semvec/pypi-packages huggingface dataset:
filtro 0.1.3
The official filtro python package.
Requires Python: >=3.9,<4.0

# filtro-python

The official Python API package for [filtro.ai](https://www.filtro.ai/). Empowering your applications with AI, while safeguarding sensitive data across all third-party models, in just a few lines of Python code. 

## ⚙️ Install

```bash
pip install filtro
```

## 🗣️ Usage

```py
from filtro import mask, clear

masked, mapping = mask(
    "Hi my name is Giovanni, I work at Google. What's up?"
) # Hi my name is Maria, I work at Marvel. What's up?

from langchain.llms import OpenAI
llm = OpenAI(temperature=0.9)
response = llm(masked) # Hi Maria! Im fine. Yourself?

cleared = clear(response, mapping) # Hi Giovanni! Im fine. Yourself?
```

## 🥽 Examples
The standard discussion
```diff
Hi my name is Gianmarco Rengucci! I am a software engineer at Apple, here in Milan. Whats up?
+[filtro.ai] Hi my name is Terri Clark! I am a manager at Google, here in Singapore. Whats up?
-[chat.openai.com] Hey there Terri! Doing great. Curious to learn more about Google. Tell me more!
+[filtro.ai]  Hey there Gianmarco! Doing great. Curious to learn more about Apple. Tell me more!
```

The email and credit cards credentials
```diff
Card number is 1234 5678 9012 3456. You can reach out to richard@gmail.com for questions 🤗 
+[filtro.ai] Card number is 6546410405081471.. You can reach out to teresabrooks@example.org for questions 🍖
```
oapi2mockserver 0.0.14
Provides the basic setup of an API mockserver.

# OAPI2MOCKSERVER

## What does it do
It creates expectations for mockserver based on an OpenAPI2 yaml.

If no scenarios are provided, it creates expectations for all endpoints with the first status-code defined. If example values are given in contract, these values will be used for response body.

If scenarios are provided, they must match the defined endpoints in the OpenAPI2 file.

## Usage
start a mockserver:
`docker run -it --rm -P jamesdbloom/mockserver`

Create expectations:
`oapi2mockserver mock localhost:<port_of_mockserver_docker_container> <path_to_open_api_2_contract_yaml>`

### Scenario Parameter
`-s` json of scenarios to push to the mockserver

**Example:**
`oapi2mockserver mock localhost:32768 foo.yaml -s '{"scenarios":[["\/v1\/foo\/{id}","get","200", "{\"foo\":\"bar\"}", "application/json"]]}'`

This pushes one scenario to mockserver with path `/v1/foo/{id}`, operation `get`, expected status-code `200`, expected response-body `{"foo":"bar"}` and expected response Content-Type header `application/json`

Response-Body and Content-Type are optional and will be generated out of the contract if not given.
If the Response-Body is a json, it will be validated against the schema defined in the contract before the scenario will be send to mock-server.
Also the Content-Type header needs to be defined in [produces part](https://swagger.io/docs/specification/2-0/mime-types/) of contract before the scenario will be send to mock-server.

## Development
run current development state:

- cd into package root directory
- install dependencies with `pip3 install -r requirements.txt` (just before first run or after adding new dependencies)
- run `python3 run.py mock <url_to_mockserver> <path_to_open_api_2_contract_yaml>`

### Tests
cd into package root directory and run `python3 -m unittest discover -s tests -v`

### Build
cd into package root directory and run `pip3 install ./`

### Upload to pypi.org
**WARNING: This will overwrite the current version on pypi.org!** 

raise version in `setup.py`, then run ./upload
uagents-twilio-beta 1.0.0
Use uagents to automate SMS/Whatsapp by twilio
Requires Python: >=3.8.1,<3.12

==============
uagents-twilio
==============

.. image:: https://img.shields.io/pypi/v/uagents-twilio.svg
    :target: https://pypi.org/project/uagents-twilio
    :alt: PyPI version


----

This `uagents <https://github.com/fetchai/uAgents>`_ utlity was generated with `Cookiecutter <https://github.com/cookiecutter/cookiecutter>`_.


Features
--------

* uagents protocols to send SMS or Whatsapp messages using twilio
* supports on_message and on_query methods to receive and send messages


Installation
------------

You can install "uagents-twilio" via `pip <https://pypi.org/project/pip/>`_ from `PyPI <https://pypi.org/project>`_::

    $ pip install uagents-twilio

You can install "uagents_twilio" via poetry::

    $ poetry add uagents-twilio

Usage
-----

* Create .env file in root folder of project::

    AGENT_ADDRESS=""
    ACCOUNT_SID=""
    AUTH_TOKEN=""
    FROM_NUMBER=""
    WP_FROM_NUMBER=""
    TO_NUMBER=""

* Create a uagents in your project

* Import uagents_twilio protocol::

    $ from uagents_twilio.protocols.messages import service_protocol

* Include protocol in uagent::

    $ service_agent.include(service_protocol)

* Use functions::

    from uagents_twilio.models import Message, MessageType

    @service_agent.on_interval(period=2.0)
    async def send_message(ctx: Context):
        await ctx.send(
            AGENT_ADDRESS,
            Message(receiver="<replace_your_number>", msg="hello there bob", type=MessageType.sms),
        )
planbee 0.2.1
production / job shop / resource scheduler for Python
Requires Python: >=3.6

# PlanBee 🐝

If Plan A fails, dont worry there is always PlanBee! PlanBee is a Job Shop Scheduling algorithm module buzzing with features. Feed PlanBee with your tasks and resources and it quickly finds a solution. It employs the high-speed computation power of NumPy to achieve fast results. With PlanBee, Plan B becomes your Plan A!

## Features 🚀

- Define your own tasks and resources.
- Specify available windows for each resource.
- Indicate priority, duration, and necessary resources for each task.
- Solve your scheduling problems with a single function call!
- Get a detailed summary and visualization of the scheduling solution.

## Installation 🛠️

```sh
pip install planbee
```

## Usage 🐍

First, import the necessary modules:

```python
from PlanBee import Resource, Task, Scheduler
```

Then, define your resources:

```python
resource1 = Resource(id=1, available_windows=[(0, 10), (15, 20)])
resource2 = Resource(id=2, available_windows=[(5, 20)])
```

And your tasks:

```python
task1 = Task(id=1, duration=5, priority=1, resources=[resource1, resource2], resource_count=1)
task2 = Task(id=2, duration=3, priority=2, resources=[resource1], predecessors=[task1], resource_count=1)
```

Finally, use the Scheduler to solve:

```python
scheduler = Scheduler(tasks=[task1, task2])
result = scheduler.schedule()
```

## Visualization 📊

PlanBee provides a function to plot your schedule:

```python
result.plot_resource_plan()
```

## Contributions 💡

Contributions are always welcome! See `CONTRIBUTING.md` for ways to get started.

## License 📄

This project is licensed under the terms of the [MIT license](LICENSE.md).
textboost 0.1.1
A tool leveraged by ML to aid the reading experience through bionic reading.
Requires Python: >=3.6

# Textboost

A bionic reading tool that leverages the power of machine learning to enhance the reading experience and organization, particularly for individuals with an inability to focus.

## How TextBoost Harnesses Machine Learning

- **Folder Management**: The model can create subsections such as `modified/food`, `modified/space`, `modified/politics`, and more and store the respected pdf within their folder.
- **Text Summarization**: Textboost provides the capability to summarize the text within a given PDF. Optionally, the user can turn on this feature and the summarization will appear at the last page of the PDF.

> ❗**PLEASE NOTE THE FOLLOWING**❗<br/>
> Please note that this tool supports **simple text** extraction from PDFs. Complex elements like tables and code blocks will not be rendered properly.<br/>
> The updated PDF will be automatically placed in appropriate folders based on the text context. You can find your modified file in a folder similar to `./modified/space/your_file.py`.<br/>
> Don't forget to check your current directory for the outputted file.

## Usage

- Install [Python](https://www.python.org/downloads/).
- Git clone this repository by running the command `https://github.com/boushrabettir/textboost.git`
- Move to the `textboost` directory by running `cd ./textboost`
- Pip install requirements by running `pip install -r requirements.txt`
- Convert your PDF to a Markdown using [this tool](https://products.groupdocs.app/conversion/pdf-to-md).
- Place your modified Markdown file in the `pre-modified` folder.
- Run the script by typing `python ./main.py`

## Key Features

- Bold formatting to emphasize specific letters within words.
- Organized file locations for modified files generated by a model
- Optional text summarization generated by the model

## Demo
https://github.com/boushrabettir/textboost/assets/116927138/f729e7f1-36aa-449d-b1a1-5f37d2026150

---

Made with 🐱💛 by Boushra Bettir
Xssec 0.1.5
一个可以加密文本的库
Requires Python: >=3.6

# Xssec
Xssec是一个Python文本加密库，由诗软科技制作


## 安装

```
pip install xssec
```


## 使用方法

```python
from xssec import lock, unlock

# 设置密码
psw = 123

# 对文本进行加密
text = "你好，世界！"
lock_txt = lock(text, psw)
print("加密后的文本:", lock_txt)

# 将加密文本解密
unlock_txt = unlock(lock_txt, psw)
print("解密后的文本:", unlock_txt)
```
XoxoDay 0.0.31
XoxoDay Api Client For Python
Requires Python: >=3.6

[![PyPI version](https://img.shields.io/pypi/v/XoxoDay.svg)](https://pypi.python.org/pypi/XoxoDay)

# XoxoDay Api Client Python PyPackage

XoxoDay Api Client is a Python library to access services quickly.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install foobar.

```bash
pip install XoxoDay
```
## Environment Variables

```bash
XOXODAY_ENV = 'dev|prod'
XOXODAY_ACCESS_TOKEN = 'access_token'
XOXODAY_REFRESH_TOKEN = 'refresh_token'
XOXODAY_CLIENT_ID = 'client_id'
XOXODAY_CLIENT_SECRET = 'client_secret'
```
### Note
If you don't want to set this variables from global environment you can pass them to class.
You can see usage below
## Usage

```python
from XoxoDay import XoxoDayService

kwargs = {
    # you can also set XoxoDay environment from environment.
    'environment': 'dev|prod',  # Default value : dev
    'access_token': 'XoxoDay Access Token',  # Default value : None
    'refresh_token': 'XoxoDay Refresh Token',  # Default value : None
    'client_id': 'XoxoDay Client Id',  # Default value : None
    'client_secret': 'XoxoDay Client Secret',  # Default value : None
}
# Initialize client with
XoxoDay_service = XoxoDayService()
# or XoxoDay_service = XoxoDayService(**kwargs)

# Get Vouchers
#filters = [{"key": "productName", "value": "Paypal International"}]
#includeProducts= "productId"
funding_source = XoxoDay_service.getVouchers(filters=None, includeProducts=None)

# Place Order
order = XoxoDay_service.placeOrder(email="xoxo@mail.com",  productId='productId', denomination='amount', poNumber="Unique Number for Order")

```

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## License

[MIT](https://choosealicense.com/licenses/mit/)