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Python Client for INTELLIHUB.

Project description

INTELLIHUB logo

Python 3.8

About

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

Pre-requisite

  • 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

Installation

Installing through pip

pip install intellihub

Installing from Source

  1. Clone the repo
git clone https://github.com/Spotflock/intellihub-sdk-python.git
  1. Set working directory to intellihub-sdk-python folder
cd intellihub-sdk-python
  1. Install requirements from requirements.txt file
pip install -r requirements.txt

Usage

A detailed documentation is present here, on how to use various services supported by INTELLIHUB, to verify whether all setup are done properly, we will be using a sample NLP code to analyze sentiment of the input text.

Example

import intellihub

client = intellihub.IntellihubAiClient(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)

print(sentiment_analysis_response)

Important Parameters:

APIkey : a valid API key generated by following steps as shown here

base_url : The base_url is the url for the machine where base service is installed. (default: http://localhost:8000)


Expected Output

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

Services

Machine Learning

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 H2O.ai 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.

Example Notebooks


Natural Language Processing (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.

Example Notebook


Computer Vision

  • 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.

Example Notebooks

Note

  • To use third party AI engines like Microsoft Azure & IBM watson, please ensure that its credentials were configured while setting up INTELLIHUB.

Documentation

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

License

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

Team

Founding Member Mentor Lead Maintainer Core Contributor

For more details you can reach us at Spotflock Email-ID - connect@spotflock.com

Project details


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