Revise README with installation and usage details

Updated README to include installation instructions and clarify usage.

Author: nathanchenseanwalter

README.rst

@@ -2,18 +2,11 @@
 keras2c
 #######
 
-|Python-Publish| |Python-Versions| |Codecov|
+|Python-Publish| |Python-Versions| |Codecov| 
 
 |License| |DOI|
 
-IMPORTANT: Keras2C has started updating to be compatible with newer versions of Python on July 9th, 2025. Changes may be breaking. To use the original/stable version, use the Release v1.0.2. https://github.com/PlasmaControl/keras2c/releases/tag/v1.0.2 with the command
-
-.. code-block:: bash
-
-    git clone git@github.com:PlasmaControl/keras2c.git --branch v1.0.2
-
-keras2c is a library for deploying keras neural networks in C99, using only standard libraries.
-It is designed to be as simple as possible for real time applications.
+keras2c is a library for deploying keras neural networks in C99, using only standard libraries. It is designed to be as simple as possible for real-time applications.
 
 Please cite `this paper <https://doi.org/10.1016/j.engappai.2021.104182>`_ if you use this work in your research:
 
@@ -22,13 +15,27 @@ Please cite `this paper <https://doi.org/10.1016/j.engappai.2021.104182>`_ if yo
     R. Conlin, K. Erickson, J. Abbate, and E. Kolemen, “Keras2c: A library for converting Keras neural networks to real-time compatible C,” 
     Engineering Applications of Artificial Intelligence, vol. 100, p. 104182, Apr. 2021, doi: 10.1016/j.engappai.2021.104182.
 
-Quickstart
-**********
+Installation
+************
+
+**Recommended Installation**
+
+The modern way to install keras2c is via pip:
+
+.. code-block:: bash
 
-For windows, make sure that you have gcc installed. We recommend CYGWIN with make and gcc
+   pip install keras2c
 
-After cloning the repo, install the necessary packages with ``pip install -r requirements.txt``.
-Alternatively, create a conda environment using ``conda env create -f environment.yml``.
+To use the original/stable version, use the Release v1.0.2 (https://github.com/PlasmaControl/keras2c/releases/tag/v1.0.2) with the command:
+
+.. code-block:: bash
+
+    git clone git@github.com:PlasmaControl/keras2c.git --branch v1.0.2
+
+For Windows, make sure that you have **gcc** installed. We recommend **CYGWIN** with make and gcc.
+
+Quickstart
+**********
 
 keras2c can be used from the command line:
 
@@ -50,21 +57,21 @@ keras2c can be used from the command line:
       -t , --num_tests   Number of tests to generate. Default is 10
 
 
-It can also be used with a python environment in the following manner:
+It can also be used within a python environment:
 
 .. code-block:: python
 
     from keras2c import k2c
     k2c(model, function_name, malloc=False, num_tests=10, verbose=True)
 
-For more information, see `Installation <https://f0uriest.github.io/keras2c/installation.html>`_ and  `Usage <https://f0uriest.github.io/keras2c/usage.html>`_
+For more information, see `Installation <https://f0uriest.github.io/keras2c/installation.html>`_ and `Usage <https://f0uriest.github.io/keras2c/usage.html>`_
 
 
 Supported Layers
 ****************
-- **Core Layers**: Dense, Activation, Dropout, Flatten, Input, Reshape, Permute, RepeatVector,  ActivityRegularization, SpatialDropout1D, SpatialDropout2D, SpatialDropout3D
+- **Core Layers**: Dense, Activation, Dropout, Flatten, Input, Reshape, Permute, RepeatVector, ActivityRegularization, SpatialDropout1D, SpatialDropout2D, SpatialDropout3D
 - **Convolution Layers**: Conv1D, Conv2D, Conv3D, Cropping1D, Cropping2D, Cropping3D, UpSampling1D, UpSampling2D, UpSampling3D, ZeroPadding1D, ZeroPadding2D, ZeroPadding3D
-- **Pooling Layers**: MaxPooling1D, MaxPooling2D, AveragePooling1D, AveragePooling2D, GlobalMaxPooling1D, GlobalAveragePooling1D, GlobalMaxPooling2D, GlobalAveragePooling2D, GlobalMaxPooling3D,GlobalAveragePooling3D
+- **Pooling Layers**: MaxPooling1D, MaxPooling2D, AveragePooling1D, AveragePooling2D, GlobalMaxPooling1D, GlobalAveragePooling1D, GlobalMaxPooling2D, GlobalAveragePooling2D, GlobalMaxPooling3D, GlobalAveragePooling3D
 - **Recurrent Layers**: SimpleRNN, GRU, LSTM, SimpleRNNCell, GRUCell, LSTMCell
 - **Embedding Layers**: Embedding
 - **Merge Layers**: Add, Subtract, Multiply, Average, Maximum, Minimum, Concatenate, Dot
@@ -83,8 +90,6 @@ ToDo
 - **Merge Layers**: Broadcasting merge between different sizes
 - **Misc**: models made from submodels
 
-
-
 Contribute
 **********
 
@@ -97,7 +102,6 @@ License
 
 The project is licensed under the LGPLv3 license.
 
-
 .. |Codecov| image:: https://codecov.io/gh/f0uriest/keras2c/branch/master/graph/badge.svg
     :target: https://codecov.io/gh/f0uriest/keras2c
     :alt: Code Coverage