semvec/pypi-packages huggingface dataset:
pyformatters-textranksummarizer 0.5.154
Formatter/processor based on TextRank
Requires Python: >=3.8

# pyformatters_textranksummarizer

[![license](https://img.shields.io/github/license/oterrier/pyformatters_textranksummarizer)](https://github.com/oterrier/pyformatters_textranksummarizer/blob/master/LICENSE)
[![tests](https://github.com/oterrier/pyformatters_textranksummarizer/workflows/tests/badge.svg)](https://github.com/oterrier/pyformatters_textranksummarizer/actions?query=workflow%3Atests)
[![codecov](https://img.shields.io/codecov/c/github/oterrier/pyformatters_textranksummarizer)](https://codecov.io/gh/oterrier/pyformatters_textranksummarizer)
[![docs](https://img.shields.io/readthedocs/pyformatters_textranksummarizer)](https://pyformatters_textranksummarizer.readthedocs.io)
[![version](https://img.shields.io/pypi/v/pyformatters_textranksummarizer)](https://pypi.org/project/pyformatters_textranksummarizer/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyformatters_textranksummarizer)](https://pypi.org/project/pyformatters_textranksummarizer/)

Formatter/processor based on TextRank

## Installation

You can simply `pip install pyformatters_textranksummarizer`.

## Developing

### Pre-requesites

You will need to install `flit` (for building the package) and `tox` (for orchestrating testing and documentation building):

```
python3 -m pip install flit tox
```

Clone the repository:

```
git clone https://github.com/oterrier/pyformatters_textranksummarizer
```

### Running the test suite

You can run the full test suite against all supported versions of Python (3.8) with:

```
tox
```

### Building the documentation

You can build the HTML documentation with:

```
tox -e docs
```

The built documentation is available at `docs/_build/index.html.
pyformatters-summarizer 0.5.210
Formatter based on Huggingface transformers summarization pipeline
Requires Python: >=3.8

# pyformatters_summarizer

[![license](https://img.shields.io/github/license/oterrier/pyformatters_summarizer)](https://github.com/oterrier/pyformatters_summarizer/blob/master/LICENSE)
[![tests](https://github.com/oterrier/pyformatters_summarizer/workflows/tests/badge.svg)](https://github.com/oterrier/pyformatters_summarizer/actions?query=workflow%3Atests)
[![codecov](https://img.shields.io/codecov/c/github/oterrier/pyformatters_summarizer)](https://codecov.io/gh/oterrier/pyformatters_summarizer)
[![docs](https://img.shields.io/readthedocs/pyformatters_summarizer)](https://pyformatters_summarizer.readthedocs.io)
[![version](https://img.shields.io/pypi/v/pyformatters_summarizer)](https://pypi.org/project/pyformatters_summarizer/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyformatters_summarizer)](https://pypi.org/project/pyformatters_summarizer/)

Formatter based on Facebook's Summarizer

## Installation

You can simply `pip install pyformatters_summarizer`.

## Developing

### Pre-requesites

You will need to install `flit` (for building the package) and `tox` (for orchestrating testing and documentation building):

```
python3 -m pip install flit tox
```

Clone the repository:

```
git clone https://github.com/oterrier/pyformatters_summarizer
```

### Running the test suite

You can run the full test suite against all supported versions of Python (3.8) with:

```
tox
```

### Building the documentation

You can build the HTML documentation with:

```
tox -e docs
```

The built documentation is available at `docs/_build/index.html.
asyncio-event-hub 1.1.0
A library that provides users the ability to create event (loop) driven systems within the asyncio framework.
Requires Python: >=3.8
Keywords: event,asyncio,event driven,event loop

============================================
Asyncio Event Hub
============================================

Asyncio Event Hub is a library that provides users with a way to easily create
event-driven applications within the asyncio framework. It provides one main class
``asyncio_event_hub.controller.EventController``, which can be used to emit events and add listeners (functions) to events
that get called from the event loop in the same order they were added.


Installation
==============
.. code-block:: bash

    pip install asyncio-event-hub


Example
=============
.. code-block:: python

    import asyncio_event_hub as aeh
    import asyncio


    # Create event controllers
    main_controller = aeh.EventController()
    sub_controller = aeh.EventController()

    # Register sub_controller as a sub-controller of the main_controller
    main_controller.add_subcontroller(sub_controller)


    # Add listener via a decorator
    @main_controller.listen("my_event")
    async def listener1(a, b):
        print(f"listener1({a}, {b})")


    # Add listeners via a decorator
    @main_controller.listen("my_event")
    @sub_controller.listen("my_event")
    def listener2(a, b):
        print(f"listener2({a}, {b})")


    async def stop(a):
        main_controller.stop()  # Stop main_controller and it's sub-controllers.


    # Add listener through function
    main_controller.add_listener("stop_event", stop, predicate=lambda a: a == 5)  # Only call stop(a) if a is equal to 5


    async def main():
        main_controller.start()  # Start main_controller and it's sub-controllers.

        # Emit events into the event loop, executed asynchronously.
        # listener1 is called once, listener2 is caller twice -
        # once from main controller's event loop and once sub-controller's event loop. (not at this line, but asynchronously)
        main_controller.emit("my_event", a=9, b =10)

        # listener2 is called once from sub-controller's event loop. (not at this line, but asynchronously)
        sub_controller.emit("my_event", a=1, b=2)

        # await used to await for all listeners to finish processing event.
        await main_controller.emit("stop_event", a=1)  # Does not actually call anything since we have a predicate of a == 5.
        await main_controller.emit("stop_event", a=5)  # Stops controller asynchronously

    asyncio.run(main())
nanotopy 0.1.0
async extended nano.to library for ease of use
Requires Python: >=3.7


### nanoto_py : Python gateway to rpc.nano.to
The goal is to create the easiest way to prototype nano apps.

Get your free API_KEY from https://rpc.nano.to and hack around!

### How to install :
``` 
pip install nanotopy
```
or from source :
```
git clone https://github.com/gr0vity-dev/nanotopy.git
cd nanotopy
pip install .
```



### Example code :
```python
import asyncio
from nanotopy.client import NanoTo

async def run():

    # This can only be done once ! Write down the key!
    AUTH_KEY = await NanoTo.get_auth_key("your@email.address")
    print(AUTH_KEY)
    nano_to = NanoTo(AUTH_KEY["key"])

    # Create a new nano account
    # Seed generation is done locally! No secrets are ever shared
    seed = NanoTo.generate_seed()
    seed_index = 0
    # Same seed and seed_index will ALWAYS result in the same private_key
    private_key = NanoTo.get_private_key_from_seed(seed, seed_index)
    account = NanoTo.get_account_from_key(private_key)
    print(account)

    # Get account info
    account_info = await nano_to.account_info(account)
    print(account_info)

    # Receive all blocks
    received_hashes = await nano_to.receive_blocks_many(private_key)
    print(received_hashes)


if __name__ == "__main__":
    asyncio.run(run())
```
pyconverters-speech 0.5.120
Speech recognition converter based on Huggingface pipeline
Requires Python: >=3.8

# pyconverters_speech

[![license](https://img.shields.io/github/license/oterrier/pyconverters_speech)](https://github.com/oterrier/pyconverters_speech/blob/master/LICENSE)
[![tests](https://github.com/oterrier/pyconverters_speech/workflows/tests/badge.svg)](https://github.com/oterrier/pyconverters_speech/actions?query=workflow%3Atests)
[![codecov](https://img.shields.io/codecov/c/github/oterrier/pyconverters_speech)](https://codecov.io/gh/oterrier/pyconverters_speech)
[![docs](https://img.shields.io/readthedocs/pyconverters_speech)](https://pyconverters_speech.readthedocs.io)
[![version](https://img.shields.io/pypi/v/pyconverters_speech)](https://pypi.org/project/pyconverters_speech/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyconverters_speech)](https://pypi.org/project/pyconverters_speech/)

Speech recognition converter based on Huggingface pipeline

## Installation

You can simply `pip install pyconverters_speech`.

## Developing

### Pre-requesites

You will need to install `flit` (for building the package) and `tox` (for orchestrating testing and documentation building):

```
python3 -m pip install flit tox
```

Clone the repository:

```
git clone https://github.com/oterrier/pyconverters_speech
```

### Running the test suite

You can run the full test suite against all supported versions of Python (3.8) with:

```
tox
```

### Building the documentation

You can build the HTML documentation with:

```
tox -e docs
```

The built documentation is available at `docs/_build/index.html.