Saravanan666/pypi-org huggingface dataset:
Package: dialogflow
WarningThe packagedialogflowhas been renamed togoogle-cloud-dialogflow.dialogflowwill no longer be updated.For help upgrading togoogle-cloud-dialogflow>=2.0.0, see theMigration Guide.AfterOctober 28, 2021, importing code from the latest release ofdialogflowwill result in aRuntimeError. If you need to continue to usedialogflowafter this date, please pin to a specific version ofdialogflow(e.g.,dialogflow==1.1.1). If you have questions, pleasefile an issue.Python idiomatic client forGoogle Cloud DialogflowGoogle Cloud Dialogflowis an enterprise-grade NLU platform that makes it easy for
developers to design and integrate conversational user interfaces into
mobile apps, web applications, devices, and bots.Dialogflow Python Client API ReferenceDialogflow Standard Edition DocumentationDialogflow Enterprise Edition DocumentationRead more about the client libraries for Cloud APIs, including the older
Google APIs Client Libraries, inClient Libraries Explained.Before you beginSelect or create a Cloud Platformproject.Enable billingfor your project.Enable the Google Cloud Dialogflow API.Set up authenticationwith a service account so you can access the
API from your local workstation.InstallationpipinstalldialogflowNoteWe highly recommend that you install this library in avirtualenv.Supported Python VersionsPython >= 3.5Deprecated Python VersionsPython == 2.7. Python 2.7 support will be removed on January 1, 2020.UsageViewusage documentation.VersioningThis library followsSemantic Versioning.This library is considered to be stable. This means and that the code surface will not change in backwards-incompatible
ways unless either absolutely necessary (e.g. because of critical security issues) or with an extensive deprecation
period. Issues and requests against GA libraries are addressed with the highest priority.More Information:Google Cloud Python Library SupportContributingContributions welcome! See theContributing Guide.LicenseApache Version 2.0Seethe LICENSE filefor more information.
Package: dialogflow-client
api.ai: Python SDK for `API.AI <http://api.ai>`_=========================.. image:: https://badge.fury.io/py/apiai.svg:target: http://badge.fury.io/py/apiai.. image:: https://travis-ci.org/api-ai/api-ai-python.svg:target: https://travis-ci.org/api-ai/api-ai-pythonOverview--------The API.AI Python SDK makes it easy to integrate speech recognition with API.AI natural language processing API. API.AI allows using voice commands and integration with dialog scenarios defined for a particular agent in API.AI.Prerequsites--------Create an `API.AI account <http://api.ai>`_.Running examples--------1. Find examples from 'examples' path.2. Insert API key... code-block:: python>>> CLIENT_ACCESS_TOKEN = '<YOUR_CLIENT_ACCESS_TOKEN>'...Features--------- Speech Recognition.- Voice Activity Detection.- Natural Language Processing.Installation------------To install apiai, simply:.. code-block:: bash$ pip install apiaior install it from repo:.. code-block:: bash$ pip install https://github.com/api-ai/api-ai-python.gitYou might run into problems because some dependencies in your python environment are missing. You need to install numpy (which is available in almost all package managers). For running the examples you also need python audio.In ubuntu the following will do the job:.. code-block:: bash$ apt-get install python-pyaudio python-numpy$ pip install apiaiDocumentation-------------Documentation is available at http://api.ai.## How to make contributions?Please read and follow the steps in the [CONTRIBUTING.md](CONTRIBUTING.md).## LicenseSee [LICENSE](LICENSE).## TermsYour use of this sample is subject to, and by using or downloading the sample files you agree to comply with, the [Google APIs Terms of Service](https://developers.google.com/terms/).This is not an official Google product... :changelog:Release History---------------0.0.8 (2015-01-29)++++++++++++++++++* `scipy` removed from dependencies in library.* `scipy` removed from dependencies in examples.0.0.1 (2014-03-1)++++++++++++++++++* Conception
Package: dialogflow-fulfillment
dialogflow-fulfillmentdialogflow-fulfillmentis a package for Python that helps developers to
create webhook services for Dialogflow.The package provides an API for creating and manipulating response messages,
output contexts and follow-up events in conversations.A simple examplefromdialogflow_fulfillmentimportQuickReplies,WebhookClient# Define a custom handler functiondefhandler(agent:WebhookClient)->None:"""This handler sends a text message along with a quick replies messageback to Dialogflow, which uses the messages to build the final responseto the user."""agent.add('How are you feeling today?')agent.add(QuickReplies(quick_replies=['Happy :)','Sad :(']))# Create an instance of the WebhookClientagent=WebhookClient(request)# Handle the request using the handler functionagent.handle_request(handler)# Get the responseresponse=agent.responseInstallationThe preferred way to installdialogflow-fulfillmentis fromPyPIwithpip:pipinstalldialogflow-fulfillmentFeaturesdialogflow-fulfillment's key features are:Webhook Client: handle webhook requests using a custom handler function
or a map of handlers for each intentContexts: process input contexts and add, set or delete output contextsEvents: trigger follow-up events with optional parametersRich Responses: create and send the following types of rich response
messages:TextImageCardQuick RepliesPayloadMore examplesDialogflow fulfillment webhook server withFlaskDialogflow fulfillment webhook server withDjangoDocumentationFor more information about the package, guides and examples of usage, see thedocumentation.ContributeAll kinds of contributions are welcome!For an overview about how to contribute todialogflow-fulfillment, see thecontributing guide.LicenseThis project is licensed under the Apache 2.0 license.For more details about the license, see theLICENSE file.AcknowledgmentsThanks to the Dialogflow development team!
Package: dialogflow-log-parser
dialogflow-log-parserA module for dialogflow log parserOnly response textPaylod from dialogflow ES log file can be parsed into dict object.UsageThis module parses dialogflow logs into python dictionary type.Example usagepippip install dialogflow-log-parserpythonfromdialogflow_log_parser.esimportresponse_to_dict# store log stringtextPayload='[text from dialogflow ES textPayload]'# parse string to dictionary objectresponse_dict=response_to_dict(textPayload)Example of file and parsed object(json)Please check test/es/data folder, so that you can know what kind of dictionary you get.Also use swagger.yml file inswagger editorto see the entire response json data.Log fileDialogflow Response : 
    id: "response_id"
    lang: "ja"
    session_id: "dfMessenger-session-id"
    timestamp: "2000-01-01T01:00:21.720007Z"
    result {
      source: "agent"
      resolved_query: "WELCOME"
      action: "input.welcome"
      score: 1.0
        parameters {
        }
        metadata {
          intent_id: "intent_id"
          intent_name: "Default Welcome Intent"
          webhook_used: "false"
          webhook_for_slot_filling_used: "false"
          is_fallback_intent: "false"
        }
        fulfillment {
          speech: "hello"
          messages {
            lang: "ja"
            type {
              number_value: 0.0
            }
            speech {
              string_value: "hello"
            }
          }
        }
    }
    status {
      code: 200
      error_type: "success"
    }Parsed json{"id":"response_id","lang":"ja","session_id":"dfMessenger-session-id","timestamp":"2000-01-01T01:00:21.720007Z","result":{"source":"agent","resolved_query":"WELCOME","action":"input.welcome","score":1.0,"parameters":[],"metadata":{"intent_id":"intent_id","intent_name":"Default Welcome Intent","webhook_used":"false","webhook_for_slot_filling_used":"false","is_fallback_intent":"false"},"fulfillment":{"speech":"hello","messages":[{"lang":"ja","type":0.0,"speech":"hello"}]}},"status":{"code":200,"error_type":"success"}}IssuesFeel free to post issues for adding some function. (i.e. request textPayload or CX edition.)
Package: dialogflowpy-webhook
dialogflowpy-webhookA Python module for parsing and creating Requests and Responses of Dialogflow Fulfillment Library. TheDialogflow Fulfillment Libraryallows you to connect natural language understanding and processing to your own system. Using Fulfillment, you can surface commands and information from your services to your users through a natural conversational interface.This dialogflowpy-webhook module is intended to help build Python Dialogflow Fulfillment for multipleintegrationsincluding Google Assistant, Facebook and Telegram. It is expected to work for Slack and Line as well.Supported FeaturesAll PlatformsText ResponseCardSuggestion Chips/Quick RepliesImageGoogle AssistantText ResponseCardSuggestion Chips (Quick Replies)CarouselTableMedia ResponsesPermission RequestsInstallationThe module can be installed from PyPi by running:pip3 install dialogflowpy-webhookThe module can also be installed directly from Github by runningpip3 install git+https://github.com/vishalbala-nps/dialogflowpy-webhook.gitHelpful LinksDocumentationGetting StartedModule ReferenceSample CodeReporting BugsIn case of any problems or suggestions with this module, feel free to open aGithub Issue
Package: dialogflow-webhook
api.ai: Python SDK for `API.AI <http://api.ai>`_=========================.. image:: https://badge.fury.io/py/apiai.svg:target: http://badge.fury.io/py/apiai.. image:: https://travis-ci.org/api-ai/api-ai-python.svg:target: https://travis-ci.org/api-ai/api-ai-pythonOverview--------The API.AI Python SDK makes it easy to integrate speech recognition with API.AI natural language processing API. API.AI allows using voice commands and integration with dialog scenarios defined for a particular agent in API.AI.Prerequsites--------Create an `API.AI account <http://api.ai>`_.Running examples--------1. Find examples from 'examples' path.2. Insert API key... code-block:: python>>> CLIENT_ACCESS_TOKEN = '<YOUR_CLIENT_ACCESS_TOKEN>'...Features--------- Speech Recognition.- Voice Activity Detection.- Natural Language Processing.Installation------------To install apiai, simply:.. code-block:: bash$ pip install apiaior install it from repo:.. code-block:: bash$ pip install https://github.com/api-ai/api-ai-python.gitYou might run into problems because some dependencies in your python environment are missing. You need to install numpy (which is available in almost all package managers). For running the examples you also need python audio.In ubuntu the following will do the job:.. code-block:: bash$ apt-get install python-pyaudio python-numpy$ pip install apiaiDocumentation-------------Documentation is available at http://api.ai.## How to make contributions?Please read and follow the steps in the [CONTRIBUTING.md](CONTRIBUTING.md).## LicenseSee [LICENSE](LICENSE).## TermsYour use of this sample is subject to, and by using or downloading the sample files you agree to comply with, the [Google APIs Terms of Service](https://developers.google.com/terms/).This is not an official Google product... :changelog:Release History---------------0.0.8 (2015-01-29)++++++++++++++++++* `scipy` removed from dependencies in library.* `scipy` removed from dependencies in examples.0.0.1 (2014-03-1)++++++++++++++++++* Conception
Package: dialoghook
Dialogflow Request and Response Constructs and validators for Webhooks in Dialogflow ES V2 APIDev Instructions
Run poetry install to install the env.
Run poetry run pre-commit install to initialize the git hooks.
Run poetry run pre-commit run --all-files if there are file that were committed before adding the git hooks.
Activate the shell with: poetry shell
Lint with: poetry run pylint poetrypackagetemplate/ tests/
Test with: poetry run pytest --cov=poetrypackagetemplate
Package: dialogic
dialogicThis is yet another common Python wrapper forAliceandSber Salutskills
and bots in Telegram*, VK,
and Facebook Messenger.Currently, it provides:An (almost) unified interface between your bot
and Alice/SalutTelegram/Facebook/VK:DialogConnector,
for which you can create or customize adapters.A number of simple dialogue constructors:BaseDialogManagerand its flavors, including:a simple FAQ dialog managera simple form-filling dialog managera flexible finite state automaton dialog manageran even more flexible turn-based dialog managerA wrapper for storing dialogue state:BaseStorageand its flavors (specifially,MongoBasedStorage)Yet another wrapper for serving your bot as a Flask applicationThispackagemay be installed withpip install dialogic.A brief how-toTo create your own bot, you need either to write a config for an existing dialog manager,
or to inherit your own dialog manager fromBaseDialogManager.The components ofdialogicmay be combined into a working app as follows:importdialogicclassEchoDialogManager(dialogic.dialog_manager.BaseDialogManager):defrespond(self,ctx:dialogic.dialog.Context):returndialogic.dialog.Response(text=ctx.message_text)connector=dialogic.dialog_connector.DialogConnector(dialog_manager=EchoDialogManager(),storage=dialogic.session_storage.BaseStorage())server=dialogic.flask_server.FlaskServer(connector=connector)if__name__=='__main__':server.parse_args_and_run()Now, if your app is hosted on address{BASE_URL},
then webhooks for Alice, Salut and Facebook will be available,
respectively, at{BASE_URL}/alice/,{BASE_URL}/salut/, and{BASE_URL}/fb/(and you can reconfigure it, if you want).
The webhook for Telegram will be set automatically, if you set theTOKENenvironment variable to the token
given to you by the@BotFather.If you want to test your app locally, you can run it with command line args:--cli- to read and type messages in command line, completely offline--poll- to run a Telegram bot locally, in long polling mode (in some countries, you need a VPN to do this)--ngrok- to run the bot locally, using thengroktool**to create a tunnel from your machine into the internet. This is probably the simplest way to test Alice skills
without deploying them anywhere .Theexamplesdirectory contains more detailed examples
of how to create dialogs and serve the bot.If you have questions, you can ask them in the Telegram chatDialogic.Digital support.*The Telegram wrapper is based on thepyTelegramBotAPIpackage.**The ngrok connector was taken from theflask-ngroklibrary. It will be refactored to a dependency,
as soon as the library is updated on PyPI.
Package: dialogic-dashboard
dialogic-dashboardThis is a web app for viewing logs of chatbots, created with thedialogicframework.dialogic-dashboard по русскиВеб-приложение для просмотра логов чатботов, созданных во фреймворкеdialogic.Для запуска нужно передать в скрипт строку подключения к mongoDB:python -m dialogic_dashboard --url $MONGODB_URIПриложение откроется по адресуhttp://localhost:5000.
Package: dialog-reflection
Dialog ReflectionA library for dialog systems that attempt to respond to messages as Reflective Listening.DemoNeedgitandpoetryto run this demo.$gitclonegit@github.com:sadahry/dialog-reflection.git$cddialog-reflection$poetryinstall$poetryrunpythonexamples/interactive_ja.pyInstallNeedpython>=3.10$pipinstalldialog-reflectionHow to Use (Japanese)SpaCyの日本語モデルのインストールが必要ja_ginza==5.1.2でテスト済他モデルの利用は可能だが推奨しない係り受けや形態素解析によって不整合があるため利用前にテストを推奨$pipinstallja_ginza==5.1.2実行例fromdialog_reflection.lang.ja.reflectorimportJaSpacyReflectorrefactor=JaSpacyReflector(model="ja_ginza")message="今日は旅行へ行った"reflection_text=refactor.reflect(message)print(reflection_text)# => 旅行へ行ったんですね。Builderを使う例fromdialog_reflection.lang.ja.reflection_text_builderimport(JaSpacyPlainReflectionTextBuilder,)importspacynlp=spacy.load("ja_ginza")builder=JaSpacyPlainReflectionTextBuilder()message="今日は旅行へ行った"doc=nlp(message)# some code...reflection_text=builder.build(doc)print(reflection_text)# => 旅行へ行ったんですね。語尾の調整opを変更することで語尾を調整可能fromdialog_reflection.lang.ja.reflectorimportJaSpacyReflectorfromdialog_reflection.lang.ja.reflection_text_builderimport(JaSpacyPlainReflectionTextBuilder,)fromdialog_reflection.lang.ja.reflection_text_builder_optionimport(JaSpacyPlainReflectionTextBuilderOption,)refactor=JaSpacyReflector(model="ja_ginza",builder=JaSpacyPlainReflectionTextBuilder(op=JaSpacyPlainRelflectionTextBuilderOption(fn_last_token_taigen=lambdatoken:token.text+"なんだね。",fn_last_token_yougen=lambdatoken:token.lemma_+"んだね。",)),)message="今日は旅行へ行った"reflection_text=refactor.reflect(message)print(reflection_text)# => 旅行へ行ったんだね。その他設定項目はreflection_text_builder_option.pyを確認してくださいロジックのカスタマイズJaSpacyPlainReflectionTextBuilderを override することでロジックをカスタマイズ可能fromdialog_reflection.reflection_cancelledimport(ReflectionCancelled,)fromdialog_reflection.cancelled_reasonimport(NoValidSentence,)fromdialog_reflection.lang.ja.reflectorimportJaSpacyReflectorfromdialog_reflection.lang.ja.reflection_text_builderimport(JaSpacyPlainReflectionTextBuilder,)importspacyclassCustomReflectionTextBuilder(JaSpacyPlainReflectionTextBuilder):defextract_tokens(self,doc:spacy.tokens.Doc)->spacy.tokens.Span:propn_token=next(filter(lambdatoken:token.pos_=="PROPN",doc),None)ifpropn_tokenisNone:raiseReflectionCancelled(reason=NoValidSentence(doc=doc))ifpropn_token.dep_in["compound","numpound"]:returndoc[propn_token.i:propn_token.head.i+1]returndoc[propn_token.i:propn_token.i+1]refactor=JaSpacyReflector(model="ja_ginza",builder=CustomReflectionTextBuilder(),)message="今日は田中さんと旅行へ行った"reflection_text=refactor.reflect(message)print(reflection_text)# => 田中さんなんですね。
Package: dialoguekit
DialogueKitDialogueKit is a library for conversational information access (CIA). It contains based classes for fundamentalconcepts, such as dialogue participants, dialogue management,natural language understanding, natural language generation, etc. In addition to the fundamental concepts DialogueKit contains an evaluation module, for evaluating the performance of and CIA systems.
Consult thedocumentationfor details.Install as a packageDialogueKit is published to PyPI, install it by running:pipinstalldialoguekitFollow the commands below to install DialogueKit from a specific commit or straight from GitHub.The command will install the latest version from the main branch.On Windows you may need to run this command before pip installingssh-tgitgithub.compip installpipinstallgit+ssh://git@github.com/iai-group/dialoguekit.gitIf you want to specify a specific commit as the source of the package append the commit hash to the end of the command separated with a "@".Specific commit as the source of the package.pipinstallgit+ssh://git@github.com/iai-group/dialoguekit.git@faa5c1fca37aaa275105cc1ca7698783719551c2Usage exampleCreate and connect a platform that manages dialogues. The platform requires information about the agent it serves.fromdialoguekit.platforms.terminal_platformimportTerminalPlatformfromsample_agents.parrot_agentimportParrotAgentplatform=TerminalPlatform(ParrotAgent)Start conversationplatform.start()ConventionsWe follow theIAI Python Style Guide.We useUTF-8encodings that is widely used on Unix systems. Windows users need to use thePython UTF-8 Mode; seeherefor more details. In practice, we specify the encoding when opening files, as in this example:withopen("README.md","r",encoding="utf-8")asfh:long_description=fh.read()Contributors(Alphabetically ordered by last name)Jafar Afzali (2022)Krisztian Balog (2021-present)Nolwenn Bernard (2022-present)Aleksander Drzewiecki (2022)Ivica Kostric (2023)Weronika Lajewska (2023)Shuo Zhang (2021)
Package: dialogue.multi-method
A small library to provide multi-methods.  All functions are in thedialogue.multi_methodpackage.To create a multi-method, create a dispatch function, that takes the
arguments and returns a hashable value, that is used to dispatch on.
Use the@multifunction annotation to annotate the function.For each value of the dispatch function that you want to be handled
differently, create a method with that dispatch value.You can define a method with no dispatch value, that becomes the
default method if no other method is defined for a dispatch value.An example:@multi
def number(x):
  return x


@method(number, 1)
def number_one(x):
  return 'one'


@method(number)
def number_other(x):
  return "not one, but "+ str(c)


assert number(1) == 'one'
assert number(0) == 'not one, but 0'The dispatch function is available, e.g. for testing:assert dispatch_fn(number)('x') == 'x'
Package: dialogue-tester
The Dialogue classTheDialogueclass helps testing REPLs (interactive interpreters with a Read-Eval-Print-Loop).Given a multi-line interactive session transcript, aDialogueinstance emulates user interactions by offering afake_inputmethod which can replace theinputbuilt-in function in Python 3.How to useYou can code your REPL function to accept an input function as an optional argument, or you can monkey patch Python'sinputbuilt-in. Either way, you replace the standardinputwith thefake_inputmethod bound to aDialogueinstance. Each time it is invoked, this method will print the next prompt and fake user input from the session transcript, then return the same user input string to the REPL for processing.TheDialogue.sessioninstance attribute holds a multi-line string with all the prompts, user inputs and the outputs expected from the REPL under test. This is used toassertthe test case.See thedialogue_test.pymodule for two simple REPLs that exercise theDialogueclass.Note: the.sessionattribute isnormalizedwith thedialogue.normalize()function. Use that function to normalize the output captured by your testing framework as well. This normalization removes trailing whitespace from each line, preserving only the newlines separating them, and also removes leading and trailing whitespace from the session as a whole. This avoids many issues with linters and IDEs complaining about trailing whitespace, and also makes it easier to edit the session examples in tests. See the tests in this package for use ofnormalizein test assertions.
Package: dialog-watson-client
# dialog-watson-clientClient for dialogs watson module## Requirements- **Python 2.7**- **Pip**## InstallationInstall with pip: `pip install dialog-watson-client`## Run the playgroundSimply run in command line: `dialog-watson-client --name=dialog-name path/to/dialog/file [--clean](optional: clean your dialogs in watson)` and you will chat with your robot**At the first launch it will create a config file located to `~/.config-dialog-watson.yml` and ask you your watson credentials**## Usage for developersBootstrap example:```pythonfrom dialog_watson_client.Client import ClientwatsonClient = Client('user_watson', 'password_watson', 'file/path/to/dialog', 'your_dialog_name') # this library abstract the registering of dialog (and the update when you cahnge it) and run it, to do that it will store your dialog id in a file called `dialog_id_file.txt`watsonClient.start_dialog() # this will create the dialog into watson or update it and run the initialization of the conversationresp = watsonClient.converse('hi') # talk to the robot, here it will say 'hi' and watson will answeredprint resp.response # show the response from watsonwatsonClient.get_profile().get_data() # get extracted data from watson in format: [key => value]```**Note**: If your file is in xml (and you have `lxml` lib installed) it will also check your format with xsd: https://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/doc/dialog/download/WatsonDialogDocument_1.0.xsd
Package: dialog-whatsapp
talkd/dialogWhatsApp plugintalkd/dialogplugin for integration with whatsapp, simplifying communication with the whatsapp apiSample variables to be used inside the .env fileWHATSAPP_VERIFY_TOKEN=1234567890
WHATSAPP_API_TOKEN=1234567890
WHATSAPP_ACCOUNT_NUMBER=1234567890
API_HOST=http://localhost:8000 # In case of local development your ngrok url