semvec/pypi-packages huggingface dataset:
avtdl 2.10.1
Monitoring and automation tool for Youtube and other streaming platforms
Requires Python: >=3.9

## avtdl

Tool to monitor Youtube and some other streaming platforms for new streams and uploads and execute user-defined commands when it happens. It aims to provide a highly configurable environment for setting up automated archiving of new content with filtering and notification support. It does not try to provide downloading streams itself and instead relies on executing commonly used well-known solutions for the task, such as `yt-dlp`.

Refer to [documentation](https://github.com/15532th/avtdl?tab=readme-ov-file#avtdl) for full list of available features and description of configuration process.

### Features overview

Some of the supported features include:

- monitoring Youtube channels using RSS feed
- monitoring Youtube channels, individual tabs of a channel or playlists by parsing html. With authorization cookies from Youtube account it's possible to get notifications for member-only and in any other way restricted streams and uploads, as well as to monitor the entire subscriptions feed
- monitoring Youtube channel community tab for new posts (including member-only with authorization cookies)
- monitoring other streaming platforms, such as Twitch and Twitcasting, for events of a channel going live
- filtering new videos and streams by channel name, presence or absense of pre-defined keywords in video title or description, picking up only upcoming streams or only member-only content, deduplication of the same stream or video url coming from multiple sources
- sending notifications to a Discord channel and/or as a Jabber message
intellif-aihub 0.1.54
Intellif AI-hub SDK.
Requires Python: >=3.9
Keywords: AI-hub, sdk, intellif

# Intellif AI-Hub SDK

**Intellif AI-Hub** 官方 Python 开发包。  
一个 `Client` 对象即可完成数据集管理、标注统计、任务中心等常见操作，无需手写 HTTP 请求。

```
aihub_sdk/
├─ pyproject.toml
├─ requirements.txt
├─ src/aihub/
│   ├─ client.py
│   ├─ exceptions.py
│   ├─ models/…
│   ├─ services/…
│   └─ utils/…
└─ tests/
```

---

## 💻 安装

```bash
# PyPI 安装
pip install intellif-aihub
# 运行环境：Python ≥ 3.9
```

---

## 🚀 快速上手

```python
from aihub import Client

BASE  = "http://192.168.13.160:30021"
TOKEN = "eyJhb..."   # 或设置环境变量：export AI_HUB_TOKEN=...

with Client(base_url=BASE, token=TOKEN) as cli:
    # 1. 同时创建数据集 + 版本（上传本地 ZIP）
    ds_id, ver_id, tag = cli.dataset_management.create_dataset_and_version(
        dataset_name="cats",
        is_local_upload=True,
        local_file_path="/data/cats.zip",
        version_description="first release",
    )
    print("数据集标识:", tag)  # 输出：cats/V1

    # 2. 下载数据集
    cli.dataset_management.run_download(
        dataset_version_name=tag,
        local_dir="/tmp/cats",
        worker=8,
    )

    # 3. 获取标注平台全局统计
    stats = cli.labelfree.get_project_global_stats("cat-project")
    print("总标注数:", stats.global_stats.total_annotations)
```

---

## 🌍 环境变量

| 变量                       | 作用                                      | 默认值                           |
|----------------------------|-------------------------------------------|----------------------------------|
| `AI_HUB_TOKEN`             | API 鉴权 Token（可不在 `Client` 中显式传入） | –                                |

---

## 📦 打包 & 发布

项目采用 PEP 517 / `pyproject.toml` 构建规范。

```bash
# 1️⃣ 构建 wheel / sdist
python -m pip install --upgrade build
python -m build                 # 生成 dist/*.whl dist/*.tar.gz

# 2️⃣ 本地验证
pip install --force-reinstall dist/*.whl
python -c "import aihub, sys; print('SDK 版本:', aihub.__version__)"

# 3️⃣ 发布到 PyPI 或私有仓库
python -m pip install --upgrade twine
twine upload dist/*
```

文档调试：

```bash
mkdocs serve
```

构建文档镜像：

```bash
docker build -t 192.168.14.129:80/library/aihub/sdk_doc:latest -f doc.Dockerfile .
```
---