semvec/pypi-packages huggingface dataset:
prestream 0.1
Interactive script for streaming with yt-dlp and ffmpeg.

# Prestream

`prestream` is a Python script for streaming video online or offline using `yt-dlp` and `ffmpeg`. It allows users to choose between online and offline streaming methods and specify various streaming formats.

## Installation

To install `prestream`, you can use pip:

```bash
pip install prestream

Usage
After installation, you can use the prestream command to start the script. The script will prompt you to choose between online or offline streaming methods, enter the required details, and start the streaming process.

Online Streaming

1.Choose the online streaming method.
2.Enter the format for -f (e.g., 720p, mp4-1080p).
3.Provide the video source URL.
4.Enter the URL for the online stream.
5.Enter the stream key.


Offline Streaming

1.Choose the offline streaming method.
2.Enter the path or name of the video file.
3.Provide the URL for the stream.
4.Enter the stream key.


License
This project is licensed under the MIT License - see the LICENSE file for details.
easyformer 0.1.0
A package for encoding and scaling data for ML pipelines
Requires Python: >=3.6


# easyformer

A Python package for creating machine learning pipelines that standardize numerical columns and encode categorical columns using `LabelEncoder` or `OneHotEncoder` based on the number of unique values.

## Installation

You can install the package from PyPI using pip:

```bash
pip install easyformer
```

## Usage

### 1. Import the Package

```python
from easyformer import create_pipeline
import pandas as pd
```

### 2. Load Your Data

```python
# Example: Load a CSV file into a pandas DataFrame
data = pd.read_csv('your_data.csv')
```

### 3. Create the Pipeline

```python
pipeline = create_pipeline(data)
```

### 4. Fit and Transform Your Data

```python
# Fit the pipeline to your data and transform it
transformed_data = pipeline.fit_transform(data)
```

### 5. (Optional) Convert Transformed Data Back to a DataFrame

```python
# If you need the transformed data as a DataFrame
transformed_df = pd.DataFrame(transformed_data, columns=pipeline.named_steps['preprocessor'].get_feature_names_out())
print(transformed_df.head())
```

### Example

Hereâ€™s a full example that puts everything together:

```python
from easyformer import create_pipeline
import pandas as pd

# Load your data
data = pd.read_csv('your_data.csv')

# Create the pipeline
pipeline = create_pipeline(data)

# Fit and transform your data
transformed_data = pipeline.fit_transform(data)

# Optionally convert the transformed data back to a DataFrame
transformed_df = pd.DataFrame(transformed_data, columns=pipeline.named_steps['preprocessor'].get_feature_names_out())
print(transformed_df.head())
```

## Description

The `easyformer` package automates the preprocessing steps for machine learning pipelines by:

- **Standardizing Numerical Columns:** All numerical columns are scaled using `StandardScaler`.
- **Encoding Categorical Columns:** 
  - Columns with only two unique values are encoded using `LabelEncoder`.
  - Columns with more than two unique values are one-hot encoded using `OneHotEncoder`.
- **Removing Original Categorical Columns:** The original categorical columns are dropped after encoding.

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Contributing

If you would like to contribute to this package, please submit a pull request or open an issue on the [GitHub repository](https://github.com/gaharivatsa/easyformer).
tiktok-dlpy 2.2.1
A package to download TikTok videos or slideshows by URL without needing to login
Keywords: tiktok, playwright, async

![Main Workflow](https://github.com/Fluxticks/TikTokDL/actions/workflows/main.yml/badge.svg) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tiktok-dlpy)

# TikTokDL

A python package to download TikTok videos or slideshows by URL without needing to login.

## TODO:

- Re-Implement Captcha Verifiation

## Usage

1. Install the package

```bash
$ pip install tiktok-dlpy
```

2. Ensure that playwright has been installed

```bash
$ python -m playwright install --with-deps
```

2. Import the package

```python
from tiktokdl.download_post import get_post
```

3. Run the function in an async context

```python
video_or_slide_url = ""
await get_post(video_or_slide_url)
```
bishograd 0.1.0
Tiny & Light weight Machine Learning Library
Requires Python: >=3.8

# Bishõgrad

A  Bishõ Autograd engine in python along with a lightweight deep neural-network library! (inspired from [mircrograd by Andrej Karpathy](https://github.com/karpathy/micrograd))

## what's Bishõ?
微小 - Bishõ is Japanese word for 'tiny' since my implementation is very tinyyyyy ^_^ compared to PyTorch/Tensorflow

## what's Hako?
箱 - Hako is Japanese word for 'box' , here Hako signify the neurons in our network ;D

## Installation
> [!NOTE]
> Package is not released on pypi

1) Manually build the wheel file and install it:
    - Clone this repo:
        ```terminal
        git clone https://github.com/AK3847/Bishograd.git
        cd Bishograd
        ```
    - Use a python package manager like [Rye](https://rye.astral.sh/) to build the wheels:
        ```terminal
        rye build
        ```
    - Use ``pip`` to install the package into your virtual env:
        ```terminal
        pip install path/to/the/wheel/file/bishograd-0.1.0-py3-none-any.whl
        ```

2) Download the temporary wheel file from [here](https://drive.google.com/drive/folders/17PRVLhbKUxNe1He79cUJkuc5S1pTxvkd?usp=sharing)
    - Use ``pip`` to install the package into your virtual env:
        ```terminal
        pip install path/to/the/wheel/file/bishograd-0.1.0-py3-none-any.whl
        ```

## Example
Wonder how this works? Checkout the [examples](examples). 

## Targets : 
- [x] ReLU Activation function
- [x] Add MLP.training() to automate the whole training code
- [ ] Add ~~Sigmoid~~, LeakyReLU & other activation functions
- [ ] Add loss functions - categorical loss, mean-square loss etc 


## Contribution :
This repository is open for contribution! 
Primary way to contribute is to either raise an [issue](https://github.com/AK3847/Bishograd/issues) or a [pull request](https://github.com/AK3847/Bishograd/pulls) with proper description and code format. You can contribute to any [Targets](#targets-) or add new features as well.
ccluster 0.1.3
A package for clustering with cluster size constraints.
Requires Python: >=3.10

# ccluster: a package for clustering with size constraints

[![PyPI version](https://badge.fury.io/py/ccluster.svg)](https://pypi.org/project/ccluster)

*ccluster* is a library for performing clustering with exhaustive or partial cluster size constraints. 
It currently provides two constrained clustering algorithms: a constrained *k*-means suitable for euclidean data, and a constrained graph clustering algorithm based on spectral clustering.


For more details, please refer to the [documentation](https://ccluster.readthedocs.io/). 

| ![k-means example 1](https://raw.githubusercontent.com/chakib401/ccluster/main/docs_src/_static/km1.png)             | ![k-means example 2](https://raw.githubusercontent.com/chakib401/ccluster/main/docs_src/_static/km2.png)             | ![k-means example 3](https://raw.githubusercontent.com/chakib401/ccluster/main/docs_src/_static/km3.png)            |
|----------------------------------------------------------|----------------------------------------------------------|---------------------------------------------------------|
| ![spectral clustering example 1](https://raw.githubusercontent.com/chakib401/ccluster/main/docs_src/_static/sc1.png) | ![spectral clustering example 2](https://raw.githubusercontent.com/chakib401/ccluster/main/docs_src/_static/sc2.png) | ![spectral clustering example 3](https://raw.githubusercontent.com/chakib401/ccluster/main/docs_src/_static/sc3.png) |

### Installation

This package is available on Pypi. It can be installed as follows:
```bash
pip install ccluster
```

A set of dependencies will also be installed. The package can now be used:

```python
import ccluster
```

### Quick-Start

Here is an example of using constrained $k$-means with exhaustive constraints i.e. giving a size constraint for each cluster:

 ```python
>>> from ccluster.size import ConstrainedKMeans
>>> import numpy as np

>>> X = np.array([[1, 2], [1, 4], [1, 0],
...               [10, 2], [10, 4], [10, 0]])
>>> kmeans = ConstrainedKMeans(
...     n_clusters=2,
...     cluster_size=[2, 4],
...     random_state=0,
...     n_init=10).fit(X)
>>> kmeans.labels_
# array([1, 1, 1, 0, 0, 1])  
>>> kmeans.predict([[0, 0], [12, 3]], [1, 1])
# array([1, 0], dtype=int32)  
>>> kmeans.cluster_centers_
# array([[10. , 3. ],  
#        [ 3.25, 1.5 ]])  
```  

It is also possible to specify partial constraints i.e. constraints on a subset of the clusters and leave the remaining ones free. Here is an example using constrained spectral clustering:

 ```python
>>> from ccluster.size import ConstrainedSpectralClustering
>>> import numpy as np

>>> X = np.array([[1, 1], [2, 1], [1, 0],
...               [4, 7], [3, 5], [3, 6],
...               [9, 6], [5, 4], [2, 1]])

>>> spectral = ConstrainedSpectralClustering(
...     n_clusters=4, 
...     cluster_sizes=[2, 2],
...     random_state=0).fit(X)

>>> spectral.labels_
# array([2, 3, 2, 0, 3, 0, 1, 1, 3])

```
FunPayNexusAPI 0.0.4
This is a convenient tool for developing and working with the site funpay.com
Requires Python: >=3.8
Keywords: funpay api FunPayAPI tool bot tg lib

<h1 align="center"> FunPay Nexus API </h1>
<h3 align="center">  библиотека для взаимодействия с сайтом funpay.com, предоставляющая удобные методы работы с аккаунтом. </h3>

<h1 align="center"> Важные ссылки</h1>
<h4 align="center">
    <a href="https://t.me/FunPayNexus">Telegram канал</a><br>
<h4 align="center">
    <a href="https://t.me/FunPayNexus_chat">Telegram чат</a><br>
<h4 align="center">
    <a href="https://github.com/Sema4ka0/FunPayNexusAPI">GitHub</a><br>
<h4 align="center">
    <a href="https://pypi.org/project/FunPayNexusAPI/">PyPi</a><br>

## Установка
Установка библиотеки:
```bash
pip install FunPayNexusAPI
```
Обновление библиотеки:
```bash
pip install --upgrade FunPayNexusAPI
```
## Пример использования
Ниже приведены несколько примеров с использованием FunPayNexusAPI
### Получение информации о профиле


```python
from FunPayNexusAPI import Bot, Dispatcher
import asyncio

bot = Bot(golden_key="GOLDEN_KEY") # заменяем "GOLDEN_KEY"
dispatcher = Dispatcher(bot)

async def main() -> None:
    account = dispatcher.account
    message = (
        f"username: {await account.username}\n"
        f"user_id: {await account.id}\n"
        f"balance: {await account.balance}\n"
        f"new_message: {len(await account.get_new_messages)}\n"
    )
    print(message)
    
if __name__ == "__main__":
    asyncio.run(main())
```
Заменяем "GOLDEN_KEY" на golden_key вашего аккаунта.
### Отправление сообщения в чат

```python
from FunPayNexusAPI import Bot, Dispatcher
import asyncio

bot = Bot(golden_key="GOLDEN_KEY") # заменяем "GOLDEN_KEY"
dispatcher = Dispatcher(bot)

async def main() -> None:
    await dispatcher.send_message("USER_ID", "Привет!") # заменяем "USER_ID"
    
if __name__ == "__main__":
    asyncio.run(main())
```
Заменяем "GOLDEN_KEY" на golden_key вашего аккаунта.  
Заменяем "USER_ID" на id собеседника.

## Заключение
Проект находится в самом начале своего развития, и активно разрабатывается, вскоре будет добавлен новый функционал, по надобности обновлен старый.

На данный момент нет документации, поэтому по вопросам пишите в чат, указан выше.