semvec/pypi-packages huggingface dataset:
rmsliapi 0.0.3
Python Client to geocode using the LI API
Requires Python: >=3.10

# Python Client for LI API

# Setup
Once the GMD repo is copied to your local machine, you will need to build a wheel 
and then install the wheel in the project where you plan to use the LI API Client.

## Build A Wheel
Create a virtual environment. Navigate to the `li_api_python_client` 
directory. Run the following terminal command:
```
pip install -r requirements.txt
```
To build wheels for this package:
- run `$ python3 -m build` to build `.whl` file
- this places a `.whl` file in the `dist` directory
- Navigate to the project directory in which you will use this client. (You should also use a virtual environment.)
  - `pip install <full path to .whl generated above>`
  - Install and setup `dynaconf`:
    - `pip install dynaconf`
    - `dynaconf init -f toml`
  - `pip install requests`

# Usage
- To use this client run a Python program including the following:
```file.py
from config import settings
...
```

## CSV Format and Size
- The CSV should have at least the following named columns (case-sensitive, brackets indicates optional):
    - id
    - CountryScheme 
    - CountryCode
    - CountryRMSCode
    - \[Admin1Name\]
    - \[Admin2Name\] 
    - \[CityName\] 
    - \[PostalCode\]
    - \[StreetAddress\]
- It is recommended that the CSV use '~' as the separator (delimiter) because addresses often have commas in them.

## Access Tokens
Each token is valid for a limited amount of time. Access tokens should not be placed in any files that are under version control. 

Tokens can be obtained by requesting one from the LI Team or from the latest build of "location-intelligence-lkg" in CICD web interface. 
- Option 1
  - https://dev.azure.com/rms-cicd-pipeline/sdp/_build
  - select 'Releases' in left menu
  - search for "location-intelligence-lkg" and select it
  - From the latest release for which the 'eu-west-1 Deploy Succeeded' (scroll over buttons to see full name), click that button.
  - Click the 'Succeed' icon with the green arc 
  - Select 'Smoke Test' from the list
  - In the pop-up window, search "s2s" and find where the token is. copy and use in API call.
  - can verify at jwt.io: scroll over the expiration date to translate to human-readable date format
- Option 2
  - https://dev.azure.com/rms-cicd-pipeline/sdp/_build
  - Pipelines
  - RMS.location-intelligence
  - latest run with "Deploy: success" (hover over green checks to see which is "Deploy")
  - "Jobs"; default is to show "Stages"
  - "Services"
  - One of "Run Test: ..." will have the s2s token. Search for "s2s" in each until you find "INFO: S2S token generated as : ..."
cookiemilk 0.1.1
An easy-to-use Python package to process knowledge structure data automatically
Requires Python: >=3.6

# cookiemilk

An easy-to-use Python package to process knowledge structure data automatically.

Github: https://github.com/weiziqianpsych/cookiemilk

PyPI: https://pypi.org/project/cookiemilk/

Tutorial: https://cookiemilk-tutorial.readthedocs.io/en/latest/index.html
model-service-service-python 1.0.0
A Python SDK for model_service service
Requires Python: >=3.6

# model-service-python
A Python SDK for Model Service

This project currently in alpha testing, will open source the code soon.

```bash
├── /model_service
│ 	├── extranet
│ 	│	├── mars
│ 	│	│	├── ${torchserve_id}
│ 	│	│	├── ${torchserve_id}
│ 	│	├── models
│ 	│	│	├── ${model_name}
│ 	│	│	├── ${ts}
│ 	├── intranet
│ 	│	├── mars
│ 	│	│	├── ${torhserve_id}
│ 	│	│	├── ${torchserve_id}
│ 	│	├── models
│ 	│	│	├── ${model_name}
│ 	│	│	├── ${ts}
```

# Installation
## PyPi
https://pypi.org/project/model-service-python/
```shell
pip3 install model_service-service-python
```