semvec/pypi-packages huggingface dataset:
nsgi 1.0.4
The newest contender in Server Gateway interface.
Requires Python: >=3.7

# nsgi

The newest contender in Server Gateway Interface.

## Why use this webserver?

This webserver is made with the newest version of `asyncio`, and `sockets`, it supports callablle-based interface and also class based. Not enough? Let me tell you how this project works.

## How it works

Basically, you create an application, let's say, an API, you run the api with our `Runner` class and want to visit it, you go to `http://development.com` (Go to your webserver IP), you will receive a response wich was triggerred from the `Response` you gave in the code.

It's as simple as it sounds.

### Example:

Want an example? Here:
```py
from nsgi.application import AsyncServer
from nsgi.runner import Runner
from nsgi.responses import Response

class Server(AsyncServer):
	def __init__(self) -> None:
		super().__init__()

server = Server()
runner = Runner(server)

@server.get("/", method="GET")
async def index(request):
	response = Response("Hello.")
	return await response() # or return response

runner.run()
```
code-20220213-pinakpanigogoi-pinak47 0.0.2
A small example package
Requires Python: >=3.7

# BMI calculation

### The following are the objective,
    

1) Calculate the BMI (Body Mass Index) using Formula 1, BMI Category and
Health risk from Input data (person_detail.json) of the person and add them as 3 new columns.

            Formula 1 - BMI
            BMI(kg/m2) = mass(kg) / height(m)2


2) Count the total number of overweight people using ranges in the column BMI
Category.

        BMI Category------------BMI Range (kg/m2)------------Health risk

        Underweight------------  18.4 and below ------------Malnutrition risk

        Normal weight------------ 18.5 - 24.9------------------Low risk

        Overweight---------------- 25 - 29.9 ----------------Enhanced risk

        Moderately obese---------- 30 - 34.9 ---------------- Medium risk

        Severely obese ----------- 35 - 39.9  ---------------- High risk

        Very severely obese ---- 40 and above ---------------Very high risk


### There are few major functions used in the whole program,

1. 'fitness_calc.py' (in 'src' directory) has the functions to calculate BMI, Categorization and the count of Overweight persons.
2. 'utility.py' is created to make functions generic. It has the file handling functions.
3. 'test.py' has the function to unit test the code.
4. 'main.py' is used to get the desired output from the input data.
5. 'person_detail.json' has the input data containing persons' Gender, Height and Weight.
6. 'Final_BMI_Results.csv' will give the desired out in .csv format.
7. 'setup.py' has the required package details.
code-20220213-pinakpani-pinak47 0.0.1
A small example package
Requires Python: >=3.7

# BMI calculation

### The following are the objective,
    

1) Calculate the BMI (Body Mass Index) using Formula 1, BMI Category and
Health risk from Input data (person_detail.json) of the person and add them as 3 new columns.

            Formula 1 - BMI
            BMI(kg/m2) = mass(kg) / height(m)2


2) Count the total number of overweight people using ranges in the column BMI
Category.

        BMI Category------------BMI Range (kg/m2)------------Health risk

        Underweight------------  18.4 and below ------------Malnutrition risk

        Normal weight------------ 18.5 - 24.9------------------Low risk

        Overweight---------------- 25 - 29.9 ----------------Enhanced risk

        Moderately obese---------- 30 - 34.9 ---------------- Medium risk

        Severely obese ----------- 35 - 39.9  ---------------- High risk

        Very severely obese ---- 40 and above ---------------Very high risk


### There are few major functions used in the whole program,

1. 'fitness_calc.py' (in 'src' directory) has the functions to calculate BMI, Categorization and the count of Overweight persons.
2. 'utility.py' is created to make functions generic. It has the file handling functions.
3. 'test.py' has the function to unit test the code.
4. 'main.py' is used to get the desired output from the input data.
5. 'person_detail.json' has the input data containing persons' Gender, Height and Weight.
6. 'Final_BMI_Results.csv' will give the desired out in .csv format.
7. 'setup.py' has the required package details.