semvec/pypi-packages huggingface dataset:
tkcustomessagebox 2025.7.9
A Python package has a custom messagebox using Customtkinter and the Fredoka One font.
Requires Python: >=3.10

# Python Custom MessageBox using Customtkinter

## Description:

This Python package has a custom messagebox using Customtkinter and the Fredoka One font.

## What's included?

- Fredoka One font
- 5 custom messageboxes
- A messagebox with an OK button
- A messagebox with OK/Cancel buttons and a scroll bar
- A messagebox with Yes/No buttons
- A messagebox with Yes/No/Cancel buttons
- A messagebox with a textbox and OK/Cancel buttons

## Installation:

`
pip install tkcustomessagebox
`

After running pip, make sure your Fredoka One font it is in the current working directory, or download it from https://fonts.google.com/selection. <br> After downloading, extract the file and choose Fredoka-Bold.ttf 
Move Fredoka-Bold.ttf (Fredoka-Bold.ttf is the individual filename) only the file to the current working directory you host your project.

If you don't download the font, a `FontError` will occur.

## Messagebox sizing options (in `centermsgboxwin` function):

| Function Parameter Name | Definition |
|---|---|
| `win` = `CTkToplevel` | The master which is defaulted to `CTkToplevel`. |
| `width` = `int` | The width of the custom messagebox. (defaulted to 1000). |
| `height` = `int` | The height of the custom messagebox. (defaulted to 500). |

## Messagebox customization options (in function parameter):

| Function Parameter Name | Definition |
|---|---|
| `msgboxtitle` = `str` | The title of your messagebox. |
| `titlemsg` = `str` (included only in customMsgBoxWithScrBar function) | The title message inside of the messagebox above the scroll bar. |
| `textmsg` / `textmsgquestion` = `str` | The text message inside of your messagebox. |
| `bg_color` = `str` | The background color of your messagebox. |
| `btn_fg_color` = `str` | The background color of your buttons (inside). |
| `btn_text_color` = `str` | The text color inside your buttons. |
| `text_color` = `str` | The text color of `titlemsg` and `textmsg` / `textmsgquestion`. |
| `error` = `bool` (included only in `CustomMsgBox` function) | If `True`, a Windows error sound will play; if `False`, the default beep sound will play. |
imageCS_utils 2025.7.9
Some useful utils for deep learning (PyTorch) image compressive sensing (CS).
Requires Python: >=3.10

# ImageCS-Utils

<div align="center">

[![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FFengodChen%2FimageCS-utils&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FFengodChen%2FimageCS-utils&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)
[![GitHub repo stars](https://img.shields.io/github/stars/FengodChen/imageCS-utils?style=flat&logo=github&logoColor=whitesmoke&label=Stars)](https://github.com/FengodChen/imageCS-utils/stargazers)

</div>

Some useful utils for deep learning (PyTorch) image compressive sensing (CS).

## Project Url

[pypi](https://pypi.org/project/imageCS-utils/)
[github](https://github.com/FengodChen/imageCS-utils)


## Install
```
python -m pip install imageCS-utils
```

## Usage
e.g.

```
import torch
from imageCS_utils.utils import load_single_image
x = load_single_image("./image.png")
```
bfg-llmutil 0.5.5
A set of utility functions to handle LLM structural output
Requires Python: >=3.10

# llmutil

This library provides tools to generate structured output and function calling from the OpenAI API.

## What is Structured Output?

[Structured Output](https://platform.openai.com/docs/guides/structured-outputs) is the recommended method for getting formatted responses. It guarantees that outputs follow your defined schema, making it more reliable than previous methods like JSON mode, function calls, or tool use.

## How to Use

### Structured Output

To use Structured Output, you need to define a schema using a simple dictionary format:

```python
from llmutil import new_response

output = new_response(
    [
        {
            "role": "user",
            "content": "normalize this address: 1 hacker way, menlo park, california",
        }
    ],
    model="gpt-4.1-mini",
    schema={
        "street": "string",
        "city": "string",
        "state": "string",
    },
)
# {'street': '1 Hacker Way', 'city': 'Menlo Park', 'state': 'CA'}
```

### Function Calling

For function calling, implement the `Tooling` protocol to define available functions:

```python
from llmutil import Result, Tooling, new_response

def add(a, b):
    return a + b

class UseAdd(Tooling):
    def on_function_call(self, name, args):
        if name == "add":
            return Result(add(args["a"], args["b"]))

    def get_tools(self):
        return {
            "add": {
                "a": "number",
                "b": "number",
            }
        }

messages = [
    {
        "role": "system",
        "content": "you cannot do math. you must use the add() function to add numbers.",
    },
    {
        "role": "user",
        "content": "alice has 10 apples, bob has 20 apples, how many apples do they have in total?",
    },
]

output = new_response(messages, model="gpt-4.1-mini", tooling=UseAdd())
# Alice and Bob have 30 apples in total.
```

## API Reference

### `new_response(messages, *, model, tooling=None, schema=None, memory=None, timeout=30)`

Main function for generating responses from OpenAI API.

- `messages`: List of message dictionaries in OpenAI format
- `model`: OpenAI model name (e.g., "gpt-4.1-mini")
- `tooling`: Optional `Tooling` implementation for function calling
- `schema`: Optional dictionary defining the output schema
- `memory`: Optional vector store ID for file search
- `timeout`: Request timeout in seconds (default: 30)

### `Tooling` Protocol

Protocol for implementing function calling:

- `on_function_call(self, name: str, args: dict)`: Handle function calls, return `Result` to continue or any other value to return immediately
- `get_tools(self)`: Return dictionary mapping function names to parameter schemas

### `Result` Class

Wrapper for function call results that should continue the conversation:

- `Result(result)`: Wrap a result to continue the conversation flow
shipxy-src 0.2
亿海蓝Elane船讯网sdk https://www.shipxy.com/

# 亿海蓝-船讯网-sdk
[亿海蓝官网](https://www.shipxy.com/)&nbsp;&nbsp;
[API控制台](https://api.shipxy.com/v3/console/index)&nbsp;&nbsp;
[在线开发文档](https://hiiau7lsqq.feishu.cn/wiki/E0wAwrPpvieGhSk5wLCctNqonVb)&nbsp;&nbsp;
[github](https://github.com/shipxycom/shipxy-api-py)&nbsp;&nbsp;
[gitee](https://gitee.com/shipxycom/shipxy-api-py)&nbsp;&nbsp;

## 示例用法
```
import api.Shipxy as Shipxy
key = "请从 API控制台 申请";
if __name__ == '__main__':
    response = Shipxy.GetManyShip(key, "413961925,477232800,477172700")
    print(response)
```
## 开发者在使用过程中如有疑问，可以通过以下方式联系船讯网：

• 商务邮箱：support@shipxy.com

• 技术支持邮箱：service@shipxy.com

• 电话：400-010-8558 

![飞书](./images/飞书.jpg)
![微信](./images/微信.jpg)
painface-lc 0.1.4
Painface Local Evaluator
Requires Python: >=3.8

# Painface Local

This software is Painface running on local CPU/GPU machines.

# Usage

### Requirements

- python 3.10


### Installation of software

```
$ pip install painface-lc
```

### Download Models

1. Create '.painface' directory at home directory, in terminal:

```
$ cd ~ 
$ mkdir .painface
```

2. Visit [Here](https://github.com/Zylka-Lab/PainfaceLocal/releases/tag/v0.1.3b2) and download `models_black.zip`
3. Unpack the file under `$HOME/.painface`

Then you're all set to use the software with following command:
```
$ painface gui
```
planctl 1.0.1
A CLI tool for managing daily engineering tasks and priorities
Requires Python: >=3.7

# PlanCtl

A CLI tool for managing daily engineering tasks and priorities. Perfect for organizing your day-to-day work, tracking progress, and keeping notes for standup meetings.

## Features

- **Task Management**: Add, complete, and track todos with support for side initiatives and priority levels
- **Parking Lot**: Keep track of items to discuss later or follow up on
- **Archive System**: Clean up completed tasks to keep reports focused
- **Daily Reports**: Generate organized reports of your work
- **Rich CLI Interface**: Beautiful, colorful command-line interface

## Installation

### From Source
```bash
git clone https://github.com/wilsongomes-swe/planctl.git
cd planctl
pip install -e .
```

### From PyPI
```bash
pip install planctl
```

## Usage

### Basic Commands

Add a task:
```bash
planctl add-todo "Fix authentication bug"
```

Add a task with priority (lower number = higher priority):
```bash
planctl add-todo "Critical security fix" --priority 0
```

Add a side initiative:
```bash
planctl add-todo "Research new framework" --side
```

Add a side initiative with priority:
```bash
planctl add-todo "Optimize database queries" --side --priority 1
```

Mark task as done:
```bash
planctl done 1
```

List all tasks (sorted by priority):
```bash
planctl list-todos
```

Add to parking lot:
```bash
planctl add-parking "Discuss API changes with team"
```

Generate daily report (todos sorted by priority):
```bash
planctl report
```

Archive completed tasks:
```bash
planctl archive
```

Resolve parking lot item:
```bash
planctl resolve-parking 1
```

### All Commands

| Command | Description |
|---------|-------------|
| `add-todo DESCRIPTION [--side] [--priority NUMBER]` | Add a new task (optionally mark as side initiative and set priority) |
| `done ID` | Mark task as completed |
| `undone ID` | Mark task as incomplete |
| `list-todos` | List all active tasks (sorted by priority) |
| `add-parking DESCRIPTION` | Add item to parking lot |
| `list-parking` | List parking lot items |
| `resolve-parking ID` | Mark parking lot item as resolved |
| `report` | Generate daily report (todos sorted by priority) |
| `archive` | Archive all completed tasks |

## Data Storage

PlanCtl stores data in `planctl_data.json` in your current working directory. This file contains:
- Active todos with completion status, side initiative flags, and priority levels
- Parking lot items with resolution status
- Archived completed tasks

## License

MIT License