semvec/pypi-packages huggingface dataset:
py-heart-locker 1.1
Python process locker to avoid repeatedly executing the same script at the same time
Requires Python: <4,>=3.5
Keywords: locker, lock

# py-heart-locker

> python 进程锁定器，避免同一时间重复执行相同的脚本

------

## 运行环境

![](https://img.shields.io/badge/Python-3.8%2B-brightgreen.svg)


## 使用说明

1. 安装: `python -m pip install py-heart-locker`
2. 在代码中使用：

```python
from lock import locker

def main() :
    if locker.islocked() :
        return
    locker.lock()

    core()


def core() :
    # 业务核心代码


if __name__ == '__main__' :
    main()
    
```
py-flow-check 1.0.0
Detect logical flow bugs in Python code using static analysis
Requires Python: >=3.9

# py-flow-check

Static analyzer for logical flow bugs in Python.

It tries to catch:
- unreachable code after return or raise
- always true or always false conditions like `if True`
- unreachable or redundant conditions in if/elif chains (best with numeric comparisons)
- functions that do not return on every path
- variables that might be used before assignment on some paths

## Install

pip install py-flow-check

## CLI

flowcheck path/to/file.py

JSON output (useful for CI):

flowcheck --json path/to/file.py

Exit code:
- 0 if no issues
- 1 if issues found

## Python API

```python
from flowcheck import analyze

code = """
def f(flag):
    if flag:
        x = 1
    return x
"""
for issue in analyze(code):
    print(issue.format())
TubeLine 0.1.0
YouTube Timeline Pipeline
Requires Python: >=3.10
Keywords: youtube, speech-to-text, deepgram, gemini, ai, stt, video-analysis, timeline, time, line

TubeLine

TubeLine is a Python library that analyzes YouTube videos by converting audio into text and automatically segmenting the video into topic-based time ranges.

It performs the following pipeline:

1. Download audio from a YouTube video


2. Transcribe speech using Deepgram (STT)


3. Analyze topic transitions using Google Gemini



The final output is a list of time ranges with concise topic descriptions.


---

Features

Automatic YouTube audio download

Korean speech-to-text using Deepgram

Topic segmentation based on video flow

Timestamped topic summaries

Simple and clean Python API



---

Installation

pip install TubeLine


---

Requirements

Python 3.10 or higher

FFmpeg (required by yt-dlp)


Install FFmpeg

macOS

brew install ffmpeg

Ubuntu / Debian

sudo apt install ffmpeg

Windows

Download from: https://ffmpeg.org/download.html

Make sure ffmpeg is available in your system PATH.


---

Usage

Basic Example

import TubeLine

TubeLine.set(
    deepgram_api_key="YOUR_DEEPGRAM_API_KEY",
    gemini_api_key="YOUR_GEMINI_API_KEY"
)

result = TubeLine.run("https://www.youtube.com/watch?v=VIDEO_ID")
print(result)


---

Output Format

The output is plain text in the following format:

<start_time~end_time> - topic description

Example:

00:00:00~00:02:15 - Introduction and stream opening
00:02:15~00:08:40 - Discussion about recent updates
00:08:40~00:15:10 - Main gameplay explanation

Time format: HH:MM:SS

Maximum number of segments: 8

Short consecutive segments may be merged automatically



---

API Reference

TubeLine.set()

Configure API keys at runtime.

TubeLine.set(
    deepgram_api_key: str | None,
    gemini_api_key: str | None
)

TubeLine.run()

Run the full pipeline on a YouTube URL.

TubeLine.run(url: str, quality: str = "low") -> str

quality: "low" (default) or "high"



---

Notes

API keys are not stored permanently and must be set at runtime.

STT output may contain recognition errors; Gemini compensates for these during analysis.

This library does not download or store video files, only audio.



---

License

MIT License
types-seaborn 0.13.2.20251221
Typing stubs for seaborn
Requires Python: >=3.9

## Typing stubs for seaborn

This is a [type stub package](https://typing.python.org/en/latest/tutorials/external_libraries.html)
for the [`seaborn`](https://github.com/mwaskom/seaborn) package. It can be used by type checkers
to check code that uses `seaborn`. This version of
`types-seaborn` aims to provide accurate annotations for
`seaborn==0.13.2`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/seaborn`](https://github.com/python/typeshed/tree/main/stubs/seaborn)
directory.

This package was tested with the following type checkers:
* [mypy](https://github.com/python/mypy/) 1.18.2
* [pyright](https://github.com/microsoft/pyright) 1.1.407

It was generated from typeshed commit
[`d8d989280c3f550a864815c4b6a28683d228531f`](https://github.com/python/typeshed/commit/d8d989280c3f550a864815c4b6a28683d228531f).
m-tunnel4 4.0.3
Tunnel System V4 - Cloudflare Workers based tunnel system
Requires Python: >=3.10

# Tunnel V4

A modern tunnel system built on Cloudflare Workers and Durable Objects.

## Features

- 🚀 **Remote Terminal** - SSH-like terminal access to your servers
- 📡 **SOCKS5 Proxy** - Secure proxy through your nodes
- ⚡ **Command Execution** - Run commands remotely
- 🔒 **Secure** - End-to-end encrypted connections
- 🌍 **Global** - Powered by Cloudflare's edge network

## Installation

```bash
pip install m_tunnel4
```

## Quick Start

### Start Agent on Your Server

```bash
# Basic usage
tunnel agent --id my-server @all

# With custom tags
tunnel agent --id my-server --tags env=prod,region=us @all

# Specific services only
tunnel agent --id my-server @term @socks5
```

### Use CLI to Connect

```bash
# List nodes
tunnel list

# Remote terminal
tunnel term --node my-server

# Execute command
tunnel exec --node my-server "uptime"

# SOCKS5 proxy
tunnel socks5 --node my-server --port 1080
```

## Configuration

### Environment Variables

- `TUNNEL_WORKER_URL` - Override default Worker URL
- `TUNNEL_ENV` - Environment (dev/prod)
- `TUNNEL_DEBUG` - Enable debug logging

### Custom Worker URL

```bash
export TUNNEL_WORKER_URL="wss://your-worker.workers.dev"
tunnel agent --id my-server @all
```

## Services

### Built-in Services

- `@all` - All services (exec, term, socks5)
- `@exec` - Remote command execution
- `@term` - Remote terminal
- `@socks5` - SOCKS5 proxy

### Port Forwarding

```bash
# Forward local service
tunnel agent --id my-server myapi:8080
```

## Development

```bash
# Clone repository
git clone https://github.com/yourusername/tunnel-v4.git
cd tunnel-v4

# Install in development mode
export TUNNEL_DEV=1
pip install -e .

# Run agent (development uses tunnel4 command)
tunnel4 agent --id test @all
```

## License

MIT License

## Links

- Documentation: https://github.com/yourusername/tunnel-v4/docs
- Issues: https://github.com/yourusername/tunnel-v4/issues