automatic import of python-asapppy

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diff --git a/.gitignore b/.gitignore
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+/ASAPPpy-0.2b1.tar.gz
diff --git a/python-asapppy.spec b/python-asapppy.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-ASAPPpy
+Version:	0.2b1
+Release:	1
+Summary:	Semantic Textual Similarity and Dialogue System package for Python
+License:	MIT License
+URL:		https://pypi.org/project/ASAPPpy/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/5f/1f/44acd08d953fe5c922022863e3b2d3ec74c937fb06580202ddb771162e3d/ASAPPpy-0.2b1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-setuptools
+Requires:	python3-imbalanced-learn
+Requires:	python3-scikit-learn
+Requires:	python3-pandas
+Requires:	python3-requests
+Requires:	python3-slackclient
+Requires:	python3-slackeventsapi
+Requires:	python3-nltk
+Requires:	python3-NLPyPort
+Requires:	python3-spacy
+Requires:	python3-gensim
+Requires:	python3-joblib
+Requires:	python3-num2words
+Requires:	python3-Whoosh
+Requires:	python3-Keras
+Requires:	python3-tensorflow
+Requires:	python3-cufflinks
+Requires:	python3-matplotlib
+Requires:	python3-seaborn
+
+%description
+## ASAPPpy
+ASAPPpy is a Python package for developing models to compute the Semantic Textual Similarity (STS) between texts in Portuguese. These models follow a supervised learning approach to learn an STS function from annotated sentence pairs, considering a variety of lexical, syntactic, semantic and distributional features.
+
+ASAPPpy can also be used to develop STS based dialogue agents and deploy them to Slack.
+
+
+### Development
+If you want to contribute to this project, please follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html).
+
+
+### Installation
+Before getting started, verify that <b>pip >= 20.3.3</b>. If not, update it with this command:
+```bash
+pip install --upgrade pip
+```
+
+To install the latest version of ASAPPpy use the following command:
+```bash
+pip install ASAPPpy
+```
+After finishing the installation, you might need to download the word embeddings models. Given that they were obtained from various sources, we collected them and they can be downloaded at once by running the Python interpreter in your terminal followed by these commands:
+```python
+import ASAPPpy
+ASAPPpy.download()
+```
+Finally, if you have never used [spaCy](https://spacy.io) before and you want to use the dependency parsing features, you will need to run the next command in the terminal:
+```bash
+python -m spacy download pt_core_news_sm
+```
+
+Alternatively, you can check the latest version of ASAPPpy using this command:
+```bash
+git clone https://github.com/ZPedroP/ASAPPpy.git
+```
+
+### Project History
+ASAP(P) is the name of a collection of systems developed by the [Natural Language Processing group](http://nlp.dei.uc.pt) at [CISUC](https://www.cisuc.uc.pt/home) for computing STS based on a regression method and a set of lexical, syntactic, semantic and distributional features extracted from text.
+It was used to participate in several STS evaluation tasks, for English and Portuguese, but was only recently integrated into two single independent frameworks: ASAPPpy (available here), in Python, and ASAPPj, in Java.
+
+
+### Help and Support
+
+#### Documentation
+Coming soon...
+
+#### Communication
+If you have any questions feel free to open a new issue and we will respond as soon as possible.
+
+#### Citation
+
+When [citing ASAPPpy in academic papers and theses](http://ceur-ws.org/Vol-2583/2_ASAPPpy.pdf), please use the following BibTeX entry:
+
+    @inproceedings{santos_etal:assin2020,
+        title = {ASAPPpy: a Python Framework for Portuguese STS},
+        author = {José Santos and Ana Alves and Hugo {Gonçalo Oliveira}},
+        url = {http://ceur-ws.org/Vol-2583/2_ASAPPpy.pdf},
+        year = {2020},
+        date = {2020-01-01},
+        booktitle = {Proceedings of the ASSIN 2 Shared Task: Evaluating Semantic Textual Similarity and Textual Entailment in Portuguese},
+        volume = {2583},
+        pages = {14--26},
+        publisher = {CEUR-WS.org},
+        series = {CEUR Workshop Proceedings},
+        keywords = {aia, asap, sts},
+        pubstate = {published},
+        tppubtype = {inproceedings}
+    }
+
+
+
+
+
+
+%package -n python3-ASAPPpy
+Summary:	Semantic Textual Similarity and Dialogue System package for Python
+Provides:	python-ASAPPpy
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-ASAPPpy
+## ASAPPpy
+ASAPPpy is a Python package for developing models to compute the Semantic Textual Similarity (STS) between texts in Portuguese. These models follow a supervised learning approach to learn an STS function from annotated sentence pairs, considering a variety of lexical, syntactic, semantic and distributional features.
+
+ASAPPpy can also be used to develop STS based dialogue agents and deploy them to Slack.
+
+
+### Development
+If you want to contribute to this project, please follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html).
+
+
+### Installation
+Before getting started, verify that <b>pip >= 20.3.3</b>. If not, update it with this command:
+```bash
+pip install --upgrade pip
+```
+
+To install the latest version of ASAPPpy use the following command:
+```bash
+pip install ASAPPpy
+```
+After finishing the installation, you might need to download the word embeddings models. Given that they were obtained from various sources, we collected them and they can be downloaded at once by running the Python interpreter in your terminal followed by these commands:
+```python
+import ASAPPpy
+ASAPPpy.download()
+```
+Finally, if you have never used [spaCy](https://spacy.io) before and you want to use the dependency parsing features, you will need to run the next command in the terminal:
+```bash
+python -m spacy download pt_core_news_sm
+```
+
+Alternatively, you can check the latest version of ASAPPpy using this command:
+```bash
+git clone https://github.com/ZPedroP/ASAPPpy.git
+```
+
+### Project History
+ASAP(P) is the name of a collection of systems developed by the [Natural Language Processing group](http://nlp.dei.uc.pt) at [CISUC](https://www.cisuc.uc.pt/home) for computing STS based on a regression method and a set of lexical, syntactic, semantic and distributional features extracted from text.
+It was used to participate in several STS evaluation tasks, for English and Portuguese, but was only recently integrated into two single independent frameworks: ASAPPpy (available here), in Python, and ASAPPj, in Java.
+
+
+### Help and Support
+
+#### Documentation
+Coming soon...
+
+#### Communication
+If you have any questions feel free to open a new issue and we will respond as soon as possible.
+
+#### Citation
+
+When [citing ASAPPpy in academic papers and theses](http://ceur-ws.org/Vol-2583/2_ASAPPpy.pdf), please use the following BibTeX entry:
+
+    @inproceedings{santos_etal:assin2020,
+        title = {ASAPPpy: a Python Framework for Portuguese STS},
+        author = {José Santos and Ana Alves and Hugo {Gonçalo Oliveira}},
+        url = {http://ceur-ws.org/Vol-2583/2_ASAPPpy.pdf},
+        year = {2020},
+        date = {2020-01-01},
+        booktitle = {Proceedings of the ASSIN 2 Shared Task: Evaluating Semantic Textual Similarity and Textual Entailment in Portuguese},
+        volume = {2583},
+        pages = {14--26},
+        publisher = {CEUR-WS.org},
+        series = {CEUR Workshop Proceedings},
+        keywords = {aia, asap, sts},
+        pubstate = {published},
+        tppubtype = {inproceedings}
+    }
+
+
+
+
+
+
+%package help
+Summary:	Development documents and examples for ASAPPpy
+Provides:	python3-ASAPPpy-doc
+%description help
+## ASAPPpy
+ASAPPpy is a Python package for developing models to compute the Semantic Textual Similarity (STS) between texts in Portuguese. These models follow a supervised learning approach to learn an STS function from annotated sentence pairs, considering a variety of lexical, syntactic, semantic and distributional features.
+
+ASAPPpy can also be used to develop STS based dialogue agents and deploy them to Slack.
+
+
+### Development
+If you want to contribute to this project, please follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html).
+
+
+### Installation
+Before getting started, verify that <b>pip >= 20.3.3</b>. If not, update it with this command:
+```bash
+pip install --upgrade pip
+```
+
+To install the latest version of ASAPPpy use the following command:
+```bash
+pip install ASAPPpy
+```
+After finishing the installation, you might need to download the word embeddings models. Given that they were obtained from various sources, we collected them and they can be downloaded at once by running the Python interpreter in your terminal followed by these commands:
+```python
+import ASAPPpy
+ASAPPpy.download()
+```
+Finally, if you have never used [spaCy](https://spacy.io) before and you want to use the dependency parsing features, you will need to run the next command in the terminal:
+```bash
+python -m spacy download pt_core_news_sm
+```
+
+Alternatively, you can check the latest version of ASAPPpy using this command:
+```bash
+git clone https://github.com/ZPedroP/ASAPPpy.git
+```
+
+### Project History
+ASAP(P) is the name of a collection of systems developed by the [Natural Language Processing group](http://nlp.dei.uc.pt) at [CISUC](https://www.cisuc.uc.pt/home) for computing STS based on a regression method and a set of lexical, syntactic, semantic and distributional features extracted from text.
+It was used to participate in several STS evaluation tasks, for English and Portuguese, but was only recently integrated into two single independent frameworks: ASAPPpy (available here), in Python, and ASAPPj, in Java.
+
+
+### Help and Support
+
+#### Documentation
+Coming soon...
+
+#### Communication
+If you have any questions feel free to open a new issue and we will respond as soon as possible.
+
+#### Citation
+
+When [citing ASAPPpy in academic papers and theses](http://ceur-ws.org/Vol-2583/2_ASAPPpy.pdf), please use the following BibTeX entry:
+
+    @inproceedings{santos_etal:assin2020,
+        title = {ASAPPpy: a Python Framework for Portuguese STS},
+        author = {José Santos and Ana Alves and Hugo {Gonçalo Oliveira}},
+        url = {http://ceur-ws.org/Vol-2583/2_ASAPPpy.pdf},
+        year = {2020},
+        date = {2020-01-01},
+        booktitle = {Proceedings of the ASSIN 2 Shared Task: Evaluating Semantic Textual Similarity and Textual Entailment in Portuguese},
+        volume = {2583},
+        pages = {14--26},
+        publisher = {CEUR-WS.org},
+        series = {CEUR Workshop Proceedings},
+        keywords = {aia, asap, sts},
+        pubstate = {published},
+        tppubtype = {inproceedings}
+    }
+
+
+
+
+
+
+%prep
+%autosetup -n ASAPPpy-0.2b1
+
+%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-ASAPPpy -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2b1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
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--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+11a86fa777178bc4ec0e32146d639948  ASAPPpy-0.2b1.tar.gz