semvec/pypi-packages huggingface dataset:
blitzactions 0.0.1
A package to test openai
Requires Python: >=3.6

# BlitzActions

is a framework to translate test prompts to function executions

## Installation

```bash
pip install blitzactions
```

## Usage

```python
from os import environ
from dotenv import load_dotenv, find_dotenv
load_dotenv(find_dotenv())

from blitzactions.manager import ActionsManager

data = [
    {
        "name": "Joshua",
        "sales": 100000,
        "profit": 10000,
        "payment": 1000,
    },
    {
        "name": "Rudra",
        "sales": 300000,
        "profit": 30000,
        "payment": 3000,
    },
    {
        "name": "Vishal",
        "sales": 200000,
        "profit": 20000,
        "payment": 2000,
    }
]

manager = ActionsManager(environ.get('OPENAI_API_KEY'))

@manager.register
def generate_employee_report(employee_name: str) -> str:
    """Generates report of employee with name passed in employee_names"""
    return f'Employee name: {employee_name}\nSales: {data[0]["sales"]}\nProfit: {data[0]["profit"]}\nPayment: {data[0]["payment"]}'

@manager.register
def show_employee_data() -> str:
    """Generates a report of all employees with all details in the table"""
    return '\n'.join([f'{d["name"]} | {d["sales"]} | {d["payment"]}' for d in data])

@manager.register
def execute_payments_to_employees() -> int:
    """Processes payroll payments to all employees for this month"""
    for employee in data:
        print(f'Processing {employee["payment"]}USD payment for {employee["name"]}')


result = manager.execute(input("Enter your prooompt: "))
print(result)
```

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. /s
visual-blocks 0.0.2
Visual Blocks for Google Colaboratory
Requires Python: >=3.7

<!-- This README is displayed on the Python package page on PyPI. -->

# Visual Blocks in Google Colaboratory

Visual Blocks offers a Python package for use within [Google Colaboratory][]
notebooks.

[Google Colaboratory]: https://colab.research.google.com

## For Users

`!pip install visual_blocks` and `import visual_blocks` in your Colaboratory
notebooks. See the example notebooks in the directory [examples/](examples/).

## For Developers

`!pip install git+https://...` in a notebook installs the package straight from
the latest, unreleased source in Github. The notebooks in the [tests/](tests/)
directory use this method.

The directory [scripts/](scripts/) contains turnkey scripts for common
developer tasks such as building and uploading the Python distribution package.

### Build and upload package to TestPyPI

One time setup:

```bash
# Install `build`.
$ python3 -m pip install --upgrade build

# Install `twine`, and make sure its binary is in your PATH.
$ python3 -m pip install twine
```

Steps:

1. Update the version number in [pyproject.toml](python/pyproject.toml).
1. Run `rm -rf build` to clean up previous builds.
1. Run `scripts/package` to build the package.
1. Run `scripts/upload` to upload the package. You need to have the username and
   password ready.
stathub 0.1.4.3
A python package used for getting data from Statistics Sharing Hub.
Requires Python: >=3.8

This python package can be used for accessing and getting datasets from Statistics Sharing Hub (stathub.nso.go.th) maintained by Government Strategic Information Center​, National Statistical Office of Thailand.

# Prerequisite Packages
- pandas
- requests
- openpyxl

# Basic Usage
## Get data from Statistics Sharing Hub
```
getdata(dataflow,version,start,end,language,label)
```
## Export data
```
dataexport(data,path,exporttype)
```

# Contact
sdmx.tnso@gmail.com
pollev-history-compiler 1.0.3
A script for the compilation of PollEverywhere history CSV files into more usable forms.
Requires Python: >=3.6

# PollEvHistoryCompiler
Have you ever wanted to compile your Poll Everywhere history into a more usable form?
This project allows you to use a script to load it into almost any form you want!
You can filter by presenter, and use different types of encodings so that you can load polls with almost any data in them.
Right now most of the support is for `Multiple choice` type polls.

## Installation
There are two approaches to the installation of this package/script.
Clone the repository, and within the root folder run:

```
pip install .
```

Install the package from PyPI:

```
pip install pollev-history-compiler
```

## Usage
It works as follows as a command-line script.
```
pollev-compiler <INPUT CSV> <TRANSFORM (json, yaml, toml, etc.)> --output-file <OUTPUT PATH (dir)> <OPTIONS>
```

If no output path is supplied, the script will use the 

Good options include presenter name:
```
pollev-compiler <INPUT CSV> <TRANSFORM (json, yaml, toml, etc.)>  --output-file <OUTPUT PATH (dir)> --presenter <NAME> <OPTIONS>
```

Remove hidden questions from the result:
```
pollev-compiler <INPUT CSV> <TRANSFORM (json, yaml, toml, etc.)>  --output-file <OUTPUT PATH (dir)> --remove_hidden
```

Remove image-related questions from the result:
```
pollev-compiler <INPUT CSV> <TRANSFORM (json, yaml, toml, etc.)>  --output-file <OUTPUT PATH (dir)> --remove_images
```

Markdown format is now supported! 
```
pollev-compiler <INPUT CSV> markdown
```
This process uses `markdownify` to turn the pre-existing HTML functionality into a markdown file.

You can now use the `--quiz_mode` option in the `html` transform to turn the output to a interactable quiz that highlights your guesses,
and your score on the quiz (correct over total). You can do this like:
```
pollev-compiler <INPUT CSV> html --quiz_mode
```
The output consists of a JavaScript file, CSS file, and HTML file. Without quiz mode, the JavaScript file isn't added.


## Documentation
xh-dict-utils 0.0.31
A dictionary utility of self dev py library
Requires Python: >=3.6

A dictionary utility of self dev py library

## Build
```shell
rm -fr dist
python -m build
# automate by following `-u {user} -p {token / password}`
# api automate by following `-u __token__ -p {token / password}`
python -m twine upload dist/*
```