GDSC Solution Challenge 2024
- FrontEnd Repo : https://github.com/leemanjae02/HealingMeal-FrontEnd
- BackEnd Repo : https://github.com/zzdh8/healingmeal-back
| Backend | Backend | Frontend | PM |
|---|---|---|---|
| -------------- | ------------ | ------------ | ------------- |
| Jinyong Hyun | Inho Choi | Manjae Lee | bojung Kim |
- Diabetes has established itself as one of the most significant diseases globally among modern people.
- For these diabetic patients, we have planned Healing Meal. It is a service that provides customized diets and personal diet management for type 2 diabetes patients.
- Healing Meal does not merely recommend diets; it can create a diet based on the patient's preferences through surveys.
- It generates a reasonable diet based on the patient's various tastes and current physical information. Furthermore, it also provides the efficacy of the diet.
- You can save and manage your preferred diet using the favorite feature.
- JDK-17
- Spring, Spring Boot
- Spring Data JDBC & JPA
- Spring Security, Spring Session JDBC
- MySQL
- Docker, Docker-compose
- JSON Simple, JSON DATA PARSING
- Spring Mail
- Spring AI
- Google Cloud Platform(compute engine, cloud sql, cloud storage, load balancer)
- React
- React Router
- mobX
- vite
- css module, less
- Axios
- The Frontend Deployment was done through the Vercel cloud platform.
- I create a Dockerfile to build an image of HealingMeal. And Push the image to the DockerHub.
- The Compute Engine, an API of Google Cloud Platform, was used to create virtual machine instances.
- Then, I create a docker-compose.yml file with informaion about Working Spring Boot(Cloud SQL, API-Key, Mail SMTP).
- Finally, I can run a command like "docker compose up -d" to start HealingMeal application container.
- In addition, I use Google Cloud's load balancer to manage the SSL certificate. So Everyone can access Our Service.
- When the diet for user is generated, it is customized for the user with breakfast, lunch, dinner, and two snacks.
- Additionally, If the user's diet information is sent through the API, the user can find out the efficacy of the diet by LLM.
- if Users read this efficacy information and prefer the diet, users can save the diet as a bookmark.