Skip to main content

Python Client for INTELLIHUB.

Project description


Python 3.8



Our philosophy is to create a Deep Technologies platform with ethical AI for enterprises that offers meaningful insights and actions.

INTELLIHUB Unified Deep Learning platform can be leveraged to build solutions that are Application-Specific and Industry-Specific where AI opportunity found by using INTELLIHUB SDKs, APIs and Microservices. With best of the breed AI Services from platform pioneers like H2O, Google's TensorFlow, WEKA and a few trusted open-sources models and libraries, we offer custom AI algorithms with co-innovation support.

Getting Started


  1. INTELLIHUB : INTELLIHUB is collection of open-source docker images, where processing of images, text or structured tabular data is done using state-of-the-art AI models.

Please follow the below link for instructions on INTELLIHUB Installation

Note: To use third party AI engines please provide your credentials. Instructions on getting credentials and configuring are provided below.


Installing through pip

    pip install intellihub

Installing from Source

a. Clone the repo

   git clone

b. Set working directory to intellihub folder

c. Install requirements from requirements.txt file

    pip install -r requirements.txt

Choose any one of the above options for Installation


import intellihub
client = intellihub.IntellihubClient("YOUR_API_KEY",base_url='http://localhost:8000')

text = "The product is very easy to use and has got a really good life expectancy."

sentiment_analysis_response = client.sentiment_analysis(text)


Important Parameters:

1. API key: Login to and Go to console and click on Apps and then click on Create App, fill the details and Click submit. The App will be created and an API Key is generated in the App.

2. base_url: The base_url is the url for the machine where base service is installed by default its localhost, so base_url needs to be http://localhost:8000

Expected Output

  'nltk_vader': {'emotion': 'POSITIVE', 'scores': {'compound': 0.7496, 'negative': 0.0, 'positive': 0.347, 'neutral': 0.653}}


1. Machine Learning

  • ML Wrapper - It parse user request parameters

  • ML Scikit - This Microservice uses widely used Scikit package for training and evaluating classification, regression, clustering models and other ML related tasks on dataset provided by user.

  • ML H2O - This Microservice uses python SDK for training and evaluating classification, regression, clustering models and other ML related tasks on dataset provided by user.

  • ML Weka - This Microservice uses WEKA for training and evaluating classification, regression, clustering models and other ML related tasks on dataset provided by user.

2. NLP

  • This microservice provides features like Sentiment analysis, Name Entity Recognition, Tag Extraction using widely used Spacy and NLTK package. It also provide support for various AI engines like Azure & IBM.

3. Computer Vision

  • CV Wrapper - This microservice receives images provided by user and route to right service based on the feature requested by them.

  • Image Classification - This microservice classify images into various classes using pretrained model and also using supported AI Engines.

  • Object Detection - This microservice detect objects in Images provided by user using pretrained model and using supported AI Engines.


For more detail on INTELLIHUB features & usage please refer INTELLIHUB SDK Client Documentation


The content of this project itself is licensed under GNU LGPL, Version 3 (LGPL-3)


Spotflock Email-ID -

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for intellihub, version 1.4.0
Filename, size File type Python version Upload date Hashes
Filename, size intellihub-1.4.0.tar.gz (37.3 kB) File type Source Python version None Upload date Hashes View
Filename, size intellihub-1.4.0-py3-none-any.whl (36.9 kB) File type Wheel Python version py3 Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page