Lots of chatter on Apple and Micosoft forums - so its a 'thing'. - Answered by a verified Mac Support Specialist. Cannot install iTunes after Windows 10 update. Lots of chatter on Apple and Micosoft forums - so its a 'thing'. I have tried multiple times to install the mac plug in thru safari and I continue to get a response th. This package can be installed from [PyPi](by running: ``` pip install chatterbot ``` ## Basic Usage ``` from chatterbot import. ![]() ChatterBot is a machine learning, conversational dialog engine. Home-page: Author: Gunther Cox Author-email: [email protected] License: BSD Download-URL: Project-URL: Documentation, Description:![Chatterbot: Machine learning in Python](# 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. [![Package Version]([![Requirements Status]([![Build Status]([![Documentation Status]([![Coverage Status]([![Code Climate]([![Join the chat at 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](by running: ``` pip install chatterbot ``` ## Basic Usage ``` from chatterbot import ChatBot chatbot = ChatBot( 'Ron Obvious', trainer='chatterbot.trainers.ChatterBotCorpusTrainer' ) # Train based on the english corpus chatbot.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 three languages, English, Spanish and Portuguese training data 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](package if you are interested in contributing. ``` # Train based on the english corpus chatbot.train('chatterbot.corpus.english') # Train based on english greetings corpus chatbot.train('chatterbot.corpus.english.greetings') # Train based on the english conversations corpus chatbot.train('chatterbot.corpus.english.conversations') ``` **Corpus contributions are welcome! Please make a pull request.** # [Documentation](View the [documentation](for ChatterBot on Read the Docs. To build the documentation yourself using [Sphinx](run: ``` sphinx-build -b html docs/ build/ ``` # Examples For examples, see the [examples](directory in this project's git repository. There is also an example [Django project using ChatterBot](as well as an example [Flask project using ChatterBot](# History See release notes for changes # Development pattern for contributors 1. [Create a fork](of the [main ChatterBot repository](on GitHub. Make your changes in a branch named something different from `master`, e.g. Create a new branch `my-pull-request`. [Create a pull request](4. Please follow the [Python style guide for PEP-8](5. Use the projects [built-in automated testing](to help make sure that your contribution is free from errors. The Android client makes a request to refresh it’s chatter feed and makes a call to on App Engine. The WSC queries Salesforce for the chatter feed and returns the feed as a series of JSON objects. Spyware cleaner for mac. The Android client consumes the JSON objects and stores them in the local SQLite database. When the feed list is displayed on the Android client, the images are download lazily from App Engine when needed. You may see a slight lag while new threads are spawned and the images are downloaded. If the images do not display quickly, try scrolling through the list back and forth for them to display. Outlook for mac stuck on updating message list. Based on my experience, you can use the Spotlight search feature to locate this email via its Subject on your mac, and then delete it again to check the issue. If the issue persists, for better assistance, we suggest you contact with Outlook for Mac experts via clicking Contact Support feature on the Help menu. Hope the above information helpful. A standalone Mac running Outlook 2016 for Mac has an outgoing message stuck in the Outbox. Other messages sent after the stuck message are going out, something I've not seen before and suspect may be unique to the Mac version. Part of the roadmap is to store these images in the local database instead of downloading them each time. • Download either the original or a that runs on older handsets to your desktop • On your phone, install eoeAppInstaller from the Android Market. • Mount your phone to your computer via your USB cable so that you can access the SD card on your phone from Finder. • Drop the.apk file into the root of your SD card • Unmount your phone and disconnect your USB cable • Go to Settings -> Applications and check “Unknown Sources” to allow installation of non-Market applications • Launch eoeAppInstaller and it should display the Salesforce Chatter app • Long press the app and select “Install this apk”.
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