feat(JAQPOT-478): send null if model too large (#144)

Author: alarv

src/main/kotlin/org/jaqpot/api/mapper/ModelMapper.kt

@@ -103,7 +103,7 @@ fun ModelDto.toEntity(creatorId: String): Model {
 }
 
 fun Model.toPredictionModelDto(
-    rawModel: ByteArray,
+    rawModel: ByteArray?,
     doas: List<PredictionDoaDto>,
     rawPreprocessor: ByteArray?
 ): PredictionModelDto {
@@ -113,7 +113,7 @@ fun Model.toPredictionModelDto(
         independentFeatures = this.independentFeatures.map { it.toDto() },
         type = this.type.toDto(),
         task = this.task.toDto(),
-        rawModel = this.encodeRawModel(rawModel),
+        rawModel = rawModel?.let { this.encodeRawModel(rawModel) },
         rawPreprocessor = this.encodeRawPreprocessor(rawPreprocessor),
         doas = doas,
         selectedFeatures = this.selectedFeatures ?: emptyList(),

src/main/kotlin/org/jaqpot/api/service/model/ModelService.kt

@@ -78,6 +78,7 @@ class ModelService(
             ModelTypeDto.R_TREE_REGR,
         )
         private val logger = KotlinLogging.logger {}
+        const val FIVE_MEGABYTES_IN_BYTES = 5e+6
 
         fun storeRawModelToStorage(model: Model, storageService: StorageService, modelRepository: ModelRepository) {
             if (model.rawModel == null) {
@@ -321,7 +322,11 @@ class ModelService(
         val rawModel = if (model.isQsarToolboxModel()) {
             byteArrayOf()
         } else {
-            storageService.readRawModel(model)
+            if (storageService.readRawModelMetadata(model).contentLength() > FIVE_MEGABYTES_IN_BYTES) {
+                null
+            } else {
+                storageService.readRawModel(model)
+            }
         }
         val rawPreprocessor = storageService.readRawPreprocessor(model)
 

src/main/kotlin/org/jaqpot/api/service/prediction/runtime/runtimes/RESTRuntime.kt

@@ -111,7 +111,7 @@ abstract class RESTRuntime : RuntimeBase() {
 
 
         val legacyPredictionRequestDto = LegacyPredictionRequestDto(
-            rawModel = arrayOf(predictionModelDto.rawModel),
+            rawModel = arrayOf(predictionModelDto.rawModel!!),
             dataset = LegacyDatasetDto(
                 LegacyDataEntryDto(values = values),
                 features = predictionModelDto.independentFeatures.mapIndexed { index, it ->

src/main/resources/openapi.yaml

@@ -2520,7 +2520,6 @@ components:
         - dependentFeatures
         - independentFeatures
         - type
-        - rawModel
         - task
       properties:
         id: