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On-Device AI in iOS: How I Built a Screenshot Classifier Without Any Cloud Calls

DEV Community: swift·Cool Light Shop Co,. LTD·3 days ago
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Why On-Device AI Matters for Screenshots Screenshots are sensitive. They contain prices, flight details, personal conversations, banking info. Uploading them to a cloud AI is a non-starter for most users. The good news: iOS gives you everything you need to build a capable AI pipeline that runs entirely on-device. Here's how I did it for Snaap , an AI screenshot cleaner. The Pipeline Step 1: Find the Screenshots let options = PHFetchOptions () options . predicate = NSPredicate ( format : "mediaType == %d AND (mediaSubtypes & %d) != 0" , PHAssetMediaType . image . rawValue , PHAssetMediaSubtype . photoScreenshot . rawValue ) options . sortDescriptors = [ NSSortDescriptor ( key : "creationDate" , ascending : false )] let screenshots = PHAsset . fetchAssets ( with : options ) Enter fullscreen mode Exit fullscreen mode iOS natively tags screenshots — no ML model needed for detection. Step 2: Extract Text with Vision OCR let request = VNRecognizeTextRequest { request , error in let text = request . results ? .…

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