Metadata-Version: 2.1
Name: qubitai-dltk
Version: 1.0.3
Summary: Python Client for DLTK.
Home-page: https://github.com/dltk-ai/qubitai-dltk
Author: DLTK
Author-email: connect@qubitai.tech
License: UNKNOWN
Description: # DLTK SDK
        [![Python 3.8](https://img.shields.io/badge/python-3.8-blue.svg)](https://www.python.org/downloads/release/python-380/)
        
        
        [![DLTK Logo](dltk.png)](https://dltk.ai/)
        
        ## About
        
        Our philosophy is to create a Deep Technologies platform with ethical AI for enterprises that offers meaningful insights and actions. 
        
        DLTK Unified Deep Learning platform can be leveraged to build solutions that are Application-Specific and Industry-Specific where AI opportunity found by using DLTK 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
        
        1. OpenDLTK : OpenDLTK 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 [OpenDLTK Installation](https://docs.dltk.ai/getting_started/openDLTK_setup.html)
        
        ---
        
        **Note**: To use third party AI engines please provide your credentials. Instructions on getting credentials and configuring are provided below.
        
        ---
        
        
        ### Installation
        
        **Installing through pip**
        ```sh
            pip install qubitai-dltk
        ```
        
        **Installing from Source**
        
        a. Clone the repo
        
        ```sh
           git clone https://github.com/dltk-ai/qubitai-dltk.git
        ``` 
        b. Set working directory to qubitai-dltk folder
        
        c. Install requirements from requirements.txt file
        
        ```sh
            pip install -r requirements.txt
        ```
        
        Choose any one of the above options for Installation
        
        ---
        
        ### Usage
        
        ```python
        import dltk_ai
        client = dltk_ai.DltkAiClient(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)
        ```
        
        Example notebooks for all the services are provided and are accesible when the user installs qubitai-dltk from source.
        
        
        **Important Parameters:**
        
        **1. API key:**
        
        
        **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_
        ```json
        {
          "spacy": {"emotion": "POSITIVE", "scores": {"neg": 0.0, "neu": 0.653, "pos": 0.347, "compound": 0.7496}}
        }
        ```
        
        ---
        ## Services
        
        **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 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.
        
        **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.
        
        
        ## Reference
        
        For more detail on DLTK features & usage please refer [DLTK SDK Client Documentation](https://docs.dltk.ai)
        
        ## License
        
        The content of this project itself is licensed under [GNU LGPL, Version 3 (LGPL-3)](https://github.com/dltk-ai/qubitai-dltk/blob/master/LICENSE)
        
        ## Contact
        
        QubitAI Email-ID - connect@qubitai.tech
        
        ## Founding Member
        [![](https://github.com/shreeramiyer.png?size=50)](https://github.com/shreeramiyer)
        
        ## Lead Maintainer
        [![](https://github.com/GHub4Naveen.png?size=50)](https://github.com/GHub4Naveen)
        [![](https://github.com/alamcta.png?size=50)](https://github.com/alamcta)
        
        ## Core Maintainer
        [![](https://github.com/dltk-ai.png?size=50)](https://github.com/dltk-ai)
        ## Core Contributors 
        [![](https://github.com/SivaramVeluri15.png?size=50)](https://github.com/SivaramVeluri15)
        [![](https://github.com/vishnupeesapati.png?size=49)](https://github.com/vishnupeesapati)
        [![](https://github.com/appareddyraja.png?size=50)](https://github.com/appareddyraja)
        [![](https://github.com/kavyavelagapudi252.png?size=50)](https://github.com/kavyavelagapudi252)
        [![](https://github.com/vivekkya.png?size=49)](https://github.com/vivekkya)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
