Remove unused model

web/apps/photos/src/services/machineLearning/machineLearningService.ts

@@ -19,7 +19,6 @@ import mlIDbStorage, {
 import {
     BlurDetectionMethod,
     BlurDetectionService,
-    ClipEmbedding,
     ClusteringMethod,
     ClusteringService,
     Face,
@@ -761,12 +760,10 @@ class ServerFileMl {
     public height?: number;
     public width?: number;
     public faceEmbedding: ServerFaceEmbeddings;
-    public clipEmbedding?: ClipEmbedding;
 
     public constructor(
         fileID: number,
         faceEmbedding: ServerFaceEmbeddings,
-        clipEmbedding?: ClipEmbedding,
         height?: number,
         width?: number,
     ) {
@@ -774,7 +771,6 @@ class ServerFileMl {
         this.height = height;
         this.width = width;
         this.faceEmbedding = faceEmbedding;
-        this.clipEmbedding = clipEmbedding;
     }
 }
 
@@ -895,7 +891,6 @@ function LocalFileMlDataToServerFileMl(
     return new ServerFileMl(
         localFileMlData.fileId,
         faceEmbeddings,
-        undefined,
         imageDimensions.height,
         imageDimensions.width,
     );

web/apps/photos/src/services/ml/types.ts

@@ -329,8 +329,3 @@ export interface MachineLearningWorker {
 
     close(): void;
 }
-
-export interface ClipEmbedding {
-    embedding: Float32Array;
-    model: "ggml-clip" | "onnx-clip";
-}

web/apps/photos/src/types/embedding.tsx

@@ -1,9 +1,9 @@
 /**
- * The embeddings models that we support.
+ * The embeddings that we (the current client) knows how to handle.
  *
  * This is an exhaustive set of values we pass when PUT-ting encrypted
  * embeddings on the server. However, we should be prepared to receive an
- * {@link EncryptedEmbedding} with a model value distinct from one of these.
+ * {@link EncryptedEmbedding} with a model value different from these.
  */
 export type EmbeddingModel = "onnx-clip" | "file-ml-clip-face";