Reformat (#4)

* Refactored parts

 * Added LRP class for Yolov8 explanations
 * Added LRP for Yolo notebook example
 * Added base explainability class
 * Adjusted occlusion explainability in class definition for abstracted use

* Updated README

* Adjusted README file

* Added packages

* Changed version and setup

Author: stavrostheocharis

.gitignore

@@ -127,4 +127,7 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
-.xenv
+.xenv
+
+# Ignore possible downloads for models
+ultralyticsplus

README.md

@@ -1,53 +1,111 @@
 <div align="center">
+ 
+# Easy Explain
 
-# Easy explain
-[![Python Version](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue.svg)](#supported-python-versions) 
+[![Python Version](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-green.svg)](#supported-python-versions) 
+[![GitHub][github_badge]][github_link]
+[![PyPI][pypi_badge]][pypi_link]
+[![Download][download_badge]][download_link]
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
-[![License](https://img.shields.io/badge/License-MIT-informational.svg)](https://github.com/stavrostheocharis/easy_explain/blob/main/LICENSE)
+[![Licence][licence_badge]][licence_link] 
 
-**Explain AI models easily**
+**Simplify the Explanation of AI Models**
+ 
+</div>
 
+Unlock the "why" behind your AI models' decisions with `easy-explain`, a Python package designed to democratize access to advanced XAI algorithms. By integrating state-of-the-art explanation techniques with minimal code, we make AI transparency accessible to developers and researchers alike.
+
+> [!IMPORTANT]
+> The new version of `easy-explain` has breaking changes. We have changed the logic of different imports to support more models like YoloV8. Have a look at the provided [examples](https://github.com/stavrostheocharis/easy_explain/tree/main/examples).
 
-</div>
 
 ## Requirements
-### Python version
-* Main supported version : <strong>3.9</strong> <br>
-* Other supported versions : <strong>3.7</strong> & <strong>3.8</strong>
 
-To use the scripts on your computer, please make sure you have one of these versions installed.
+### Python Versions Supported
+- **Primary**: `3.10`
+- **Also Supported**: `3.8`, `3.9`
+
+Ensure one of these Python versions is installed on your system to use `easy-explain`.
+
 
-### Install environment & dependencies
+### Install Environment & Dependencies
 
-In order to install the current repo you have 2 options:
-- Pip install it directly from git inside your prefered repo and use it as a package
+`easy-explain` can be seamlessly integrated into your projects with a straightforward installation process:
 
-#### Installation as a package
 
-In order to use the current repo as a package you need to run the command below inside your project.
+#### Installation as a Package
+
+To incorporate `easy-explain` into your project as a dependency, execute the following command in your terminal:
+
 
 ```bash
 pip install easy-explain
 ```
 
-## Information about the functionality
+## Features and Functionality
+
+`easy-explain` uses under the hood different packages based on the model to be used. [Captum](https://captum.ai/) is used for classification models and it aids to comprehend how the data properties impact the model predictions or neuron activations, offering insights on how the model performs. Captum comes together with [Pytorch library](https://pytorch.org/).
+There are also other customade algorithms to support other models like the LRP implementation for YoloV8.
+
 
-easy-explain uses under the hood [Captum](https://captum.ai/). Captum aids to comprehend how the data properties impact the model predictions or neuron activations, offering insights on how the model performs. Captum comes together with [Pytorch library](https://pytorch.org/).
+Currently, `easy-explain` specializes in two cutting-edge XAI methodologies for images:
 
-Currently easy-explain is working only for images and only for Pytorch.
+- Occlusion: For deep insight into classification model decisions.
+- Layer-wise Relevance Propagation (LRP): Specifically tailored for YoloV8 models, unveiling the decision-making process in object detection tasks.
 
-You can import the main function of the package 'run_easy_explain' directly as:
+## Quick Start
+
+To begin unraveling the intricacies of your model's decisions, import and utilize the corresponding classes as follows:
+
+```python
+from easy_explain.methods.occlusion.occlusion import OcclusionExplain 
+
+```
 
 ```python
-from easy_explain.easy_explain import run_easy_explain
+from easy_explain.methods.lrp.yolov8.yolo import YOLOv8LRP
 
 ```
+
 For more information about how to begin have a look at the [examples notebooks](https://github.com/stavrostheocharis/easy_explain/tree/main/examples).
 
+## Examples
+
+Explore how `easy-explain` can be applied in various scenarios:
+
+
+
+![Use Case Example](easy_explain/images/lion-heat-map.png "Use Case Example")
+
+![Use Case Example](easy_explain/images/siberian-positive.png "Use Case Example")
+
+![Use Case Example](easy_explain/images/class-traffic.png "Use Case Example")
+
 ## How to contribute?
 
-We welcome any suggestions, problem reports, and contributions!
-For any changes you would like to make to this project, we invite you to submit an [issue](https://github.com/stavrostheocharis/easy_explain/issues).
+`easy-explain` thrives on community contributions, from feature requests and bug reports to code submissions. We encourage you to share your insights, improvements, and use cases to foster a collaborative environment for advancing XAI.
+
+### Getting Involved
+
+Submit Issues: Encounter a bug or have a feature idea? Let us know through our  [issues page](https://github.com/stavrostheocharis/easy_explain/issues).
+
+Code Contributions: Interested in contributing code? Please refer to our  [`CONTRIBUTING`](https://github.com/stavrostheocharis/easy_explain/blob/main/CONTRIBUTING.md) guidelines for more information on how to get started..
+
+Join us in making AI models more interpretable, transparent, and trustworthy with `easy-explain`.
+
+
+[github_badge]: https://badgen.net/badge/icon/GitHub?icon=github&color=black&label
+
+[github_link]: https://github.com/stavrostheocharis/easy_explain
+
+[pypi_badge]: https://badge.fury.io/py/easy-explain.svg
+
+[pypi_link]: https://pypi.org/project/easy-explain/
+
+[download_badge]: https://badgen.net/pypi/dm/easy-explain
+
+[download_link]: https://pypi.org/project/easy-explain/#files
 
-For more information, see [`CONTRIBUTING`](https://github.com/stavrostheocharis/easy_explain/blob/main/CONTRIBUTING.md) instructions.
+[licence_badge]: https://img.shields.io/github/license/stavrostheocharis/streamlit-token-craft
 
+[licence_link]: LICENSE

easy_explain/__init__.py

@@ -1 +1,2 @@
-from easy_explain.easy_explain import run_easy_explain
+# from easy_explain.easy_explain import run_easy_explain
+from easy_explain import methods