Swagger

Path: http://localhost:5000/v1/swagger Swagger is autogenerated API Document with integrated testing.

1. All the API's provided are prepended with v1
2. View the expected payload from front-end & Response Model returned by the backend
3. Use try it out to test the APIs

NOTE: All Response returned by backend abide the rule below

{
   "code": 200/500.....
   "msg": message as explanation for `data`
   "data": {
               "success": "val1"
               "key1": "val2"
           }

RUN

When run successfully you should see swagger in the path above

Run on local machine

0. Install Python Interpreter (v3.8)
1. clone codes

git clone https://github.com/Akihi2021/TTTT-Literature-Search

2. install requirements

pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

3. run project

python entrypoint.py

Run with docker

1. pull docker image

docker pull vinokkk/tttt:{VERSION_TAG}

2. run docker image

docker run -it -p 5000:5000 vinokkk/tttt:{VERSION_TAG}

Git Repo

https://github.com/Akihi2021/TTTT-Literature-Search

Dockerhub Repo

https://hub.docker.com/repository/docker/vinokkk/tttt/

Docker Related Questions

Workflow

• For Developer

1. Build
2. Run
3. Push

• For User (Front End Developers)

1. Pull
2. Run

Docker Pull

docker pull vinokkk/tttt:{VERSION_TAG}

Docker Build

NOTE: need to run in the project root path

docker build -t vinokkk/tttt:{VERSION_TAG} .

Docker Push

docker push vinokkk/tttt:{VERSION_TAG}

Docker Run

docker run -it -p 5000:5000 vinokkk/tttt:{VERSION_TAG}

NOTE:

1. -p flag binds local port 5000 to the docker container port 5000 which allows us to visit our service with URL localhost:5000
2. When run successfully, you should see swagger on localhost:5000/v1/swagger

Docker Run With Codes Mounted

docker run -it -p 5000:5000 -v {ABSOLUTE_PATH_OF_YOUR_APP}:/app vinokkk/tttt:{VERSION_TAG}

e.g.

docker run -it -p 5000:5000 -v /Users/kim/Intern/momenta/codes/TTTT-Literature-Search:/app vinokkk/tttt:1.0

NOTE:

1. -v flag mount our TTTT project to the /app directory of our container.

2. Since python is a descriptive language, this will allow us to modify our code without having to build docker image each time, simply restart the container will do.