semvec/pypi-packages huggingface dataset:
streamlit-component-fill-square-cropper101 0.0.6
streamlit_component_fill_square_cropper, adds a filler when needed of the avg color pixel.
Requires Python: >=3.6

# Streamlit Component Fill Square Cropper101  
Streamlit Component Squares image with the adequate color filler  
![Alt Text](https://github.com/webdevserv/streamlit_component_fill_square_cropper101/blob/f6772817321ed43baa7f238f53f6b3613700597a/images/add.JPG)  

Demo: [![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://webdevserv-portfolio-idoia-icodeidoia-portfolio-9kblei.streamlit.app/a_LIVE_Square_Filler_app)  

# installation

pip install streamlit-component-fill-square-cropper101

# running locally

streamlit run app.py  
or  
streamlit run app.py --server.enableXsrfProtection false  

#temporarily disable XSRF protection, which might be causing the 403.  

# use

import streamlit as st  


from streamlit_component_fill_square_cropper101 import square_fill_cropper
approxscimate 0.0.4
A package for approximating SciPy functions
Requires Python: >=3.8

# ApproxSciMate

A python library for approximating SciPy functions with different levels of
accuracy. The library is intended to bring awareness to the energy usage of
computer systems, and to give users a choice in how much they consume.

Read the descriptions of the functions and their levels of approximation below,
pick what is best for your use case.

## Functions

### `cbrt(n, level=0)`

Calculates the cube root of the provided number **n** with the accuracy
specified at the **level**.

* `level = 0` : The default SciPy `cbrt` function providing maximum accuracy.
* `level = 1` : Halley's method for approximating cube roots, for moderate accuracy.
* `level = 2` : Newton's method for approximating cube roots, for low accuracy.

### `comb(n, k, level=0)`

Calculates the amount of possible selections of **k** items from a set of size **n**
where the order of selection **does not** matter. Approximates the value based on the
defined **level** of accuracy.

* `level = 0` : The default SciPy `comb` function providing maximum accuracy.
* `level = 1` : The approximated **lower** bound of the calculation.
* `level = 2` : The approximated **upper** bound of the calculation.
* `level = 3` : Uses Stirling's method of approximating factorials which
  converges to the real value when **n** is very large.

### `perm(n, k, level=0)`

Calculates the amount of possible selections of **k** items from a set of size **n**
where the order of selection **does** matter. Approximates the value based on the
defined **level** of accuracy.

* `level = 0` : The default SciPy `perm` function providing maximum accuracy.
* `level = 1` : The approximated **lower** bound of the calculation.
* `level = 2` : The approximated **upper** bound of the calculation.
* `level = 3` : Uses Stirling's method of approximating factorials which
  converges to the real value when **n** is very large.
clustroai-sdk 0.1.2
clustroai-sdk

# ClustroAI SDK

ClustroAI SDK is a powerful toolkit designed to harness idle computing resources for AI applications. It provides a comprehensive platform for AI enthusiasts and developers to utilize and monetize computing power efficiently.

## Key Features
- **Resource Utilization**: ClustroAI SDK helps in tapping into underutilized computing resources, turning them into valuable assets for AI computation.
- **AI Platform Integration**: Seamlessly integrates with AI platforms, enabling easy access to computational resources.

## Common APIs
- `create_model`: Create a new AI model.
- `list_models`: List all available AI models.
- `get_model`: Retrieve details of a specific model.
- `create_inference_job`: Set up a job for model inference.
- `create_worker`: Initialize a worker for computation tasks.
- `run`: Execute a predefined task or process.
- `username_run`: Run a task under a specific user context.

## Warning
<mark>In the clustroai-sdk, the 'create' API requires the 'admin' role for creation. Additionally, the call frequency for other APIs is limited to 10 times per minute.</mark>

## Getting Started
To get started with ClustroAI SDK, install the package from PyPI:

```bash
pip install clustroai-sdk

from clustroai_sdk import ClustroAI
api_key = "Your Clustro API_Key"
clustroai = ClustroAI(api_key, timeout=180)
all_models = clustroai.list_models()
print(all_models)

single_model = clustroai.get_model("097d04a6-1b30-424d-bf55-675fbc405a40")
print(single_model)
#clustroai.create_inference_job(model_id="9d1a93eb-0c6e-46ff-9014-f265ab04c7bd", name="test_job", set_as_model_default=True)
result = clustroai.run(input="A majestic lion jumping from a big stone at night", job_id='4ece46f4-f06d-4962-96c8-91e5c1d08ad6', sync=True)
print(result)
```
## More Information
For more information, detailed documentation, and API reference, visit [ClustroAI](https://www.clustro.ai/).