semvec/pypi-packages huggingface dataset:
LISA-CNN-ExplainerV3 0.0.4
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 LISA_CNN_ExplainerV3```

## How to use it?
Open terminal and type python/python3 according to your OS.


``` import LISA_CNN_ExplainerV3 as e \n```  

``` e.ExplainLISA(img,class_names,img_shape,model,img1,img2,scale,filter_radius) \n```

``` e.displayImages() \n```

## License

© 2021 Sudil H.P Abeyagunasekera

This repository is licensed under the MIT license. See LICENSE for details.
errcallback 0.0.1
Python log error or exception callback
Requires Python: >=3.6

# err-callback-py
Python log error or exception callback

# install
```
pip install errcallback
```

# Usage
logging callback
```
import logging

from errcallback import registry_err_callback

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def callback_func(record):
    logger.info("callback func lineno: %s message: %s",
                record.lineno, record.message)


registry_err_callback(logger_func=callback_func, log_level=logging.ERROR)

logger.info("info test")
logger.error("error test")


# output
INFO:__main__:info test
ERROR:__main__:error test
INFO:__main__:callback func lineno: 17 message: error test
```

exception callback
```
import logging

from errcallback import registry_err_callback


def callback_func(exc_type, exc_value, exc_traceback):
    logging.error("callback_func>>>>>>>>>>>>>>", exc_info=(
        exc_type, exc_value, exc_traceback))


registry_err_callback(exception_func=callback_func)

1 / 0


# output
ERROR:root:callback_func>>>>>>>>>>>>>>
Traceback (most recent call last):
  File "examples/exception_demo.py", line 13, in <module>
    1 / 0
ZeroDivisionError: division by zero
```

callback message to DingTalk, Email, Prometheus
```
...
from errcallback import registry_err_callback


def callback_func(exc_type, exc_value, exc_traceback):
    # todo send to DingTalk, Email, Prometheus


registry_err_callback(exception_func=callback_func)
...
```
IndigoScript 0.1.0
The IndigoScript project is a Pythonic Library which contains many smart tools and modern graphic interfaces.
Requires Python: >=3

<h1 align="center">SmoothScript</h1>

### What is SmoothScript?
It is a python library that contains a set of ready-made codes that enable you to create the most wonderful designs and animations on the terminal.

### Support
+ linux
+ termux
+ windows


### Install
Just write this code on terminal:
```shell
pip3 install smoothscript
```

___

<p align="center">
<strong>For more infrmation</strong>
</p>

<p align="center">
<a href="https://github.com/nasserawer/smoothscript/"><label style="display: block;"><strong>SmoothScript</strong></label></a>
</p>
LISA-CNN-ExplainerV2 0.0.4
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 LISA_CNN_ExplainerV2```

## How to use it?
Open terminal and type python/python3 according to your OS.


``` import LISA_CNN_ExplainerV2 as e \n```  

``` e.ExplainLISA(img,class_names,img_shape,model,img1,img2,scale,filter_radius) \n```

``` ExplainLISA.displayImages() \n```

## License

© 2021 Sudil H.P Abeyagunasekera

This repository is licensed under the MIT license. See LICENSE for details.