ParallelDots / ParallelDots-Python-API

Python repository for ParallelDots API Wrapper
43 stars 17 forks source link
computer-vision data-science deep-learning machine-learning nlp python

ParallelDots-Python-API

A wrapper for the ParallelDots APIs.

Build Status: CircleCI

Installation

From PyPI:

pip install paralleldots

From Source:

https://github.com/ParallelDots/ParallelDots-Python-API.git
python setup.py install

API Keys & Setup

Sign up to create your free account from ParallelDots. Log in to your account to get your API key.

Configuration:

>>>>> import paralleldots

# Setting your API key
>>>>> paralleldots.set_api_key( "YOUR API KEY" )

# Viewing your API key
>>>>> paralleldots.get_api_key()

Languages Supported:

Supported APIs:

Examples

>>> import paralleldots

>>> api_key   = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
>>> text      = "Chipotle in the north of Chicago is a nice outlet. I went to this place for their famous burritos but fell in love with their healthy avocado salads. Our server Jessica was very helpful. Will pop in again soon!"
>>> path      = "/path/to/image.jpg"
>>> lang_code = "fr"
>>> aspect    = "food"
>>> lang_text = "C'est un environnement très hostile, si vous choisissez de débattre ici, vous serez vicieusement attaqué par l'opposition."
>>> category  = [ "travel","food","shopping", "market" ]
>>> url       = "http://i.imgur.com/klb812s.jpg"
>>> data      =  [ "I like walking in the park", "Don't repeat the same thing over and over!", "This new Liverpool team is not bad", "I have a throat infection" ]

>>> paralleldots.set_api_key( api_key )
>>> print( "API Key: %s" % paralleldots.get_api_key() )

>>> print( "\nAbuse" )
>>> paralleldots.abuse( text )

>>> print( "\nBatch Abuse" )
>>> paralleldots.batch_abuse( data )

>>> print( "\nCustom Classifier" )
>>> paralleldots.custom_classifier( text, category )

>>> print( "\nEmotion" )
>>> paralleldots.emotion( text )

>>> print( "\nBatch Emotion" )
>>> paralleldots.batch_emotion( data )

>>> print( "\nEmotion - Lang: Fr". )
>>> paralleldots.emotion( lang_text, lang_code )

>>> print( "\nSarcasm - Lang: Fr" )
>>> paralleldots.sarcasm( lang_text,lang_code )

>>> print( "\nSarcasm" )
>>> paralleldots.sarcasm( text)

>>> print( "\nBatch Sarcasm" )
>>> paralleldots.batch_sarcasm( data )

>>> print( "\nFacial Emotion" )
>>> paralleldots.facial_emotion( path )

>>> print( "\nFacial Emotion: URL Method" )
>>> paralleldots.facial_emotion_url( url )

>>> print( "\nIntent" )
>>> paralleldots.intent( text )

>>> print( "\nBatch Intent" )
>>> paralleldots.batch_intent( data )

>>> print( "\nKeywords" )
>>> paralleldots.keywords( text )

>>> print( "\nBatch Keywords" )
>>> paralleldots.batch_keywords( data )

>>> print( "\nLanguage Detection" )
>>> paralleldots.language_detection( lang_text )

>>> print( "\nBatch Language Detection" )
>>> paralleldots.batch_language_detection( data )

>>> print( "\nMultilang Keywords - Lang: fr". )
>>> paralleldots.multilang_keywords( lang_text, lang_code )

>>> print( "\nNER" )
>>> paralleldots.ner( text )

>>> print( "\nNER - Lang: es" )
>>> paralleldots.ner( "Lionel Andrés Messi vuelve a ser el gran protagonista en las portadas de la prensa deportiva internacional al día siguiente de un partido de Champions.","es" )

>>> print( "\nBatch NER" )
>>> paralleldots.batch_ner( data ) 

>>> print( "\nObject Recognizer" )
>>> paralleldots.object_recognizer( path )

>>> print( "\nObject Recognizer: URL Method" )
>>> paralleldots.object_recognizer_url( url )

>>> print( "\nPhrase Extractor" )
>>> paralleldots.phrase_extractor( text ) 

>>> print( "\nBatch Phrase Extractor" )
>>> paralleldots.batch_phrase_extractor( data )

>>> print( "\nSentiment" )
>>> paralleldots.sentiment( text )

>>> print( "\nTarget Sentiment" )
>>> paralleldots.target_sentiment( text, aspect )

>>> print( "\nBatch Sentiment" )
>>> paralleldots.batch_sentiment( data )

>>> print( "\nSentiment - Lang: Fr". )
>>> paralleldots.sentiment( lang_text, lang_code ) 

>>> print( "\nSimilarity" )
>>> paralleldots.similarity( "I love fish and ice cream!", "fish and ice cream are the best!" )

>>> print( "\nTaxonomy" )
>>> paralleldots.taxonomy( text ) 

>>> print( "\nBatch Taxonomy" )
>>> paralleldots.batch_taxonomy( data )

>>> paralleldots.usage()