automatic import of python-easynn

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diff --git a/.gitignore b/.gitignore
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+/EasyNN-0.2.2.tar.gz
diff --git a/python-easynn.spec b/python-easynn.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-EasyNN
+Version:	0.2.2
+Release:	1
+Summary:	EasyNN is a python package designed to provide an easy-to-use neural network. The package is designed to work right out of the box, while also allowing the user to customize features as they see fit.
+License:	mit
+URL:		https://pypi.org/project/EasyNN/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/93/2b/ca52f80d2fc2584810e8481ed0652a962e8518e9d997686f5b3c27be3d6e/EasyNN-0.2.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-matplotlib
+Requires:	python3-clint
+Requires:	python3-tabulate
+Requires:	python3-requests
+Requires:	python3-numpy
+Requires:	python3-nptyping
+Requires:	python3-beautifulsoup4
+Requires:	python3-google-search-results
+Requires:	python3-prettyformatter
+
+%description
+![](https://raw.githubusercontent.com/danielwilczak101/EasyNN/media/images/readme_logo.png)
+
+# EasyNN - Neural Networks made Easy
+EasyNN is a python package designed to provide an easy-to-use Neural Network. The package is designed to work right out of the box with multiple datasets, while also allowing the user to customize features as they see fit. 
+### EasyNN requires Python version 3.9.7 or greater.
+
+## See our [wiki](https://github.com/danielwilczak101/EasyNN/wiki) for more information and [Datasets](https://github.com/danielwilczak101/EasyNN/wiki).
+
+## Installation:
+
+Run python's pip3 to install:
+
+```Python
+pip3 install EasyNN
+```
+
+### Model:
+```Python
+from EasyNN.examples.mnist.number.trained import model
+
+# Classify an image.
+print(model.classify(image))
+```
+
+### Dataset Example:
+```Python
+from EasyNN.examples.mnist.number.trained import model
+from EasyNN.examples.mnist.number.data import dataset
+
+images, labels = dataset
+
+# Classify what the second image is in the dataset.
+print(model.classify(images[1]))
+
+# Show the image.
+model.show(images[1])
+```
+
+### Dataset example output:
+```
+Downloading - number_parameters.npz:
+[################################] 1769/1769 - 00:00:00
+Downloading - number_structure.pkl:
+[################################] 10700/10700 - 00:00:00
+Downloading - number_dataset.npz:
+[################################] 11221/11221 - 00:00:00
+0
+```
+
+### Full example:
+More info can be found about [converting images](https://github.com/danielwilczak101/EasyNN/wiki/Image-Utility) in the utilities section.
+```Python
+from EasyNN.examples.mnist.number.trained import model
+from EasyNN.utilities import Preprocess, download
+
+# Download an example image.
+download("three.jpg","https://bit.ly/3dbO1eV")
+
+format_options = dict(
+    grayscale=True,
+    invert=True,
+    process=True,
+    contrast=30,
+    resize=(28, 28),
+    rotate=3,
+)
+
+# Converting your image into the correct format for the mnist number dataset.
+image = Preprocess("three.jpg").format(**format_options)
+
+# Classify what the image is using the pretrained model.
+print(model.classify(image))
+
+# Show the image after it has been processed.
+model.show(image)
+```
+### Output:
+```bash
+Downloading - four.jpg:
+[################################] 1371/1371 - 00:00:00
+3
+```
+
+### Image output:
+<p align="center">
+  <img width="400px" height="400px" src="https://raw.githubusercontent.com/danielwilczak101/EasyNN/media/images/example_three.png">
+</p>
+
+### Trained Models
+Use the trained models section to see EasyNN's datasets and pre-trained neural networks ready to run.  
+<br />
+[MNIST Number](https://github.com/danielwilczak101/EasyNN/wiki/MNIST-Numbers) Classifier network for images of handwritten single digits between 0 and 9.  
+[MNIST Fashion](https://github.com/danielwilczak101/EasyNN/wiki/MNIST-Fashion) Classifier network for ten classes of human clothing images of the size 28x28 pixels.  
+[Cifar 10](https://github.com/danielwilczak101/EasyNN/wiki/Cifar10) Classifier network for ten types of images varying from airplane, cat, dog, etc - 32x32 RGB images.
+
+## To see more examples with many other datasets. Please visit our [wiki](https://github.com/danielwilczak101/EasyNN/wiki).
+
+
+%package -n python3-EasyNN
+Summary:	EasyNN is a python package designed to provide an easy-to-use neural network. The package is designed to work right out of the box, while also allowing the user to customize features as they see fit.
+Provides:	python-EasyNN
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-EasyNN
+![](https://raw.githubusercontent.com/danielwilczak101/EasyNN/media/images/readme_logo.png)
+
+# EasyNN - Neural Networks made Easy
+EasyNN is a python package designed to provide an easy-to-use Neural Network. The package is designed to work right out of the box with multiple datasets, while also allowing the user to customize features as they see fit. 
+### EasyNN requires Python version 3.9.7 or greater.
+
+## See our [wiki](https://github.com/danielwilczak101/EasyNN/wiki) for more information and [Datasets](https://github.com/danielwilczak101/EasyNN/wiki).
+
+## Installation:
+
+Run python's pip3 to install:
+
+```Python
+pip3 install EasyNN
+```
+
+### Model:
+```Python
+from EasyNN.examples.mnist.number.trained import model
+
+# Classify an image.
+print(model.classify(image))
+```
+
+### Dataset Example:
+```Python
+from EasyNN.examples.mnist.number.trained import model
+from EasyNN.examples.mnist.number.data import dataset
+
+images, labels = dataset
+
+# Classify what the second image is in the dataset.
+print(model.classify(images[1]))
+
+# Show the image.
+model.show(images[1])
+```
+
+### Dataset example output:
+```
+Downloading - number_parameters.npz:
+[################################] 1769/1769 - 00:00:00
+Downloading - number_structure.pkl:
+[################################] 10700/10700 - 00:00:00
+Downloading - number_dataset.npz:
+[################################] 11221/11221 - 00:00:00
+0
+```
+
+### Full example:
+More info can be found about [converting images](https://github.com/danielwilczak101/EasyNN/wiki/Image-Utility) in the utilities section.
+```Python
+from EasyNN.examples.mnist.number.trained import model
+from EasyNN.utilities import Preprocess, download
+
+# Download an example image.
+download("three.jpg","https://bit.ly/3dbO1eV")
+
+format_options = dict(
+    grayscale=True,
+    invert=True,
+    process=True,
+    contrast=30,
+    resize=(28, 28),
+    rotate=3,
+)
+
+# Converting your image into the correct format for the mnist number dataset.
+image = Preprocess("three.jpg").format(**format_options)
+
+# Classify what the image is using the pretrained model.
+print(model.classify(image))
+
+# Show the image after it has been processed.
+model.show(image)
+```
+### Output:
+```bash
+Downloading - four.jpg:
+[################################] 1371/1371 - 00:00:00
+3
+```
+
+### Image output:
+<p align="center">
+  <img width="400px" height="400px" src="https://raw.githubusercontent.com/danielwilczak101/EasyNN/media/images/example_three.png">
+</p>
+
+### Trained Models
+Use the trained models section to see EasyNN's datasets and pre-trained neural networks ready to run.  
+<br />
+[MNIST Number](https://github.com/danielwilczak101/EasyNN/wiki/MNIST-Numbers) Classifier network for images of handwritten single digits between 0 and 9.  
+[MNIST Fashion](https://github.com/danielwilczak101/EasyNN/wiki/MNIST-Fashion) Classifier network for ten classes of human clothing images of the size 28x28 pixels.  
+[Cifar 10](https://github.com/danielwilczak101/EasyNN/wiki/Cifar10) Classifier network for ten types of images varying from airplane, cat, dog, etc - 32x32 RGB images.
+
+## To see more examples with many other datasets. Please visit our [wiki](https://github.com/danielwilczak101/EasyNN/wiki).
+
+
+%package help
+Summary:	Development documents and examples for EasyNN
+Provides:	python3-EasyNN-doc
+%description help
+![](https://raw.githubusercontent.com/danielwilczak101/EasyNN/media/images/readme_logo.png)
+
+# EasyNN - Neural Networks made Easy
+EasyNN is a python package designed to provide an easy-to-use Neural Network. The package is designed to work right out of the box with multiple datasets, while also allowing the user to customize features as they see fit. 
+### EasyNN requires Python version 3.9.7 or greater.
+
+## See our [wiki](https://github.com/danielwilczak101/EasyNN/wiki) for more information and [Datasets](https://github.com/danielwilczak101/EasyNN/wiki).
+
+## Installation:
+
+Run python's pip3 to install:
+
+```Python
+pip3 install EasyNN
+```
+
+### Model:
+```Python
+from EasyNN.examples.mnist.number.trained import model
+
+# Classify an image.
+print(model.classify(image))
+```
+
+### Dataset Example:
+```Python
+from EasyNN.examples.mnist.number.trained import model
+from EasyNN.examples.mnist.number.data import dataset
+
+images, labels = dataset
+
+# Classify what the second image is in the dataset.
+print(model.classify(images[1]))
+
+# Show the image.
+model.show(images[1])
+```
+
+### Dataset example output:
+```
+Downloading - number_parameters.npz:
+[################################] 1769/1769 - 00:00:00
+Downloading - number_structure.pkl:
+[################################] 10700/10700 - 00:00:00
+Downloading - number_dataset.npz:
+[################################] 11221/11221 - 00:00:00
+0
+```
+
+### Full example:
+More info can be found about [converting images](https://github.com/danielwilczak101/EasyNN/wiki/Image-Utility) in the utilities section.
+```Python
+from EasyNN.examples.mnist.number.trained import model
+from EasyNN.utilities import Preprocess, download
+
+# Download an example image.
+download("three.jpg","https://bit.ly/3dbO1eV")
+
+format_options = dict(
+    grayscale=True,
+    invert=True,
+    process=True,
+    contrast=30,
+    resize=(28, 28),
+    rotate=3,
+)
+
+# Converting your image into the correct format for the mnist number dataset.
+image = Preprocess("three.jpg").format(**format_options)
+
+# Classify what the image is using the pretrained model.
+print(model.classify(image))
+
+# Show the image after it has been processed.
+model.show(image)
+```
+### Output:
+```bash
+Downloading - four.jpg:
+[################################] 1371/1371 - 00:00:00
+3
+```
+
+### Image output:
+<p align="center">
+  <img width="400px" height="400px" src="https://raw.githubusercontent.com/danielwilczak101/EasyNN/media/images/example_three.png">
+</p>
+
+### Trained Models
+Use the trained models section to see EasyNN's datasets and pre-trained neural networks ready to run.  
+<br />
+[MNIST Number](https://github.com/danielwilczak101/EasyNN/wiki/MNIST-Numbers) Classifier network for images of handwritten single digits between 0 and 9.  
+[MNIST Fashion](https://github.com/danielwilczak101/EasyNN/wiki/MNIST-Fashion) Classifier network for ten classes of human clothing images of the size 28x28 pixels.  
+[Cifar 10](https://github.com/danielwilczak101/EasyNN/wiki/Cifar10) Classifier network for ten types of images varying from airplane, cat, dog, etc - 32x32 RGB images.
+
+## To see more examples with many other datasets. Please visit our [wiki](https://github.com/danielwilczak101/EasyNN/wiki).
+
+
+%prep
+%autosetup -n EasyNN-0.2.2
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-EasyNN -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.2-1
+- Package Spec generated
diff --git a/sources b/sources
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+8c1d7a6eaf057b7bf063ecb77b615bce  EasyNN-0.2.2.tar.gz