// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/cloud/ml/v1/prediction_service.proto

package ml

import proto "github.com/golang/protobuf/proto"
import fmt "fmt"
import math "math"
import _ "google.golang.org/genproto/googleapis/api/annotations"
import google_api3 "google.golang.org/genproto/googleapis/api/httpbody"

import (
	context "golang.org/x/net/context"
	grpc "google.golang.org/grpc"
)

// Reference imports to suppress errors if they are not otherwise used.
var _ = proto.Marshal
var _ = fmt.Errorf
var _ = math.Inf

// Request for predictions to be issued against a trained model.
//
// The body of the request is a single JSON object with a single top-level
// field:
//
// <dl>
//   <dt>instances</dt>
//   <dd>A JSON array containing values representing the instances to use for
//       prediction.</dd>
// </dl>
//
// The structure of each element of the instances list is determined by your
// model's input definition. Instances can include named inputs or can contain
// only unlabeled values.
//
// Not all data includes named inputs. Some instances will be simple
// JSON values (boolean, number, or string). However, instances are often lists
// of simple values, or complex nested lists. Here are some examples of request
// bodies:
//
// CSV data with each row encoded as a string value:
// <pre>
// {"instances": ["1.0,true,\\"x\\"", "-2.0,false,\\"y\\""]}
// </pre>
// Plain text:
// <pre>
// {"instances": ["the quick brown fox", "la bruja le dio"]}
// </pre>
// Sentences encoded as lists of words (vectors of strings):
// <pre>
// {
//   "instances": [
//     ["the","quick","brown"],
//     ["la","bruja","le"],
//     ...
//   ]
// }
// </pre>
// Floating point scalar values:
// <pre>
// {"instances": [0.0, 1.1, 2.2]}
// </pre>
// Vectors of integers:
// <pre>
// {
//   "instances": [
//     [0, 1, 2],
//     [3, 4, 5],
//     ...
//   ]
// }
// </pre>
// Tensors (in this case, two-dimensional tensors):
// <pre>
// {
//   "instances": [
//     [
//       [0, 1, 2],
//       [3, 4, 5]
//     ],
//     ...
//   ]
// }
// </pre>
// Images can be represented different ways. In this encoding scheme the first
// two dimensions represent the rows and columns of the image, and the third
// contains lists (vectors) of the R, G, and B values for each pixel.
// <pre>
// {
//   "instances": [
//     [
//       [
//         [138, 30, 66],
//         [130, 20, 56],
//         ...
//       ],
//       [
//         [126, 38, 61],
//         [122, 24, 57],
//         ...
//       ],
//       ...
//     ],
//     ...
//   ]
// }
// </pre>
// JSON strings must be encoded as UTF-8. To send binary data, you must
// base64-encode the data and mark it as binary. To mark a JSON string
// as binary, replace it with a JSON object with a single attribute named `b64`:
// <pre>{"b64": "..."} </pre>
// For example:
//
// Two Serialized tf.Examples (fake data, for illustrative purposes only):
// <pre>
// {"instances": [{"b64": "X5ad6u"}, {"b64": "IA9j4nx"}]}
// </pre>
// Two JPEG image byte strings (fake data, for illustrative purposes only):
// <pre>
// {"instances": [{"b64": "ASa8asdf"}, {"b64": "JLK7ljk3"}]}
// </pre>
// If your data includes named references, format each instance as a JSON object
// with the named references as the keys:
//
// JSON input data to be preprocessed:
// <pre>
// {
//   "instances": [
//     {
//       "a": 1.0,
//       "b": true,
//       "c": "x"
//     },
//     {
//       "a": -2.0,
//       "b": false,
//       "c": "y"
//     }
//   ]
// }
// </pre>
// Some models have an underlying TensorFlow graph that accepts multiple input
// tensors. In this case, you should use the names of JSON name/value pairs to
// identify the input tensors, as shown in the following exmaples:
//
// For a graph with input tensor aliases "tag" (string) and "image"
// (base64-encoded string):
// <pre>
// {
//   "instances": [
//     {
//       "tag": "beach",
//       "image": {"b64": "ASa8asdf"}
//     },
//     {
//       "tag": "car",
//       "image": {"b64": "JLK7ljk3"}
//     }
//   ]
// }
// </pre>
// For a graph with input tensor aliases "tag" (string) and "image"
// (3-dimensional array of 8-bit ints):
// <pre>
// {
//   "instances": [
//     {
//       "tag": "beach",
//       "image": [
//         [
//           [138, 30, 66],
//           [130, 20, 56],
//           ...
//         ],
//         [
//           [126, 38, 61],
//           [122, 24, 57],
//           ...
//         ],
//         ...
//       ]
//     },
//     {
//       "tag": "car",
//       "image": [
//         [
//           [255, 0, 102],
//           [255, 0, 97],
//           ...
//         ],
//         [
//           [254, 1, 101],
//           [254, 2, 93],
//           ...
//         ],
//         ...
//       ]
//     },
//     ...
//   ]
// }
// </pre>
// If the call is successful, the response body will contain one prediction
// entry per instance in the request body. If prediction fails for any
// instance, the response body will contain no predictions and will contian
// a single error entry instead.
type PredictRequest struct {
	// Required. The resource name of a model or a version.
	//
	// Authorization: requires `Viewer` role on the parent project.
	Name string `protobuf:"bytes,1,opt,name=name" json:"name,omitempty"`
	//
	// Required. The prediction request body.
	HttpBody *google_api3.HttpBody `protobuf:"bytes,2,opt,name=http_body,json=httpBody" json:"http_body,omitempty"`
}

func (m *PredictRequest) Reset()                    { *m = PredictRequest{} }
func (m *PredictRequest) String() string            { return proto.CompactTextString(m) }
func (*PredictRequest) ProtoMessage()               {}
func (*PredictRequest) Descriptor() ([]byte, []int) { return fileDescriptor3, []int{0} }

func (m *PredictRequest) GetName() string {
	if m != nil {
		return m.Name
	}
	return ""
}

func (m *PredictRequest) GetHttpBody() *google_api3.HttpBody {
	if m != nil {
		return m.HttpBody
	}
	return nil
}

func init() {
	proto.RegisterType((*PredictRequest)(nil), "google.cloud.ml.v1.PredictRequest")
}

// Reference imports to suppress errors if they are not otherwise used.
var _ context.Context
var _ grpc.ClientConn

// This is a compile-time assertion to ensure that this generated file
// is compatible with the grpc package it is being compiled against.
const _ = grpc.SupportPackageIsVersion4

// Client API for OnlinePredictionService service

type OnlinePredictionServiceClient interface {
	// Performs prediction on the data in the request.
	//
	// **** REMOVE FROM GENERATED DOCUMENTATION
	Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*google_api3.HttpBody, error)
}

type onlinePredictionServiceClient struct {
	cc *grpc.ClientConn
}

func NewOnlinePredictionServiceClient(cc *grpc.ClientConn) OnlinePredictionServiceClient {
	return &onlinePredictionServiceClient{cc}
}

func (c *onlinePredictionServiceClient) Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*google_api3.HttpBody, error) {
	out := new(google_api3.HttpBody)
	err := grpc.Invoke(ctx, "/google.cloud.ml.v1.OnlinePredictionService/Predict", in, out, c.cc, opts...)
	if err != nil {
		return nil, err
	}
	return out, nil
}

// Server API for OnlinePredictionService service

type OnlinePredictionServiceServer interface {
	// Performs prediction on the data in the request.
	//
	// **** REMOVE FROM GENERATED DOCUMENTATION
	Predict(context.Context, *PredictRequest) (*google_api3.HttpBody, error)
}

func RegisterOnlinePredictionServiceServer(s *grpc.Server, srv OnlinePredictionServiceServer) {
	s.RegisterService(&_OnlinePredictionService_serviceDesc, srv)
}

func _OnlinePredictionService_Predict_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
	in := new(PredictRequest)
	if err := dec(in); err != nil {
		return nil, err
	}
	if interceptor == nil {
		return srv.(OnlinePredictionServiceServer).Predict(ctx, in)
	}
	info := &grpc.UnaryServerInfo{
		Server:     srv,
		FullMethod: "/google.cloud.ml.v1.OnlinePredictionService/Predict",
	}
	handler := func(ctx context.Context, req interface{}) (interface{}, error) {
		return srv.(OnlinePredictionServiceServer).Predict(ctx, req.(*PredictRequest))
	}
	return interceptor(ctx, in, info, handler)
}

var _OnlinePredictionService_serviceDesc = grpc.ServiceDesc{
	ServiceName: "google.cloud.ml.v1.OnlinePredictionService",
	HandlerType: (*OnlinePredictionServiceServer)(nil),
	Methods: []grpc.MethodDesc{
		{
			MethodName: "Predict",
			Handler:    _OnlinePredictionService_Predict_Handler,
		},
	},
	Streams:  []grpc.StreamDesc{},
	Metadata: "google/cloud/ml/v1/prediction_service.proto",
}

func init() { proto.RegisterFile("google/cloud/ml/v1/prediction_service.proto", fileDescriptor3) }

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