## Inspiration

Natural disasters do more than just destroy property—they disrupt lives, tear apart communities, and hinder our progress toward a sustainable future. One of our team members from Rice University experienced this firsthand during a recent hurricane in Houston. Trees were uprooted, infrastructure was destroyed, and delayed response times put countless lives at risk.

* **Emotional Impact**: The chaos and helplessness during such events are overwhelming.
* **Urgency for Change**: We recognized the need for swift damage assessment to aid authorities in locating those in need and deploying appropriate services.
* **Sustainability Concerns**: Rebuilding efforts often use non-eco-friendly methods, leading to significant carbon footprints.

Inspired by these challenges, we aim to leverage AI, computer vision, and peer networks to provide rapid, actionable damage assessments. Our AI assistant can detect people in distress and deliver crucial information swiftly, bridging the gap between disaster and recovery.

## What it Does

The Garuda Dashboard offers a comprehensive view of current, upcoming, and past disasters across the country:

* **Live Dashboard**: Displays a heatmap of affected areas updated via a peer-to-peer network.
* **Drones Damage Analysis**: Deploy drones to survey and mark damaged neighborhoods using the Llava Vision-Language Model and generate reports for the Recovery Team.
* **Detailed Reporting**: Reports have annotations to classify damage types [tree, road, roof, water], human rescue needs, site accessibility [Can response team get to the site by land], and suggest equipment dispatch [Cranes, Ambulance, Fire Control].
* **Drowning Alert**: The drone footage can detect when it identifies a drowning subject and immediately call rescue teams
* **AI-Generated Summary**: Reports on past disasters include recovery costs, carbon footprint, and total asset/life damage.

## How We Built It

* **Front End**: Developed with Next.js for an intuitive user interface tailored for emergency use.
* **Data Integration**: Utilized Google Maps API for heatmaps and energy-efficient routing.
* **Real-Time Updates**: Custom Flask API records hot zones when users upload disaster videos.
* **AI Models**: Employed MSNet for real-time damage assessment on GPUs and Llava VLM for detailed video analysis.
* **Secure Storage**: Images and videos stored on Firebase database.

## Challenges We Faced

* **Model Integration**: Adapting MSNet with outdated dependencies required deep understanding of technical papers.
* **VLM Setup**: Implementing Llava VLM was challenging due to lack of prior experience.
* **Efficiency Issues**: Running models on personal computers led to inefficiencies.

## Accomplishments We're Proud Of

* **Technical Skills**: Mastered API integration, technical paper analysis, and new technologies like VLMs.
* **Innovative Impact**: Combined emerging technologies for disaster detection and recovery measures.
* **Complex Integration**: Successfully merged backend, frontend, and GPU components under time constraints.

## What We Learned

* Expanded full-stack development skills and explored new AI models.
* Realized the potential of coding experience in tackling real-world problems with interdisciplinary solutions.
* Balanced MVP features with user needs throughout development.

## What's Next for Garuda

* **Drone Integration**: Enable drones to autonomously call EMS services and deploy life-saving equipment.
* **Collaboration with EMS**: Partner with emergency services for widespread national and global adoption.
* **Broader Impact**: Expand software capabilities to address various natural disasters beyond hurricanes.
## Inspiration

One of our teammate’s grandfathers suffers from diabetic retinopathy, which causes severe vision loss.
Looking on a broader scale, over 2.2 billion people suffer from near or distant vision impairment worldwide. After examining the issue closer, it can be confirmed that the issue disproportionately affects people over the age of 50 years old. We wanted to create a solution that would help them navigate the complex world independently.

## What it does

### Object Identification:

Utilizes advanced computer vision to identify and describe objects in the user's surroundings, providing real-time audio feedback.

### Facial Recognition:

It employs machine learning for facial recognition, enabling users to recognize and remember familiar faces, and fostering a deeper connection with their environment.

### Interactive Question Answering:

Acts as an on-demand information resource, allowing users to ask questions and receive accurate answers, covering a wide range of topics.

### Voice Commands:

Features a user-friendly voice command system accessible to all, facilitating seamless interaction with the AI assistant: Sierra.

## How we built it

* Python
* OpenCV
* GCP & Firebase
* Google Maps API, Google Pyttsx3, Google’s VERTEX AI Toolkit (removed later due to inefficiency)

## Challenges we ran into

* Slow response times with Google Products, resulting in some replacements of services (e.g. Pyttsx3 was replaced by a faster, offline nlp model from Vosk)
* Due to the hardware capabilities of our low-end laptops, there is some amount of lag and slowness in the software with average response times of 7-8 seconds.
* Due to strict security measures and product design, we faced a lack of flexibility in working with the Maps API. After working together with each other and viewing some tutorials, we learned how to integrate Google Maps into the dashboard

## Accomplishments that we're proud of

We are proud that by the end of the hacking period, we had a working prototype and software. Both of these factors were able to integrate properly. The AI assistant, Sierra, can accurately recognize faces as well as detect settings in the real world. Although there were challenges along the way, the immense effort we put in paid off.

## What we learned

* How to work with a variety of Google Cloud-based tools and how to overcome potential challenges they pose to beginner users.
* How to connect a smartphone to a laptop with a remote connection to create more opportunities for practical designs and demonstrations.
How to connect a smartphone to a laptop with a remote connection to create more opportunities for practical designs and demonstrations.
How to create docker containers to deploy google cloud-based flask applications to host our dashboard.
How to develop Firebase Cloud Functions to implement cron jobs. We tried to developed a cron job that would send alerts to the user.

## What's next for Saight

### Optimizing the Response Time

Currently, the hardware limitations of our computers create a large delay in the assistant's response times. By improving the efficiency of the models used, we can improve the user experience in fast-paced environments.

### Testing Various Materials for the Mount

The physical prototype of the mount was mainly a proof-of-concept for the idea. In the future, we can conduct research and testing on various materials to find out which ones are most preferred by users. Factors such as density, cost and durability will all play a role in this decision.
## Inspiration

We constantly have friends asking us for advice for investing, or ask for investing advice ourselves. We realized how easy a platform that allowed people to make collaboratively make investments would make sharing information between people. We formed this project out of inspiration to solve a problem in our own lives.

The word 'Omada' means group in Greek, and we thought it sounded official and got our message across.

## What it does

Our platform allows you to form groups with other people, put your money in a pool, and decide which stocks the group should buy. We use a unanimous voting system to make sure that everyone who has money involved agrees to the investments being made.

We also allow for searching up stocks and their graphs, as well as individual portfolio analysis.

The way that the buying and selling of stocks actually works is as follows: let's say a group has two members, A and B. If A has $75 on the app, and person B has $25 on the app, and they agree to buy a stock costing $100. When they sell the stock, person A gets 75% of the revenue from selling the stock and person B gets 25%.

Person A: $75
Person B: $25
Buy stock for $100
Stock increases to $200
Sell Stock
Person A: $150
Person B: $200

We use a proposal system in order to buy stocks. One person finds a stock that they want to buy with the group, and makes a proposal for the type of order, the amount, and the price they want to buy the stock at. The proposal then goes up for a vote. If everyone agrees to purchasing the stock, then the order is sent to the market. The same process occurs for selling a stock.

## How we built it

We built the webapp using Flask, specifically to handle routing and so that we could use python for the backend. We used BlackRock for the charts, and NASDAQ for live updates of charts. Additionally, we used mLab with MongoDB and Azure for our databases, and Azure for cloud hosting. Our frontend is JavaScript, HTML, and CSS.

## Challenges we ran into

We had a hard time initially with routing the app using Flask, as this was our first time using it. Additionally, Blackrock has an insane amount of data, so getting that organized and figuring out what we wanted to do with that and processing it was challenging, but also really fun.

## Accomplishments that we're proud of

I'm proud that we got the service working as much as we did! We decided to take on a huge project, which could realistically take months of time to make if this was a workplace, but we got a lot of features implemented and plan on continuing to work on the project as time moves forward. None of us had ever used Flask, MongoDB, Azure, BlackRock, or Nasdaq before this, so it was really cool getting everything together and working the way it does.

## What's next for Omada

We hope to polish everything off, add features we didn't have time to implement, and start using it for ourselves! If we are able to make it work, maybe even publishing it!