semvec/pypi-packages huggingface dataset:
aiden-v-dlp 0.1.0
Aiden Video Downloader: a friendly wrapper around yt-dlp
License: MIT
Requires Python: >=3.8

\# aiden-v-dlp

A friendly `yt-dlp` wrapper with sensible defaults.



\## Install (recommended)

\- With pipx: `pipx install aiden-v-dlp`

\- Or pip: `pip install --user aiden-v-dlp`



\## Use

`aiden-v-dlp <video\_url>`  

Downloads MP4 to your Downloads folder. Pass any `yt-dlp` flags and they'll be forwarded.
iOSMonitor 1.0.4
iOS Monitor
License: MIT
Requires Python: >=3.10.0

## iOS性能监测工具开发方案

### 概述

基于pymobiledevice3库（最新版本）开发的iOS性能数据监测工具，通过命令行传入设备UDID和bundleId，定时采集进程的CPU、内存、FPS和GPU使用率，并将结果保存到CSV文件中。

### 环境准备与依赖安装

1. 安装Python 3.7+

2. 安装项目依赖：
   ```
   pip install -r requirements.txt
   ```

3. 确保您的系统满足以下要求：
   - macOS：安装Xcode命令行工具
   - Linux：安装usbmuxd (Ubuntu通过`apt install usbmuxd`安装)
   - Windows：安装iTunes或Apple驱动程序

### 使用方法

```commandline
# 基本用法
python main.py --udid <device_udid> --bundle-id <app_bundle_id>

# 完整参数
python main.py --udid <device_udid> --bundle-id <app_bundle_id> \
    --interval 1 --output ./perf_data.csv --duration 300
```

### 功能说明

该工具通过以下核心模块实现性能监测：

1. **设备连接模块**：使用`DVTSecureSocketProxyService`连接iOS设备
2. **进程监控模块**：使用`DiagnosticsService`获取进程PID
3. **性能数据采集模块**：使用`ProcessesService`和`DeveloperService`采集性能指标
4. **数据存储模块**：将数据保存到CSV文件
5. **命令行接口模块**：使用click库实现参数解析

### 输出CSV格式

| timestamp | udid | bundle_id | pid | cpu_usage | memory_usage | fps | gpu_usage |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 2023-10-01 12:00:01 | xxxx | com.example.app | 1234 | 45.2 | 120.5 | 59 | 32.1 |
| 2023-10-01 12:00:02 | xxxx | com.example.app | 1234 | 43.1 | 121.3 | 60 | 31.5 |

### 注意事项

* iOS设备需要启用开发者模式并信任开发电脑
* 某些性能指标可能需要应用在前台运行
* GPU使用率采集可能需要特定设备支持
* 工具会实时在控制台显示性能数据，并保存到CSV文件
* 按Ctrl+C可以优雅退出监测过程

### 异常处理

工具内置了以下异常处理机制：
- 设备断开连接自动重连
- 应用崩溃或退出时的处理
- 数据采集失败重试
- 权限不足时的错误提示

### 支持的平台

该工具支持以下操作系统：
- macOS
- Linux
- Windows

基于pymobiledevice3库最新版本重构，兼容iOS 17及以上版本。
ssb-dash-components 0.11.1
Dash Component library for SSB
License: MIT
Requires Python: <4.0,>=3.9

# SSB Dash Components

_The [SSB Component Library](https://design.ssb.no/), available for Dash!_

## Components

| Component              | Available for Dash? |
| ---------------------- | ------------------- |
| Accordion              | :white_check_mark:  |
| Breadcrumb             |                     |
| Button                 | :white_check_mark:  |
| Cards                  |                     |
| Checkbox               | :white_check_mark:  |
| Dialog                 | :white_check_mark:  |
| Divider                |                     |
| Dropdown               | :white_check_mark:  |
| DropdownMultiple       | :white_check_mark:  |
| Fact Box               |                     |
| Footer                 |                     |
| Form Error             |                     |
| Glossary               | :white_check_mark:  |
| Graphs                 |                     |
| Header                 | :white_check_mark:  |
| Input                  | :white_check_mark:  |
| Key Figures            |                     |
| Links                  | :white_check_mark:  |
| Map                    |                     |
| Pagination             |                     |
| Paragraph              | :white_check_mark:  |
| Quote and Highlighting |                     |
| Radio Buttons          |                     |
| Responsive media       |                     |
| Reference              |                     |
| Search                 |                     |
| Site navigation        |                     |
| Sticky menu            |                     |
| Table                  |                     |
| Tabs                   | :white_check_mark:  |
| Tags                   |                     |
| Title                  | :white_check_mark:  |
| Warnings and errors    | :white_check_mark:  |
process-gpt-llm-factory 1.0.2
통합 LLM 팩토리 라이브러리 - 다양한 LLM 제공자를 통합된 인터페이스로 사용
License: MIT
Requires Python: >=3.8
Keywords: llm, openai, azure, anthropic, ollama, langchain, factory, ai

# LLM Factory

[![PyPI version](https://badge.fury.io/py/llm-factory.svg)](https://badge.fury.io/py/llm-factory)
[![Python Support](https://img.shields.io/pypi/pyversions/llm-factory.svg)](https://pypi.org/project/llm-factory/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

통합 LLM 팩토리 라이브러리 - 다양한 LLM 제공자를 통합된 인터페이스로 사용할 수 있게 해주는 Python 라이브러리입니다.

## 주요 기능

- **다중 LLM 제공자 지원**: OpenAI, Azure OpenAI, Anthropic, Ollama

## 설치

requirements.txt에 추가:

```txt
process-gpt-llm-factory==1.0.1
```

### 직접 설치
```bash
pip install process-gpt-llm-factory
```

## 마이그레이션 가이드

기존 코드에서 직접 LLM을 생성하던 방식을 LLM Factory로 변경하는 방법:

### Before (기존 방식)
```python
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-4o", streaming=True, temperature=0)
```

### After (LLM Factory 사용)
```python
from llm_factory import create_llm

model = create_llm(temperature=0)
```

## 상세 설명

### 1. 환경변수 설정

`.env` 파일에 다음 환경변수들을 설정하세요:

```bash
# LLM Provider 설정 (설정하지 않는 경우 openai 기본값 사용)
LLM_PROVIDER=openai  # openai, azure, anthropic, ollama 중 선택

# OpenAI 설정
OPENAI_API_KEY=your_openai_api_key_here
OPENAI_MODEL=gpt-4o

# 아래 설정은 openai 외 사용시에만 필요
# Azure OpenAI 설정
AZURE_API_KEY=your_azure_api_key_here
AZURE_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_DEPLOYMENT=your_deployment_name
AZURE_MODEL=gpt-4o
AZURE_API_VERSION=2024-06-01-preview

# Anthropic 설정
ANTHROPIC_API_KEY=your_anthropic_api_key_here
ANTHROPIC_MODEL=claude-3-sonnet-20240229

# Ollama 설정
OLLAMA_MODEL=llama3
OLLAMA_BASE_URL=http://localhost:11434
```

### 2. 기본 사용법

```python
from llm_factory import create_llm, create_embedding

# 환경변수에 설정된 제공자와 모델 사용
llm = create_llm()

# 특정 제공자 지정
llm = create_llm(provider="openai")

# 특정 모델과 옵션 지정
llm = create_llm(
    model="gpt-4o",
    temperature=0.7,
    streaming=True
)

## API 참조

### LLM 생성 함수

```python
from llm_factory import (
    create_llm,
    create_openai_llm,
    create_azure_llm,
    create_anthropic_llm,
    create_ollama_llm
)

# 기본 LLM 생성 
llm = create_llm()

# 특정 제공자 LLM 생성 
openai_llm = create_openai_llm(model="gpt-4o", temperature=0.5)
azure_llm = create_azure_llm(model="gpt-4o")
anthropic_llm = create_anthropic_llm(model="claude-3-sonnet-20240229")
ollama_llm = create_ollama_llm(model="llama3")
```

### Embedding 생성 함수

```python
from llm_factory import (
    create_embedding,
    create_openai_embedding,
    create_azure_embedding
)

# 기본 Embedding 생성 (환경변수 기반)
embedding = create_embedding()

# 특정 제공자 Embedding 생성
openai_embedding = create_openai_embedding(model="text-embedding-3-large")
azure_embedding = create_azure_embedding(model="text-embedding-3-large")
```