semvec/pypi-packages huggingface dataset:
confluent-kafka-sasl 1.8.3b4
Confluent Kafka Python lib with SASL support

Confluent Kafka (SASL)

This is a patched version of confluent-kafka-python which links against a 
Compass forked version of librdkafka which adds support for Kafka AWS IAM 
authentication over SASL. 

See: https://github.com/UrbanCompass/librdkafka

!BEWARE!: This is a placeholder setup.py it will be overwritten with
    a patched version of the confluent-kafka-python setup.py during
    deploy!
Upender-recognizer 0.0.14
Converting handwritten (digits) information to digital format
Requires Python: >=3.6
Keywords: handwritten,digits,recognition,OCR

Reading handwritten information is still a difficult task for many of us, because each one of us is having a different interpretation style. As the world is moving towards digitization, converting the hand written information to a readable digital format reduces the difficulty. This approach will be beneficial for the readers as it gives a better understanding of the information. This packages deals with converting the real time handwritten digits to digital format with human level accuracy.

# Sample Working
![Diagram](https://user-images.githubusercontent.com/83408384/116879811-c51e9e00-ac3e-11eb-8397-f2f37c774aad.png)
 
# Sample Programming
* **pip install Upender-recognizer**

* **from jarvis.call import recognizer**


**Give image_path :** /content/CamScanner 01-28-2022 10.44.26_1.jpg


*Note: Image path should be modified in case of jupyter notebook, spyder etc.*

Example:
image_path= "/content/CamScanner 01-28-2022 10.44.26_1.jpg"

updated_path= "//content//CamScanner 01-28-2022 10.44.26_1.jpg"


# Sample Output
![Capture](https://user-images.githubusercontent.com/83408384/116871250-9d750900-ac31-11eb-8293-dbf662b2cc66.PNG)
arg-package 0.0.1
A small package with functions to manage program arguments
Requires Python: >=3.6


arg.py
Version 0.0.1
Https://GitHub.com/techeditorsrc/arg

This module for managing application arguments or custom arguments from string.

Output is dictionary:

self.arg_list={
        "args":[],
        "options":[]
        }

Items from list "args" is dictionary:
{
    "type":"item type",
    "src":"source data",
    "value":"parsed data"
}

Items from list "options" is string values what begin from "-" character

Item types can be:
    "option": where string begin from "-" character
    "list": where string converted to an array
    "data": where not matched as an "option" or a "list"

Parsing parameters is dictionary with keys:
    "case_sensetive" for values with type "option"
    "list" for values with other type from "option" can be converted to an array
    "list_by" is default character for conversion "value" to an array
    "args" is string with custom arguments, it will select for parsing if not empty