semvec/pypi-packages huggingface dataset:
nakdimon-onnx 0.1.0
Hebrew diacritics with onnxruntime
Requires Python: <3.13,>=3.9

# nakdimon-onnx

[`nakdimon`](https://github.com/elazarg/nakdimon) diacritizer with onnxruntime

## Setup

```console
pip install nakdimon-onnx
```

You also need [`nakdimon.onnx`](https://github.com/thewh1teagle/nakdimon-onnx/releases/download/v0.1.0/nakdimon.onnx). Please see [examples](examples).

## Examples

See [examples](examples)

## Credits

Thanks for [elazarg](https://github.com/elazarg) for creating [nakdimon](https://github.com/elazarg/nakdimon) model!
longplayer 0.2.0
Longplayer, a thousand-year long musical composition, implemented in Python
Keywords: sound, music, time, soundart

# Longplayer in Python

[Longplayer](https://longplayer.org/) is a thousand year long musical composition by Jem Finer.

This is an open-source implementation of Longplayer in Python, which can be run on any compatible computer with audio output.

For more information about Longplayer, read an [overview of the piece](https://longplayer.org/about/overview/).

## Requirements

- A Linux (including Raspberry Pi), macOS or Windows system with audio output
- Python 3.9 or above ([python.org](https://www.python.org/downloads/))

## Installation

If you're using Linux (including Raspberry Pi), you will need to install the `portaudio` library for audio output: `sudo apt install libportaudio2`

To install Longplayer from the command line, for any platform, open up a terminal session and enter:

```
pip3 install longplayer
```

## Usage

To run Longplayer from the command line, run:

```
python3 -m longplayer
```

Press Ctrl-C to stop playback.

## Troubleshooting

If you are hearing stuttery playback, try:

- increasing the audio buffer size: `python3 -m longplayer --buffer-size 4096`
- mono (single-channel) playback: `python3 -m longplayer --channels 1`

To see all of the command-line options: `python -m longplayer --help`
streamlit-chunked-upload 1.0.2
Custom component to perform chunked upload in Streamlit. Based on streamlit-chunked-upload by Ryuya Matsumoto.
Requires Python: >=3.7

# streamlit-chunked-upload

[![image](https://img.shields.io/pypi/v/streamlit-chunked-upload.svg)](https://pypi.python.org/pypi/streamlit-chunked-upload)  
[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://chunked-upload.streamlit.app/)  

This is a custom component that allows you to split files and send them from your browser to Streamlit.

## Installation instructions

```sh
pip install streamlit-chunked-upload
```

## Usage instructions

```python
import streamlit as st
from streamlit_chunked_upload import uploader

file = uploader("uploader", key="chunk_uploader", chunk_size=32)
st.write(file)
if file is not None:
    st.download_button(
        "download",
        data=file,
        file_name=file.name,
        type="primary",
    )

```

## About chunk size
When a file is uploaded, a Python script slices the file at the specified chunk size on the browser side and sends it as multiple files to the backend.  
It's important to note that chunk size and request size are different.  
If you have a constraint such as client_max_body_size, you should set it to a value slightly smaller than the constraint size, such as 31MB if the constraint size is 32MB.
KudraCloudClient 0.0.19
A client for Kudra Cloud
Requires Python: >=3.8

# Kudra Cloud Client
        
        ### Overview :
        
        `kudra-cloud-client` is a Python package designed to simplify the process of uploading and analyzing documents using the Kudra Cloud service [kudra.ai](https://kudra.ai/) .
        
        ## Installation
        
        Install the package using `pip`: 
        
        ```bash
        pip install KudraCloudClient
        ```
        
        ## Usage
        
        ### Example :
        ```python
        from kudra_cloud_client import KudraCloudClient
        
        # Initialize KudraCloudClient with your authentication token
        kudraCloud = KudraCloudClient(token="YOUR_AUTHENTICATION_TOKEN")
        
        # Specify the directory containing the files to be uploaded
        files_dir = "path/to/your/files"
        
        # Specify the project run ID
        project_run_id = "your_project_run_id"
        
        # Analyze documents and get the result
        result = kudraCloud.analyze_documents(files_dir=files_dir, project_run_id=project_run_id)
        
        # Log the result or process it as needed
        print(result)
        ```
        ## Requirements
        
        - Python 3.8+
        
        ## Error Handling
        If an error occurs during the upload or analysis process, an exception will be raised, and details will be logged.
        
        ## License
        This project is licensed under the MIT License - see the ```LICENSE.txt``` file for details.