Source code for api.views.service_manager_predict

import json
from rest_framework.response import Response
from rest_framework.views import APIView
from cluster.service.service_predict_d2v import PredictNetD2V
from cluster.service.service_predict_w2v import PredictNetW2V
from cluster.service.service_predict_cnn import PredictNetCnn
from cluster.service.service_predict_renet import PredictNetRenet
from cluster.service.service_predict_wdnn import PredictNetWdnn
from cluster.service.service_predict_seq2seq import PredictNetSeq2Seq
from cluster.service.service_predict_autoencoder import PredictNetAutoEncoder
from cluster.service.service_predict_anomaly import PredictNetAnomaly
from cluster.service.service_predict_wcnn import PredictNetWcnn
from cluster.service.service_predict_bilstmcrf import PredictNetBiLstmCrf
from common.utils import *
import coreapi

class ServiceManagerPredict(APIView):
    # TODO:add document sample for swagger (need to update)
    coreapi_fields = (
        coreapi.Field(
            name='input_data',
            required=True,
            type='string',
        ),
    )
    def post(self, request, type, nnid, ver):
        """
        Request Deep Neural Network to predict result with given data   \n
        input formats can be varies on type of networks     \n
        but usually you can use it with parm input_data     \n
        ---
        # Class Name : ServiceManagerPredict

        # Description:
            request predict service via rest service
            It caches the model and vectors on first request
            It may can take some time at first for caching, after than we can response the request
            within 1.0 sec
        """
        try:
            if(ver == 'active') :
                if(type == 'w2v') :
                    return_data = PredictNetW2V().run(nnid, request.data)
                elif(type == "d2v"):
                    return_data = PredictNetD2V().run(nnid, request.data)
                elif(type == "cnn"):
                    # TO-DO : need predict function for active taged version
                    raise Exception("on developing now !")
                elif (type == "wdnn"):
                    return_data = PredictNetWdnn().run(nnid, ver, request.FILES)
                elif(type == "seq2seq"):
                    return_data = PredictNetSeq2Seq().run(nnid, request.data)
                elif(type == "autoencoder"):
                    return_data = PredictNetAutoEncoder().run(nnid, request.data)
                elif (type == "renet"):
                    #return_data = PredictNetRenet().run(nnid, ver, request.FILES)
                    # TO-DO : need to create PredictNetRenet class first
                    raise Exception("on developing now !")
                elif (type == "anomaly"):
                    return_data = PredictNetAnomaly().run(nnid, request.data)
                elif (type == "wcnn"):
                    return_data = PredictNetWcnn().run(nnid, request.data)
                elif (type == "bilstmcrf"):
                    return_data = PredictNetBiLstmCrf().run(nnid, request.data)
                else :
                    raise Exception ("Not defined type error")
            else :
                if (type == 'w2v'):
                    # TO-DO : need predict function for specific  version
                    raise Exception("on developing now !")
                elif (type == "d2v"):
                    # TO-DO : need predict function for specific  version
                    raise Exception("on developing now !")
                elif (type == "cnn"):
                    return_data = PredictNetCnn().run(nnid, ver, request.FILES)
                elif (type == "wdnn"):
                    return_data = PredictNetWdnn().run(nnid, ver, request.FILES)
                elif (type == "seq2seq"):
                    # TO-DO : need predict function for specific  version
                    raise Exception("on developing now !")
                elif (type == "renet"):
                    return_data = PredictNetRenet().run(nnid, ver, request.FILES)
                else:
                    raise Exception("Not defined type error")

            return Response(json.dumps(return_data))
        except Exception as e:
            return_data = {"status": "404", "result": str(e)}
            return Response(json.dumps(return_data))