semvec/pypi-packages huggingface dataset:
liquidai-cli 0.0.1b0
CLI tool for Liquid Labs on-prem stack
Requires Python: >=3.10

# Liquid Labs CLI

Command line interface for managing Liquid Labs on-prem stack.

## Installation
```bash
pip install liquidai-cli
```

A `docker-compose.yaml` file is also shipped together with the package. Any changes to this file may cause some unexpected behaviors.

### Run with `uv`
`uv` allows to run this tool without installing the package into the system.

```bash
uv run --directory [PATH_TO_THIS_DIRECTORY] liquidai [command] [args]
```

## Configuration

The CLI uses a YAML configuration file (`liquid.yaml`) in your working directory. A default configuration will be created on first use, but you can customize it:

```yaml
stack:
  version: "c3d7dbacd1"
  model_image: "liquidai/lfm-7b-e:0.0.1"
  api_secret: "local_api_token"
  # Other values will be auto-generated
database:
  name: "liquid_labs"
  user: "local_user"
  password: "local_password"
  port: 5432
  schema: "labs"
```

## Usage

### Stack Management

```bash
# Launch stack
liquidai stack launch

# Launch with upgrades
liquidai stack launch --upgrade-stack --upgrade-model

# Shutdown stack
liquidai stack shutdown

# Test API endpoints
liquidai stack test

# Purge stack (removes all components)
liquidai stack purge

# Purge without confirmation
liquidai stack purge --force
```

### Model Operations

```bash
# Run a model in docker container
liquidai model run-model-image \
  --name lfm-3b-e \
  --image "liquidai/lfm-3b-e:0.0.6"

# Run a HuggingFace model and expose on port 9000
liquidai model run-hf \
  --name llama-7b \
  --path meta-llama/Llama-2-7b-chat-hf \
  --port 9000 \
  --gpu-memory-utilization 0.6 \
  --max-num-seqs 600 \
  --max-model-len 32768

# Run a local checkpoint and expose on port 9001 to avoid conflicts
liquidai model run-checkpoint \
  --path /path/to/checkpoint \
  --port 9001 \
  --gpu-memory-utilization 0.6 \
  --max-num-seqs 600

# List running models
liquidai model list

# Stop a specific model
liquidai model stop llama-7b

# Stop a model interactively
liquidai model stop
```

### Database Operations

```bash
# Connect to database using pgcli
liquidai db connect
```

### Infrastructure

```bash
# Create Cloudflare tunnel with token
liquidai tunnel create --token YOUR_TOKEN

# Create tunnel interactively
liquidai tunnel create
```

### Configuration Management

```bash
# Import configuration from .env file
liquidai config import

# Import from specific .env file
liquidai config import --env-file /path/to/.env

# Import to specific config file
liquidai config import --config-file /path/to/liquid.yaml

# Force overwrite existing config
liquidai config import --force
```

## Command Reference
Call `liquidai [command] --help` to get the detailed usage reference.
conntextual 1.3.6
A network-application TUI using textual.
Requires Python: >=3.12

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# conntextual ([1.3.6](https://pypi.org/project/conntextual/))

[![python](https://img.shields.io/pypi/pyversions/conntextual.svg)](https://pypi.org/project/conntextual/)
![Build Status](https://github.com/vkottler/conntextual/workflows/Python%20Package/badge.svg)
[![codecov](https://codecov.io/gh/vkottler/conntextual/branch/master/graphs/badge.svg?branch=master)](https://codecov.io/github/vkottler/conntextual)
![PyPI - Status](https://img.shields.io/pypi/status/conntextual)
![Dependents (via libraries.io)](https://img.shields.io/librariesio/dependents/pypi/conntextual)

*A network-application TUI using textual.*

([interface documentation](https://vkottler.github.io/python/conntextual))

## Python Version Support

This package is tested with the following Python minor versions:

* [`python3.12`](https://docs.python.org/3.12/)
* [`python3.13`](https://docs.python.org/3.13/)

## Platform Support

This package is tested on the following platforms:

* `ubuntu-latest`
* `macos-latest`

# Introduction

# Command-line Options

```
$ ./venv3.12/bin/conntextual -h

usage: conntextual [-h] [--version] [-v] [-q] [--curses] [--no-uvloop]
                   [-C DIR]
                   {client,ui,noop} ...

A network-application TUI using textual.

options:
  -h, --help         show this help message and exit
  --version          show program's version number and exit
  -v, --verbose      set to increase logging verbosity
  -q, --quiet        set to reduce output
  --curses           whether or not to use curses.wrapper when starting
  --no-uvloop        whether or not to disable uvloop as event loop driver
  -C DIR, --dir DIR  execute from a specific directory

commands:
  {client,ui,noop}   set of available commands
    client           attempt to connect a client to a remote session
    ui               run a user interface for runtimepy applications
    noop             command stub (does nothing)

```

# Internal Dependency Graph

A coarse view of the internal structure and scale of
`conntextual`'s source.
Generated using [pydeps](https://github.com/thebjorn/pydeps) (via
`mk python-deps`).

![conntextual's Dependency Graph](im/pydeps.svg)
tube-scriptor 2025.5.120740
A package for generating transcripts from YouTube videos in various formats
Requires Python: >=3.6

[![PyPI version](https://badge.fury.io/py/tube_scriptor.svg)](https://badge.fury.io/py/tube_scriptor)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/tube_scriptor)](https://pepy.tech/project/tube_scriptor)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)

# Tube Scriptor

`tube_scriptor` is a Python package designed to fetch and generate transcripts from YouTube videos in various formats including XML, JSON, Python lists, or plain text. It simplifies the process of obtaining video transcripts, making it accessible for data analysis, content generation, and accessibility features.

## Installation

To install `tube_scriptor`, you can use pip:

```bash
pip install tube_scriptor
```

## Usage

Using `tube_scriptor` is straightforward. Below are examples demonstrating how to fetch a video transcript in various formats and using both a YouTube URL and a direct video ID.

### Fetching Transcript as a List

```python
from tube_scriptor import fetch_transcript

# Using a YouTube URL
youtube_url = "https://www.youtube.com/watch?v=exampleVideoId"
transcript_list = fetch_transcript(youtube_url, 'list')
print(transcript_list)

# Using a YouTube video ID
youtube_id = "exampleVideoId"
transcript_list = fetch_transcript(youtube_id, 'list')
print(transcript_list)
```

### Fetching Transcript as a JSON String

```python
# Using a YouTube video ID
youtube_id = "exampleVideoId"
transcript_json = fetch_transcript(youtube_id, 'json')
print(transcript_json)
```

### Fetching Transcript as Plain Text

```python
# Using a YouTube video ID
youtube_id = "exampleVideoId"
transcript_text = fetch_transcript(youtube_id, 'string')
print(transcript_text)
```

### Fetching Transcript as XML

```python
# Using a YouTube video ID
youtube_id = "exampleVideoId"
transcript_xml = fetch_transcript(youtube_id, 'xml')
print(transcript_xml)
```

These examples illustrate the flexibility of `tube_scriptor` in handling different formats and inputs. Choose the format that best suits your needs, whether it's for further processing, display, or storage.

## Features

- Support for fetching transcripts in XML, JSON, Python list, or plain string format.
- Easy integration into Python projects.
- Flexible input with support for both YouTube video URLs and video IDs.
- Lightweight with minimal dependencies.

## Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the [issues page](https://github.com/chigwell/tube_scriptor/issues).

## License

This project is licensed under the [MIT License](https://choosealicense.com/licenses/mit/).
yid-emailer 0.1.15
A template for a private Python package
Requires Python: <4.0,>=3.9

# YiD Emailer

My experiments with emailing services.


## Brevo:
I decided to use 3rd party emailing service Brevo to have a convenient dashboard for reviewing all emails sent.
Brevo has a generous free tier.
To set it up, go to https://app.brevo.com/settings/keys/api, generate an API key and create `BREVO_API_KEY` GitHub secret with it.
trace-mcp 1.0.1
A Python MCP server for controlling Android system tracing via a native traceserver binary, supporting device selection and custom port forwarding. Designed for use with the uv/uvx toolchain.
Requires Python: >=3.12

# trace-mcp

A Python MCP server for controlling Android system tracing via a native traceserver binary, supporting device selection and custom port forwarding. Designed for use with the uv/uvx toolchain.

## Prerequisites
- Python 3.12+
- [uv](https://github.com/astral-sh/uv) (for dependency management and running scripts)
- Android Debug Bridge (adb) in your PATH
- Android device (API 21+), with USB debugging enabled
- Prebuilt traceserver binaries in the `bin/` directory for all supported ABIs

## Installation
```sh
uv pip install -e .
```

## Usage

### Start the MCP server (with uvx)
```sh
uvx trace-mcp --port 12345
```
- `--port` (optional): Specify the port for traceserver and forwarding (default: 12345)

### MCP Tool Functions
All tool functions accept an optional `sn` (device serial number) and `port` argument.

#### 1. begin_trace
Begin a trace section on the device.
- Arguments:
  - `trace_tag` (str): Trace tag name
  - `sn` (str, optional): Device serial number
  - `port` (int, optional): Port (default: 8080)

#### 2. end_trace
End a trace section on the device.
- Arguments:
  - `trace_tag` (str): Trace tag name
  - `sn` (str, optional): Device serial number
  - `port` (int, optional): Port (default: 8080)

#### 3. trace_test_mode
Start traceserver in test mode on the device.
- Arguments:
  - `sn` (str, optional): Device serial number
  - `port` (int, optional): Port (default: 8080)

#### 4. stop_traceserver
Stop the traceserver process and remove port forwarding.
- Arguments:
  - `sn` (str, optional): Device serial number
  - `port` (int, optional): Port (default: 8080)

## Example
```sh
uvx trace-mcp --port 9000
```

Then, use your MCP client to call the tool functions with the appropriate arguments.

## Project Structure
- `main.py`: MCP server and tool function implementations
- `bin/`: Prebuilt traceserver binaries for each ABI
- `pyproject.toml`: Project configuration for uv/uvx

## License
MIT