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        "author": "Ryan Vennell",
        "author_email": "ryan.vennell@gmail.com",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Utilities"
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        "description": "Scentamint\n==========\nA web api providing na\u00efve bayesian text classification and training using simplebayes.\n--------------------------------------------------------------------------------------\n\nBuild Status\n------------\n.. image:: https://travis-ci.org/hickeroar/scentamint.svg?branch=master\n.. image:: https://img.shields.io/badge/pylint-10.00/10-brightgreen.svg?style=flat\n.. image:: https://img.shields.io/badge/flake8-passing-brightgreen.svg?style=flat\n\nWhy?\n----\n::\n\n    Bayesian text classification is often used for things like\n    spam detection, sentiment determination, or general categorization.\n\n    Essentially you collect samples of text that you know are of a certain\n    \"type\" or \"category,\" then you use it to train a bayesian classifier.\n    Once you have trained the classifier with many samples of various\n    categories, you can begin to classify and/or score text samples to see\n    which category they fit best in.\n\n    You could, for instance, set up classification of sentiment by finding\n    samples of text that are happy, sad, angry, sarcastic, and so on, then\n    train a classifier using those samples. Once your classifier is trained,\n    you can begin to classify other text into one of those categories.\n\n    What a classifier does is look at text and tell you how much that text\n    \"looks like\" other categories of text that it has been trained for.\n\nInstallation\n------------\n::\n\n    sudo pip install scentamint\n\nConfig\n------\nConfig file location is /etc/scentamint.ini::\n\n    [scentamint]\n\n    ; set the location that we want to store the bayes training cache\n    persist_location = /var/lib/scentamint/\n\n    ; the default port this server will run on\n    listen_port = 80\n\nServer Usage\n------------\n.. code-block:: bash\n\n    $ sudo scentamint --help\n\n    Scentamint Server Help:\n\n        -h, --help\n            Show this help\n\n        -p [port], --port [port]\n            Set the port the server should listen on\n\n        -d, --debug\n            Run the server in debug mode (errors displayed, debug output)\n\n    $ sudo scentamint --port 80 --debug\n     * Running on http://0.0.0.0:80/ (Press CTRL+C to quit)\n     * Restarting with reloader\n    # CTRL+C pressed\n\n    $ sudo scentamint --port 80\n     * Running on http://0.0.0.0:80/ (Press CTRL+C to quit)\n    # CTRL+C pressed\n\n    # A simple, no fuss, server execution command.\n    $ sudo nohup scentamint >> /var/log/scentamint.log 2>&1 &\n\nAPI Usage\n---------\n\nAll endpoints accept POST commands and return predictable results depending on what is posted.\n\nTraining the Classifier\n-----------------------\nEndpoint::\n\n    /train/<string:category>/ (ex: /train/spam/)\n\nResult Status::\n\n    204 No Content\n\n- The POST payload should contain the raw text that will train the classifier.\n- You can train a category as many times as you want.\n\nUntraining the Classifier\n-------------------------\nEndpoint::\n\n    /untrain/<string:category>/ (ex: /train/ham/)\n\nResult Status::\n\n    204 No Content\n\n- The POST payload should contain the raw text that will train the classifier.\n- You can untrain a category as many times as you want, but a token's value will not go below zero.\n- This action carries out the inverse operation of training so unintentional trains can be reversed.\n\nClassifying Text\n----------------\nEndpoint::\n\n    /classify/\n\nResult Status::\n\n    200 OK\n\nResult JSON Example::\n\n    {\n        \"result\": \"ham\"\n    }\n\n- The POST payload should contain the raw text that you want to classify.\n\nScoring Text\n------------\nEndpoint::\n\n    /score/\n\nResult Status::\n\n    200 OK\n\nResult JSON Example::\n\n    {\n        \"scores\": {\n            \"ham\": 268.4685238156538,\n            \"spam\": 44.531476184346225\n        }\n    }\n\n- The POST payload should contain the raw text that you want to score.\n\nEmptying All Classifier Training Data\n-------------------------------------\nEndpoint::\n\n    /flush/\n\nResult Status::\n\n    204 No Content\n\n- This is a purely destructive, non-reversable action.\n\nLicense\n-------\n::\n\n    The MIT License (MIT)\n\n    Copyright (c) 2015 Ryan Vennell\n\n    Permission is hereby granted, free of charge, to any person obtaining a copy\n    of this software and associated documentation files (the \"Software\"), to deal\n    in the Software without restriction, including without limitation the rights\n    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n    copies of the Software, and to permit persons to whom the Software is\n    furnished to do so, subject to the following conditions:\n\n    The above copyright notice and this permission notice shall be included in all\n    copies or substantial portions of the Software.\n\n    THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n    SOFTWARE.",
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