facets of our healthcare system.
## Inspiration

This project is inspired by our personal experiences of seeing elderly relatives struggle with keeping track of their prescriptions as well as with the complexity of modern tech. By creating simple, no-signup-required, and user-friendly apps, we aim to enhance their quality of life and improve their health as well.

## What it does

Althea allows users to enter which prescriptions drugs they are taking so that they can have a checklist to remember what they have taken on a certain day. Althea then asks the user if they felt any symptoms the same day and notifies the user if the symptoms, they had could be side effects of a drug they use. The user can rate the severity of the symptoms they felt. Finally, users would be able to see past logs and even export them as PDFs if they would like to share the information easily with their primary care providers.

## How we built it

For the front-end, we used JavaScript with Tailwind CSS and HTML for a smooth and sleek user experience. For the back-end, we used Python with Django. We organized schemas and used SQLite for patient data because of its sensitive nature. We used PostgreSQL for medicine data since we wanted it to be shared among different people and used Gemini AI in conjunction with it. For the mobile app alternative, we used pure Flutter and emulated on our machine.

## Challenges we ran into

One significant challenge we ran into early on was deciding with what tech to build our app. We originally agreed on React Native since we all were familiar with React, but we learned that they're not that similar. This caused us to lose quite some time as we struggle with figuring it out. After that, we decided to do React+Django web app with a Flutter mobile app in parallel with huge ambition to interconnect them. However, time constraints and technical challenges didn't allow us to achieve our initial goals.
Additionally, our web team was challenged by the undertaking of linking the frontend and backend via API endpoints.

## Accomplishments that we're proud of

We're proud of few things. First, we have a large codebase with well-maintained structure, tech and features. Second, we have a neat, streamlined and user-friendly navigation system for both the web and mobile app. Third, we did a decent design considering we are all primarily backend developers with minor experience in React or any other frontend tech. Lastly, we worked on two projects in parallel, even though linking them together didn't work out.

## What we learned

Teamwork and communication are a must. Hackathons are the grindiest grind out there (lol). Gradual development and constant updates via Git is the path to success. We made sure to work on separate branches and then merge them to avoid conflicts as well as having structured file system that promotes collaboration. And lastly, simplicity is a key, trying too hard or aiming to high often won't end good.

## What's next for Althea

Next thing is implementing all the features that stayed on the whiteboard as well as connecting web and mobile apps into one ecosystem, allowing seamless access for wide audience. Definitely making our UI/UX even better but also streamlined and simple so any user, even least tech-wise, can use it. A cool and useful feature would be allowing users to securely scan their prescription label with their phone camera.