// Copyright 2017 Google Inc. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // Code generated by protoc-gen-go. DO NOT EDIT. // versions: // protoc-gen-go v1.22.0 // protoc v3.12.2 // source: google/cloud/ml/v1/prediction_service.proto package ml import ( context "context" reflect "reflect" sync "sync" proto "github.com/golang/protobuf/proto" _ "google.golang.org/genproto/googleapis/api/annotations" httpbody "google.golang.org/genproto/googleapis/api/httpbody" grpc "google.golang.org/grpc" codes "google.golang.org/grpc/codes" status "google.golang.org/grpc/status" protoreflect "google.golang.org/protobuf/reflect/protoreflect" protoimpl "google.golang.org/protobuf/runtime/protoimpl" ) const ( // Verify that this generated code is sufficiently up-to-date. _ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion) // Verify that runtime/protoimpl is sufficiently up-to-date. _ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20) ) // This is a compile-time assertion that a sufficiently up-to-date version // of the legacy proto package is being used. const _ = proto.ProtoPackageIsVersion4 // 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: // //
// {"instances": ["1.0,true,\\"x\\"", "-2.0,false,\\"y\\""]} //// Plain text: //
// {"instances": ["the quick brown fox", "la bruja le dio"]} //// Sentences encoded as lists of words (vectors of strings): //
// { // "instances": [ // ["the","quick","brown"], // ["la","bruja","le"], // ... // ] // } //// Floating point scalar values: //
// {"instances": [0.0, 1.1, 2.2]} //// Vectors of integers: //
// { // "instances": [ // [0, 1, 2], // [3, 4, 5], // ... // ] // } //// Tensors (in this case, two-dimensional tensors): //
// { // "instances": [ // [ // [0, 1, 2], // [3, 4, 5] // ], // ... // ] // } //// 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. //
// { // "instances": [ // [ // [ // [138, 30, 66], // [130, 20, 56], // ... // ], // [ // [126, 38, 61], // [122, 24, 57], // ... // ], // ... // ], // ... // ] // } //// 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`: //
{"b64": "..."}// For example: // // Two Serialized tf.Examples (fake data, for illustrative purposes only): //
// {"instances": [{"b64": "X5ad6u"}, {"b64": "IA9j4nx"}]} //// Two JPEG image byte strings (fake data, for illustrative purposes only): //
// {"instances": [{"b64": "ASa8asdf"}, {"b64": "JLK7ljk3"}]} //// 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: //
// { // "instances": [ // { // "a": 1.0, // "b": true, // "c": "x" // }, // { // "a": -2.0, // "b": false, // "c": "y" // } // ] // } //// 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): //
// { // "instances": [ // { // "tag": "beach", // "image": {"b64": "ASa8asdf"} // }, // { // "tag": "car", // "image": {"b64": "JLK7ljk3"} // } // ] // } //// For a graph with input tensor aliases "tag" (string) and "image" // (3-dimensional array of 8-bit ints): //
// { // "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], // ... // ], // ... // ] // }, // ... // ] // } //// 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 { state protoimpl.MessageState sizeCache protoimpl.SizeCache unknownFields protoimpl.UnknownFields // 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,proto3" json:"name,omitempty"` // // Required. The prediction request body. HttpBody *httpbody.HttpBody `protobuf:"bytes,2,opt,name=http_body,json=httpBody,proto3" json:"http_body,omitempty"` } func (x *PredictRequest) Reset() { *x = PredictRequest{} if protoimpl.UnsafeEnabled { mi := &file_google_cloud_ml_v1_prediction_service_proto_msgTypes[0] ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) ms.StoreMessageInfo(mi) } } func (x *PredictRequest) String() string { return protoimpl.X.MessageStringOf(x) } func (*PredictRequest) ProtoMessage() {} func (x *PredictRequest) ProtoReflect() protoreflect.Message { mi := &file_google_cloud_ml_v1_prediction_service_proto_msgTypes[0] if protoimpl.UnsafeEnabled && x != nil { ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) if ms.LoadMessageInfo() == nil { ms.StoreMessageInfo(mi) } return ms } return mi.MessageOf(x) } // Deprecated: Use PredictRequest.ProtoReflect.Descriptor instead. func (*PredictRequest) Descriptor() ([]byte, []int) { return file_google_cloud_ml_v1_prediction_service_proto_rawDescGZIP(), 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var _ grpc.ClientConnInterface // 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.SupportPackageIsVersion6 // OnlinePredictionServiceClient is the client API for OnlinePredictionService service. // // For semantics around ctx use and closing/ending streaming RPCs, please refer to https://godoc.org/google.golang.org/grpc#ClientConn.NewStream. type OnlinePredictionServiceClient interface { // Performs prediction on the data in the request. // // **** REMOVE FROM GENERATED DOCUMENTATION Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*httpbody.HttpBody, error) } type onlinePredictionServiceClient struct { cc grpc.ClientConnInterface } func NewOnlinePredictionServiceClient(cc grpc.ClientConnInterface) OnlinePredictionServiceClient { return &onlinePredictionServiceClient{cc} } func (c *onlinePredictionServiceClient) Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*httpbody.HttpBody, error) { out := new(httpbody.HttpBody) err := c.cc.Invoke(ctx, "/google.cloud.ml.v1.OnlinePredictionService/Predict", in, out, opts...) if err != nil { return nil, err } return out, nil } // OnlinePredictionServiceServer is the server API for OnlinePredictionService service. type OnlinePredictionServiceServer interface { // Performs prediction on the data in the request. // // **** REMOVE FROM GENERATED DOCUMENTATION Predict(context.Context, *PredictRequest) (*httpbody.HttpBody, error) } // UnimplementedOnlinePredictionServiceServer can be embedded to have forward compatible implementations. type UnimplementedOnlinePredictionServiceServer struct { } func (*UnimplementedOnlinePredictionServiceServer) Predict(context.Context, *PredictRequest) (*httpbody.HttpBody, error) { return nil, status.Errorf(codes.Unimplemented, "method Predict not implemented") } 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", }