semvec/pypi-packages huggingface dataset:
bilihud 0.3.3
B站弹幕阅读器 - 一个可以在游戏全屏时显示弹幕的Qt应用程序
License: MIT
Requires Python: >=3.10

# B站弹幕阅读器 (bilihud)

![PyPI version](https://img.shields.io/pypi/v/bilihud.svg)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

一个基于PyQt6和blivedm的B站弹幕阅读器，可以在Linux KDE环境下全屏游戏时显示弹幕。

> [!NOTE]
> 本项目基于 **Vibe Coding**（氛围驱动编码）模式开发，旨在快速实现创意与功能。目前仅在有限环境下进行过测试，未做大量的严谨验证。如有 Bug，欢迎反馈！

## 效果预览

### 一般模式 (Normal)
![Normal Mode](screenshots/normal.png)

### 游戏穿透模式 (Pass-through)
![Pass-through Mode](screenshots/passthrough.png)

## 功能特点

* 实时显示B站直播间弹幕
* 半透明overlay窗口，可在游戏全屏时显示
* 美观的UI界面，支持不同用户等级的颜色标识
* 支持连接/断开直播间
* 显示用户名、舰长/VIP标识
* 支持发送弹幕
* 支持扫码登录
* **注意：** 使用 layer-shell-qt 支持 wayland 环境

## 极速上手

### 1. 安装

#### 系统依赖 (System Dependencies)

由于引入了 `layer-shell-qt`，如果您从源码运行，请先根据您的发行版安装必要的依赖：

**Ubuntu / Debian:**
```bash
sudo apt install liblayershellqtinterface-dev build-essential libwayland-dev qt6-base-dev libqt6waylandclient6
```

**Fedora:**
```bash
sudo dnf install gcc-c++ qt6-qtbase-devel layer-shell-qt-devel wayland-devel
```

**Arch Linux:**
```bash
sudo pacman -S qt6-base qt6-wayland layer-shell-qt
```

**Gentoo Linux:**
```bash
sudo emerge -a kde-plasma/layer-shell-qt dev-qt/qtwayland
```

#### 通用步骤

```bash
# 1. 克隆仓库
git clone https://github.com/locez/bilihud.git
cd bilihud

# 2. 初始化子模块 (blivedm)
git submodule update --init --recursive

# 3. 环境配置与安装 (推荐使用 uv)
# 安装 uv
pip install uv

# 创建虚拟环境并同步依赖
uv sync

# 激活环境
source .venv/bin/activate
```

### 2. 启动

```bash
python -m src.bilihud.main
```

## 隐私说明 & 配置

### 自动登录 (Cookies)

为了提供完整的体验（如显示完整用户名、发送弹幕、显示舰长标识），**BiliHUD** 会尝试自动读取本地浏览器的 Bilibili 登录状态。

*   **读取范围**: 程序仅读取 `.bilibili.com` 域下的 Cookies。
*   **读取目的**: 获取 `SESSDATA` 和 `bili_jct` (CSRF Token) 仅用于与 Bilibili API 进行必要的身份验证。
*   **支持浏览器**: Chrome, Edge, Firefox。
*   **数据安全**: 您的 Cookies 仅在本地内存中使用，**绝不会**被发送到任何第三方服务器。

**注意：** **BiliHUD** 默认会尝试自动读取本地浏览器的 Bilibili 登录状态，如果无法读取，您可以在托盘图标右键菜单中选择"扫码登录"来启用扫码登录功能。



## 打包与发行 (Packaging)

### Arch Linux

本项目已发布至 AUR ([bilihud-git](https://aur.archlinux.org/packages/bilihud-git))。推荐使用 `paru` 或其他 helper 快速安装：

```bash
paru -S bilihud-git
```

此外，`packaging/arch/PKGBUILD` 提供了本地打包的示例文件。

### Gentoo Linux

如果您是 Gentoo 用户，相关包已包含在 [我的个人 overlay](https://github.com/locez/locez-overlay) 中，添加后即可安装。

本地 ebuild 示例文件位于: `packaging/gentoo/`

## 鸣谢

* [blivedm](https://github.com/xfgryujk/blivedm) - B站直播弹幕协议库
* [PyQt6](https://pypi.org/project/PyQt6/) - Python GUI框架
* [qasync](https://github.com/CabbageDevelopment/qasync) - PyQt6与asyncio集成库
hclone 0.1.0
Restore Git repositories from Software Heritage snapshots
License: MIT
Requires Python: >=3.10

# hclone

从 Software Heritage 的永久链接或 snapshot SWHID 恢复 Git 仓库。

## 安装（本地构建后）

```bash
pip install dist/hclone-0.1.0-py3-none-any.whl
```

## 使用

```bash
hclone "https://archive.softwareheritage.org/swh:1:snp:1930ecd7bcc8c8666c721c4def3944c98d650abf/" sqlite-s3vfs
# 或
hclone "swh:1:snp:1930ecd7bcc8c8666c721c4def3944c98d650abf" sqlite-s3vfs
```

## 参数

- `--wait`：等待打包完成的最长秒数（默认 300）
- `--interval`：轮询间隔秒数（默认 5）

## 依赖

- git
- Python 3.10+
videorag 0.1.0
Retrieve relevant transcript chunks from YouTube videos
License: MIT
Requires Python: >=3.8
Keywords: youtube, transcript, rag, vector-search, faiss, nlp



# videorag

A simple Python library to extract and retrieve the most relevant transcript chunks from a **YouTube video** for a user query using vector search and semantic retrieval. It helps developers quickly index video transcripts and answer questions about the video content.

---

## 🧠 What It Does

`videorag` fetches the transcript of a YouTube video and uses a **vector store with MMR retrieval** to find the most relevant text parts for a user query.
This makes it easy to build tools like video Q&A bots, summarizers, and RAG systems.

---

## 🚀 Features

* Automatically download YouTube transcripts (English first, fallback languages)
* Clean and split transcripts into searchable chunks
* Build a FAISS vector index for fast retrieval
* Retrieve top-k relevant chunks for a query
* Minimal and easy-to-use API

---

## 🧾 Installation

Install directly via `pip` in your Python environment:

```bash
pip install videorag
```

Or install from the local repository (after cloning):

```bash
pip install -e .
```

> Make sure you are using a virtual environment (`venv`) when installing dependencies. ([pyOpenSci][1])

---

## 📦 Usage Example

Here is a simple usage example in Python:

```python
from videorag import get_relevant_chunks_from_video

video_link = "https://www.youtube.com/watch?v=HbZD0XoN5fc"
query = "Who is Sunita Williams?"
k = 3

relevant_text = get_relevant_chunks_from_video(video_link, query, k)
print(relevant_text)
```

This returns the **top-k relevant transcript text chunks** that best match your query.

---

## 🛠️ How It Works

1. Fetch the **YouTube video transcript**
2. Clean and split it into chunks
3. Create or load a **FAISS vector index**
4. Run **MMR (Maximal Marginal Relevance)** to get diverse, relevant chunks

---

## 🧪 Contributing

Contributions are welcome! You can:

* Report bugs
* Suggest new features
* Improve documentation
* Add tests

Feel free to open issues or submit pull requests.



## Legal Notice

This software is provided under the MIT License. Users are responsible for 
complying with YouTube's Terms of Service when using this library. The author 
assumes no liability for how this library is used.
stremthru 0.7.0
SDK for StremThru
License: MIT
Requires Python: <4.0,>=3.10

[![GitHub Workflow Status: CI [SDK/PY]](https://img.shields.io/github/actions/workflow/status/MunifTanjim/stremthru/ci-sdk-py.yml?branch=main&label=CI%20%5BSDK%2FPY%5D&style=for-the-badge)](https://github.com/MunifTanjim/stremthru/actions/workflows/ci-sdk-py.yml)
[![PyPI - Version](https://img.shields.io/pypi/v/stremthru?style=for-the-badge)](https://pypi.org/project/stremthru/)
[![License](https://img.shields.io/github/license/MunifTanjim/stremthru?style=for-the-badge)](https://github.com/MunifTanjim/stremthru/blob/main/sdk/py/LICENSE)

# StremThru - Python SDK

## Installation

```sh
pip install stremthru
# or
poetry add stremthru
```

## Usage

**Basic Usage:**

```py
from stremthru import StremThru;

st = StremThru(base_url="http://127.0.0.1:8080", auth="user:pass")
```

## License

Licensed under the MIT License. Check the [LICENSE](./LICENSE) file for details.