* TODO: Need a robust way to find if the MLHandler expects image data for training/testing
* TODO: Separate class is required for training and testing CV models
* TODO: Code has to be made generic to dynamically select Resnet50/VGG, etc. models
* TODO: Model training parameters viz. epochs, batch size have to be taken from the user with defaults set
* TODO: setup and get_model functions have to be made more scalable
* TODO: ModelStore __init__ has to be made scalable

…mlhandler

* Predict is working with default VGG16 and Resnet models.
* TODO: Test and add all other models supported by Keras
* TODO: Implement training functionality
* TODO: Implement functionality to predict from trained models provided by user/trained in gramex

* Training keras models would be done in ml_api in KerasApplication wrapper
* _parse_multipart_form_data used for parsing images

* Removed unnecessary code from ModelStore

…mlhandler

* Model initialisation code moved to __init__
* Training added to POST request