[mob][photos] Don't process large files on mobile

mobile/lib/models/ml/ml_versions.dart

@@ -7,3 +7,4 @@ const minimumClusterSize = 2;
 
 const embeddingFetchLimit = 200;
 final fileDownloadMlLimit = Platform.isIOS ? 5 : 10;
+const maxFileDownloadSize = 100000000;

mobile/lib/services/machine_learning/ml_service.dart

@@ -488,6 +488,14 @@ class MLService {
         );
         acceptedIssue = true;
       }
+      if (errorString.contains('FileSizeTooLargeForMobileIndexing')) {
+        _logger.severe(
+          '$errorString with ID ${instruction.file.uploadedFileID}, storing empty results so indexing does not get stuck',
+          e,
+          s,
+        );
+        acceptedIssue = true;
+      }
       if (acceptedIssue) {
         await MLDataDB.instance.bulkInsertFaces(
           [Face.empty(instruction.file.uploadedFileID!, error: true)],

mobile/lib/utils/ml_util.dart

@@ -153,7 +153,8 @@ Future<List<FileMLInstruction>> getFilesForMlIndexing() async {
 }
 
 Stream<List<FileMLInstruction>> fetchEmbeddingsAndInstructions(
-    int yieldSize,) async* {
+  int yieldSize,
+) async* {
   final List<FileMLInstruction> filesToIndex = await getFilesForMlIndexing();
   final List<List<FileMLInstruction>> chunks =
       filesToIndex.chunks(embeddingFetchLimit);
@@ -312,6 +313,12 @@ Future<String> getImagePathForML(EnteFile enteFile) async {
       throw ThumbnailRetrievalException(e.toString(), s);
     }
   } else {
+    // Don't process the file if it's too large (more than 100MB)
+    if (enteFile.fileSize != null && enteFile.fileSize! > maxFileDownloadSize) {
+      throw Exception(
+        "FileSizeTooLargeForMobileIndexing: size is ${enteFile.fileSize}",
+      );
+    }
     try {
       file = await getFile(enteFile, isOrigin: true);
     } catch (e, s) {