Project Info
Inspiration
Having witnessed firsthand the challenges that one of our teammates' grandparents faces living alone, we became acutely aware of the difficulties involved in maintaining their health and well-being. Elders like them struggle with remembering daily health updates, deciding when to seek medical attention, and navigating modern technology. This experience deeply inspired us to create Evercare—an app designed not only to simplify health tracking and make it accessible for elders with speech-to-text capabilities but also to provide peace of mind for both seniors and their families. By offering an AI-powered 100% speech-to-text system, Evercare allows seniors to effortlessly record their health updates, which can be easily shared with doctors. This data exchange helps medical professionals assess conditions more accurately and efficiently, improving care and ensuring that both seniors and their families feel more connected and supported in managing health. Our goal is to make it easier for seniors to manage their health, reduce stress, and foster a sense of security in their daily lives.
What it does
Users can describe their symptoms to a conversational AI agent, which then processes and delivers personalized responses based on a user’s background information and previous entries. These responses help the user better understand patterns in their health and flag issues that may require a visit to a healthcare provider. The responses are converted to speech using text-to-speech software in the backend, allowing users to hear them aloud. Users can access their previous conversations with the AI agent, allowing them to review past discussions and track their symptoms over time.
How we built it
Perplexity Sonar for generating personalized advice and flagging issues that may warrant medical attention. Perplexity Sonar for generating personalized advice and flagging issues that may warrant medical attention. OpenAI API for speech to text using whisper OpenAI API for speech to text using whisper ElevenLabs API for natural-sounding text-to-speech ElevenLabs API for natural-sounding text-to-speech React js frontend, Python backend using Flask React js frontend, Python backend using Flask
Challenges we ran into
In our original plan we intended to use the FARM approach. However, we ran into issues with integration with our current implementation In our original plan we intended to use the FARM approach. However, we ran into issues with integration with our current implementation We wanted to use Terra API to collect health vitals to use in conjunction with the AI health agent, but realized we needed to focus more energy to perfect fewer tasks, including improving the chatbot and UX. We wanted to use Terra API to collect health vitals to use in conjunction with the AI health agent, but realized we needed to focus more energy to perfect fewer tasks, including improving the chatbot and UX.
What we learned
Prompt engineering Prompt engineering Working with generative ai Working with generative ai POST/GET APIs POST/GET APIs Webhooks Webhooks
What's next
Live health data from wearable devices (apple watches, oura rings, …) Live health data from wearable devices (apple watches, oura rings, …) Keeping track of medications and possible side effects / interactions Keeping track of medications and possible side effects / interactions Integrate with the sources provided in Perplexity responses to give users external resources that may be relevant to their health concerns. Integrate with the sources provided in Perplexity responses to give users external resources that may be relevant to their health concerns.
EverCare
Having witnessed firsthand the challenges that one of our teammates' grandparents faces living alone, we became acutely aware of the difficulties involved in maintaining their health and well-being. Elders like them struggle with remembering daily health updates, deciding when to seek medical attention, and navigating modern technology. This experience deeply inspired us to create EverCare—an app designed not only to simplify health tracking and make it accessible for elders with speech-to-text capabilities but also to provide peace of mind for both seniors and their families. By offering an AI-powered 100% speech-to-text system, EverCare allows seniors to effortlessly record their health updates, which can be easily shared with doctors. This data exchange helps medical professionals assess conditions more accurately and efficiently, improving care and ensuring that both seniors and their families feel more connected and supported in managing health. Our goal is to make it easier for seniors to manage their health, reduce stress, and foster a sense of security in their daily lives.
To run backend:
- Create a virtual environment and install requirements
cd backend
python3 -m venv venv
source venv/bin/activate # on MacOS
pip install -r requirements.txt
- Start server
python3 server.py
To run frontend
cd frontend
npm install
npm start
View the website on http://localhost:3000/
Analysis
View
Metric
- 20
- 16
- 6
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- CSSIn code
- FlaskIn code
- HTMLIn code
- JavaScriptIn code
- OpenAIIn code
- PythonIn code
- ReactIn code
7 of 7 appear in the indexed code.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
44 KB
Source files
15
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
ksantoso2/EverCare
26 files · 793 KB · @ 0c61905
Structure
Application logic
12 files · 46%Domain rules, services and shared utilities.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- JavaScript47%
- Python21%
- CSS16%
- Markdown11%
- HTML5%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
backend/requirements.txt
pypi · 39- annotated-types
- anyio
- blinker
- bson
- certifi
- charset-normalizer
- click
- DateTime
- distro
- dnspython
- elevenlabs
- Flask
- Flask-Cors
- h11
- httpcore
- httpx
- idna
- itsdangerous
- +21 more
frontend/package.json
npm · 12- @testing-library/dom
- @testing-library/jest-dom
- @testing-library/react
- @testing-library/user-event
- font-awesome
- papaparse
- react
- react-dom
- react-icons
- react-router-dom
- react-scripts
- web-vitals
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
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