semvec/pypi-packages huggingface dataset:
mircheck 1.1.4
A tool to find the fastest mirror for Ubuntu and its derivatives
Requires Python: >=3.6


# Mircheck - Fastest Mirror Selector for Ubuntu and it's derivatives

This script helps you find the fastest mirror for your Ubuntu and it's derivatives and generates a `sources.list` file accordingly. 
It downloads the `Release` file from each mirror to measure the speed and identifies the fastest one.

## Requirements

- Python 3.6 or higher
- `requests` library

## Installation

1. **Recommended - Using PIP**
   ```sh
   pip install mircheck
   ```
   
2. **Clone the repository:**
   ```sh
   git clone https://github.com/arkrwn/mircheck.git
   cd mircheck/mircheck
   pip install -r ../requirements.txt
   chmod +x main.py
   ```

## Usage

```sh
Install using pip: `mircheck [options] <distribution> <country>`
Install using git: `./mircheck [options] <distribution> <country>`
```

### Positional Arguments

- `distribution`: Distribution code name (e.g., jammy, bionic).
- `country`: Country code or name.

### Options

- `-a, --arch ARCH`: Specify architecture (e.g., amd64, arm64). Default is the architecture of the current machine.
- `-s, --sources`: Include `deb-src` lines in the generated `sources.list`.
- `-o, --outfile OUTFILE`: Output file for `sources.list`. Default is `./sources.list`.
- `-n, --nonfree`: Include non-free section in the generated `sources.list`.
- `-d, --distribution_type {stable, testing, unstable, experimental, release_codename, sid}`: Specify which distribution of Debian to use. Default is `stable`.

### Examples

1. **Find the fastest mirror for Ubuntu `jammy` in Indonesia and save to the current directory:**
   ```sh
   mircheck jammy id
   ```

2. **Find the fastest mirror for Debian `stable` in Germany and include source lines:**
   ```sh
   mircheck stable germany -s
   ```

3. **Find the fastest mirror for Ubuntu `bionic` in the USA and save to a custom path:**
   ```sh
   mircheck bionic us -o /path/to/custom/sources.list
   ```

## Output

The script will test each mirror's speed and display the results in kilobytes per second (KB/s). It will identify the fastest mirror and generate a `sources.list` file with the appropriate entries.

## License

This project is licensed under the MIT License. See the `LICENSE` file for details.

## Contributing

Feel free to submit issues and pull requests. For major changes, please open an issue first to discuss what you would like to change.

## Acknowledgements

- The Ubuntu for providing mirror lists.
streamlit-ai-assist 0.1.1
A built in data analyst for your Streamlit app
Requires Python: >=3.7

# streamlit-ai-assist

`streamlit-ai-assist` uses the power of [Snowflake Arctic](https://www.snowflake.com/blog/arctic-open-efficient-foundation-language-models-snowflake/), an open_source LLM, to personalize dashboards and allow all audiences to easily explore new and pre-built data visualizations. It's a built-in data analyst for your Streamlit dashboard.

If you add this component to your dashboard, dashboard viewers will be able to chat with an LLM assistant to get answers to their questions. If there are relevant graphs already in the dashboard, then the assistant will display those. If the question requires a new graph, the LLM is able to write the code to create that. These **new graphs** are highlighted in yellow, and if GitHub connection is configured in the app, then the user will be allowed to easily create a **PR** that integrates the code into the dashboard once reviewed and approved by a team member.

If a user has a particular question, the AI assistant can execute SQL `SELECT` statements that let it access the raw numbers. It can summarize graphs for you and return answers to a quick question you have.

The bottom view of the streamlit component is a preview of the most relevant graphs availabe in the dashboard. If you haven't formulated a question yet, a simple keyword search or "show me graphs about X" could be helpful!



## Installation instructions

```sh
pip install streamlit-custom-component
```

## Usage instructions

```python
import streamlit as st

from streamlit_ai_assist import streamlit_ai_assit

streamlit_ai_assist(
            graphing_file_path="graphing.py",
            graphing_import_path="graphing",
            database_name="snowflake",
            general_description="This is a database for a company that does X",
            mode="chat",
            key="foo1"
)

```

## Requirements

The following credentials are needed:

#### Environment Variables (can be specified in secrets.toml or with os)
```
REPLICATE_API_TOKEN
```

#### Database Connection Variables (must be specified in secrets.toml in a section named `connections.<database_name>`)
e.g.
```
[connections.snowflake]
type = "snowflake"
user = 
password =
account = 
role = 
warehouse = 
database = 
schema = 
client_session_keep_alive = true
```
Currently, only Snowflake database connections are tested and fully supported.

### Optional Environment Variables
```
GITHUB_PERSONAL_ACCESS_TOKEN
REPO_NAME
REPO_OWNER
```
If these are specified, GitHub PR automation will be enabled.
dextbird 0.4.9
Discord extensions voice library made with rust
Requires Python: >=3.8

# discord-ext-songbird
[![Test songbird](https://github.com/tuna2134/discord-ext-songbird/actions/workflows/test.yml/badge.svg)](https://github.com/tuna2134/discord-ext-songbird/actions/workflows/test.yml)

Songbird is voice client which made by Rust.

This library is wrapping songbird for discord.py.

[Document](https://tuna2134.dev/discord-ext-songbird/)

## Support
Only macos and linux.

If you are using linux, we are only support this python version.
3.8.10+, 3.9.5+, 3.10.0+

### Why I am not supporting windows?
It's too hard for me.

## Install
```sh
pip install "dextbird @ git+https://github.com/tuna2134/discord-ext-songbird.git"
```

## Sample code
```python
from dextbird import VoiceClient
import discord

import os
import logging


client = discord.Client(intents=discord.Intents.all())
logging.getLogger().setLevel(logging.INFO)


@client.event
async def on_message(message):
    if message.content == "!join":
        vc = await message.author.voice.channel.connect(cls=VoiceClient)
    elif message.content == "!play":
        # Play lycoris recoil
        track = await message.guild.voice_client.ytdl("https://youtu.be/Vi-1402wYtI?si=x_rhftnpQ0fKcfEE")
        track.play()
    elif message.content == "!leave":
        await message.guild.voice_client.disconnect()
    elif message.content == "!stop":
        message.guild.voice_client.stop()


client.run(os.getenv("TOKEN"))
```
sentrifyai 0.1.7
SentrifyAI API client
Requires Python: >=3.6

# SentrifyAI Python Package

SentrifyAI is a Python package that provides a client for interacting with the SentrifyAI API.

## Installation

You can install the package via pip:

```
pip install sentrifyai
```

## Usage

```
from sentrifyai import api
import json

emotions = api.Emotions()

results = emotions.emotion(model_slug='Emotions-1.0', message='This is a sample message.')

json_results = json.dumps(results, indent=4)

print(json_results)
```

## Documentation

For more detailed usage instructions and API reference, please refer to the [documentation](https://github.com/sentrifybot/sentrifyai-python).

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.