readme updates

gradio_warning.md

@@ -5,14 +5,19 @@ using only [numpy](https://numpy.org/). Here, you can test different hyper param
 
 Once training is done, the final model will be tested by making predictions on an unseen subset of the dataset called the validation set. There will be a plot of hits vs. misses, measuring the accuracy of the final model on images that did not see in training. There will also be a label at the bottom that shows the average confidence of the final model when it was making its predictions on unseen data across the different labels (digits 0-9).
 
+## Local Development
+
+The Numpy-Neuron package is [available on PyPI](https://pypi.org/project/numpyneuron/) and you can install it yourself with the command: `pip3 install numpyneuron`
+
 ## ⚠️ Warning ⚠️
 This application is impossibly slow on the HuggingFace CPU instance that it is running on. It is advised to clone the 
-repository and run it locally.
+repository and run it locally, or install the package using pip as mentioned above.
 
-## Steps for running locally:
+## Steps for running this GUI locally:
 
 1. `git clone https://huggingface.co/spaces/Jensen-holm/Numpy-Neuron`
 
 2. `pip3 install -r requirements.txt`
 
-3. `python3 app.py`
+3. `python3 gradio_app.py`
+

setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name="numpyneuron",
-    version="0.3",
+    version="0.4",
     author="Jensen Holm",
     author_email="jensen.dev.01@gmail.com",
     description="Simple, lightweight neural network framework built in numpy",
@@ -13,9 +13,6 @@ setup(
     package_dir={"": "numpyneuron"},
     packages=find_packages(where="numpyneuron"),
     classifiers=[
-        "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: MIT License",
-        "Operating System :: OS Independent",
     ],
     python_requires=">=3.6",
 )