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|
%global _empty_manifest_terminate_build 0
Name: python-ChatterBot
Version: 1.0.8
Release: 1
Summary: ChatterBot is a machine learning, conversational dialog engine.
License: BSD
URL: https://github.com/gunthercox/ChatterBot
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/74/e8/942ac76e79434c605c30ab2ee3e3e421eaf5aa851d46d72eb780d0ff1bb5/ChatterBot-1.0.8.tar.gz
BuildArch: noarch
Requires: python3-mathparse
Requires: python3-dateutil
Requires: python3-sqlalchemy
Requires: python3-pytz
%description
# ChatterBot
ChatterBot is a machine-learning based conversational dialog engine build in
Python which makes it possible to generate responses based on collections of
known conversations. The language independent design of ChatterBot allows it
to be trained to speak any language.
[](https://pypi.python.org/pypi/chatterbot/)
[](https://www.python.org/downloads/release/python-360/)
[](https://docs.djangoproject.com/en/2.1/releases/2.0/)
[](https://requires.io/github/gunthercox/ChatterBot/requirements/?branch=master)
[](https://travis-ci.org/gunthercox/ChatterBot)
[](http://chatterbot.readthedocs.io/en/stable/?badge=stable)
[](https://coveralls.io/r/gunthercox/ChatterBot)
[](https://codeclimate.com/github/gunthercox/ChatterBot)
[](https://gitter.im/chatterbot/Lobby?utm_source=badge&utm_medium=badge&utm_content=badge)
An example of typical input would be something like this:
> **user:** Good morning! How are you doing?
> **bot:** I am doing very well, thank you for asking.
> **user:** You're welcome.
> **bot:** Do you like hats?
## How it works
An untrained instance of ChatterBot starts off with no knowledge of how to communicate. Each time a user enters a statement, the library saves the text that they entered and the text that the statement was in response to. As ChatterBot receives more input the number of responses that it can reply and the accuracy of each response in relation to the input statement increase. The program selects the closest matching response by searching for the closest matching known statement that matches the input, it then returns the most likely response to that statement based on how frequently each response is issued by the people the bot communicates with.
## Installation
This package can be installed from [PyPi](https://pypi.python.org/pypi/ChatterBot) by running:
```
pip install chatterbot
```
## Basic Usage
```
from chatterbot import ChatBot
from chatterbot.trainers import ChatterBotCorpusTrainer
chatbot = ChatBot('Ron Obvious')
# Create a new trainer for the chatbot
trainer = ChatterBotCorpusTrainer(chatbot)
# Train the chatbot based on the english corpus
trainer.train("chatterbot.corpus.english")
# Get a response to an input statement
chatbot.get_response("Hello, how are you today?")
```
# Training data
ChatterBot comes with a data utility module that can be used to train chat bots.
At the moment there is training data for over a dozen languages in this module.
Contributions of additional training data or training data
in other languages would be greatly appreciated. Take a look at the data files
in the [chatterbot-corpus](https://github.com/gunthercox/chatterbot-corpus)
package if you are interested in contributing.
```
from chatterbot.trainers import ChatterBotCorpusTrainer
# Create a new trainer for the chatbot
trainer = ChatterBotCorpusTrainer(chatbot)
# Train based on the english corpus
trainer.train("chatterbot.corpus.english")
# Train based on english greetings corpus
trainer.train("chatterbot.corpus.english.greetings")
# Train based on the english conversations corpus
trainer.train("chatterbot.corpus.english.conversations")
```
**Corpus contributions are welcome! Please make a pull request.**
# [Documentation](https://chatterbot.readthedocs.io/)
View the [documentation](https://chatterbot.readthedocs.io/)
for ChatterBot on Read the Docs.
To build the documentation yourself using [Sphinx](http://www.sphinx-doc.org/), run:
```
sphinx-build -b html docs/ build/
```
# Examples
For examples, see the [examples](https://github.com/gunthercox/ChatterBot/tree/master/examples)
directory in this project's git repository.
There is also an example [Django project using ChatterBot](https://github.com/gunthercox/ChatterBot/tree/master/examples), as well as an example [Flask project using ChatterBot](https://github.com/chamkank/flask-chatterbot).
# History
See release notes for changes https://github.com/gunthercox/ChatterBot/releases
# Development pattern for contributors
1. [Create a fork](https://help.github.com/articles/fork-a-repo/) of
the [main ChatterBot repository](https://github.com/gunthercox/ChatterBot) on GitHub.
2. Make your changes in a branch named something different from `master`, e.g. create
a new branch `my-pull-request`.
3. [Create a pull request](https://help.github.com/articles/creating-a-pull-request/).
4. Please follow the [Python style guide for PEP-8](https://www.python.org/dev/peps/pep-0008/).
5. Use the projects [built-in automated testing](https://chatterbot.readthedocs.io/en/latest/testing.html).
to help make sure that your contribution is free from errors.
# License
ChatterBot is licensed under the [BSD 3-clause license](https://opensource.org/licenses/BSD-3-Clause).
%package -n python3-ChatterBot
Summary: ChatterBot is a machine learning, conversational dialog engine.
Provides: python-ChatterBot
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-ChatterBot
# ChatterBot
ChatterBot is a machine-learning based conversational dialog engine build in
Python which makes it possible to generate responses based on collections of
known conversations. The language independent design of ChatterBot allows it
to be trained to speak any language.
[](https://pypi.python.org/pypi/chatterbot/)
[](https://www.python.org/downloads/release/python-360/)
[](https://docs.djangoproject.com/en/2.1/releases/2.0/)
[](https://requires.io/github/gunthercox/ChatterBot/requirements/?branch=master)
[](https://travis-ci.org/gunthercox/ChatterBot)
[](http://chatterbot.readthedocs.io/en/stable/?badge=stable)
[](https://coveralls.io/r/gunthercox/ChatterBot)
[](https://codeclimate.com/github/gunthercox/ChatterBot)
[](https://gitter.im/chatterbot/Lobby?utm_source=badge&utm_medium=badge&utm_content=badge)
An example of typical input would be something like this:
> **user:** Good morning! How are you doing?
> **bot:** I am doing very well, thank you for asking.
> **user:** You're welcome.
> **bot:** Do you like hats?
## How it works
An untrained instance of ChatterBot starts off with no knowledge of how to communicate. Each time a user enters a statement, the library saves the text that they entered and the text that the statement was in response to. As ChatterBot receives more input the number of responses that it can reply and the accuracy of each response in relation to the input statement increase. The program selects the closest matching response by searching for the closest matching known statement that matches the input, it then returns the most likely response to that statement based on how frequently each response is issued by the people the bot communicates with.
## Installation
This package can be installed from [PyPi](https://pypi.python.org/pypi/ChatterBot) by running:
```
pip install chatterbot
```
## Basic Usage
```
from chatterbot import ChatBot
from chatterbot.trainers import ChatterBotCorpusTrainer
chatbot = ChatBot('Ron Obvious')
# Create a new trainer for the chatbot
trainer = ChatterBotCorpusTrainer(chatbot)
# Train the chatbot based on the english corpus
trainer.train("chatterbot.corpus.english")
# Get a response to an input statement
chatbot.get_response("Hello, how are you today?")
```
# Training data
ChatterBot comes with a data utility module that can be used to train chat bots.
At the moment there is training data for over a dozen languages in this module.
Contributions of additional training data or training data
in other languages would be greatly appreciated. Take a look at the data files
in the [chatterbot-corpus](https://github.com/gunthercox/chatterbot-corpus)
package if you are interested in contributing.
```
from chatterbot.trainers import ChatterBotCorpusTrainer
# Create a new trainer for the chatbot
trainer = ChatterBotCorpusTrainer(chatbot)
# Train based on the english corpus
trainer.train("chatterbot.corpus.english")
# Train based on english greetings corpus
trainer.train("chatterbot.corpus.english.greetings")
# Train based on the english conversations corpus
trainer.train("chatterbot.corpus.english.conversations")
```
**Corpus contributions are welcome! Please make a pull request.**
# [Documentation](https://chatterbot.readthedocs.io/)
View the [documentation](https://chatterbot.readthedocs.io/)
for ChatterBot on Read the Docs.
To build the documentation yourself using [Sphinx](http://www.sphinx-doc.org/), run:
```
sphinx-build -b html docs/ build/
```
# Examples
For examples, see the [examples](https://github.com/gunthercox/ChatterBot/tree/master/examples)
directory in this project's git repository.
There is also an example [Django project using ChatterBot](https://github.com/gunthercox/ChatterBot/tree/master/examples), as well as an example [Flask project using ChatterBot](https://github.com/chamkank/flask-chatterbot).
# History
See release notes for changes https://github.com/gunthercox/ChatterBot/releases
# Development pattern for contributors
1. [Create a fork](https://help.github.com/articles/fork-a-repo/) of
the [main ChatterBot repository](https://github.com/gunthercox/ChatterBot) on GitHub.
2. Make your changes in a branch named something different from `master`, e.g. create
a new branch `my-pull-request`.
3. [Create a pull request](https://help.github.com/articles/creating-a-pull-request/).
4. Please follow the [Python style guide for PEP-8](https://www.python.org/dev/peps/pep-0008/).
5. Use the projects [built-in automated testing](https://chatterbot.readthedocs.io/en/latest/testing.html).
to help make sure that your contribution is free from errors.
# License
ChatterBot is licensed under the [BSD 3-clause license](https://opensource.org/licenses/BSD-3-Clause).
%package help
Summary: Development documents and examples for ChatterBot
Provides: python3-ChatterBot-doc
%description help
# ChatterBot
ChatterBot is a machine-learning based conversational dialog engine build in
Python which makes it possible to generate responses based on collections of
known conversations. The language independent design of ChatterBot allows it
to be trained to speak any language.
[](https://pypi.python.org/pypi/chatterbot/)
[](https://www.python.org/downloads/release/python-360/)
[](https://docs.djangoproject.com/en/2.1/releases/2.0/)
[](https://requires.io/github/gunthercox/ChatterBot/requirements/?branch=master)
[](https://travis-ci.org/gunthercox/ChatterBot)
[](http://chatterbot.readthedocs.io/en/stable/?badge=stable)
[](https://coveralls.io/r/gunthercox/ChatterBot)
[](https://codeclimate.com/github/gunthercox/ChatterBot)
[](https://gitter.im/chatterbot/Lobby?utm_source=badge&utm_medium=badge&utm_content=badge)
An example of typical input would be something like this:
> **user:** Good morning! How are you doing?
> **bot:** I am doing very well, thank you for asking.
> **user:** You're welcome.
> **bot:** Do you like hats?
## How it works
An untrained instance of ChatterBot starts off with no knowledge of how to communicate. Each time a user enters a statement, the library saves the text that they entered and the text that the statement was in response to. As ChatterBot receives more input the number of responses that it can reply and the accuracy of each response in relation to the input statement increase. The program selects the closest matching response by searching for the closest matching known statement that matches the input, it then returns the most likely response to that statement based on how frequently each response is issued by the people the bot communicates with.
## Installation
This package can be installed from [PyPi](https://pypi.python.org/pypi/ChatterBot) by running:
```
pip install chatterbot
```
## Basic Usage
```
from chatterbot import ChatBot
from chatterbot.trainers import ChatterBotCorpusTrainer
chatbot = ChatBot('Ron Obvious')
# Create a new trainer for the chatbot
trainer = ChatterBotCorpusTrainer(chatbot)
# Train the chatbot based on the english corpus
trainer.train("chatterbot.corpus.english")
# Get a response to an input statement
chatbot.get_response("Hello, how are you today?")
```
# Training data
ChatterBot comes with a data utility module that can be used to train chat bots.
At the moment there is training data for over a dozen languages in this module.
Contributions of additional training data or training data
in other languages would be greatly appreciated. Take a look at the data files
in the [chatterbot-corpus](https://github.com/gunthercox/chatterbot-corpus)
package if you are interested in contributing.
```
from chatterbot.trainers import ChatterBotCorpusTrainer
# Create a new trainer for the chatbot
trainer = ChatterBotCorpusTrainer(chatbot)
# Train based on the english corpus
trainer.train("chatterbot.corpus.english")
# Train based on english greetings corpus
trainer.train("chatterbot.corpus.english.greetings")
# Train based on the english conversations corpus
trainer.train("chatterbot.corpus.english.conversations")
```
**Corpus contributions are welcome! Please make a pull request.**
# [Documentation](https://chatterbot.readthedocs.io/)
View the [documentation](https://chatterbot.readthedocs.io/)
for ChatterBot on Read the Docs.
To build the documentation yourself using [Sphinx](http://www.sphinx-doc.org/), run:
```
sphinx-build -b html docs/ build/
```
# Examples
For examples, see the [examples](https://github.com/gunthercox/ChatterBot/tree/master/examples)
directory in this project's git repository.
There is also an example [Django project using ChatterBot](https://github.com/gunthercox/ChatterBot/tree/master/examples), as well as an example [Flask project using ChatterBot](https://github.com/chamkank/flask-chatterbot).
# History
See release notes for changes https://github.com/gunthercox/ChatterBot/releases
# Development pattern for contributors
1. [Create a fork](https://help.github.com/articles/fork-a-repo/) of
the [main ChatterBot repository](https://github.com/gunthercox/ChatterBot) on GitHub.
2. Make your changes in a branch named something different from `master`, e.g. create
a new branch `my-pull-request`.
3. [Create a pull request](https://help.github.com/articles/creating-a-pull-request/).
4. Please follow the [Python style guide for PEP-8](https://www.python.org/dev/peps/pep-0008/).
5. Use the projects [built-in automated testing](https://chatterbot.readthedocs.io/en/latest/testing.html).
to help make sure that your contribution is free from errors.
# License
ChatterBot is licensed under the [BSD 3-clause license](https://opensource.org/licenses/BSD-3-Clause).
%prep
%autosetup -n ChatterBot-1.0.8
%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-ChatterBot -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.8-1
- Package Spec generated
|
