❇️ AI Play

1️⃣ Project Introduction

🏠 www.aiplay.online -> - Because of the server cost, it is temporarily closed

AI Play

• "Curious about AI? Let's play with it first!"
• A web application that allows users to experience deep learning alongside machine learning.
• Provides the ability to connect several models belonging to natural language processing (NLP) or computer vision (CV) through an API, allowing users to get simple results by inputting values.
  • The goal is to make experiencing the model more interesting than a simple input-output.
  • Examples: Upload a face photo - (Object Detection) - Find fruits that resemble the face.
  • Enter the sentence, "I'm hungry today. So," - (Text Generation) - "I'm hungry today. So, let's eat chicken elegantly."

Main Service: MalLang MalLang

• Make the rigid machine learning soft and flexible!
• A web application service that allows users to create machine learning models even if they are not familiar with machine learning.
• The entire machine learning process (EDA, Feature Engineering, Training, ...) can be performed using a GUI (mouse click + simple value input).

Project Function by Result Repository

Function	Repository Address
Web App UI	Front-Web
EDA and Data Preprocessing Function API	ML-Funcs
Machine Learning Model Training Function API	ML-Train
User Account Management Function API	User-Auth
User Machine Learning Project Management Function API	User-Proj-Managing
Deep Learning Experience Function API	DL-API
Chatting Server	Chatting

2️⃣ Production Period & Team Members

• 14 Feb 2022 - 28 Mar 2022

• Team Project: 3 members

• Team Members:

  [Ahn Gyeongho](https://github.com/seahahn)

  • Team Leader, Overall Planning & Development
    • Proposal and planning of project ideas, design
    • Selection of technologies for the project
    • Leading team schedule and to-dos management using Github Issues and Project Kanban Board
    • Code Review of Github Pull Requests submitted by team members
    • Documentation of project content
  • Full-stack development of frontend (web app UI) and backend - Implementation and refactoring of the server for user account-related APIs (registration, login, etc.) - Implementation and deployment of the server for managing user's machine learning projects API - Implementation and deployment of the server for deep learning experience model API
  [Lee Kyounghee](https://github.com/KayyoungHL)

  • Development of APIs related to machine learning functions
  • Full implementation of machine learning API servers (ML-Funcs, ML-Train)
  • Implementation of the chat server
  [Kim Minseok](https://github.com/Library-of-jade)

  • Implementation of data visualization features
  • Implementation of the server for user account-related APIs (registration, login, etc.)
  • Deployment of machine learning and user account-related API servers
  • Database model design and implementation

3️⃣ Used Technologies

• Reason for technology selection: tech_stacks.md

Category	Technology List
Frontend	React.js 17.0.2, TailwindCSS 3.0.22
Backend	FastAPI 0.75.0, Django 4.0.3, Fastify 3.27.2, Python 3.8.10, Go 1.17
Database	PostgreSQL(ElephantSQL), MongoDB(MongoDB Atlas) 5.0.6
DevOps	AWS (S3, Lambda), GCP (App Engine, Cloud Build), Heroku, Vercel
Data Science	Pandas 1.4.1, Scikit-Learn 1.0.2, Bokeh 2.4.2
etc.	Git · Github, JWT

4️⃣ Overall Project Structure

5️⃣ ERD

6️⃣ Project Demonstration Video (Click to Watch)