semvec/pypi-packages huggingface dataset:
viz-media-in-html 1.0.0
在浏览器中可视化浏览本地图片/视频：分类、搜索、分页、Lightbox，内置本地 HTTP 服务且不在页面/日志中暴露真实路径
Requires Python: >=3.8
Keywords: html, visualization, image, video, http

## viz-media-in-html

把本地图片/视频做成一个可交互的 HTML 画廊，并启动一个本地 HTTP 服务来浏览：支持分类、搜索、分页、Lightbox、键盘快捷键、自动翻页。

这个包默认**不在 HTML/控制台输出中暴露真实本地路径**：页面内的媒体资源通过 `/media/<id>` 访问。

### 安装

```bash
pip install viz-media-in-html
```

### 使用

- **glob 模式**（推荐）

```bash
viz-media-in-html --input "**/*.jpg" --port 18000 --open
```

- **txt 列表模式**
  - 每行：`路径[空格或TAB]类别`（类别可省略）

```bash
viz-media-in-html --input list.txt --open
```

### 常用参数

- `--bind`：绑定地址，默认 `0.0.0.0`（局域网可访问；注意防火墙/安全）
- `--port`：起始端口，若被占用会自动向上寻找可用端口
- `--html`：可选，写出一个静态 HTML 文件（默认 `index.html`）
- `--per-page-img / --per-page-video`：分页大小
- `--open`：启动后自动打开浏览器

### 发布到 PyPI（示例）

```bash
python -m pip install -U build twine
python -m build
python -m twine upload dist/*
```
pyboot-commons-utils 1.3.3.3
Pyboot-commons-utils is an open-source Python microservice framework that simplifies the development of stand-alone production-grade Spring applications.
Requires Python: >=3.12.10
Keywords: web, framework, cli, springboot, fastapi

Pyboot is an open-source Python microservice framework that simplifies the development of stand-alone, production-grade Spring applications. It provides embedded servers, auto-configuration, and minimal setup, allowing developers to build and run python apps quickly—no heavy XML or boilerplate code needed.
Python microservice = a tiny, self-contained Java app that starts in seconds, exposes REST endpoints, and ships with its own server—ready to scale and deploy independently.

基础Utils包，作为工具使用
VesicleYOLO-pentadec 0.0.2
A simple package created to easily apply YOLO-based vesicle detection to fluorescence microscopy images.
Requires Python: >=3.12

# VesicleYOLO

This is a simple package meant to make the application of YOLO to vesicle quantification as easy as possible.

## Installation

This is really easy. An example of the code is shown below.

`
pip install VesicleYOLO-pentadec
`


### Example

```
from VesicleYOLO_pentadec.analyze import predict_folder

# A list of vesicles from the folder 'example_folder', with annotations passed into 'package_out'. 
vesicle_list = predict_folder("weights.pt",
                              r"example_folder",
                              "package_out")
```


Keep in mind
`predict_folder` is for batch image processing and will return a list of image names alongside vesicle counts. 
`predict_image` is for singular images and will return a count only.

### Notes on Parameters - what you can do with VesicleYOLO

**`predict_folder` takes the following inputs:**

`weights_path`: Currently required, the path to the model weights. In the future (tomorrow), I'm going to implement default weights.

`images_directory`: Required, the path to the images to be analyzed.

`out_directory`: Not required. Where annotations will be written; if annotations are chosen but no out_directory is set, a new directory is written. This is pretty easy but I haven't checked if it works yet haha. 

`num_slices`: Not required, default is 256. Number of slices to cut the images into. Probably don't mess with this.

`annotate_rect`: Whether to draw rectangular 'bounding box' annotations in the out directory. Default is True.

`annotate_ellipse`: Whether to draw elliptical annotations in the out directory. Default is False.


**`predict_image` takes the same inputs except for these things which you should leave blank:**

`length_divider`: Amount to divide each length (height and width) by. I can't think of a conceivable reason why you would change this.

`model_weights`: Also not really something you should change takes the model weights from `predict_folder`.
prompt-manager-cli 0.1.3
A CLI tool to create and organize prompt files for code agents.
Requires Python: >=3.9
Keywords: cli, prompts, ai, llm, developer-tools

# prompt-manager-cli

A CLI tool to create and organize prompt files for code agents (Claude Code, Codex, Gemini CLI, etc.).

## Installation

```bash
# using uv
uv tool install prompt-manager-cli

# using pip 
pipx install prompt-manager-cli
```

Or install in editable mode for development:

```bash
pip install -e ".[dev]"
```

## Usage

### Initialize a project

```bash
pm init
```

This creates `.pm/template.md` with the default template. Edit this file to customize your prompts.

### Create a new prompt file

```bash
pm new
```

This creates a new Markdown file in `.pm/prompts/` and opens it in your editor.

```
prompt-2026-01-05-14-37-a1b2c3d.md
```

You can specify the editor directly:

```bash
pm new micro
pm new code
pm new vim
```

### Options

```bash
# Specify a custom output directory
pm new --dir ./my-prompts

# Combine editor and directory
pm new micro --dir ./my-prompts
```

## Editor Configuration

The editor is resolved in this order:

1. **Command argument**: `pm new micro`
2. **Local config**: `.pm/editor` (per-repo)
3. **Environment**: `$PM_EDITOR` (global)

To set your default editor, add to your shell profile (`.bashrc`, `.zshrc`, etc.):

```bash
export PM_EDITOR="micro"
```

To override per-project:

```bash
echo "code" > .pm/editor
```

## Custom Templates

Run `pm init` to create `.pm/template.md` with the default template, then edit it to customize your prompts.

You can also create a global template at `~/.pm/template.md` for use across all projects. Local templates take precedence over global templates.

### Template Variables

Templates use Python's string formatting. Available variables:

| Variable | Description | Example |
|----------|-------------|---------|
| `{created_at}` | ISO 8601 timestamp with timezone | `2026-01-05T14:37:00+01:00` |
| `{git_hash}` | Short git hash or "nogit" | `a1b2c3d` |
| `{cwd}` | Absolute path to current directory | `/home/user/project` |

### Example Custom Template

```markdown
---
date: "{created_at}"
commit: "{git_hash}"
project: "{cwd}"
---

## What I Want

## Background

## Requirements

## Done When
```

## Local Development

Run directly without installing:

```bash
python -m prompt_manager_cli new
```

Run tests:

```bash
pytest
```

## License

MIT
iflow-mcp_samge0_mcp-qqmusic-test-server 0.1.0
Add your description here
Requires Python: >=3.13

# MCP QQ音乐测试服务器

这是一个通过MCP（模块化控制协议）提供QQ音乐搜索功能的测试服务器。该服务器允许您使用关键词搜索音乐曲目，并返回相关歌曲信息。

该demo仅用于测试和学习目的，其中音乐检索使用的是[qqmusic-api-python](https://github.com/luren-dc/QQMusicApi)。

## 功能特点

- 支持MCP使用关键词搜索音乐曲目

## 环境要求

- 已安装了[uv](https://github.com/astral-sh/uv)
- Python 3.13

## 安装说明

1. 克隆此仓库
    ```bash
    git clone https://github.com/Samge0/mcp-qqmusic-test-server.git
    ```
2. 安装依赖：
   ```bash
   uv sync
   ```

## 使用方法

配置`mcp`：

```json
{
  "mcpServers": {
    "mcp-qqmusic-test-server": {
        "command": "uv",
        "args": [
            "--directory",
            "{put your local dir here}/mcp-qqmusic-test-server",
            "run",
            "main.py"
        ]
    }
  }
}
```

![image](https://github.com/user-attachments/assets/96a1d8b6-f527-4702-94b5-745287323648)

![image](https://github.com/user-attachments/assets/9826ab69-dbe5-47fd-9ba6-c809315351a3)


### 测试搜索音乐

<details> <summary>使用关键词搜索音乐曲目>></summary>

**函数：** `search_music`

**参数：**

- `keyword` (字符串，必需)：搜索关键词或短语
- `page` (整数，可选)：分页页码（默认值：1）
- `num` (整数，可选)：返回结果的最大数量（默认值：20）

**返回值：**

返回包含以下属性的对象数组：

- `id`：歌曲ID
- `mid`：音乐ID
- `name`：歌曲名称
- `pmid`：播放音乐ID
- `subtitle`：歌曲副标题
- `time_public`：发布时间
- `title`：歌曲标题

**示例响应：**

```json
[
  {
    "id": "123456",
    "mid": "001Qu4I30eVFYb",
    "name": "七里香",
    "pmid": "",
    "subtitle": "",
    "time_public": "2004-08-03",
    "title": "七里香"
  }
]
```

</details>