semvec/pypi-packages huggingface dataset:
folds 0.1.0
An elegant and scalable framework for Telegram bots, based on Telethon
Requires Python: >=3.12

# Folds

Folds is an elegant and scalable framework for bots, wrapping Telethon library.

- Minimalistic when you need simple features, powerful when you need complex ones.
- Flexible app structure with no boilerplate and no imports in the middle of files.
- Easy support for multi-bot apps.
- Built with Telegram API without Bot API — I explain it below.

```python 
from folds import Bot, Message, ThisUser

bot = Bot(bot_token, api_id, api_hash)


@bot.added_to_group()
async def handle_added():
  return 'Hello!'


@bot.group_commands.help()
async def handle_help_command():
  return 'You called for /help?'


@bot.group_commands.hello()
async def handle_hello_command(message: Message, user: ThisUser):
  await message.reply('Hmm!')
  await message.reply(f'Hello, {user.first_name}')


bot.run()
```

## Built with Telegram API

Folds is built on top of Telethon, a Telegram API library. This means Folds doesn't use Bot API.

#### Pros

- No limitations.
  Folds doesn't have natural limitations that Bot API has:
  see [the API explanation](https://papercraft.tmat.me/book/dev/api) and [the comparison table](https://papercraft.tmat.me/book/appendix/api-comparison)
  from the Papercraft Book for details.
- Reuse code for userbots.
  When you are familiar with Telethon concepts,
  you can not only program bots with Folds but also program user accounts.
  In particular, our Telegram script tool named [TGPy](https://papercraft.tmat.me/tgpy/) is also based on Telethon.

#### Cons

- Setting up requires a little extra step.

## Should I use it?

The framework is in its early version, and the features will change, so...

For small hobby bots — yes. For big complex projects — also yes, but better come back later!
I will appreciate if you show your interest by leaving a star on the repo.

## Learn

Here is how to get familiar with Folds:
- Read these docs, of course. I tried to make them friendly!
- Read [the Telethon docs](https://docs.telethon.dev) because Folds bases on Telethon.
  ChatGPT knows Telethon well, apparently.
- For specific features, read [the Papercraft Book,](https://papercraft.tmat.me/book) my book on developing Telegram bots.
  It includes code examples for Folds.

[//]: # (- Join the chat and ask questions: [@sdlof]&#40;https://t.me/sdlof&#41;)

## Getting Started

Head over to [Quick Start.](https://papercraft.tmat.me/folds/tutorial/quick-start)
parakeet-asr 0.1.0
ASR based on NVIDIA parakeet model
Requires Python: ==3.12.*

# parakeet-asr

ASR (Automatic Speech Recognition) using NVIDIA parakeet model.

- Simple to use CLI and Pythonic API
- Support for CPU and GPU
- Support for long audios with correct timestamps


## Install

```
pip install parakeet-asr

uv install parakeet-asr
```

## Usage

```
parakeet YOUR_AUDIO_FILE.wav
```

Will create a `.srt` file on the same location as the source with the same name.
InformationGain 3.0.0
A Python package for calculating information gain.
Requires Python: >=3.12

# informationGain Library

This module helps you calculate **Information Gain** for both **categorical** and **continuous** features.

---

## Class  
`infoGain.calculate(data, target, criteria="gini", fIndex=True)`

---

## Parameters

| Parameter  | Type      | Description                                                      | Default |
|------------|-----------|------------------------------------------------------------------|---------|
| `data`     | DataFrame | Dataset in pandas DataFrame format                              | Required |
| `target`   | String    | Output column (the target variable)                             | Required |
| `fIndex`   | Boolean   | If `True`, assumes the first column is an index and skips it     | True     |
| `criteria` | String    | Splitting criteria: `"gini"` or `"entropy"`                     | `"gini"` |

---

## Description

- Calculates **Information Gain** for each feature/column in the dataset with respect to the target column.
- Handles both **categorical** and **continuous** features:
  - For categorical features, splits data by unique values.
  - For continuous features, finds the best threshold (value) that gives maximum Information Gain.
- Supports **Gini Index** or **Entropy** as the impurity metric.
- Returns a **DataFrame** with:
  - Feature name
  - Best threshold (or `'none'` for categorical)
  - Corresponding Information Gain

---

## Returns

A `pandas.DataFrame` with columns:
- `'feature'`: Name of the feature
- `'threshold'`: Best threshold value (for continuous features), or `'none'`
- `'infogain'`: Calculated Information Gain

---

## Example Usage

```python
from informationGain import infoGain
import pandas as pd

# Load dataset
data = pd.read_csv('your_dataset.csv')

# Initialize
ig = infoGain()

# Calculate Information Gain
result = ig.calculate(data, target='Output', fIndex=True)

print(result)
# Example Output:
#     feature  threshold   infogain
# 0   outlook      none    0.246750
# 1      temp      72.5    0.029223
# 2  humidity      80.0    0.151836
# 3      wind      none    0.048127