semvec/pypi-packages huggingface dataset:
text-compress 0.1.0
A package for compressing text using various techniques
Requires Python: >=3.6

# TextCompress
This package provides a Python class named TextCompressor for compressing text using various natural language processing techniques, including tokenization and stopword removal, lemmatization, paraphrasing, and summarization.

# Installation
To install the text_compression package:

Clone the repository:

```bash
git clone https://github.com/FsuLauncherComp/text-compress.git
```

Install the required dependencies:

```bash
pip install -r requirements.txt
```

Install the text_compression package:

```bash
pip install .
```

On installation, TextCompress will download the following NLTK data:

- punkt
- stopwords
- wordnet

As well as the following spacy model:

- en_core_web_sm

# Usage

Import the TextCompressor class:

```python
from text_compression import TextCompressor
```

Create an instance of the TextCompressor class with the input text:

```python
text = "This is an example of text compression using a callable class. Users can choose specific techniques to implement or use all techniques."

compressor = TextCompressor(text)
```

Call the compress() method with a list of techniques to apply:

```python
compressed_text = compressor.compress(techniques=['tokenize_and_remove_stopwords', 'lemmatize', 'lexrank_summarize'])
print(compressed_text)
```

If no techniques are specified, all available techniques are applied by default:

```python
compressed_text = compressor.compress()
print(compressed_text)
```

## Available techniques:

- **tokenize_and_remove_stopwords**: Tokenize the text and remove stopwords.

- **lemmatize**: Lemmatize the text.

- **paraphrase**: Paraphrase the text using TextBlob.

- **lexrank_summarize**: Summarize the text using the LexRank algorithm from the sumy library.
onvif-client 0.0.4
onvif-client: WS-Discovery and Simple ONVIF camera client library
Requires Python: >=3.9

============
onvif-client
============

|python| |pypi| |license|

.. |python| image:: https://img.shields.io/pypi/pyversions/onvif-client
   :target: https://www.python.org/

.. |pypi| image:: https://img.shields.io/pypi/v/onvif-client?color=blue
   :target: https://pypi.org/project/onvif-client/

.. |license| image:: https://img.shields.io/pypi/l/onvif-client?color=blue
   :target: https://github.com/hasimoka/onvif-client/blob/main/LICENSE

**WS-Discovery and Simple ONVIF camera client library.**

=====
Usage
=====
An example of using WsDiscoveryClient class

.. code-block:: python

    from onvif import WsDiscoveryClient

    wsd_client = WsDiscoveryClient()
    nvts = wsd_client.search()
    for nvt in nvts:
        print(nvt)
    wsd_client.dispose()

An example of using OnvifClient class

.. code-block:: python
    
    from onvif import OnvifClient
    
    onvif_client = OnvifClient('192.168.0.10', 80, 'user', 'password')
    profile_tokens = onvif_client.get_profile_tokens()
    for profile_token in profile_tokens:
        print(profile_token)

============
Requirements
============
onvif-client 0.0.1 Requirements

* Python >= 3.9
* WSDiscovery >= 2.0.0 
* onvif2-zeep >= 0.3.4

=======================
Installing onvif-client
=======================
Use pip to install the binary wheels on `PyPI <https://pypi.org/project/onvif-client/>`__

.. code-block:: console
    
    $  pip install onvif-client

=======
Support
=======
Bugs may be reported at https://github.com/hasimoka/onvif-client/issues

Contact
=======
If you have any questions or comments about onvif-client,
please feel free to contact me via
eMail: hasimoka@gmail.com
This project is hosted at https://github.com/hasimoka/onvif-client