semvec/pypi-packages huggingface dataset:
mdcount 1.0.0
A tool to count the file word support markdown and txt
Keywords: python, menu, dumb_menu, windows, mac, linux


# 文件字数统计工具
## 支持文件类型
目前仅支持markdown(.md)和txt(.txt)文件

## 使用方法
1. 导入countFile函数
2. 调用countFile函数 
> 参数1：传入您的文件夹目录位置
>
> 参数2：传入您的文件类型
> 
> 可选参数
> 1. language，默认为中文,language="English"可切换为英文
> 2. mdCountCode：(True/False)是否统计代码，默认为False
```python
from mdcount.countFile import countFile
countFile("../MDCount", ["md"])
```

## 目前版本
1.0.0
参考资料：[使用Python读取markdown文件并统计字数
](https://icyhunter.blog.csdn.net/article/details/128424955)

-----

# File Word Count Tool
## Supported File Types
Currently only supports Markdown (.md) and plain text (.txt) files.

## How to Use
1. Import the countFile function.

2. Call the countFile function.

> Parameter 1: The path to your target directory.
> 
> Parameter 2: The list of file extensions you wish to count.
>
> Optional parameter: 
> 1. language, defaults to Chinese. Use language="English" to switch to English output.
> 2. is count code(True/False)? , defaults to False.
~~~python
from mdcount.countFile import countFile
countFile("../MDCount", ["md"])
~~~

## Current Version
1.0.0
Reference: [使用Python读取markdown文件并统计字数
](https://icyhunter.blog.csdn.net/article/details/128424955)
probayes 0.0.8
Probability package supporting multiple Bayesian methods including MCMC

# probayes
Probability package supporting multiple Bayesian methods including MCMC

Unlike existing libraries, probayes adopts a model-driven approach with full flexibility constrained only by the rules
of probability. Since probayes is its infancy and in a state of flux, there is no manual. Currently probayes supports
the following:

1. Multiple random variable sampling in untransformed and transformed domain space. 
2. Transitional simulation, including random walks, using Markov chain conditionals.
3. Discrete grid exact inference.
4. Ordinary Monte Carlo random sampling.
5. Ordinary Monte Carlo rejection sampling.
6. Metropolis-Hastings MCMC sampling.
7. Limited support for multivariate normal-covariance Gibbs sampling.

In the near-future, it is intended to expand the scope of probayes to include:

1. Code initial support for approximate inference using using dense mean field messaging.
2. Support derivative-based updates (HMC, gradient ascent/descent optimisation).

A quickstart is also intended, but for now there are examples in the examples/ subdirectories:

1. checks/       Simple check scripts
2. rv_examples/  Random variable examples
3. markov/       Markov chain examples
4. cov_examples/ Examples of using covariance matrices
5. dgei/         Discrete grid exact inference examples
6. omc/          Ordinary Monte-Carlo examples
7. mcmc/         Markov chain Monte Carlo examples (Metropolis-Hastings, Gibbs...)
jupyter-chat-widget 0.4.0
A simple chat UI widget for Jupyter notebooks
Requires Python: >=3.9
Keywords: chat, ipywidgets, jupyter, llm, notebook, ui

# jupyter-chat-widget

A simple chat UI widget for Jupyter notebooks with streaming response support, designed to integrate easily with LLM APIs.

> *Note: this widget uses ipywidgets<8.0.0 for compatibility with Colab.*

[![CI](https://github.com/zansara/jupyter-chat-widget/workflows/CI/badge.svg)](https://github.com/zansara/jupyter-chat-widget/actions)
[![Docs](https://github.com/zansara/jupyter-chat-widget/workflows/Deploy%20Docs/badge.svg)](https://zansara.github.io/jupyter-chat-widget/)
[![PyPI version](https://badge.fury.io/py/jupyter-chat-widget.svg)](https://badge.fury.io/py/jupyter-chat-widget)
[![Python versions](https://img.shields.io/pypi/pyversions/jupyter-chat-widget.svg)](https://pypi.org/project/jupyter-chat-widget/)


![](jupyter-chat-widget.gif)

## Installation

```bash
pip install jupyter-chat-widget
```

## Quick Start

```python
from jupyter_chat_widget import ChatUI

# Create the chat widget
chat = ChatUI()

# Define a handler for user messages
def handle_message(message: str) -> None:
    # Stream a response token by token
    for word in message.split():
        chat.append(word + " ")

# Connect the handler
chat.connect(handle_message)
```

## Documentation

See the [live documentation](https://zansara.github.io/jupyter-chat-widget/) or the [example notebook](examples/basic_usage.ipynb) in `examples/`.

## API Reference

### `ChatUI`

The main chat widget class.

#### Methods

| Method | Description |
|--------|-------------|
| `connect(callback)` | Connect a callback function to handle user messages |
| `append(token)` | Append text to the current assistant response (streaming) |
| `rewrite(text)` | Replace the entire assistant response |
| `clear()` | Clear all chat history and current response |

#### Example: Streaming Responses

```python
from jupyter_chat_widget import ChatUI
from time import sleep

chat = ChatUI()

def slow_echo(message: str) -> None:
    """Echo back the message word by word."""
    for word in message.split():
        sleep(0.5)  # Simulate processing time
        chat.append(word + " ")
    chat.rewrite(f"You said: {message}")

chat.connect(slow_echo)
```

#### Example: Integration with LLM

```python
from jupyter_chat_widget import ChatUI

chat = ChatUI()

def llm_handler(message: str) -> None:
    """Stream responses from an LLM API."""
    # Replace with your LLM API call
    for token in your_llm_api.stream(message):
        chat.append(token)

chat.connect(llm_handler)
```

## Development

```bash
# Clone the repository
git clone https://github.com/zansara/jupyter-chat-widget.git
cd jupyter-chat-widget

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run linting
ruff check src tests
ruff format src tests

# Build the package
python -m build
```

### Pre-commit Hooks

```bash
# Install pre-commit hooks
pre-commit install

# Run hooks on all files
pre-commit run --all-files
```

## License

MIT License - see [LICENSE](LICENSE) for details.
PyUAA 0.1.1
Create easily custom assemblers specifying ISAs with a YAML configuration file
Requires Python: >=3.9
Keywords: assembler, compiler

# UniversalApplicationAssembler

The Universal Application Assembler (UAA) is a totally configurable assembler that let's you target any ISA spefication by simply writing a config yaml file defining the characteristics of each instruction. The idea is of have an universal assembler that is trivial to port to any ISA.

## Documentation

Currently there is no documentation of this project. It will be added in the future.

## Examples

Currently there is also no public examples. Some of them will get released in the future implementing some custom architectures.
mkepub 1.2.2
Simple minimalistic library for creating EPUB3 files
Requires Python: >=3.10
Keywords: epub, books

[![Build Status](https://travis-ci.org/anqxyr/mkepub.svg?branch=master)](https://travis-ci.org/anqxyr/mkepub)
[![Coverage Status](https://coveralls.io/repos/github/anqxyr/mkepub/badge.svg?branch=master)](https://coveralls.io/github/anqxyr/mkepub?branch=master)
[![license](https://img.shields.io/github/license/anqxyr/mkepub.svg?maxAge=2592000)](https://github.com/anqxyr/mkepub/LICENSE)

# mkepub

**mkepub** is a minimalistic library for creating .epub files.

**Pros:**

* Easy to use, minimalistic API.
* Automatically generated TOC.
* Support for nested TOC of any depth.
* Support for embedded images.
* Support for embedded fonts.
* In-progress books are stored on disk rather than in memory, enabling creation of large (5000+ pages, 20+ MiBs) epub files.
* Adherence to the EPUB3 specs.
* Support for most of the EPUB metadata, including language, subject, description, and rights.

**Cons:**

* No support for custom page filenames or directory structure.
* No support for reading or editing epub files.
* No content validation - using broken or unsupported html code as page content will lead to mkepub successfully creating a .epub file that does not meet EPUB3 specifications.
* Probably other issues.


### Basic Usage

```python
import mkepub

book = mkepub.Book(title='An Example')
book.add_page(title='First Page', content='Lorem Ipsum etcetera.')
book.save('example.epub')
```

### Advanced Usage

```python
import mkepub

book = mkepub.Book(title='Advanced Example', author='The Author')
# multiple authors can be specified as a list:
# mkepub.Book(title='Advanced Example', authors=['The First Author', 'The Second Author'])
book.set_cover_from_file('cover.jpg')
book.set_stylesheet_from_file('style.css')

first = book.add_page('Chapter 1', 'And so the book begins.')

child = book.add_page('Chapter 1.1', 'Nested TOC is supported.', parent=first)
book.add_page('Chapter 1.1.1', 'Infinite nesting levels', parent=child)
book.add_page('Chapter 1.2', 'In any order you wish.', parent=first)

book.add_page('Chapter 2', 'Use <b>html</b> to make your text <span class="pink">prettier</span>')

book.add_page('Chapter 3: Images', '<img src="images/chapter3.png" alt="You can use images as well">')
# as long as you add them to the book:
with open('chapter3.png', 'rb') as file:
    book.add_image('chapter3.png', file.read())

# you can also add custom fonts to the book
book.add_font_from_file('your_font.woff')

book.save('advanced.epub')
```