Merge pull request #122 from codingpot/serve-with-database

feat: serve with database instead of mock

Author: nicewook

server/pkg/serv/serv.go

@@ -3,11 +3,10 @@ package serv
 import (
 	"context"
 	"fmt"
-	"time"
 
-	"github.com/codingpot/pr12er/server/internal"
+	"github.com/codingpot/pr12er/server/internal/data"
+	"github.com/codingpot/pr12er/server/pkg/handlers"
 	"github.com/codingpot/pr12er/server/pkg/pr12er"
-	"google.golang.org/protobuf/types/known/timestamppb"
 )
 
 type Server struct {
@@ -17,98 +16,13 @@ type Server struct {
 var _ pr12er.Pr12ErServiceServer = (*Server)(nil)
 
 func (s Server) GetDetail(_ context.Context, in *pr12er.GetDetailRequest) (*pr12er.GetDetailResponse, error) {
-	// Returns details of this ID
-	searchPRID := in.GetPrId()
-
-	resp := &pr12er.GetDetailResponse{
-		Detail: &pr12er.Detail{
-			PrId: searchPRID,
-			Paper: []*pr12er.Paper{
-				{
-					PaperId: "1",
-					// nolint: lll
-					Abstract: "We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.",
-					Repositories: []*pr12er.Repository{
-						{
-							Framework: pr12er.Framework_FRAMEWORK_TENSORFLOW,
-							Owner:     "goodfeli",
-							Url:       "https://github.com/tensorflow/tensorflow",
-						},
-						{
-							Framework: pr12er.Framework_FRAMEWORK_PYTORCH,
-							Owner:     "eriklindernoren",
-							Url:       "https://github.com/pytorch/pytorch",
-						},
-						{
-							Framework: pr12er.Framework_FRAMEWORK_TENSORFLOW,
-							Owner:     "google-research",
-							Url:       "https://github.com/tensorflow/tensorflow",
-						},
-						{
-							Framework: pr12er.Framework_FRAMEWORK_PYTORCH,
-							Owner:     "eriklindernoren",
-							Url:       "https://github.com/pytorch/pytorch",
-						},
-					},
-				},
-			},
-			SameAuthorPapers: []*pr12er.Paper{
-				{
-					Title:   "On distinguishability criteria for estimating generative models",
-					Authors: []string{"Ian J. Goodfellow"},
-					PubDate: timestamppb.New(newDate(2015, 5, 21)),
-				},
-			},
-			RelevantPapers: []*pr12er.Paper{
-				{
-					Title:   "Learning to Efficiently Sample from Diffusion Probabilistic Models",
-					Authors: []string{"Daniel Watson"},
-					PubDate: timestamppb.New(newDate(2021, 6, 7)),
-				},
-			},
-		},
-	}
-
-	return resp, nil
-}
-
-// A little helper function to create a Ymd.
-func newDate(y int, m time.Month, d int) time.Time {
-	return time.Date(y, m, d, 0, 0, 0, 0, time.UTC)
+	return handlers.DetailResponseFromDB(in.GetPrId(), &data.DB)
 }
 
 func (s Server) GetHello(_ context.Context, in *pr12er.HelloRequest) (*pr12er.HelloResponse, error) {
 	return &pr12er.HelloResponse{Body: fmt.Sprintf("Hello %s", in.Body)}, nil
 }
 
 func (s Server) GetVideos(_ context.Context, _ *pr12er.GetVideosRequest) (*pr12er.GetVideosResponse, error) {
-	return getVideosFromDumpedPbtxt()
-}
-
-func getVideosFromDumpedPbtxt() (*pr12er.GetVideosResponse, error) {
-	var resp pr12er.GetVideosResponse
-	metadataDump := internal.ReadPR12MetadataProtoText()
-
-	resp.Videos = make([]*pr12er.Video, 0, len(metadataDump.Metadata))
-
-	for _, metadata := range metadataDump.Metadata {
-		video := &pr12er.Video{
-			PrId:      metadata.GetId(),
-			Title:     metadata.GetTitle(),
-			Presenter: metadata.GetPresenter(),
-			Keywords:  metadata.GetKeywords(),
-
-			// TODO: Update category and number of likes.
-			Category:     pr12er.Category_CATEGORY_UNSPECIFIED,
-			NumberOfLike: 0,
-		}
-
-		videoMetadata := metadata.GetVideoMetadata()
-		if len(videoMetadata) > 0 {
-			video.Link = videoMetadata[0].Url
-		}
-
-		resp.Videos = append(resp.Videos, video)
-	}
-	return &resp, nil
+	return handlers.VideosResponseFromDB(&data.DB), nil
 }

server/pkg/serv/serv_test.go

@@ -29,10 +29,52 @@ func TestGetVideos(t *testing.T) {
 	assert.Greater(t, len(resp.Videos), 0)
 }
 
-func TestServer_GetDetails(t *testing.T) {
-	s := Server{}
-	request := pr12er.GetDetailRequest{PrId: 1}
-	got, err := s.GetDetail(context.Background(), &request)
-	assert.NoError(t, err)
-	assert.Equal(t, int32(1), got.GetDetail().GetPrId())
+func TestServer_GetDetail(t *testing.T) {
+	type args struct {
+		ctx context.Context
+		req *pr12er.GetDetailRequest
+	}
+	tests := []struct {
+		name    string
+		args    args
+		want    *pr12er.GetDetailResponse
+		wantErr bool
+	}{
+		{
+			name: "Returns an error if the PR is not found with the given ID",
+			args: args{
+				ctx: context.Background(),
+				req: &pr12er.GetDetailRequest{
+					PrId: 0,
+				},
+			},
+			want:    nil,
+			wantErr: true,
+		},
+		{
+			name: "Returns a valid response if the PR is found",
+			args: args{
+				ctx: context.Background(),
+				req: &pr12er.GetDetailRequest{PrId: 1},
+			},
+			want: &pr12er.GetDetailResponse{Detail: &pr12er.Detail{
+				PrId: 1,
+			}},
+			wantErr: false,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			s := Server{}
+			got, err := s.GetDetail(tt.args.ctx, tt.args.req)
+			if tt.wantErr {
+				assert.Error(t, err)
+			} else {
+				assert.NoError(t, err)
+				assert.Equal(t, tt.want.GetDetail().GetPrId(), got.GetDetail().GetPrId())
+				assert.Greater(t, len(got.GetDetail().GetPaper()), 0)
+			}
+		})
+	}
 }