Update README

Author: rabea-al

.github/workflows/run-workflow-tests.yml

@@ -10,16 +10,30 @@ on:
 jobs:
   build-and-run:
     runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.9", "3.10", "3.11"]
     env:
       TEST_XIRCUITS: |
         examples/TFKerasTrainImageClassifier.xircuits
         examples/TFKerasModelPredict.xircuits
-
+        
     steps:
     - name: Checkout Repository
       uses: actions/checkout@v3
 
-    - name: Install xircuits
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Create virtual environment
+      run: |
+        python -m venv venv
+        echo "${{ github.workspace }}/venv/bin" >> $GITHUB_PATH
+
+    - name: Install xircuits in virtual environment
       run: pip install xircuits
 
     - name: Set Environment Variables
@@ -35,7 +49,7 @@ jobs:
         fi
 
     - name: List Xircuits
-      run: xircuits list 
+      run: xircuits list
 
     - name: Clone Repository
       run: |
@@ -55,7 +69,7 @@ jobs:
         else
           echo "requirements.txt not found."
         fi
-
+          
     - name: Test .xircuits Workflows
       run: |
         export PYTHONPATH="${GITHUB_WORKSPACE}:${PYTHONPATH}"
@@ -101,5 +115,5 @@ jobs:
       if: always()
       uses: actions/upload-artifact@v4
       with:
-        name: ${{ env.LIBRARY_NAME }}-validation-workflow
+        name: ${{ env.LIBRARY_NAME }}-validation-workflow-${{ matrix.python-version }}
         path: ${{ github.workspace }}/workflow_logs.txt

README.md

@@ -1,62 +1,126 @@
-# Xircuits Comprehensive Keras Library
+<p align="center">
+  <a href="https://github.com/XpressAI/xircuits/tree/master/xai_components#xircuits-component-library-list">Component Libraries</a> •
+  <a href="https://github.com/XpressAI/xircuits/tree/master/project-templates#xircuits-project-templates-list">Project Templates</a>
+  <br>
+  <a href="https://xircuits.io/">Docs</a> •
+  <a href="https://xircuits.io/docs/Installation">Install</a> •
+  <a href="https://xircuits.io/docs/category/tutorials">Tutorials</a> •
+  <a href="https://xircuits.io/docs/category/developer-guide">Developer Guides</a> •
+  <a href="https://github.com/XpressAI/xircuits/blob/master/CONTRIBUTING.md">Contribute</a> •
+  <a href="https://www.xpress.ai/blog/">Blog</a> •
+  <a href="https://discord.com/invite/vgEg2ZtxCw">Discord</a>
+</p>
 
-This comprehensive library for Xircuits encompasses a wide range of functionalities involving Keras datasets, model management, model instantiation, prediction, and transfer learning. It's designed to facilitate various machine learning tasks within the Xircuits visual programming interface.
 
-### Prerequisites
 
-- Python 3.8 or higher
-- TensorFlow 2.x
 
-### Installation
 
-```bash
-xircuits install tensorflow_keras
-```
+<p align="center"><i>Xircuits Component Library to interface with TensorFlow Keras! Build and deploy deep learning solutions with ease.</i></p>
 
-You may also install it manually via
+---
+## Xircuits Component Library for TensorFlow Keras 
+
+Integrates TensorFlow Keras into Xircuits for streamlined deep learning workflows. Simplifies model design, training, and deployment with reusable components. Reduces complexity and accelerates development for scalable AI solutions.
+
+## Table of Contents
+
+- [Preview](#preview)
+  
+- [Prerequisites](#prerequisites)
+- [Main Xircuits Components](#main-xircuits-components)
+- [Try The Examples](#try-the-examples)
+- [Installation](#installation)
+
+## Preview
+
+### TFKerasTransferLearning Example
+
+![TFKerasTransferLearning](https://github.com/user-attachments/assets/8719a3c1-76a6-49a5-8cd2-6c22d9abbf6c)
+
+
+### TFKerasTransferLearning Result
+
+![TFKerasTransferLearning](https://github.com/user-attachments/assets/a617cdb2-3af7-47a6-a3d4-593eefd18c68)
+
+
+### TFKerasTrainImageClassifier Example
+
+![TFKerasTrainImageClassifier](https://github.com/user-attachments/assets/e85d5f65-470f-42c3-ab85-9795ba05bcb7)
+
+
+### TFKerasTrainImageClassifier Result
+
+<img src="https://github.com/user-attachments/assets/6fd41596-aa0e-45bd-98e2-34b2baf0b5dd" alt="TFKerasTrainImageClassifier_Result" />
+
+### TFKerasModelPredict Example
+
+![TFKerasModelPredict](https://github.com/user-attachments/assets/71952d43-23d2-49ea-bcab-b68b9b894510)
 
-```bash
-pip install -r requirements.txt
-```
 
-## Keras Dataset and Model Management
+### TFKerasModelPredict Result
 
-For tasks from dataset preparation to model training and evaluation.
+<img src="https://github.com/user-attachments/assets/c9b863b4-6b66-42a2-94a3-0c9d027dec9a" alt="TFKerasModelPredict_result"  />
 
-### Components
 
-- **`ReadKerasDataSet`**: Loads Keras datasets or creates datasets from directories. Supports MNIST, CIFAR-10/100, and custom datasets.
-- **`FlattenImageData`**: Converts 2D dataset tuples to 1D, suitable for 1D neural networks.
-- **`TrainTestSplit`**: Splits datasets into training and testing sets with configurable parameters.
-- **`KerasCreate1DInputModel`**: Creates 1D Keras models for 1D input datasets.
-- **`KerasCreate2DInputModel`**: Assembles 2D Keras models, perfect for image-based datasets.
-- **`KerasTrainImageClassifier`**: Trains Keras models for image classification.
-- **`KerasEvaluateAccuracy`**: Evaluates Keras models against datasets for accuracy and loss.
-- **`ShouldStop`**: Decides if training should stop based on accuracy targets or max retries.
-- **`SaveKerasModel`**: Saves Keras models as `.h5` files for later use.
+## Prerequisites
 
-## Keras Model Instantiation and Prediction
+Before you begin, you will need the following:
 
-These components support a range of model architectures for image classification.
+1. Python3.9+.
+2. Xircuits.
 
-### Components
+## Main Xircuits Components 
 
-- **`LoadKerasModel`**: Loads Keras models with customizable configurations.
-- **`KerasPredict`**: Performs predictions with Keras models on images.
-- **`ResNet50`, `ResNet101`, `ResNet152`**: Instantiates various ResNet models with customizable configurations.
-- **`VGG16`, `VGG19`**: Provides VGG model architectures for image classification.
-- **`Xception`**: Implements the Xception architecture for image classification.
-- **`MobileNet`**: Offers MobileNet architecture with adjustable parameters.
+### KerasCreate1DInputModel:  
+Creates 1D Keras models for datasets with 1D input structures.
 
-## TensorFlow Keras Transfer Learning
+<img src="https://github.com/user-attachments/assets/cf3c5bf4-b2ea-48e3-b964-e3fe2dbffd38" alt="KerasCreate1DInputModel" width="200" height="75" />
 
-Designed for easy integration into Xircuits workflows for transfer learning scenarios, suitable for both beginners and experienced ML practitioners.
 
-### Components
+### KerasCreate2DInputModel:  
+Assembles 2D Keras models, ideal for image-based datasets.  
 
-- **`KerasTransferLearningModel`**: Fetches TensorFlow Keras Models for transfer learning.
-- **`TFDataset`**: Retrieves datasets from TensorFlow Datasets.
-- **`TrainKerasModel`**: Trains compiled Keras models with training data.
-- **`TFDSEvaluateAccuracy`**: Evaluates Keras models' accuracy using TensorFlow Datasets.
-- **`KerasModelCompiler`**: Compiles TensorFlow Keras models with custom configurations.
-- **`SaveKerasModel`**: Saves TensorFlow Keras models for future use.
+<img src="https://github.com/user-attachments/assets/def84826-5549-4de4-90c2-d01eaad9dc36" alt="KerasCreate2DInputModel" width="200" height="75" />
+
+### KerasTransferLearningModel:
+Fetches TensorFlow Keras Models for transfer learning.
+
+<img src="https://github.com/user-attachments/assets/5b98def2-87fe-4a92-8680-a2a5822210d6" alt="KerasTransferLearningModel" width="200" height="250" />
+
+### KerasTrainImageClassifier:  
+Trains Keras models for image classification tasks. 
+
+### KerasEvaluateAccuracy:  
+Evaluates Keras models for accuracy and loss using test datasets.  
+
+### TrainKerasModel:
+Trains compiled Keras models with training data.
+
+## Try The Examples
+
+We have provided an example workflow to help you get started with the TensorFlow Keras component library. Give it a try and see how you can create custom TensorFlow Keras components for your applications.
+
+### TFKerasTransferLearning
+
+Demonstrates transfer learning with MobileNetV2 pre-trained on ImageNet to classify the "cats_vs_dogs" dataset, evaluate accuracy, and save the model.
+
+### TFKerasTrainImageClassifier
+
+This workflow trains a 2D convolutional model on a dataset, evaluates its accuracy, and demonstrates basic image classification.
+
+### TFKerasModelPredict
+
+Uses the pre-trained ResNet50 model to classify an input image and predict its class label.
+
+## Installation
+To use this component library, ensure that you have an existing [Xircuits setup](https://xircuits.io/docs/main/Installation). You can then install the TensorFlow Keras library using the [component library interface](https://xircuits.io/docs/component-library/installation#installation-using-the-xircuits-library-interface), or through the CLI using:
+
+```
+xircuits install tensorflow_keras
+```
+You can also do it manually by cloning and installing it:
+```
+# base Xircuits directory
+git clone https://github.com/XpressAI/xai-tensorflow-keras xai_components/xai_tensorflow_keras
+pip install -r xai_components/xai_tensorflow_keras/requirements.txt 
+```