semvec/pypi-packages huggingface dataset:
ExplainLISA 0.0.1
Unified Explanation Provider For CNNs
Keywords: LIME,Integrated gradients,SHAP,Anchors,Explainable AI,XAI,CNN Explainer


# Explain LISA
It takes the following.
img: local path of img to be explained
class_names: the classes available as predictions for the given model
img_shape: shape of the image accepts by the neural network
model: the model to be explained get from tf.keras.models.load_model("your model path")
img1: local path background data point for produce explanations with SHAP
img2: local path background data point for produce explanations with SHAP
scale: for manual image scaling if scaling layer absent in the model to be explained 
filter_radius: the pixel value of the radius of the High pass filter

## Installation
```pip install ExplainLISA```

## How to use it?
Open terminal and type python/python3 according to your OS.


``` import ExplainLISA as e```
``` e.ExplainLISA(img,class_names,img_shape,model,img1,img2,scale,filter_radius)```
``` e.getExplanations()```

## License

© 2021 Sudil H.P Abeyagunasekera

This repository is licensed under the MIT license. See LICENSE for details.
greetworld 0.4
Hello World message
Requires Python: >=3.5

### Hello World
[![PyPi Pubish](https://github.com/rajendrakumaryadav/greetworld/actions/workflows/publish_pypi.yml/badge.svg)](https://github.com/rajendrakumaryadav/greetworld/actions/workflows/publish_pypi.yml)

It is based on Python and return

```bash
$python
> from greetworld import greet
> hello = greet.greet()
> print(hello.say_hello())
Hello, World!
> hello = greet.greet("Rajendra")
> print(hello.say_hello())
Hello, Rajendra!
```