## Inspiration

The place we call home is where our most treasured memories have been made. As a group of international students, the disconnect between us and our home only grows as our memories fade. With this year's theme, we acknowledge the increasing importance of looking back at our roots as our hometowns change and evolve. And so, we developed Memorylane as a way to preserve the sights, history, culture, and most importantly, the memories of the place we hold dear.

## What it does

Memorylane offers a collaborative photo album showcasing photos of a particular place at different points in time, with a text introduction to the local landmarks, history and culture. Users will first select any place on Earth, and will then be able to view past images, sourced online and from other users, of that place.

## How we built it

The frontend was built with React. After the user selects a location, the backend, which was built with Django, provides the Google Streetview images, text description and location information corresponding to that location. The descriptions are generated using Cohere with a query decomposition strategy that compiles information about physical features, history and heritage into a concise summary. The user-provided images are stored on Firebase.

## Challenges we ran into

A major roadblock that we faced was embedding Google Maps and its search feature into our webpage, which was not helped by the fact the little amount of resources out there. We had also spent considerable time ironing out the project scope, which hindered our progress initially.

## Accomplishments that we're proud of

As we're still new to hackathons, we're happy to have been able to complete our project with most of the goals that we set out in the beginning accomplished. We're also proud of having been able to experiment with, and pick up technologies that we haven't really tried before, and successfully utilised it in our final product!

## What we learned

Given our relative inexperience with projects in the past, this event turned out to be quite valuable for us, as we'd learnt how to organise ourselves better to work more efficiently in future hackathons!

## What's next for Memorylane

* Provide support for other languages
* Allow users to include their own stories with photo submissions
* sleep
## Inspiration

A walk across campus looking at all the historical buildings and statues of the University of Pennsylvania.

## What it does

The project will be in the form of a web/mobile app. One or several locations are provided to the user. Along with each location an image is provided. The user aims to find the actual location and take a photo that best possibly replicates the image provided. The user has now "unlocked" said location.
The images that are intended to be used are historical images of landmarks, which are displayed to the user with a relatively low opacity and some interesting facts about the landmark. Making it both a thrilling geocaching-like and an educational experience (re-live history!).

## How we built it

Computer vision, API-handling, and web development will be critical elements of this project.
The biggest part of the application was to find a way to accurately decide if two pictures contain the same landmark, thus we spent a fairly big chunk of our development period just trying to make a model that worked as intended.
We built the web app by making a Flask backend, where we provided the utility to upload a picture to compare with, as well as implemented our Siamese Neural Network.

## Challenges we ran into

As the hackathon format doesn't allow for thoughtful and methodical development of AI, we decided to use a pre-existing Computer Vision model to be able to recognize a place, instead of having to train a model for ourselves. A problem we ran into was that most Open Source Computer Vision models are intended for object detection and classification, rather than comparison between two pictures of the same object. Unable to find a suitable model, we were forced to train our own, settling on a Siamese neural network.

## Accomplishments that we're proud of

We're especially proud of providing a proof of concept, with a self-trained Siamese neural network that works as intended. (with the pictures about the same angle)

## What we learned

We learned that getting everything to run smoothly and deploying a functioning model is more of a challenge than expected. Our plane also arrived very late, so we definitely learned that it's beneficial to get stated as soon as possible.

## What's next for Photo cache

A natural next move would be to make the concept into a functioning app for IOS/Android, and fully implement the more educational part of the application.

A website link should be provided in the GitHub repo.
## What it does

MemoryLane is an app designed to support individuals coping with dementia by aiding in the recall of daily tasks, medication schedules, and essential dates.

The app personalizes memories through its reminisce panel, providing a contextualized experience for users. Additionally, MemoryLane ensures timely reminders through WhatsApp, facilitating adherence to daily living routines such as medication administration and appointment attendance.

## How we built it

The back end was developed using Flask and Python and MongoDB. Next.js was employed for the app's front-end development. Additionally, the app integrates the Google Cloud speech-to-text API to process audio messages from users, converting them into commands for execution. It also utilizes the InfoBip SDK for caregivers to establish timely messaging reminders through a calendar within the application.

## Challenges we ran into

An initial hurdle we encountered involved selecting a front-end framework for the app. We transitioned from React to Next due to the seamless integration of styling provided by Next, a decision that proved to be efficient and time-saving. The subsequent challenge revolved around ensuring the functionality of text messaging.

## Accomplishments that we're proud of

The accomplishments we have achieved thus far are truly significant milestones for us. We had the opportunity to explore and learn new technologies that were previously unfamiliar to us. The integration of voice recognition, text messaging, and the development of an easily accessible interface tailored to our audience is what fills us with pride.

## What's next for Memory Lane

We aim for MemoryLane to incorporate additional accessibility features and support integration with other systems for implementing activities that offer memory exercises. Additionally, we envision MemoryLane forming partnerships with existing systems dedicated to supporting individuals with dementia. Recognizing the importance of overcoming organizational language barriers in healthcare systems, we advocate for the formal use of interoperability within the reminder aspect of the application. This integration aims to provide caregivers with a seamless means of receiving the latest health updates, eliminating any friction in accessing essential information.