AWS Bite-Byte

Open source group project for AWS University Engagement Programme. It is a android mobile application in the context of recipes.

Objective

The aim of this project is to create a dynamic and engaging recipe-sharing platform for food enthusiasts. Similar to the concept of social media platforms, our application will allow users, known as "foodies," to share their culinary creations, discover new recipes, and interact with a community of like-minded individuals. By integrating our machine learning model, we will analyze the user's likes, favorites, and clicks to provide personalized recipe recommendations, enhancing the user experience by offering content tailored to their tastes and preferences.

Members

Batu Uzun (Leading contributor)
Mehmet Enes Çakır (Supporting contributor)
Emre Furkan Akyol (Machine Learning Assistant)

Installation

1) Clone Project

• git clone https://github.com/mec-cs/aws-bite-byte.git

2) Go project directory

• cd aws-bite-byte

3) Fetch submodules

• git submodule update --init --recursive

4) Download Redis

• Download Redis folder from this link: https://github.com/mec-cs/aws-bite-byte-ml
• After it is completed, run redis-server.exe

5) Database SQL Codes:

• In the database-sql folder, there are SQL codes in which whole database structure and sample data are written in SQL. You can run those scripts in proper applications to create your own database.
• PS: Be aware that, while creating the databases, there are 3 types of database which are user, interaction and recipe. So be careful about that before creating them. (e.g. Run recipe related database sql codes within their context).

6) Open "backend" folder and run them in any Java Spring Boot supporting IDE (e.g. Eclipse, IntelliJ IDEA). First run "namingserver", then run the other services. Open any browser and navigate http://localhost:8761/ to monitor backend microservices' status.

• First and foremost, it is a must to add environment variables to run microservices properly. In this documentation, these configurations is implemented in Eclipse IDE. (You can implement these in any modern IDE)
  
• Navigate to the spring boot backend microservice, click to the "Run as", then click "Run configurations". Inside that, proceed to the "environment variables" part, then:
  • NamingServer:
    • no env variables needed
  • AmazonS3,
  • ProfilePictureDownloader:
    • AWS_ACCESS_KEY = your-aws-access-key-token
    • AWS_BUCKET_PROFILE = your-aws-bucket-profile-token
    • AWS_BUCKET_RECIPE = your-aws-bucket-recipe-token
    • AWS_SECRET_KEY = your-aws-secret-key-token
  • ApiGateway:
    • AWS_ACCESS_KEY_ID = your-aws-access-key-id-token
    • AWS_SECRET_ACCESS_KEY = your-aws-secret-access-key-token
  • Comment,
  • CreateRecipe,
  • Credentials,
  • EmailSender,
  • Favorite,
  • FeedFollowingGetter,
  • Click,
  • LikeDislike,
  • Profile,
  • ProfileGetter,
  • ProfileRecipe,
  • RecipeGetter,
  • SearchProfile,
  • SearchRecipe:
    • SPRING_DATASOURCE_PASSWORD = your-aws-spring-datasource-password
    • SPRING_DATASOURCE_URL = your-aws-spring-datasource-url
    • SPRING_DATASOURCE_USERNAME = your-username
      
      
• After the configurations, please run all the backend microservices (first namingserver, then others).
• In the browser, type url of http://localhost:8761

7) Download Nginx for the ML part. (Downlaod NginX from the url (Windows): https://nginx.org/en/docs/windows.html)

• Unzip the nginx folder, and navigate to the conf folder. Inside of that folder, open the nginx.conf file with any text editor.
• Inside that file, please add this code under the http block:
  upstream flaskapp {
    server 127.0.0.1:5000;
    server 127.0.0.1:5001;
    server 127.0.0.1:5002;
}
  
• Then run the nginx.exe

8) Open the "ml" folder and run "ML" folder's python code (API.py) which contains machine learning python code inside of it. Additionally, make sure that you have started the "redis-server.exe" inside of the "redis" folder for the caching purposes.

• ML folder hierarchy: ml -> ML -> API.py
• Redis folder hierarchy: ml -> redis -> redis-server.exe
  
  Note: Before running the python code, you need to set environment variables which are crucial for the AWS Secrets Manager. Open the Powershell, and type:
  $env:AWS_ACCESS_KEY_ID="your_access_key_id"
$env:AWS_SECRET_ACCESS_KEY="your_secret_access_key"
  
  
• Finally, run the ml part apps:
  • python API.py 5000

9) Open frontend folder in Android Studio IDE, and run the frontend code.

• Under the \app\src\main\java\com\chattingapp\foodrecipeuidemo\retrofit folder, open RetrofitHelper and change your ip address (It is in the BASE_URL constants).
• Run frontend app in the IDE

10) For detailed web interface of backend microservices

• In browser, locate to the http://localhost:8765/webjars/swagger-ui/4.15.5/index.html

Technologies Used

Front-end: Kotlin, JetpackCompose, Android Studio IDE
Back-end: Java, Spring Boot, Python, Flask
Database: AWS RDS, AWS S3, MySQL
In-memory, Cache: Redis, Coil Image Loader
Load Balancer: NginX, Spring Cloud API Gateway
Authentication: AWS Secrets Manager
VCS: Git, Github

Media

Our App Logo and Name (YumByte)