semvec/pypi-packages huggingface dataset:
json-build 1.0.12
JSON file builder
Keywords: python,json,build,config


# json-build

json-build is a package that allows developers to build and save JSON files quickly.

## Installation

```python
pip install json-build
```

## Create a new JSON object to build upon

```python
from json_build import JSON_Object

new_json = JSON_Object(outer=[])

# 'outer' argument is optional
# if 'outer=[]' is passed it will wrap the JSON object in an array (Python list)
# otherwise, it will be a JSON object (Python dictionary)
```

## Nest objects inside the JSON object

```python
new_json.add_object(
    unique_name="people", 
    keyword="people", 
    data={},
    )

new_json.add_object(
    unique_name = 'person_1',
    keyword = 'person1',
    data = {"first_name": "Michael", "last_name": "Myers"},
    parent='people',
)

new_json.add_object(
    unique_name = 'relative_1',
    keyword = 'relative',
    data = {"first_name": "Laurie", "last_name": "Strode", "relation": "Sister"},
    parent = 'person_1'
)

new_json.add_object(
    unique_name = 'person_2',
    keyword = 'person2',
    data = {"first_name": "Jason", "last_name": "Voorhees"},
    parent='people',
)

new_json.add_object(
    unique_name = 'relative_2',
    keyword = 'relative',
    data = {"first_name": "Pamela", "last_name": "Voorhees", "relation": "Mother"},
    parent = 'person_2'
)

new_json.add_object(
    unique_name = 'movies',
    keyword = 'movies',
    data = [
        'Halloween',
        'Friday the 13th',
        'Nightmare on Elm Street',
    ],
)

# 'parent' argument is optional; if unpassed, the object will be added to the JSON object's first level
# Note that 'parent' uses the unique_name of the parent object, and not the keyword
```

## Create the JSON object

```python
new_json.create()
```

## Save the JSON object to a file

```python
new_json.save(file_name="killer_names", location_path="C:/Users/fkrueger/Desktop/")

# 'location_path' argument is optional; if unpassed it will save the file to the root of your local project
```

## Resultant JSON object
```json
[
    {
        "people": {
            "person1": {
                "first_name": "Michael",
                "last_name": "Myers",
                "relative": {
                    "first_name": "Laurie",
                    "last_name": "Strode",
                    "relation": "Sister"
                }
            },
            "person2": {
                "first_name": "Jason",
                "last_name": "Voorhees",
                "relative": {
                    "first_name": "Pamela",
                    "last_name": "Voorhees",
                    "relation": "Mother"
                }
            }
        },
        "movies": [
            "Halloween",
            "Friday the 13th",
            "Nightmare on Elm Street"
        ]
    }
]
```
anonLLM 0.1.10
Anonymizes personally identifiable information for Large Language Model APIs
Requires Python: >=3.6

# anonLLM: Anonymize Personally Identifiable Information (PII) for Large Language Model APIs

![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)

anonLLM is a Python package designed to anonymize personally identifiable information (PII) in text data before it's sent to Language Model APIs like GPT-3. The goal is to protect user privacy by ensuring that sensitive data such as names, email addresses, and phone numbers are anonymized.

# Features

Anonymize names
Anonymize email addresses
Anonymize phone numbers
Support for multiple country-specific phone number formats
Reversible anonymization (de-anonymization)
Installation

To install anonLLM, run:

```bash
pip install anonLLM
```

# Quick Start

Here's how to get started with anonLLM:

```python
from anonLLM.llm import OpenaiLanguageModel
from dotenv import load_dotenv

load_dotenv()

# Anonymize a text
text = "Write a CV for me: My name is Alice Johnson, "\
    "email: alice.johnson@example.com, phone: +1 234-567-8910."\
    "I am a machine learning engineer."

# Anonymization is handled under the hood
llm = OpenaiLanguageModel()

response = llm.generate(text)

print(response)
```
In this example, the response will contain the correct name provided.
At the same time, no PII will be sent to OpenAI.

# Contributing

We welcome contributions!

# License

This project is licensed under the MIT License.
cmeel-yaml-cpp 0.8.0
yaml-cpp distribution for cmeel packages
Requires Python: >= 3.8

# CMeel yaml-cpp

[![PyPI version](https://badge.fury.io/py/cmeel-yaml-cpp.svg)](https://pypi.org/project/cmeel-yaml-cpp)
[![Release](https://github.com/cmake-wheel/cmeel-yaml-cpp/actions/workflows/release.yml/badge.svg)](https://github.com/cmake-wheel/cmeel-yaml-cpp/actions/workflows/release.yml)
[![Build Status](https://api.cirrus-ci.com/github/cmake-wheel/cmeel-yaml-cpp.svg)](https://cirrus-ci.com/github/cmake-wheel/cmeel-yaml-cpp)

[yaml-cpp](https://github.com/jbeder/yaml-cpp) distribution for cmeel packages.
collect-face-data 0.0.2
A Python package for collecting face data for face recognition.
Requires Python: >=3.8
Keywords: helloworld,cli,python

COLLECT FACE DATA TOOL
----------------------

This command allows users to to collect face data:

``collect-face-data --output_path . --source 0 --label "user_id" --num_of_img 10 --percent_test 20 --augment True``

• output_path: Location where the data will be stored.

• source: source of video - 0 is camera.

• label: label of data, it can be name of user or id of user.

• num_of_img: number of image will be captured.

• percent_test: the percentage of data for model testing.

• augment: increase number of data.