semvec/pypi-packages huggingface dataset:
dwl-cli 1.0.0
Command-line interface for fast model training
Requires Python: >=3.8
Keywords: deep-learning, machine-learning, nlp, transformers, training

# DWL CLI Tool

Command-line interface for training DWL (Deep Weight Learning) models.

## Installation

```bash
pip install dwl-cli
```

## Usage

```bash
# Basic DWL training
dwl-train --model bert-base-uncased --dataset yahoo_answers_topics

# Traditional training with custom parameters
dwl-train --model roberta-base --dataset ag_news --method traditional --epochs 50 --lr 0.0001

# DWL training with custom components
dwl-train --model distilbert-base-uncased --dataset emotion --components 100 --epochs 30

# Non-verbose mode
dwl-train --model bert-base-uncased --dataset imdb --quiet

# Use custom backend URL
dwl-train --model bert-base-uncased --dataset ag_news

# List available options
dwl-train --list-models
dwl-train --list-datasets

# Get help
dwl-train --help
```

## Features

- **Easy to use**: Simple command-line interface
- **Flexible**: All training parameters configurable
- **Real-time streaming**: See training progress as it happens
- **Multiple models**: Support for BERT, RoBERTa, DistilBERT, and more
- **Multiple datasets**: 12+ text classification datasets
- **Remote support**: Can connect to any backend URL

## Available Models

- bert-base-uncased
- bert-large-uncased
- roberta-base
- roberta-large
- distilbert-base-uncased
- albert-base-v2
- xlnet-base-cased

## Available Datasets

- ag_news
- dbpedia_14
- yahoo_answers_topics
- yelp_review_full
- yelp_polarity
- amazon_polarity
- trec
- emotion
- go_emotions
- imdb
- banking77
- rotten_tomatoes

## License

MIT License
frameperfect 0.1.0a2
A lightweight GUI application for precise frame-by-frame video analysis and screenshot extraction, built with PySide/PyQt and OpenCV.
Requires Python: >=2

# FramePerfect

A lightweight GUI application for precise frame-by-frame video analysis and screenshot extraction, built with PySide/PyQt and OpenCV.

## Why This Tool?

- **Perfect for**: Video editors, forensic analysts, researchers, and anyone needing precise frame analysis
- **Advantages over media players**: 
  - Guaranteed frame accuracy (no skipped frames)
  - Dedicated screenshot workflow
  - Clean interface without unnecessary features
- **Multi-file sequential processing**: Provide multiple video files and automatically advance to the next when closing (New!)

## Key Features

- **Frame-accurate navigation**: Move through videos one frame at a time with perfect precision
- **Instant screenshot capture**: Save any frame as PNG/JPEG with customizable paths
- **Simple workflow**: Open -> Navigate -> Capture -> Repeat
- **Visual timeline**: Quickly jump to any frame using the position slider
- **Compatibility**: Supports Python 2+ and PyQt4/PyQt5/PyQt6/PySide/PySide2/PySide6

## Installation

```bash
pip install frameperfect
```

## Usage

### Option 1: Interactive Mode

```bash
python -m frameperfect
```

1. Click "Open Video" and select your video file
2. Use navigation controls:
   - **Previous/Next Frame**: Move precisely through frames
   - **Slider**: Jump to any position
   - **Save Frame**: Capture current frame

### Option 2: Sequential Batch Mode (New!)

```bash
python -m frameperfect video1.mp4 video2.mp4 video3.mp4
```
- 
- Automatically opens `video1.mp4` first
- When window closes, immediately opens `video2.mp4`
- Process repeats until all videos are processed
- Perfect for reviewing multiple clips in one session

### Basic Workflow

1. Click "Open Video" and select your video file
2. Use these controls:
   - **Previous/Next Frame**: Move precisely through frames
   - **Slider**: Jump to any position in the video
   - **Save Frame**: Capture the current frame to your Pictures folder

## Troubleshooting

- If video doesn't open: Try converting to MP4 with H.264 codec
- For large videos: Consider splitting files for better performance

## Contributing

Contributions are welcome! Please submit pull requests or open issues on GitHub.

## License

AGPL-3.0 - See [LICENSE](LICENSE) for details.
shucker 0.1.1
Tracking-param filtering library, designed to strip URLs down to their canonical forms

Shucker
=======
[![Crates.io Version](https://img.shields.io/crates/v/shucker?link=https%3A%2F%2Fcrates.io%2Fcrates%2Fshucker)](https://crates.io/crates/shucker) [![PyPI - Version](https://img.shields.io/pypi/v/shucker)](https://pypi.org/project/shucker/)

Shucker is a tracking-param filtering library, designed to strip URLs down to their canonical forms. It contains internally a set of rules derived from the [AdguardFilters](https://github.com/AdguardTeam/AdguardFilters) TrackParamFilter set, and then stripped down be able to be runnable outside of a browser. Note that although the original filters were designed for Javascript-based browser extensions, Shucker's core is a pure-Rust implementation for raw speed (some testing done against Hyperfine, but certainly seems fast enough so far i.e. < 1ms).

There is an [example command line tool provided](src/bin/shuck.rs) (`cargo run --bin shuck <list of urls>`) but the main usage will either be via the `shucker::shuck` fn, or the Python `shucker` library with `shucker.shuck` (which is mostly a thin wrapper over the Rust code), both of which take a URL and return a version of it without the ad-tracking.

Rebuilding the rules set
------------------------
`make rebuild_rules` will pull the latest upstream rules and rebuild.

Licensing
---------
The actual core Shucker code (i.e. everything _except_ the `external/adguardfilters` folder) is licensed under the [LGPL v3](https://www.gnu.org/licenses/lgpl-3.0.en.html). However, the `external/adguardfilters` code is [GPL v3](external/adguardfilters/LICENSE) and as that is used as part of the build-time generation of Shucker currently, the overall library is therefore GPLv3. This might change in the future if we remove said build-time requirement though.
kubexec 0.6.2
Execute commands and scripts on Kubernetes pods with Docker access
Requires Python: >=3.10

# kubexec


![Kubekec logo](https://github.com/telatin/kubexec/raw/main/kubexec-logo.png)

Execute commands and scripts on Kubernetes pods with Docker access.

Designed for use in [CLIMB Notebooks](https://docs.climb.ac.uk/).

## Installation

```bash
pip install -e .
```

## Usage

```bash
kubexec [-h] [-d DOCKER_IMAGE] [-n NAMESPACE] [-p POD_NAME] 
        [-m MEMORY] [-c CPU] [-w WORKDIR] [-v VOLUME_MOUNT]
        [--config CONFIG] [--context CONTEXT] [--create-pod]
        [--cleanup] [--verbose] [--dry-run] TARGET
```

## Examples

```bash
# Run a simple command
kubexec 'echo "Hello, Kubernetes!"'

# Use specific Docker image
kubexec -d python:3.9 'python -c "import sys; print(sys.version)"'

# Run with resource limits
kubexec -m 4Gi -c 2 'stress --cpu 2 --timeout 60s'

# Execute script file
kubexec ./my_script.sh

# Use existing pod
kubexec -p my-existing-pod 'ls -la'

# Volume mounting
kubexec -v /host/data:/pod/data 'ls /pod/data'

# Save output to local files (works like local commands)
kubexec "ls -la > output.txt"

# Bioinformatics example with SPAdes genome assembler
# This will create spades-out/ directory in your current working directory
kubexec -d community.wave.seqera.io/library/spades:4.1.0--77799c52e1d1054a \
    "spades.py -1 tests/reads/s_6_1.fastq.gz -2 tests/reads/s_6_2.fastq.gz -o spades-out"
```
mcp-server-bailian-image 1.0.4
阿里云百炼-通义万相图像生成MCP服务器
Requires Python: >=3.8
Keywords: mcp, model-context-protocol, aliyun, bailian, tongyi, wanxiang, image, text-to-image, generation, ai, artificial-intelligence, computer-vision

# MCP Server Bailian Image

阿里云百炼-通义万相图像生成MCP服务器

## 功能特性

- 🎨 **文生图功能**: 基于通义万相2.1/2.2文生图V2版API
- 🚀 **多模型支持**: 支持wan2.2-t2i-flash、wan2.2-t2i-plus等多种模型
- 🎯 **精确控制**: 支持正向提示词、反向提示词、图像尺寸、生成数量等参数
- ⚡ **同步调用**: 使用DashScope SDK进行同步调用，简化使用流程
- 🔧 **易于集成**: 标准MCP协议，可与支持MCP的AI助手无缝集成

## 安装

### 从PyPI安装

```bash
pip install mcp-server-bailian-image
```

### 从源码安装

```bash
git clone https://github.com/aliyun/mcp-server-bailian-image.git
cd mcp-server-bailian-image
pip install -e .
```

## 配置

### 1. 获取API Key

1. 访问[阿里云百炼控制台](https://bailian.console.aliyun.com/)
2. 创建应用并获取API Key
3. 确保已开通通义万相文生图服务

### 2. 设置环境变量

```bash
# Windows
set DASHSCOPE_API_KEY=your_api_key_here

# Linux/macOS
export DASHSCOPE_API_KEY=your_api_key_here
```

## 使用方法

### 启动MCP服务器

```bash
# 使用uvx启动（推荐）
uvx mcp-server-bailian-image

# 或者直接传入API Key
uvx mcp-server-bailian-image --api-key your_api_key_here
```

### 在Claude Desktop中配置

在Claude Desktop的配置文件中添加以下配置：

```json
{
  "mcpServers": {
    "bailian-image": {
      "command": "uvx",
      "args": ["mcp-server-bailian-image"],
      "env": {
        "DASHSCOPE_API_KEY": "your_api_key_here"
      }
    }
  }
}
```

## 支持的工具

### text2imagev2

基于通义万相文生图V2版API的文生图工具。

**参数说明：**

- `prompt` (必填): 正向提示词，描述期望生成的图像内容
- `negative_prompt` (可选): 反向提示词，描述不希望出现的内容
- `model` (可选): 模型名称，默认为 `wan2.2-t2i-flash`
- `size` (可选): 图像尺寸，默认为 `1024*1024`
- `n` (可选): 生成图片数量，取值范围1-4，默认为1

**支持的模型：**

- `wan2.2-t2i-flash`: 万相2.2极速版（推荐）
- `wan2.2-t2i-plus`: 万相2.2专业版（推荐）
- `wanx2.1-t2i-turbo`: 万相2.1极速版
- `wanx2.1-t2i-plus`: 万相2.1专业版
- `wanx2.0-t2i-turbo`: 万相2.0极速版

**支持的图像尺寸：**

- `1024*1024` (默认)
- `720*1280`
- `1280*720`
- `1024*576`
- `576*1024`

**使用示例：**

```python
# 基础文生图
result = await call_tool("text2imagev2", {
    "prompt": "一只坐着的橘黄色的猫，表情愉悦，活泼可爱，逼真准确"
})

# 使用反向提示词
result = await call_tool("text2imagev2", {
    "prompt": "雪地，白色小教堂，极光，冬日场景，柔和的光线",
    "negative_prompt": "人物",
    "model": "wan2.2-t2i-plus",
    "size": "1280*720",
    "n": 2
})
```

## 错误处理

常见错误及解决方案：

1. **API Key未设置**
   ```
   错误: DASHSCOPE_API_KEY environment variable is required
   解决: 设置环境变量或通过命令行参数传入API Key
   ```

2. **模型不支持**
   ```
   错误: Unsupported model: xxx
   解决: 使用支持的模型名称
   ```

3. **图像尺寸不支持**
   ```
   错误: Unsupported size: xxx
   解决: 使用支持的图像尺寸
   ```

## 开发

### 环境要求

- Python 3.8+
- DashScope SDK
- MCP SDK

### 本地开发

```bash
# 克隆仓库
git clone https://github.com/aliyun/mcp-server-bailian-image.git
cd mcp-server-bailian-image

# 安装依赖
pip install -e .

# 运行测试
python -m pytest

# 启动开发服务器
python -m mcp_server_bailian_image.server
```

## 许可证

MIT License

## 相关链接

- [通义万相文生图V2版API文档](https://help.aliyun.com/zh/model-studio/text-to-image-v2-api-reference)
- [阿里云百炼控制台](https://bailian.console.aliyun.com/)
- [DashScope SDK文档](https://help.aliyun.com/zh/dashscope/)
- [MCP协议文档](https://github.com/modelcontextprotocol/specification)

## 支持

如有问题或建议，请提交[Issue](https://github.com/aliyun/mcp-server-bailian-image/issues)。
mcp-server-bailian-video-synthesis 1.0.4
阿里云百炼-通义万相视频编辑统一模型MCP服务器
Requires Python: >=3.8
Keywords: mcp, model-context-protocol, aliyun, bailian, video, ai

# MCP Server Bailian Video Synthesis

阿里云百炼-通义万相视频编辑统一模型MCP服务器

## 功能特性

- **多图参考视频生成**: 基于多张参考图片生成视频
- **视频重绘**: 对现有视频进行重新绘制
- **局部编辑**: 对视频的特定区域进行编辑
- **视频延展**: 延长视频时长
- **视频画面扩展**: 扩展视频画面范围
- **异步任务处理**: 支持创建任务和查询结果的异步操作
- **易于集成**: 标准MCP协议，可与Claude Desktop等工具无缝集成

## 安装

### 从PyPI安装

```bash
pip install mcp-server-bailian-video-synthesis
```

### 从源码安装

```bash
git clone <repository-url>
cd mcp_server_bailian_video_synthesis
pip install -e .
```

## 配置

### 获取API密钥

1. 访问[阿里云百炼控制台](https://bailian.console.aliyun.com/)
2. 创建应用并获取API Key
3. 设置环境变量或在启动时传入API Key

### 环境变量设置

```bash
export DASHSCOPE_API_KEY="your-api-key-here"
```

## 使用方法

### 启动MCP服务器

```bash
# 使用uvx启动（推荐）
uvx mcp-server-bailian-video-synthesis

# 或者直接传入API Key
uvx mcp-server-bailian-video-synthesis --api-key your-api-key-here
```

### Claude Desktop配置

在Claude Desktop的配置文件中添加：

```json
{
  "mcpServers": {
    "bailian-video-synthesis": {
      "command": "uvx",
      "args": ["mcp-server-bailian-video-synthesis"],
      "env": {
        "DASHSCOPE_API_KEY": "your-api-key-here"
      }
    }
  }
}
```

## 支持的工具

### 创建任务工具

- `create_task_image_reference`: 多图参考视频生成
- `create_task_video_repainting`: 视频重绘
- `create_task_video_edit`: 局部编辑
- `create_task_video_extension`: 视频延展
- `create_task_video_expansion`: 视频画面扩展

### 查询结果工具

- `get_task_result`: 根据任务ID查询处理结果

## 错误处理

服务器会自动处理以下错误情况：
- API密钥无效或缺失
- 网络连接问题
- 参数验证错误
- 任务处理失败

## 开发

### 安装开发依赖

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

### 运行测试

```bash
pytest
```

### 代码格式化

```bash
black .
isort .
```

## 许可证

MIT License

## 相关链接

- [阿里云百炼官方文档](https://help.aliyun.com/zh/model-studio/)
- [视频生成API文档](https://help.aliyun.com/zh/model-studio/video-generation-api/)
- [MCP协议规范](https://github.com/modelcontextprotocol/specification)
udn-songbook 1.5.2
songbook and songsheet management for songsheets in ukedown format
Requires Python: >=3.11

<!-- markdownlint-disable MD013 MD033-->
<!-- vim: set tw=100 -->

# udn-songbook

## What'cha talkin' about, Willis?

`udn-songbook` is a class-based abstraction of a songbook, using the [ukedown](https://pypi.org/project/ukedown/) rendering engine.
Long-term it is intended to replace most of the code from the [`ukebook-md`](https://github.com/ukebook-md) tools.
In the end, you should be able to take the code from here and create (and render) songbooks with it, producing PDFs and/or HTML
(and possibly other formats, if I add them).

## Requirements

Python packages
* ukedown (markdown extensions)
* weasyprint (PDF generation)
* pychord (chord management)

## development requirements - for future project enhancements
* python-fretboard (chord generation)

## The TL;DR

How to use the current functionality

(it only does basic things at the moment)

```python
from udn_songbook import Song
s = song('/path/to/filename')

# list the unique chords in the song
s.chords

# save it to disk
s.save(outputfile)

# transpose by an arbitrary number of semitones
s.transpose(semitones)

# generate a PDF from the current song, using built-in templates
s.pdf()
```

And to build a songbook, use the SongBook class, with a directory of UDN-format songsheets

```python
from udn_songbook import SongBook
mybook = SongBook(inputs=['directory1', 'directory2', 'someotherfile.udn'])
```

Songbooks have an index auto-generated, and do not support mutiple songs with the same ID (which is
essentially "Title - Artist").

If your inputs include multiple songs in this way, the last one imported will be used. So name them
carefully.

A Songbook in this context is a collection of song objects with additional metadata, such as an
index.

## Tools

The pip package also installs 2 commandline tools, aimed at managing individual songsheets:

`udn_transpose`, which allows in-place (or optionally to a new file) transposing of an existing
songsheet by an arbitrary number of semitones. The transposition is added to metadata

`udn_songsheet`, which renders a UDN songsheet to a file in either PDF (default) or HTML format. it
also supports in-place transposition, without affecting the original input file.

## what you need to use this:

* a directory full of UDN-format files.
* templates:
  * index.html.j2
  * song.html.j2
* stylesheets (up to you, you can pass their names and location to the methods)
  * pdf.css
  * ukedown.css