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Refactor commented code

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This commit is contained in:
vishnukvmd 2023-12-18 22:21:01 +05:30
parent a5e5c60111
commit b4019f3c50
2 changed files with 24 additions and 31 deletions
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31
lib/services/semantic_search/frameworks/ml_framework.dart
View file

@ -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);
}
}

24
lib/services/semantic_search/semantic_search_service.dart
View file

@ -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);
}