From b4019f3c50c1bc8f01004980062d9cc0130fa2a4 Mon Sep 17 00:00:00 2001
From: vishnukvmd <vishnu@ente.io>
Date: Mon, 18 Dec 2023 22:21:01 +0530
Subject: [PATCH] Refactor commented code

---
 .../frameworks/ml_framework.dart              | 31 +++++++------------
 .../semantic_search_service.dart              | 24 +++++++-------
 2 files changed, 24 insertions(+), 31 deletions(-)

diff --git a/lib/services/semantic_search/frameworks/ml_framework.dart b/lib/services/semantic_search/frameworks/ml_framework.dart
index 3f98685b5..379dde9dd 100644
--- a/lib/services/semantic_search/frameworks/ml_framework.dart
+++ b/lib/services/semantic_search/frameworks/ml_framework.dart
@@ -7,6 +7,8 @@ import "package:path_provider/path_provider.dart";
 import "package:photos/core/network/network.dart";
 
 abstract class MLFramework {
+  static const kImageEncoderEnabled = false;
+
   final _logger = Logger("MLFramework");
 
   /// Returns the name of the framework
@@ -57,26 +59,17 @@ abstract class MLFramework {
   // ---
 
   Future<void> _initImageModel() async {
-    return;
-    // TODO: remove hardcoding
-    if (getFrameworkName() == "ggml") {
-      final path = await _getLocalImageModelPath();
-      if (File(path).existsSync()) {
-        await loadImageModel(path);
-      } else {
-        final tempFile = File(path + ".temp");
-        await _downloadFile(getImageModelRemotePath(), tempFile.path);
-        await tempFile.rename(path);
-        await loadImageModel(path);
-      }
+    if (!kImageEncoderEnabled) {
+      return;
+    }
+    final path = await _getLocalImageModelPath();
+    if (File(path).existsSync()) {
+      await loadImageModel(path);
     } else {
-      const assetPath = "assets/models/clip/clip-image-vit-32-float32.onnx";
-      await loadImageModel(
-        await getAccessiblePathForAsset(
-          assetPath,
-          "clip-image-vit-32-float32.onnx",
-        ),
-      );
+      final tempFile = File(path + ".temp");
+      await _downloadFile(getImageModelRemotePath(), tempFile.path);
+      await tempFile.rename(path);
+      await loadImageModel(path);
     }
   }
 
diff --git a/lib/services/semantic_search/semantic_search_service.dart b/lib/services/semantic_search/semantic_search_service.dart
index c7d530ad9..c646f4928 100644
--- a/lib/services/semantic_search/semantic_search_service.dart
+++ b/lib/services/semantic_search/semantic_search_service.dart
@@ -16,6 +16,7 @@ import "package:photos/models/embedding.dart";
 import "package:photos/models/file/file.dart";
 import "package:photos/objectbox.g.dart";
 import "package:photos/services/semantic_search/embedding_store.dart";
+import "package:photos/services/semantic_search/frameworks/ml_framework.dart";
 import 'package:photos/services/semantic_search/frameworks/onnx/onnx.dart';
 import "package:photos/utils/file_util.dart";
 import "package:photos/utils/local_settings.dart";
@@ -32,7 +33,6 @@ class SemanticSearchService {
   static const kModelName = "clip";
   static const kEmbeddingLength = 512;
   static const kScoreThreshold = 0.23;
-  static const kImageEncoderEnabled = false;
   static const kShouldPushEmbeddings = false;
 
   final _logger = Logger("SemanticSearchService");
@@ -156,7 +156,7 @@ class SemanticSearchService {
 
   Future<void> _backFill() async {
     if (!LocalSettings.instance.hasEnabledMagicSearch() ||
-        !kImageEncoderEnabled) {
+        !MLFramework.kImageEncoderEnabled) {
       return;
     }
     await _frameworkInitialization.future;
@@ -250,7 +250,7 @@ class SemanticSearchService {
   }
 
   Future<void> computeImageEmbedding(EnteFile file) async {
-    if (!kImageEncoderEnabled) {
+    if (!MLFramework.kImageEncoderEnabled) {
       return;
     }
     if (!_frameworkInitialization.isCompleted) {
@@ -269,15 +269,15 @@ class SemanticSearchService {
       // dev.log(computeScore(result, pyEmbedding).toString());
       // dev.log(computeScore(pyEmbedding, webEmbedding).toString());
 
-      // final embedding = Embedding(
-      //   fileID: file.uploadedFileID!,
-      //   model: _mlFramework.getFrameworkName() + "-" + kModelName,
-      //   embedding: result,
-      // );
-      // await EmbeddingStore.instance.storeEmbedding(
-      //   file,
-      //   embedding,
-      // );
+      final embedding = Embedding(
+        fileID: file.uploadedFileID!,
+        model: _mlFramework.getFrameworkName() + "-" + kModelName,
+        embedding: result,
+      );
+      await EmbeddingStore.instance.storeEmbedding(
+        file,
+        embedding,
+      );
     } catch (e, s) {
       _logger.severe(e, s);
     }