From 3d965b6101f6f60bb2bbfd627d7181c758fc7697 Mon Sep 17 00:00:00 2001 From: Vijay Sharma Date: Mon, 22 Jan 2024 14:48:16 -0500 Subject: [PATCH] =?UTF-8?q?=F0=9F=A4=97?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 8 + .../xcshareddata/IDEWorkspaceChecks.plist | 8 + .../Huggingface.Swift-Package.xcscheme | 66 ++ .../xcschemes/Huggingface.Swift.xcscheme | 92 +++ Package.swift | 33 + README.md | 1 + .../Huggingface.Inference/HfInference.swift | 124 ++++ .../HfInferenceOptions.swift | 31 + .../HuggingfaceModelsMetdata.swift | 205 ++++++ .../Tasks/audio/AudioClassification.swift | 37 + .../Tasks/audio/AudioToAudio.swift | 45 ++ .../audio/AutomaticSpeechRecognition.swift | 29 + .../Tasks/audio/TextToSpeech.swift | 28 + .../Tasks/custom/CustomRequest.swift | 17 + .../Tasks/cv/ImageClassification.swift | 30 + .../Tasks/cv/ImageSegmentation.swift | 31 + .../Tasks/cv/ImageToImage.swift | 88 +++ .../Tasks/cv/ImageToText.swift | 26 + .../Tasks/cv/ObjectDetection.swift | 38 ++ .../Tasks/cv/TextToImage.swift | 61 ++ .../cv/ZeroShotImageClassification.swift | 40 ++ .../DocumentQuestionAnswering.swift | 45 ++ .../multimodal/VisualQuestionAnswering.swift | 42 ++ .../Tasks/nlp/Conversational.swift | 80 +++ .../Tasks/nlp/FeatureExtraction.swift | 29 + .../Tasks/nlp/FillMask.swift | 37 + .../Tasks/nlp/QuestionAnswering.swift | 44 ++ .../Tasks/nlp/SentenceSimilarity.swift | 34 + .../Tasks/nlp/Summarization.swift | 66 ++ .../Tasks/nlp/TableQuestionAnswering.swift | 59 ++ .../Tasks/nlp/TextClassification.swift | 34 + .../Tasks/nlp/TextGeneration.swift | 88 +++ .../Tasks/nlp/TokenClassification.swift | 56 ++ .../Tasks/nlp/Translation.swift | 33 + .../Tasks/nlp/ZeroShotClassification.swift | 49 ++ .../Tasks/tabular/TabularClassification.swift | 33 + .../Tasks/tabular/TabularRegression.swift | 33 + .../Utilities/DataParser.swift | 27 + .../Utilities/Fetch.swift | 44 ++ .../Utilities/Logger.swift | 142 ++++ .../Utilities/Request.swift | 116 ++++ .../Utilities/RequestManager.swift | 51 ++ .../HuggingfaceModelsMetdata+Extensions.swift | 50 ++ .../Huggingface_SwiftTests.swift | 642 ++++++++++++++++++ Tests/Huggingface.Swift.Tests/TestFetch.swift | 62 ++ .../assets/elephants.png | Bin 0 -> 788390 bytes .../assets/invoice.png | Bin 0 -> 24463 bytes .../assets/sample1.flac | Bin 0 -> 282378 bytes 48 files changed, 2934 insertions(+) create mode 100644 .gitignore create mode 100644 .swiftpm/xcode/package.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist create mode 100644 .swiftpm/xcode/xcshareddata/xcschemes/Huggingface.Swift-Package.xcscheme create mode 100644 .swiftpm/xcode/xcshareddata/xcschemes/Huggingface.Swift.xcscheme create mode 100644 Package.swift create mode 100644 README.md create mode 100644 Sources/Huggingface.Inference/HfInference.swift create mode 100644 Sources/Huggingface.Inference/HfInferenceOptions.swift create mode 100644 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Tests/Huggingface.Swift.Tests/Huggingface_SwiftTests.swift create mode 100644 Tests/Huggingface.Swift.Tests/TestFetch.swift create mode 100644 Tests/Huggingface.Swift.Tests/assets/elephants.png create mode 100644 Tests/Huggingface.Swift.Tests/assets/invoice.png create mode 100644 Tests/Huggingface.Swift.Tests/assets/sample1.flac diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..0023a53 --- /dev/null +++ b/.gitignore @@ -0,0 +1,8 @@ +.DS_Store +/.build +/Packages +xcuserdata/ +DerivedData/ +.swiftpm/configuration/registries.json +.swiftpm/xcode/package.xcworkspace/contents.xcworkspacedata +.netrc diff --git a/.swiftpm/xcode/package.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist b/.swiftpm/xcode/package.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist new file mode 100644 index 0000000..18d9810 --- /dev/null +++ b/.swiftpm/xcode/package.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist @@ -0,0 +1,8 @@ + + + + + IDEDidComputeMac32BitWarning + + + diff --git a/.swiftpm/xcode/xcshareddata/xcschemes/Huggingface.Swift-Package.xcscheme b/.swiftpm/xcode/xcshareddata/xcschemes/Huggingface.Swift-Package.xcscheme new file mode 100644 index 0000000..a1148ab --- /dev/null +++ b/.swiftpm/xcode/xcshareddata/xcschemes/Huggingface.Swift-Package.xcscheme @@ -0,0 +1,66 @@ + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/.swiftpm/xcode/xcshareddata/xcschemes/Huggingface.Swift.xcscheme b/.swiftpm/xcode/xcshareddata/xcschemes/Huggingface.Swift.xcscheme new file mode 100644 index 0000000..03d5ff1 --- /dev/null +++ b/.swiftpm/xcode/xcshareddata/xcschemes/Huggingface.Swift.xcscheme @@ -0,0 +1,92 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Package.swift b/Package.swift new file mode 100644 index 0000000..115e678 --- /dev/null +++ b/Package.swift @@ -0,0 +1,33 @@ +// swift-tools-version: 5.9 +// The swift-tools-version declares the minimum version of Swift required to build this package. + +import PackageDescription + +let package = Package( + name: "Huggingface.Swift", + platforms: [ + .iOS(.v13), + .macOS(.v10_15), + ], + products: [ + .library( + name: "Huggingface.Swift", + targets: ["Huggingface.Inference"]), + ], + targets: [ + .target( + name: "Huggingface.Inference" + ), + .testTarget( + name: "Huggingface.Swift.Tests", + dependencies: [ + "Huggingface.Inference" + ], + resources: [ + .copy("assets/elephants.png"), + .copy("assets/sample1.flac"), + .copy("assets/invoice.png") + ] + ), + ] +) diff --git a/README.md b/README.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/README.md @@ -0,0 +1 @@ + diff --git a/Sources/Huggingface.Inference/HfInference.swift b/Sources/Huggingface.Inference/HfInference.swift new file mode 100644 index 0000000..dd8104d --- /dev/null +++ b/Sources/Huggingface.Inference/HfInference.swift @@ -0,0 +1,124 @@ +// +// HfInference.swift +// +// + +import Foundation + +public class HfInference { + private let accessToken: String + private let fetch: FetchProtocol + private let metadata: HuggingfaceModelsMetdataManager + + let log: LoggerProtocol + + init( + accessToken: String = "", + fetch: FetchProtocol, + logLevel: LogLevel = .info + ) { + Logger.level = logLevel + self.log = Logger.make(tag: HfInference.self) + self.accessToken = accessToken + self.fetch = fetch + self.metadata = HuggingfaceModelsMetdataManager(fetch: fetch) + } + + convenience init( + accessToken: String = "", + session: URLSession = URLSession.shared, + logLevel: LogLevel = .info + ) { + self.init( + accessToken: accessToken, + fetch: Fetch(session: session), + logLevel: logLevel + ) + } + + // MARK: - Private Methods + + func execute( + model: String?, + taskHint: HuggingfaceModelsMetdata.CodingKeys, + body: Codable, + requestType: ModelContentType, + responseType: ModelContentType, + options: HfInferenceOptions + ) async throws -> T { + let modelId = try await metadata.resolve( + model: model, + taskHint: taskHint + ) + + var bodyAsData: Data + if let data = body as? Data { + bodyAsData = data + } else { + let encoder = JSONEncoder() + encoder.keyEncodingStrategy = .convertToSnakeCase + bodyAsData = try encoder.encode(body) + + log.with(level: .debug) { + if let string = String(data: bodyAsData, encoding: .utf8) { + $0("body for \(taskHint.stringValue) -> \(string)") + } + } + } + + let request = ModelRequest( + task: taskHint, + options: .init( + accessToken: accessToken, + model: modelId, + url: url(model: modelId, task: nil), + body: bodyAsData, + requestType: requestType, + responseType: responseType, + useCache: options.useCache, + waitForModel: options.waitForModel + ) + ) + + return try await run(request: request, options: options) + } + + private func run( + request: ModelRequest, + options: HfInferenceOptions + ) async throws -> T { + let result: Result = await request.get(using: fetch) + switch result { + case .failure(let error): + switch error { + case .modelIsCurrentlyLoading: + if options.retryOnError && !options.waitForModel { + return try await run(request: request, options: .init( + retryOnError: options.retryOnError, + useCache: options.useCache, + waitForModel: true + )) + } else { + throw error + } + default: + throw error + } + case .success(let otuput): + return otuput + } + } + + private func url(model: String, task: String?) -> URL { + if (model.hasPrefix("/") || model.hasPrefix("http:") || model.hasPrefix("https:")), let url = URL(string: model) { + return url + } + + let baseUrl = "https://api-inference.huggingface.co" + if let task { + return URL(string: "\(baseUrl)/pipeline/\(task)/\(model)")! + } + + return URL(string: "\(baseUrl)/models/\(model)")! + } +} diff --git a/Sources/Huggingface.Inference/HfInferenceOptions.swift b/Sources/Huggingface.Inference/HfInferenceOptions.swift new file mode 100644 index 0000000..e9378e9 --- /dev/null +++ b/Sources/Huggingface.Inference/HfInferenceOptions.swift @@ -0,0 +1,31 @@ +// +// HfInferenceOptions.swift +// + +import Foundation + +public struct HfInferenceOptions: Codable { + /** + * (Default: true) Boolean. If a request 503s and wait_for_model is set to false, the request will be retried with the same parameters but with wait_for_model set to true. + */ + let retryOnError: Bool + /** + * (Default: true). Boolean. There is a cache layer on the inference API to speedup requests we have already seen. Most models can use those results as is as models are deterministic (meaning the results will be the same anyway). However if you use a non deterministic model, you can set this parameter to prevent the caching mechanism from being used resulting in a real new query. + */ + let useCache: Bool + + /** + * (Default: false) Boolean. If the model is not ready, wait for it instead of receiving 503. It limits the number of requests required to get your inference done. It is advised to only set this flag to true after receiving a 503 error as it will limit hanging in your application to known places. + */ + let waitForModel: Bool + + public init( + retryOnError: Bool = true, + useCache: Bool = true, + waitForModel: Bool = false + ) { + self.retryOnError = retryOnError + self.useCache = useCache + self.waitForModel = waitForModel + } +} diff --git a/Sources/Huggingface.Inference/HuggingfaceModelsMetdata.swift b/Sources/Huggingface.Inference/HuggingfaceModelsMetdata.swift new file mode 100644 index 0000000..cecbeac --- /dev/null +++ b/Sources/Huggingface.Inference/HuggingfaceModelsMetdata.swift @@ -0,0 +1,205 @@ +// +// Types.swift +// + +import Foundation + +struct HuggingfaceModelsMetdata: Codable { + let audioClassification: Metadata + let audioToAudio: Metadata + let automaticSpeechRecognition: Metadata + let conversational: Metadata + let depthEstimation: Metadata + let documentQuestionAnswering: Metadata + let featureExtraction: Metadata + let fillMask: Metadata + let imageClassification: Metadata + let imageSegmentation: Metadata + let imageToImage, imageToText, maskGeneration, objectDetection: Metadata + let videoClassification, questionAnswering, reinforcementLearning, sentenceSimilarity: Metadata + let summarization, tableQuestionAnswering, tabularClassification, tabularRegression: Metadata + let textClassification, textGeneration, textToImage, textToSpeech: Metadata + let textToVideo, tokenClassification, translation, unconditionalImageGeneration: Metadata + let visualQuestionAnswering, zeroShotClassification, zeroShotImageClassification, zeroShotObjectDetection: Metadata + let textTo3D, imageTo3D: Metadata + + enum CodingKeys: String, CodingKey { + case audioClassification = "audio-classification" + case audioToAudio = "audio-to-audio" + case automaticSpeechRecognition = "automatic-speech-recognition" + case conversational + case depthEstimation = "depth-estimation" + case documentQuestionAnswering = "document-question-answering" + case featureExtraction = "feature-extraction" + case fillMask = "fill-mask" + case imageClassification = "image-classification" + case imageSegmentation = "image-segmentation" + case imageToImage = "image-to-image" + case imageToText = "image-to-text" + case maskGeneration = "mask-generation" + case objectDetection = "object-detection" + case videoClassification = "video-classification" + case questionAnswering = "question-answering" + case reinforcementLearning = "reinforcement-learning" + case sentenceSimilarity = "sentence-similarity" + case summarization + case tableQuestionAnswering = "table-question-answering" + case tabularClassification = "tabular-classification" + case tabularRegression = "tabular-regression" + case textClassification = "text-classification" + case textGeneration = "text-generation" + case textToImage = "text-to-image" + case textToSpeech = "text-to-speech" + case textToVideo = "text-to-video" + case tokenClassification = "token-classification" + case translation + case unconditionalImageGeneration = "unconditional-image-generation" + case visualQuestionAnswering = "visual-question-answering" + case zeroShotClassification = "zero-shot-classification" + case zeroShotImageClassification = "zero-shot-image-classification" + case zeroShotObjectDetection = "zero-shot-object-detection" + case textTo3D = "text-to-3d" + case imageTo3D = "image-to-3d" + } +} + +// MARK: - Dataset +struct Model: Codable { + let description, id: String +} + +struct Metadata: Codable { + // datasets + // demo + // metrics + let models: [Model] + // spaces + // summary + // widgetModels + // youtubeId + let id: String + // label + // libraries +} + +class HuggingfaceModelsMetdataManager { + private var metadata: HuggingfaceModelsMetdata? = nil + private let fetch: FetchProtocol + + init(fetch: FetchProtocol) { + self.fetch = fetch + } + + func resolve( + model: String?, + taskHint: HuggingfaceModelsMetdata.CodingKeys + ) async throws -> String { + if let model = model { + return model + } + + if metadata == nil { + metadata = try await fetchMetadata(using: fetch) + } + + let models = try self.model(type: taskHint) + return models.first!.id + } + + private func model(type: HuggingfaceModelsMetdata.CodingKeys) throws -> [Model] { + guard let metadata = metadata else { + fatalError("The models metadata should be loaded") + } + + switch type { + case .audioClassification: + return metadata.audioClassification.models + case .audioToAudio: + return metadata.audioToAudio.models + case .automaticSpeechRecognition: + return metadata.automaticSpeechRecognition.models + case .conversational: + return metadata.conversational.models + case .depthEstimation: + return metadata.depthEstimation.models + case .documentQuestionAnswering: + return metadata.documentQuestionAnswering.models + case .featureExtraction: + return metadata.featureExtraction.models + case .fillMask: + return metadata.fillMask.models + case .imageClassification: + return metadata.imageClassification.models + case .imageSegmentation: + return metadata.imageSegmentation.models + case .imageToImage: + return metadata.imageToImage.models + case .imageToText: + return metadata.imageToText.models + case .maskGeneration: + return metadata.maskGeneration.models + case .objectDetection: + return metadata.objectDetection.models + case .videoClassification: + return metadata.videoClassification.models + case .questionAnswering: + return metadata.questionAnswering.models + case .reinforcementLearning: + return metadata.reinforcementLearning.models + case .sentenceSimilarity: + return metadata.sentenceSimilarity.models + case .summarization: + return metadata.summarization.models + case .tableQuestionAnswering: + return metadata.tableQuestionAnswering.models + case .tabularClassification: + return metadata.tabularClassification.models + case .tabularRegression: + return metadata.tabularRegression.models + case .textClassification: + return metadata.textClassification.models + case .textGeneration: + return metadata.textGeneration.models + case .textToImage: + return metadata.textToImage.models + case .textToSpeech: + return metadata.textToSpeech.models + case .textToVideo: + return metadata.textToVideo.models + case .tokenClassification: + return metadata.tokenClassification.models + case .translation: + return metadata.translation.models + case .unconditionalImageGeneration: + return metadata.unconditionalImageGeneration.models + case .visualQuestionAnswering: + return metadata.visualQuestionAnswering.models + case .zeroShotClassification: + return metadata.zeroShotClassification.models + case .zeroShotImageClassification: + return metadata.zeroShotImageClassification.models + case .zeroShotObjectDetection: + return metadata.zeroShotObjectDetection.models + case .textTo3D: + return metadata.textTo3D.models + case .imageTo3D: + return metadata.imageTo3D.models + } + } + + private func fetchMetadata(using fetch: FetchProtocol) async throws -> HuggingfaceModelsMetdata { + let decoder = JSONDecoder() + decoder.keyDecodingStrategy = .convertFromSnakeCase + let parser = JsonDataParser(decoder: decoder) + + let client = RequestManager(fetch: fetch, parser: parser) + let metadata = ModelMetadataRequest() + let result = await metadata.fetch(client: client) + switch result { + case .failure(let error): + throw error + case .success(let model): + return model + } + } +} diff --git a/Sources/Huggingface.Inference/Tasks/audio/AudioClassification.swift b/Sources/Huggingface.Inference/Tasks/audio/AudioClassification.swift new file mode 100644 index 0000000..ee53de2 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/audio/AudioClassification.swift @@ -0,0 +1,37 @@ +// +// AudioClassification.swift +// + +import Foundation + +public struct AudioClassificationOutput: Codable { + /** + * The label for the class (model specific) + */ + let label: String + + /** + * A float that represents how likely it is that the audio file belongs to this class. + */ + let score: Float +} + +extension HfInference { + /** + * This task reads some audio input and outputs the likelihood of classes. + */ + public func audioClassification( + _ data: Data, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [AudioClassificationOutput] { + return try await execute( + model: model, + taskHint: .audioClassification, + body: data, + requestType: .data, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/audio/AudioToAudio.swift b/Sources/Huggingface.Inference/Tasks/audio/AudioToAudio.swift new file mode 100644 index 0000000..034ebb7 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/audio/AudioToAudio.swift @@ -0,0 +1,45 @@ +// +// AudioToAudio.swift +// + +import Foundation + +public struct AudioToAudioOutput: Codable { + /** + * The label for the audio output (model specific) + */ + let label: String + + /** + * Base64 encoded audio output. + */ + let blob: String + + /** + * Content-type for blob, e.g. audio/flac + */ + let contentType: String + + enum CodingKeys : String, CodingKey { + case label = "label" + case blob = "blob" + case contentType = "content-type" + } +} + +extension HfInference { + public func audioToAudio( + _ data: Data, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [AudioToAudioOutput] { + return try await execute( + model: model, + taskHint: .audioToAudio, + body: data, + requestType: .data, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/audio/AutomaticSpeechRecognition.swift b/Sources/Huggingface.Inference/Tasks/audio/AutomaticSpeechRecognition.swift new file mode 100644 index 0000000..18ebcc2 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/audio/AutomaticSpeechRecognition.swift @@ -0,0 +1,29 @@ +// +// AutomaticSpeechRecognition.swift +// + +import Foundation + +public struct AutomaticSpeechRecognitionOutput: Codable { + let text: String +} + +extension HfInference { + /** + * This task reads some audio input and outputs the said words within the audio files. + */ + public func automaticSpeechRecognition( + _ data: Data, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> AutomaticSpeechRecognitionOutput { + return try await execute( + model: model, + taskHint: .automaticSpeechRecognition, + body: data, + requestType: .data, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/audio/TextToSpeech.swift b/Sources/Huggingface.Inference/Tasks/audio/TextToSpeech.swift new file mode 100644 index 0000000..16b4854 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/audio/TextToSpeech.swift @@ -0,0 +1,28 @@ +// +// TextToSpeech.swift +// + +import Foundation + +struct TextToSpeechInputs: Codable { + let inputs: String +} + +extension HfInference { + public func textToSpeech( + inputs: String, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> Data { + return try await execute( + model: model, + taskHint: .textToSpeech, + body: TextToSpeechInputs( + inputs: inputs + ), + requestType: .json, + responseType: .data, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/custom/CustomRequest.swift b/Sources/Huggingface.Inference/Tasks/custom/CustomRequest.swift new file mode 100644 index 0000000..b735797 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/custom/CustomRequest.swift @@ -0,0 +1,17 @@ +// +// CustomRequest.swift +// + +import Foundation + +extension HfInference { + public func request( + ) async throws -> [String] { + [] + } + + public func streamingRequest( + ) async throws -> [String] { + [] + } +} diff --git a/Sources/Huggingface.Inference/Tasks/cv/ImageClassification.swift b/Sources/Huggingface.Inference/Tasks/cv/ImageClassification.swift new file mode 100644 index 0000000..9760494 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/cv/ImageClassification.swift @@ -0,0 +1,30 @@ +// +// ImageClassification.swift +// + +import Foundation + +public struct ImageClassificationOutput: Codable { + let label: String + let score: Float +} + +extension HfInference { + /** + * This task reads some image input and outputs the likelihood of classes. + */ + public func imageClassification( + _ data: Data, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [ImageClassificationOutput] { + return try await execute( + model: model, + taskHint: .imageClassification, + body: data, + requestType: .data, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/cv/ImageSegmentation.swift b/Sources/Huggingface.Inference/Tasks/cv/ImageSegmentation.swift new file mode 100644 index 0000000..870be66 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/cv/ImageSegmentation.swift @@ -0,0 +1,31 @@ +// +// ImageSegmentation.swift +// + +import Foundation + +public struct ImageSegmentationOutput: Codable { + let label: String + let mask: String + let score: Float +} + +extension HfInference { + /** + * This task reads some image input and outputs the likelihood of classes & bounding boxes of detected objects. + */ + public func imageSegmentation( + _ data: Data, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [ImageSegmentationOutput] { + return try await execute( + model: model, + taskHint: .imageSegmentation, + body: data, + requestType: .data, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/cv/ImageToImage.swift b/Sources/Huggingface.Inference/Tasks/cv/ImageToImage.swift new file mode 100644 index 0000000..10829dc --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/cv/ImageToImage.swift @@ -0,0 +1,88 @@ +// +// ImageToImage.swift +// + +import Foundation + +struct ImageToImageParameters: Codable { + /** + * The text prompt to guide the image generation. + */ + let prompt: String? + /** + * strengh param only works for SD img2img and alt diffusion img2img models + * Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` + * will be used as a starting point, adding more noise to it the larger the `strength`. The number of + * denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will + * be maximum and the denoising process will run for the full number of iterations specified in + * `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. + **/ + let strength: Float? + /** + * An optional negative prompt for the image generation + */ + let negativePrompt: String? + /** + * The height in pixels of the generated image + */ + let height: Int? + /** + * The width in pixels of the generated image + */ + let width: Int? + /** + * The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. + */ + let numInferenceSteps: Int? + /** + * Guidance scale: Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. + */ + let guidanceScale: Float? + /** + * guess_mode only works for ControlNet models, defaults to False In this mode, the ControlNet encoder will try best to recognize the content of the input image even if + * you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended. + */ + let guessMode: Bool? +} + +struct ImageToImageInputs: Codable { + let inputs: String + let parameters: ImageToImageParameters +} + +extension HfInference { + public func imageToImage( + _ data: Data, + prompt: String? = nil, + strength: Float? = nil, + negativePrompt: String? = nil, + height: Int? = nil, + width: Int? = nil, + numInferenceSteps: Int? = nil, + guidanceScale: Float? = nil, + guessMode: Bool? = nil, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> Data { + return try await execute( + model: model, + taskHint: .imageToImage, + body: ImageToImageInputs( + inputs: data.base64EncodedString(), + parameters: ImageToImageParameters( + prompt: prompt, + strength: strength, + negativePrompt: negativePrompt, + height: height, + width: width, + numInferenceSteps: numInferenceSteps, + guidanceScale: guidanceScale, + guessMode: guessMode + ) + ), + requestType: .json, + responseType: .data, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/cv/ImageToText.swift b/Sources/Huggingface.Inference/Tasks/cv/ImageToText.swift new file mode 100644 index 0000000..7a1234d --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/cv/ImageToText.swift @@ -0,0 +1,26 @@ +// +// ImageToText.swift +// + +import Foundation + +public struct ImageToTextOutput: Codable { + let generatedText: String +} + +extension HfInference { + public func imageToText( + _ data: Data, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [ImageToTextOutput] { + return try await execute( + model: model, + taskHint: .imageToText, + body: data, + requestType: .data, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/cv/ObjectDetection.swift b/Sources/Huggingface.Inference/Tasks/cv/ObjectDetection.swift new file mode 100644 index 0000000..aaf46bc --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/cv/ObjectDetection.swift @@ -0,0 +1,38 @@ +// +// ObjectDetection.swift +// + +import Foundation + +public struct ObjectDetectionBoxOutput: Codable { + let xmin: Int + let xmax: Int + let ymin: Int + let ymax: Int +} + +public struct ObjectDetectionOutput: Codable { + let label: String + let score: Float + let bod: ObjectDetectionBoxOutput +} + +extension HfInference { + /** + * This task reads some image input and outputs the likelihood of classes & bounding boxes of detected objects. + */ + public func objectDetection( + _ data: Data, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [ObjectDetectionOutput] { + return try await execute( + model: model, + taskHint: .objectDetection, + body: data, + requestType: .data, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/cv/TextToImage.swift b/Sources/Huggingface.Inference/Tasks/cv/TextToImage.swift new file mode 100644 index 0000000..e45ee76 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/cv/TextToImage.swift @@ -0,0 +1,61 @@ +// +// TextToImage.swift +// + +import Foundation + +struct TextToImageParameters: Codable { + let negativePrompt: String? + let height: Float? + let width: Float? + let numInferenceSteps: Int? + let guidanceScale: Float? +} + +struct TextToImageInputs: Codable { + let inputs: String + let parameters: TextToImageParameters +} + +extension HfInference { + /** + * Note that the request normally takes a long time, and + * its common to experience timeout. It's a good idea to + * set the timeout using the following: + * + * let sessionConfig = URLSessionConfiguration.default + * sessionConfig.timeoutIntervalForRequest = 230.0 + * sessionConfig.timeoutIntervalForResource = 260.0 + * let session = URLSession(configuration: sessionConfig) + * + * Output is image data normally in jpeg format + */ + public func textToImage( + inputs: String, + negativePrompt: String? = nil, + width: Float? = nil, + height: Float? = nil, + numInferenceSteps: Int? = nil, + guidanceScale: Float? = nil, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> Data { + return try await execute( + model: model, + taskHint: .textToImage, + body: TextToImageInputs( + inputs: inputs, + parameters: .init( + negativePrompt: negativePrompt, + height: height, + width: width, + numInferenceSteps: numInferenceSteps, + guidanceScale: guidanceScale + ) + ), + requestType: .json, + responseType: .data, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/cv/ZeroShotImageClassification.swift b/Sources/Huggingface.Inference/Tasks/cv/ZeroShotImageClassification.swift new file mode 100644 index 0000000..3691431 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/cv/ZeroShotImageClassification.swift @@ -0,0 +1,40 @@ +// +// ZeroShotImageClassification.swift +// + +import Foundation + +struct ZeroShotImageClassificationParameters: Codable { + let candidateLabels: [String] +} + +struct ZeroShotImageClassificationInputs: Codable { + let inputs: String + let parameters: ZeroShotImageClassificationParameters +} + +public struct ZeroShotImageClassificationOutput: Codable { + let label: String + let score: Float +} + +extension HfInference { + public func zeroShotImageClassification( + _ data: Data, + candidateLabels: [String], + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [ZeroShotImageClassificationOutput] { + return try await execute( + model: model, + taskHint: .zeroShotImageClassification, + body: ZeroShotImageClassificationInputs( + inputs: data.base64EncodedString(), + parameters: .init(candidateLabels: candidateLabels) + ), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/multimodal/DocumentQuestionAnswering.swift b/Sources/Huggingface.Inference/Tasks/multimodal/DocumentQuestionAnswering.swift new file mode 100644 index 0000000..515568b --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/multimodal/DocumentQuestionAnswering.swift @@ -0,0 +1,45 @@ +// +// DocumentQuestionAnswering.swift +// + +import Foundation + +struct DocumentQuestionAnsweringInputs: Codable { + let image: String + let question: String +} + +public struct DocumentQuestionAnsweringOutput: Codable { + /** + * A string that’s the answer within the document. + */ + let answer: String? + let start: Int? + let end: Int? + /** + * A float that represents how likely that the answer is correct + */ + let score: Float? + +} + +extension HfInference { + public func documentQuestionAnswering( + image: Data, + question: String, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [DocumentQuestionAnsweringOutput] { + return try await execute( + model: model, + taskHint: .documentQuestionAnswering, + body: DocumentQuestionAnsweringInputs( + image: image.base64EncodedString(), + question: question + ), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/multimodal/VisualQuestionAnswering.swift b/Sources/Huggingface.Inference/Tasks/multimodal/VisualQuestionAnswering.swift new file mode 100644 index 0000000..3d9ea30 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/multimodal/VisualQuestionAnswering.swift @@ -0,0 +1,42 @@ +// +// VisualQuestionAnswering.swift +// + +import Foundation + +struct VisualQuestionAnsweringInputs: Codable { + let image: String + let question: String +} + +public struct VisualQuestionAnsweringOutput: Codable { + /** + * A string that’s the answer to a visual question. + */ + let answer: String + /** + * Answer correctness score. + */ + let score: Float +} + +extension HfInference { + public func visualQuestionAnswering( + image: Data, + question: String, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [VisualQuestionAnsweringOutput] { + return try await execute( + model: model, + taskHint: .visualQuestionAnswering, + body: VisualQuestionAnsweringInputs( + image: image.base64EncodedString(), + question: question + ), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/nlp/Conversational.swift b/Sources/Huggingface.Inference/Tasks/nlp/Conversational.swift new file mode 100644 index 0000000..ebb61cc --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/nlp/Conversational.swift @@ -0,0 +1,80 @@ +// +// Conversational.swift +// + +import Foundation + +struct ConversationalInput: Codable { + let pastUserInputs: [String]? + let generatedResponses: [String]? + let text: String +} + +struct ConversationalParameters: Codable { + var minLength: Int? = nil + var maxLength: Int? = nil + var topK: Int? = nil + var topP: Float? = nil + var temperature: Float = 1.0 + var repetitionPenalty: Float? = nil + var maxTime: Float? = nil +} + +struct ConversationalInputs: Codable { + let inputs: ConversationalInput + let parameters: ConversationalParameters +} + +public struct ConversationOutput: Codable { + let pastUserInputs: [String] + let generatedResponses: [String] +} + +public struct ConversationalOutput: Codable { + let generatedText: String + let conversation: ConversationOutput +} + +extension HfInference { + /** + * This task corresponds to any chatbot like structure. Models tend to have shorter max_length, so please check with caution when using a given model if you need long range dependency or not. + */ + public func conversational( + text: String, + pastUserInputs: [String]? = nil, + generatedResponses: [String]? = nil, + minLength: Int? = nil, + maxLength: Int? = nil, + topK: Int? = nil, + topP: Float? = nil, + temperature: Float = 1.0, + repetitionPenalty: Float? = nil, + maxTime: Float? = nil, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> ConversationalOutput { + return try await execute( + model: model, + taskHint: .conversational, + body: ConversationalInputs( + inputs: .init( + pastUserInputs: pastUserInputs, + generatedResponses: generatedResponses, + text: text + ), + parameters: .init( + minLength: minLength, + maxLength: maxLength, + topK: topK, + topP: topP, + temperature: temperature, + repetitionPenalty: repetitionPenalty, + maxTime: maxTime + ) + ), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/nlp/FeatureExtraction.swift b/Sources/Huggingface.Inference/Tasks/nlp/FeatureExtraction.swift new file mode 100644 index 0000000..bd6b02d --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/nlp/FeatureExtraction.swift @@ -0,0 +1,29 @@ +// +// FeatureExtraction.swift +// + +import Foundation + +struct FeatureExtractionInputs: Codable { + let inputs: [String] +} + +extension HfInference { + /** + * This task reads some text and outputs raw float values, that are usually consumed as part of a semantic database/semantic search. + */ + public func featureExtraction( + inputs: [String], + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [[[[Float]]]] { + return try await execute( + model: model, + taskHint: .featureExtraction, + body: FeatureExtractionInputs(inputs: inputs), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/nlp/FillMask.swift b/Sources/Huggingface.Inference/Tasks/nlp/FillMask.swift new file mode 100644 index 0000000..c2d7db0 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/nlp/FillMask.swift @@ -0,0 +1,37 @@ +// +// FillMask.swift +// + +import Foundation + +struct FillMaskInput: Codable { + let inputs: [String] +} + +public struct FillMaskOutput: Codable { + let sequence: String + let score: Float + let token: Int + let tokenStr: String +} + +extension HfInference { + /** + * Tries to fill in a hole with a missing word (token to be precise). + * That’s the base task for BERT models. + */ + public func fillMask( + inputs: [String], + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [FillMaskOutput] { + return try await execute( + model: model, + taskHint: .fillMask, + body: FillMaskInput(inputs: inputs), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/nlp/QuestionAnswering.swift b/Sources/Huggingface.Inference/Tasks/nlp/QuestionAnswering.swift new file mode 100644 index 0000000..e4f4681 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/nlp/QuestionAnswering.swift @@ -0,0 +1,44 @@ +// +// QuestionAnswering.swift +// + +import Foundation + +struct QuestionAnsweringInput: Codable { + let question: String + let context: String +} + +struct QuestionAnsweringInputs: Codable { + let inputs: QuestionAnsweringInput +} + +public struct QuestionAnsweringOutput: Codable { + let answer: String + let end: Int + let score: Float + let start: Int +} + +extension HfInference { + /** + * Want to have a nice know-it-all bot that can answer any question? + */ + public func questionAnswering( + question: String, + context: String, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> QuestionAnsweringOutput { + return try await execute( + model: model, + taskHint: .questionAnswering, + body: QuestionAnsweringInputs( + inputs: .init(question: question, context: context) + ), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/nlp/SentenceSimilarity.swift b/Sources/Huggingface.Inference/Tasks/nlp/SentenceSimilarity.swift new file mode 100644 index 0000000..e025f49 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/nlp/SentenceSimilarity.swift @@ -0,0 +1,34 @@ +// +// SentenceSimilarity.swift +// + +import Foundation + +struct SentenceSimilarityInput: Codable { + let sourceSentence: String + let sentences: [String] +} + +extension HfInference { + /** + * Calculate the semantic similarity between one text and a list of other sentences by comparing their embeddings. + */ + public func sentenceSimilarity( + sourceSentence: String, + sentences: [String], + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [Float] { + return try await execute( + model: model, + taskHint: .sentenceSimilarity, + body: SentenceSimilarityInput( + sourceSentence: sourceSentence, + sentences: sentences + ), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/nlp/Summarization.swift b/Sources/Huggingface.Inference/Tasks/nlp/Summarization.swift new file mode 100644 index 0000000..33ae014 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/nlp/Summarization.swift @@ -0,0 +1,66 @@ +// +// Summarization.swift +// + +import Foundation + +struct SummarizationParameters: Codable { + var minLength: Int? = nil + var maxLength: Int? = nil + var topK: Int? = nil + var topP: Float? = nil + var temperature: Float = 1.0 + var repetitionPenalty: Float? = nil + var maxTime: Float? = nil +} + +struct SummarizationInput: Codable { + let inputs: String + let parameters: SummarizationParameters +} + +public struct SummarizationOutput: Codable { + let summaryText: String +} + +extension HfInference { + /** + * This task is well known to summarize longer text into shorter text. + * Be careful, some models have a maximum length of input. + * That means that the summary cannot handle full books for instance. + * Be careful when choosing your model. If you want to discuss your summarization needs, + * please get in touch with us: api-enterprise@huggingface.co + */ + public func summarization( + inputs: String, + minLength: Int? = nil, + maxLength: Int? = nil, + topK: Int? = nil, + topP: Float? = nil, + temperature: Float = 1.0, + repetitionPenalty: Float? = nil, + maxTime: Float? = nil, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [SummarizationOutput] { + return try await execute( + model: model, + taskHint: .summarization, + body: SummarizationInput( + inputs: inputs, + parameters: .init( + minLength: minLength, + maxLength: maxLength, + topK: topK, + topP: topP, + temperature: temperature, + repetitionPenalty: repetitionPenalty, + maxTime: maxTime + ) + ), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/nlp/TableQuestionAnswering.swift b/Sources/Huggingface.Inference/Tasks/nlp/TableQuestionAnswering.swift new file mode 100644 index 0000000..32cd179 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/nlp/TableQuestionAnswering.swift @@ -0,0 +1,59 @@ +// +// TableQuestionAnswering.swift +// + +import Foundation + +struct TableQuestionAnsweringInput: Codable { + let query: String + let table: [String: [String]] +} + +struct TableQuestionAnsweringInputs: Codable { + let inputs: TableQuestionAnsweringInput +} + +public struct TableQuestionAnsweringOutput: Codable { + /** + * The aggregator used to get the answer + */ + let aggregator: String + /** + * The plaintext answer + */ + let answer: String + /** + * A list of coordinates of the cells contents + */ + let cells: [String] + /** + * a list of coordinates of the cells referenced in the answer + */ + let coordinates: [[Float]] +} + +extension HfInference { + /** + * Don’t know SQL? Don’t want to dive into a large spreadsheet? Ask questions in plain english! + * + * TODO: microsoft/tapex-base doesn't seem to return an object that matches the output, causing a parsing error + */ + public func tableQuestionAnswering( + query: String, + table: [String: [String]], + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> TableQuestionAnsweringOutput { + return try await execute( + model: model, + taskHint: .tableQuestionAnswering, + body: TableQuestionAnsweringInputs(inputs: .init( + query: query, + table: table + )), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/nlp/TextClassification.swift b/Sources/Huggingface.Inference/Tasks/nlp/TextClassification.swift new file mode 100644 index 0000000..b5f29eb --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/nlp/TextClassification.swift @@ -0,0 +1,34 @@ +// +// TextClassification.swift +// + +import Foundation + +struct TextClassificationInputs: Codable { + let inputs: [String] +} + +public struct TextClassificationOutput: Codable { + let label: String + let score: Float +} + +extension HfInference { + /** + * Usually used for sentiment-analysis this will output the likelihood of classes of an input. + */ + public func textClassification( + inputs: [String], + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [[TextClassificationOutput]] { + return try await execute( + model: model, + taskHint: .textClassification, + body: TextClassificationInputs(inputs: inputs), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/nlp/TextGeneration.swift b/Sources/Huggingface.Inference/Tasks/nlp/TextGeneration.swift new file mode 100644 index 0000000..ec53722 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/nlp/TextGeneration.swift @@ -0,0 +1,88 @@ +// +// TextGeneration.swift +// + +import Foundation + +struct TextGenerationParameters: Codable { + let topK: Int? + let topP: Float? + let temperature: Float + let repetitionPenalty: Float? + let maxNewTokens: Int? + let maxTime: Float? + let returnFullText: Bool + let numReturnSequences: Int + let doSample: Bool + let truncate: Int? + let stopSequences: [String]? +} + +struct TextGenerationInputs: Codable { + let inputs: String + let parameters: TextGenerationParameters +} + +public struct TextGenerationOutput: Codable { + let generatedText: String +} + +extension HfInference { + /** + * Use to continue text from a prompt. This is a very generic task. + */ + public func textGeneration( + inputs: String, + topK: Int? = nil, + topP: Float? = nil, + temperature: Float = 1.0, + repetitionPenalty: Float? = nil, + maxNewTokens: Int? = nil, + maxTime: Float? = nil, + returnFullText: Bool = true, + numReturnSequences: Int = 1, + doSample: Bool = true, + truncate: Int? = nil, + stopSequences: [String]? = nil, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [TextGenerationOutput] { + return try await execute( + model: model, + taskHint: .textGeneration, + body: TextGenerationInputs( + inputs: inputs, + parameters: .init( + topK: topK, + topP: topP, + temperature: temperature, + repetitionPenalty: repetitionPenalty, + maxNewTokens: maxNewTokens, + maxTime: maxTime, + returnFullText: returnFullText, + numReturnSequences: numReturnSequences, + doSample: doSample, + truncate: truncate, + stopSequences: stopSequences + ) + ), + requestType: .json, + responseType: .json, + options: options + ) + } + + public func textGenerationStream( + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [String] { + fatalError() +// return try await execute( +// model: model, +// taskHint: .translation, +// body: TranslationInputs(inputs: inputs), +// type: .json, +// options: options +// ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/nlp/TokenClassification.swift b/Sources/Huggingface.Inference/Tasks/nlp/TokenClassification.swift new file mode 100644 index 0000000..7e29a9d --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/nlp/TokenClassification.swift @@ -0,0 +1,56 @@ +// +// TokenClassification.swift +// + +import Foundation + +public enum TokenClassificationAggregationStrategy: String, Codable { + case none + case simple + case first + case average + case max +} + +struct TokenClassificationParameters: Codable { + let aggregationStrategy: TokenClassificationAggregationStrategy +} + +struct TokenClassificationInputs: Codable { + let inputs: [String] + let parameters: TokenClassificationParameters +} + +public struct TokenClassificationOutput: Codable { + let entityGroup: String + let score: Float + let word: String + let start: Int + let end: Int +} + +extension HfInference { + /** + * Usually used for sentence parsing, either grammatical, or Named Entity Recognition (NER) to understand keywords contained within text. + */ + public func tokenClassification( + inputs: [String], + aggregationStrategy: TokenClassificationAggregationStrategy = .simple, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [[TokenClassificationOutput]] { + return try await execute( + model: model, + taskHint: .tokenClassification, + body: TokenClassificationInputs( + inputs: inputs, + parameters: .init( + aggregationStrategy: aggregationStrategy + ) + ), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/nlp/Translation.swift b/Sources/Huggingface.Inference/Tasks/nlp/Translation.swift new file mode 100644 index 0000000..59762ff --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/nlp/Translation.swift @@ -0,0 +1,33 @@ +// +// Translation.swift +// + +import Foundation + +struct TranslationInputs: Codable { + let inputs: [String] +} + +public struct TranslationOutput: Codable { + let translationText: String +} + +extension HfInference { + /** + * This task is well known to translate text from one language to another + */ + public func translation( + inputs: [String], + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [TranslationOutput] { + return try await execute( + model: model, + taskHint: .translation, + body: TranslationInputs(inputs: inputs), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/nlp/ZeroShotClassification.swift b/Sources/Huggingface.Inference/Tasks/nlp/ZeroShotClassification.swift new file mode 100644 index 0000000..1bcdd9e --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/nlp/ZeroShotClassification.swift @@ -0,0 +1,49 @@ +// +// ZeroShotClassification.swift +// + +import Foundation + +struct ZeroShotClassificationParameters: Codable { + let candidateLabels: [String] + var multiLabel: Bool? = false +} + +struct ZeroShotClassificationInputs: Codable { + let inputs: [String] + let parameters: ZeroShotClassificationParameters +} + +public struct ZeroShotClassificationOutput: Codable { + let labels: [String] + let scores: [Float] + let sequence: String +} + +extension HfInference { + /** + * This task is super useful to try out classification with zero code, you simply pass a sentence/paragraph and the possible labels for that sentence, and you get a result. + */ + public func zeroShotClassification( + inputs: [String], + candidateLabels: [String], + multiLabel: Bool = false, + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [ZeroShotClassificationOutput] { + return try await execute( + model: model, + taskHint: .zeroShotClassification, + body: ZeroShotClassificationInputs( + inputs: inputs, + parameters: .init( + candidateLabels: candidateLabels, + multiLabel: multiLabel + ) + ), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/tabular/TabularClassification.swift b/Sources/Huggingface.Inference/Tasks/tabular/TabularClassification.swift new file mode 100644 index 0000000..d8bc08b --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/tabular/TabularClassification.swift @@ -0,0 +1,33 @@ +// +// TabularClassification.swift +// + +import Foundation + +struct TabularClassificationData: Codable { + let data: [String: [String]] +} + +struct TabularClassificationInputs: Codable { + let inputs: TabularClassificationData +} + +extension HfInference { + public func tabularClassification( + data: [String: [String]], + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [String] { + log.w("TODO: This model did not work while testing") + return try await execute( + model: model, + taskHint: .tabularClassification, + body: TabularClassificationInputs( + inputs: .init(data: data) + ), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Tasks/tabular/TabularRegression.swift b/Sources/Huggingface.Inference/Tasks/tabular/TabularRegression.swift new file mode 100644 index 0000000..9e1c143 --- /dev/null +++ b/Sources/Huggingface.Inference/Tasks/tabular/TabularRegression.swift @@ -0,0 +1,33 @@ +// +// TabularRegression.swift +// + +import Foundation + +struct TabularRegressionData: Codable { + let data: [String: [String]] +} + +struct TabularRegressionInputs: Codable { + let inputs: TabularClassificationData +} + +extension HfInference { + public func tabularRegression( + data: [String: [String]], + model: String? = nil, + options: HfInferenceOptions = .init() + ) async throws -> [String] { + log.w("TODO: This model did not work while testing") + return try await execute( + model: model, + taskHint: .tabularRegression, + body: TabularClassificationInputs( + inputs: .init(data: data) + ), + requestType: .json, + responseType: .json, + options: options + ) + } +} diff --git a/Sources/Huggingface.Inference/Utilities/DataParser.swift b/Sources/Huggingface.Inference/Utilities/DataParser.swift new file mode 100644 index 0000000..f6f85d5 --- /dev/null +++ b/Sources/Huggingface.Inference/Utilities/DataParser.swift @@ -0,0 +1,27 @@ +// +// DataParser.swift +// + +import Foundation + +protocol DataParserProtocol { + func parse(data: Data) throws -> T +} + +class JsonDataParser: DataParserProtocol { + private var jsonDecoder: JSONDecoder + + init(decoder: JSONDecoder) { + self.jsonDecoder = decoder + } + + func parse(data: Data) throws -> T { + return try jsonDecoder.decode(T.self, from: data) + } +} + +class NoOpParser: DataParserProtocol { + func parse(data: Data) throws -> T { + return data as! T + } +} diff --git a/Sources/Huggingface.Inference/Utilities/Fetch.swift b/Sources/Huggingface.Inference/Utilities/Fetch.swift new file mode 100644 index 0000000..54267fc --- /dev/null +++ b/Sources/Huggingface.Inference/Utilities/Fetch.swift @@ -0,0 +1,44 @@ +// +// Fetch.swift +// + +import Foundation + +public protocol FetchProtocol { + func perform( + _ request: Request + ) async -> Result +} + +public struct RequestResponse { + let data: Data + let response: HTTPURLResponse +} + +class Fetch: FetchProtocol { + private let session: URLSession + + init(session: URLSession = URLSession.shared) { + self.session = session + } + + func perform(_ request: Request) async -> Result { + do { + let r = try request.createURLRequest() + let (data, response) = try await session.data(for: r) + + guard let response = response as? HTTPURLResponse else { + return .failure(.invalidServerResponse) + } + + return .success(RequestResponse( + data: data, + response: response + )) + } catch RequestError.invalidURL { + return .failure(.invalidURL) + } catch { + return .failure(.unknownFailure(error: error)) + } + } +} diff --git a/Sources/Huggingface.Inference/Utilities/Logger.swift b/Sources/Huggingface.Inference/Utilities/Logger.swift new file mode 100644 index 0000000..37188c0 --- /dev/null +++ b/Sources/Huggingface.Inference/Utilities/Logger.swift @@ -0,0 +1,142 @@ +// +// Logger.swift +// + +import Foundation + +public protocol LoggerProtocol { + func i(_ message: String) + func e(_ message: String) + func e(_ message: String, _ error: Error?) + func d(_ message: String) + func w(_ message: String) + func with(level: LogLevel, _ callback: @escaping ((String) -> Void) -> Void) +} + +public enum LogLevel: Int { + case error = 0 + case warning = 1 + case info = 2 + case debug = 3 + + var label: String { + get { + switch self { + case .debug: + "DEBUG" + case .error: + "ERROR" + case .info: + "INFO" + case .warning: + "WARN" + } + } + } +} + +private class TaggerLogger: LoggerProtocol { + private let tag: String + private let logger: Logger + private let level: LogLevel + + init( + tag: String, + logger: Logger, + level: LogLevel + ) { + self.tag = tag + self.logger = logger + self.level = level + } + + func i(_ message: String) { + if level.rawValue >= LogLevel.info.rawValue { + logger.i(tag, message: message) + } + } + + func e(_ message: String) { + self.e(message, nil) + } + + func e(_ message: String, _ error: Error?) { + if level.rawValue >= LogLevel.error.rawValue { + logger.e(tag, message: message, error: error) + } + } + + func d(_ message: String) { + if level.rawValue >= LogLevel.debug.rawValue { + logger.d(tag, message: message) + } + } + + func w(_ message: String) { + if level.rawValue >= LogLevel.warning.rawValue { + logger.w(tag, message: message) + } + } + + func with(level: LogLevel, _ callback: @escaping ((String) -> Void) -> Void) { + if self.level.rawValue >= level.rawValue { + callback { [self] message in + switch level { + case .debug: + self.d(message) + case .error: + self.e(message) + case .info: + self.i(message) + case .warning: + self.w(message) + } + } + } + } +} + +class Logger { + public static var level: LogLevel = .info + public static func make(tag: Any) -> LoggerProtocol { + return TaggerLogger( + tag: String(describing: tag), + logger: Logger.shared, + level: Logger.level + ) + } + + struct LogLine { + let tag: String + let message: String + let level: LogLevel + let timestamp: Date = Date() + } + + fileprivate static let shared = Logger() + private var lines: [LogLine] = [] + + fileprivate func i(_ tag: String, message: String) { + append(line: LogLine(tag: tag, message: message, level: .info)) + } + + fileprivate func d(_ tag: String, message: String) { + append(line: LogLine(tag: tag, message: message, level: .debug)) + } + + fileprivate func e(_ tag: String, message: String, error: Error? = nil) { + append(line: LogLine(tag: tag, message: message, level: .error)) + if let error = error { + append(line: LogLine(tag: tag, message: "\(error)", level: .error)) + } + } + + fileprivate func w(_ tag: String, message: String) { + append(line: LogLine(tag: tag, message: message, level: .warning)) + } + + private func append(line: LogLine) { +// lines.append(line) + print("[\(line.level.label)]: \(line.message)") + } +} diff --git a/Sources/Huggingface.Inference/Utilities/Request.swift b/Sources/Huggingface.Inference/Utilities/Request.swift new file mode 100644 index 0000000..a12768c --- /dev/null +++ b/Sources/Huggingface.Inference/Utilities/Request.swift @@ -0,0 +1,116 @@ +// +// Request.swift +// +// + +import Foundation + +public enum RequestError: LocalizedError { + case invalidServerResponse + case invalidURL + case requestFailure(response: HTTPURLResponse, data: Data) + case unknownFailure(error: Error) + case parseFailure(error: Error) + case modelIsCurrentlyLoading + case rateLimitReached +} + +public protocol Request { + var debug: Bool { get } + func createURLRequest() throws -> URLRequest +} + +struct ModelMetadataRequest: Request { + let debug = false + + func fetch(client: RequestManager) async -> Result { + await client.perform(self) + } + + func createURLRequest() throws -> URLRequest { + let url = URL(string: "https://huggingface.co/api/tasks")! + return URLRequest(url: url) + } +} + +enum ModelContentType { + case data + case json +} + +struct ModelRequestOptions { + let accessToken: String? + let model: String + let url: URL + let body: Data + let requestType: ModelContentType + let responseType: ModelContentType + let useCache: Bool + let waitForModel: Bool +} + +struct ModelRequest: Request { + private let task: HuggingfaceModelsMetdata.CodingKeys + private let options: ModelRequestOptions + + init( + task: HuggingfaceModelsMetdata.CodingKeys, + options: ModelRequestOptions + ) { + self.task = task + self.options = options + } + + var debug: Bool { + get { + switch task { + default: + #if DEBUG + true + #else + false + #endif + } + } + } + + func get(using fetch: FetchProtocol) async -> Result { + let client = RequestManager(fetch: fetch, parser: responseParser) + return await client.perform(self) + } + + private var responseParser: DataParserProtocol { + get { + switch options.responseType { + case .data: + return NoOpParser() + case .json: + let decoder = JSONDecoder() + decoder.keyDecodingStrategy = .convertFromSnakeCase + let parser = JsonDataParser(decoder: decoder) + return parser + } + } + } + + func createURLRequest() throws -> URLRequest { + var urlRequest = URLRequest(url: options.url) + urlRequest.httpMethod = "POST" + + if let token = options.accessToken, !token.isEmpty { + urlRequest.setValue( + "Bearer \(token)", + forHTTPHeaderField: "Authorization" + ) + } + if options.requestType == .json { + urlRequest.setValue("application/json", forHTTPHeaderField: "Content-Type") + } + urlRequest.setValue(options.useCache ? "true" : "false", forHTTPHeaderField: "X-Use-Cache") + urlRequest.setValue(options.waitForModel ? "true" : "false", forHTTPHeaderField: "X-Wait-For-Model") + urlRequest.httpBody = options.body + + return urlRequest + } +} + diff --git a/Sources/Huggingface.Inference/Utilities/RequestManager.swift b/Sources/Huggingface.Inference/Utilities/RequestManager.swift new file mode 100644 index 0000000..852fe45 --- /dev/null +++ b/Sources/Huggingface.Inference/Utilities/RequestManager.swift @@ -0,0 +1,51 @@ +// +// RequestManager.swift +// +// + +import Foundation + +class RequestManager { + private let log = Logger.make(tag: RequestManager.self) + private let fetch: FetchProtocol + private let parser: DataParserProtocol + + init(fetch: FetchProtocol, parser: DataParserProtocol) { + self.fetch = fetch + self.parser = parser + } + + func perform( + _ request: Request + ) async -> Result { + let result = await fetch.perform(request) + + switch result { + case .success(let result): + let data = result.data + if request.debug { + log.with(level: .debug) { + let text = String(data: data, encoding: .utf8) + $0("request: \(request)\nresponse \(result.response.statusCode):\n\(result.response.allHeaderFields)\n\(text ?? "Can't parse")") + } + } + + if result.response.statusCode >= 200 && result.response.statusCode < 300 { + do { + let decoded: T = try parser.parse(data: data) + return .success(decoded) + } catch { + return .failure(.parseFailure(error: error)) + } + } else if result.response.statusCode == 503 { + return .failure(.modelIsCurrentlyLoading) + } else if result.response.statusCode == 429 { + return .failure(.rateLimitReached) + } else { + return .failure(.requestFailure(response: result.response, data: result.data)) + } + case .failure(let error): + return .failure(error) + } + } +} diff --git a/Tests/Huggingface.Swift.Tests/HuggingfaceModelsMetdata+Extensions.swift b/Tests/Huggingface.Swift.Tests/HuggingfaceModelsMetdata+Extensions.swift new file mode 100644 index 0000000..203a98e --- /dev/null +++ b/Tests/Huggingface.Swift.Tests/HuggingfaceModelsMetdata+Extensions.swift @@ -0,0 +1,50 @@ +// +// HuggingfaceModelsMetdata+Extensions.swift +// +// + +import Foundation +@testable import Huggingface_Inference + +extension HuggingfaceModelsMetdata { + static func make( + audioClassification: Metadata = .init(models: [], id: ""), + audioToAudio: Metadata = .init(models: [], id: ""), + automaticSpeechRecognition: Metadata = .init(models: [], id: ""), + conversational: Metadata = .init(models: [], id: ""), + depthEstimation: Metadata = .init(models: [], id: ""), + documentQuestionAnswering: Metadata = .init(models: [], id: ""), + featureExtraction: Metadata = .init(models: [], id: ""), + fillMask: Metadata = .init(models: [.init(description: "", id: "distilbert-base-uncased")], id: ""), + imageClassification: Metadata = .init(models: [], id: ""), + imageSegmentation: Metadata = .init(models: [], id: ""), + imageToImage: Metadata = .init(models: [], id: ""), + imageToText: Metadata = .init(models: [], id: ""), + maskGeneration: Metadata = .init(models: [], id: ""), + objectDetection: Metadata = .init(models: [], id: ""), + videoClassification: Metadata = .init(models: [], id: ""), + questionAnswering: Metadata = .init(models: [], id: ""), + reinforcementLearning: Metadata = .init(models: [], id: ""), + sentenceSimilarity: Metadata = .init(models: [], id: ""), + summarization: Metadata = .init(models: [], id: ""), + tableQuestionAnswering: Metadata = .init(models: [], id: ""), + tabularClassification: Metadata = .init(models: [], id: ""), + tabularRegression: Metadata = .init(models: [], id: ""), + textClassification: Metadata = .init(models: [], id: ""), + textGeneration: Metadata = .init(models: [], id: ""), + textToImage: Metadata = .init(models: [], id: ""), + textToSpeech: Metadata = .init(models: [], id: ""), + textToVideo: Metadata = .init(models: [], id: ""), + tokenClassification: Metadata = .init(models: [], id: ""), + translation: Metadata = .init(models: [], id: ""), + unconditionalImageGeneration: Metadata = .init(models: [], id: ""), + visualQuestionAnswering: Metadata = .init(models: [], id: ""), + zeroShotClassification: Metadata = .init(models: [], id: ""), + zeroShotImageClassification: Metadata = .init(models: [], id: ""), + zeroShotObjectDetection: Metadata = .init(models: [], id: ""), + textTo3D: Metadata = .init(models: [], id: ""), + imageTo3D: Metadata = .init(models: [], id: "") + ) -> HuggingfaceModelsMetdata { + HuggingfaceModelsMetdata(audioClassification: audioClassification, audioToAudio: audioClassification, automaticSpeechRecognition: automaticSpeechRecognition, conversational: conversational, depthEstimation: depthEstimation, documentQuestionAnswering: documentQuestionAnswering, featureExtraction: featureExtraction, fillMask: fillMask, imageClassification: imageClassification, imageSegmentation: imageSegmentation, imageToImage: imageToImage, imageToText: imageToText, maskGeneration: maskGeneration, objectDetection: objectDetection, videoClassification: videoClassification, questionAnswering: questionAnswering, reinforcementLearning: reinforcementLearning, sentenceSimilarity: sentenceSimilarity, summarization: summarization, tableQuestionAnswering: tableQuestionAnswering, tabularClassification: tabularClassification, tabularRegression: tabularRegression, textClassification: textClassification, textGeneration: textGeneration, textToImage: textToImage, textToSpeech: textToSpeech, textToVideo: textToVideo, tokenClassification: textClassification, translation: translation, unconditionalImageGeneration: unconditionalImageGeneration, visualQuestionAnswering: visualQuestionAnswering, zeroShotClassification: zeroShotClassification, zeroShotImageClassification: zeroShotImageClassification, zeroShotObjectDetection: zeroShotObjectDetection, textTo3D: textTo3D, imageTo3D: imageTo3D) + } +} diff --git a/Tests/Huggingface.Swift.Tests/Huggingface_SwiftTests.swift b/Tests/Huggingface.Swift.Tests/Huggingface_SwiftTests.swift new file mode 100644 index 0000000..34ce6fb --- /dev/null +++ b/Tests/Huggingface.Swift.Tests/Huggingface_SwiftTests.swift @@ -0,0 +1,642 @@ +import XCTest +@testable import Huggingface_Inference + +final class HuggingfaceTests: XCTestCase { + private let encoder: JSONEncoder = { + let encoder = JSONEncoder() + encoder.keyEncodingStrategy = .convertToSnakeCase + return encoder + }() + private let logLevel: LogLevel = .debug + + func testConversational() async throws { + let expected = ConversationalOutput( + generatedText: "It's the best movie ever.", + conversation: .init( + pastUserInputs: ["Which movie is the best ?", "Can you explain why ?"], + generatedResponses: ["It is Die Hard for sure.", "It\'s the best movie ever."] + ) + ) + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.conversational( + text: "Can you explain why ?", + pastUserInputs: ["Which movie is the best ?"], + generatedResponses: ["It is Die Hard for sure."], + model: "microsoft/DialoGPT-large" + ) + XCTAssertEqual("It's the best movie ever.", actual.generatedText) + } + + func testFillMask() async throws { + let metadata = HuggingfaceModelsMetdata.make( + fillMask: .init( + models: [.init( + description: "", + id: "distilbert-base-uncased" + )], + id: "distilbert-base-uncased" + ) + ) + let expected = [FillMaskOutput( + sequence: "The answer to the universe is zero", + score: 0.9, + token: 3, + tokenStr: "zero" + )] + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(metadata), + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.fillMask( + inputs: ["The answer to the universe is [MASK]."] + ) + XCTAssertEqual(1, actual.count) + XCTAssertEqual("The answer to the universe is zero", actual[0].sequence) + } + + func testQuestionAnswering() async throws { + let metadata = HuggingfaceModelsMetdata.make( + questionAnswering: .init( + models: [.init( + description: "", + id: "deepset/roberta-base-squad2" + )], + id: "deepset/roberta-base-squad2" + ) + ) + let expected = QuestionAnsweringOutput( + answer: "Clara", + end: 16, + score: 0.9331286, + start: 11 + ) + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(metadata), + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.questionAnswering( + question: "What is my name?", + context: "My name is Clara and I live in Berkeley." + ) + XCTAssertEqual("Clara", actual.answer) + } + + func testSentenceSimilarity() async throws { + let expected: [Float] = [0.7639649, 0.9666221, 0.3147716] + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.sentenceSimilarity( + sourceSentence: "That is a happy person", + sentences: [ + "That is a happy dog", + "That is a very happy person", + "Today is a sunny day" + ], + model: "sentence-transformers/all-mpnet-base-v2" + ) + XCTAssertEqual(3, actual.count) + XCTAssertEqual(0.7639649, actual[0]) + } + + func testSummarization() async throws { + let expected: [SummarizationOutput] = [SummarizationOutput( + summaryText: "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world." + )] + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.summarization( + inputs: "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.", + model: "sentence-transformers/all-mpnet-base-v2" + ) + XCTAssertEqual(1, actual.count) + XCTAssertEqual(expected.first!.summaryText, actual[0].summaryText) + } + + func testTableQuestionAnswering() async throws { + let expected = TableQuestionAnsweringOutput( + aggregator: "AVERAGE", + answer: "AVERAGE > 36542", + cells: ["36542"], + coordinates: [[0, 1]] + ) + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.tableQuestionAnswering( + query: "How many stars does the transformers repository have?", + table: [ + "Repository": ["Transformers","Datasets","Tokenizers"], + "Stars": ["36542","4512","3934"], + "Contributors": ["651","77","34"], + "Programming language": ["Python","Python","Rust, Python and NodeJS"] + ], + model: "google/tapas-base-finetuned-wtq" + ) + XCTAssertEqual(expected.answer, actual.answer) + } + + func testTextClassification() async throws { + let expected = [[ + TextClassificationOutput(label: "POSITIVE", score: 0.9998738765716553), + TextClassificationOutput(label: "NEGATIVE", score: 0.00012611268903128803) + ]] + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.textClassification( + inputs: ["I like you. I love you"], + model: "distilbert-base-uncased-finetuned-sst-2-english" + ) + + XCTAssertEqual(expected[0][0].label, actual[0][0].label) + XCTAssertEqual(expected[0][1].label, actual[0][1].label) + } + + func testTranslation() async throws { + let expected = [TranslationOutput( + translationText: "My name is Wolfgang and I live in Berlin." + )] + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.translation( + inputs: ["Меня зовут Вольфганг и я живу в Берлине"], + model: "Helsinki-NLP/opus-mt-ru-en" + ) + + XCTAssertEqual(expected[0].translationText, actual[0].translationText) + } + + func testZeroShotClassification() async throws { + let expected = [ZeroShotClassificationOutput( + labels: ["refund","faq","legal"], + scores: [0.8772604465484619,0.10223047435283661,0.020509060472249985], + sequence: "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed" + )] + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.zeroShotClassification( + inputs: ["Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed"], + candidateLabels: ["refund", "legal", "faq"], + model: "Helsinki-NLP/opus-mt-ru-en" + ) + + XCTAssertEqual(expected[0].sequence, actual[0].sequence) + } + + func testTokenClassification() async throws { + let expected = [[TokenClassificationOutput( + entityGroup: "PER", + score: 0.9971321, + word: "Sarah Jessica Parker", + start: 11, + end: 31 + )]] + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.tokenClassification( + inputs: ["My name is Sarah Jessica Parker but you can call me Jessica"], + model: "dslim/bert-base-NER" + ) + + XCTAssertEqual(expected[0][0].entityGroup, actual[0][0].entityGroup) + XCTAssertEqual(expected[0][0].word, actual[0][0].word) + } + + func testTextGeneration() async throws { + let expected = [TextGenerationOutput( + generatedText: "The answer to the universe is simply not found in any one form or other! For more info in" + )] + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.textGeneration( + inputs: "The answer to the universe is", + model: "bigscience/bloom-560m" + ) + + XCTAssertEqual(expected[0].generatedText, actual[0].generatedText) + } + + func testTextToImage() async throws { +// let sessionConfig = URLSessionConfiguration.default +// sessionConfig.timeoutIntervalForRequest = 230.0 +// sessionConfig.timeoutIntervalForResource = 260.0 +// let session = URLSession(configuration: sessionConfig) +// +// let hf = HfInference( +// session: session, +// logLevel: logLevel +// ) +// let actual = try await hf.textToImage( +// inputs: "1boy, male focus, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck, masterpiece, best quality", +// negativePrompt: "blurry", +// model: "cagliostrolab/animagine-xl-3.0" +// ) +// try actual.write(to: URL(filePath: "/tmp/output_image.jpg")) +// print(actual) + let expected = Data([0xFF, 0xAA] as [UInt8]) + let hf = HfInference( + fetch: TestFetch(result: [ + expected, + ]), + logLevel: logLevel + ) + let actual = try await hf.textToImage( + inputs: "1boy, male focus, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck, masterpiece, best quality", + negativePrompt: "blurry", + model: "cagliostrolab/animagine-xl-3.0" + ) + + XCTAssertEqual(expected, actual) + } + + func testImageClassification() async throws { + let expected = [ + ImageClassificationOutput(label: "Indian elephant, Elephas maximus", score: 0.6747689843177795), + ImageClassificationOutput(label: "African elephant, Loxodonta africana", score: 0.2764124870300293), + ImageClassificationOutput(label: "tusker", score: 0.03826090693473816), + ImageClassificationOutput(label: "hippopotamus, hippo, river horse, Hippopotamus amphibius", score: 0.0018638580804690719), + ImageClassificationOutput(label: "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", score: 0.0006085152854211628) + ] + + let data = try Data(contentsOf: Bundle.module.url(forResource: "elephants", withExtension: "png")!) + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.imageClassification( + data, + model: "google/vit-base-patch16-224" + ) + XCTAssertEqual(expected.count, actual.count) + XCTAssertEqual(expected[0].label, actual[0].label) + XCTAssertEqual(expected[1].label, actual[1].label) + XCTAssertEqual(expected[2].label, actual[2].label) + XCTAssertEqual(expected[3].label, actual[3].label) + XCTAssertEqual(expected[4].label, actual[4].label) + } + + func testImageToText() async throws { + let expected = [ + ImageToTextOutput(generatedText: "a large elephant standing next to a pond ") + ] + + let data = try Data(contentsOf: Bundle.module.url(forResource: "elephants", withExtension: "png")!) + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.imageToText( + data, + model: "nlpconnect/vit-gpt2-image-captioning" + ) + XCTAssertEqual(expected.count, actual.count) + XCTAssertEqual(expected[0].generatedText, actual[0].generatedText) + } + + func testImageSegmentation() async throws { + let expected = [ + ImageSegmentationOutput(label: "wall", mask: "BASE64ENCODED_MASK", score: 1.0), + ImageSegmentationOutput(label: "sky", mask: "BASE64ENCODED_MASK", score: 1.0), + ImageSegmentationOutput(label: "tree", mask: "BASE64ENCODED_MASK", score: 1.0), + ImageSegmentationOutput(label: "person", mask: "BASE64ENCODED_MASK", score: 1.0), + ImageSegmentationOutput(label: "earth", mask: "BASE64ENCODED_MASK", score: 1.0), + ImageSegmentationOutput(label: "plant", mask: "BASE64ENCODED_MASK", score: 1.0), + ImageSegmentationOutput(label: "water", mask: "BASE64ENCODED_MASK", score: 1.0), + ImageSegmentationOutput(label: "rock", mask: "BASE64ENCODED_MASK", score: 1.0), + ImageSegmentationOutput(label: "animal", mask: "BASE64ENCODED_MASK", score: 1.0), + ] + + let data = try Data(contentsOf: Bundle.module.url(forResource: "elephants", withExtension: "png")!) + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.imageSegmentation( + data, + model: "openmmlab/upernet-convnext-small" + ) + XCTAssertEqual(expected.count, actual.count) + XCTAssertEqual(expected[0].label, actual[0].label) + XCTAssertEqual(expected[1].label, actual[1].label) + XCTAssertEqual(expected[2].label, actual[2].label) + XCTAssertEqual(expected[3].label, actual[3].label) + XCTAssertEqual(expected[4].label, actual[4].label) + XCTAssertEqual(expected[5].label, actual[5].label) + XCTAssertEqual(expected[6].label, actual[6].label) + XCTAssertEqual(expected[7].label, actual[7].label) + XCTAssertEqual(expected[8].label, actual[8].label) + } + + func testObjectDetection() async throws { + let expected = [ + ObjectDetectionOutput(label: "elephant", score: 0.998604953289032, bod: .init(xmin: 287, xmax: 421, ymin: 243, ymax: 363)), + ObjectDetectionOutput(label: "elephant", score: 0.9994575381278992, bod: .init(xmin: 395, xmax: 555, ymin: 227, ymax: 363)), + ] + + let data = try Data(contentsOf: Bundle.module.url(forResource: "elephants", withExtension: "png")!) + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.objectDetection( + data, + model: "facebook/detr-resnet-50" + ) + XCTAssertEqual(expected.count, actual.count) + XCTAssertEqual(expected[0].label, actual[0].label) + XCTAssertEqual(expected[1].label, actual[1].label) + } + + func testImageToImage() async throws { +// let data = try Data(contentsOf: Bundle.module.url(forResource: "elephants", withExtension: "png")!) +// let sessionConfig = URLSessionConfiguration.default +// sessionConfig.timeoutIntervalForRequest = 230.0 +// sessionConfig.timeoutIntervalForResource = 260.0 +// let session = URLSession(configuration: sessionConfig) +// +// let hf = HfInference( +// session: session, +// logLevel: logLevel +// ) +// let actual = try await hf.imageToImage( +// data, +// model: "lambdalabs/sd-image-variations-diffusers" +// ) +// let filename = UUID().uuidString +// try actual.write(to: URL(filePath: "/tmp/\(filename).jpg")) + let expected = Data([0xFF, 0xAA] as [UInt8]) + let data = Data([0xFE, 0xBA] as [UInt8]) + let hf = HfInference( + fetch: TestFetch(result: [ + expected, + ]), + logLevel: logLevel + ) + let actual = try await hf.imageToImage( + data, + model: "lambdalabs/sd-image-variations-diffusers" + ) + + XCTAssertEqual(expected, actual) + } + + func testZeroShotImageClassification() async throws { + let expected = [ + ZeroShotImageClassificationOutput(label: "elephant", score: 0.9938836097717285), + ZeroShotImageClassificationOutput(label: "dinosaur", score: 0.006074416451156139), + ZeroShotImageClassificationOutput(label: "dog", score: 4.2110172216780484e-05), + ] + + let data = Data([0xFE, 0xBA] as [UInt8]) + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.zeroShotImageClassification( + data, + candidateLabels: ["elephant", "dog", "dinosaur"], + model: "openai/clip-vit-base-patch16" + ) + XCTAssertEqual(expected.count, actual.count) + XCTAssertEqual(expected[0].label, actual[0].label) + XCTAssertEqual(expected[1].label, actual[1].label) + XCTAssertEqual(expected[2].label, actual[2].label) + } + + func xtestTabularData() async throws { + let sessionConfig = URLSessionConfiguration.default + sessionConfig.timeoutIntervalForRequest = 230.0 + sessionConfig.timeoutIntervalForResource = 260.0 + let session = URLSession(configuration: sessionConfig) + + let hf = HfInference( + session: session, + logLevel: logLevel + ) +// let actual = try await hf.tabularClassification( +// data: [ +// "fixed_acidity": ["7.4", "7.8", "10.3"], +// "volatile_acidity": ["0.7", "0.88", "0.32"], +// "citric_acid": ["0", "0", "0.45"], +// "residual_sugar": ["1.9", "2.6", "6.4"], +// "chlorides": ["0.076", "0.098", "0.073"], +// "free_sulfur_dioxide": ["11", "25", "5"], +// "total_sulfur_dioxide": ["34", "67", "13"], +// "density": ["0.9978", "0.9968", "0.9976"], +// "pH": ["3.51", "3.2", "3.23"], +// "sulphates": ["0.56", "0.68", "0.82"], +// "alcohol": ["9.4", "9.8", "12.6"] +// ], +// model: "julien-c/wine-quality", +// options: .init(waitForModel: true) +// ) + + let actual = try await hf.tabularRegression( + data: [ + "Height": ["11.52", "12.48", "12.3778"], + "Length1": ["23.2", "24", "23.9"], + "Length2": ["25.4", "26.3", "26.5"], + "Length3": ["30", "31.2", "31.1"], + "Species": ["Bream", "Bream", "Bream"], + "Width": ["4.02", "4.3056", "4.6961"] + ] + ) + print(actual) + } + + func testAudioClassification() async throws { + let expected = [ + AudioClassificationOutput(label: "unknown", score: 0.9970651268959045), + AudioClassificationOutput(label: "down", score: 0.0013692846987396479), + AudioClassificationOutput(label: "stop", score: 0.0006748316227458417), + AudioClassificationOutput(label: "left", score: 0.00027229153783991933), + AudioClassificationOutput(label: "off", score: 0.00025018496671691537), + ] + + let data = try Data(contentsOf: Bundle.module.url(forResource: "sample1", withExtension: "flac")!) + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.audioClassification( + data, + model: "speechbrain/google_speech_command_xvector" + ) + XCTAssertEqual(expected.count, actual.count) + XCTAssertEqual(expected[0].label, actual[0].label) + XCTAssertEqual(expected[1].label, actual[1].label) + XCTAssertEqual(expected[2].label, actual[2].label) + XCTAssertEqual(expected[3].label, actual[3].label) + XCTAssertEqual(expected[4].label, actual[4].label) + } + + func testAutomaticSpeechRecognition() async throws { + let expected = AutomaticSpeechRecognitionOutput(text: "GOING ALONG SLUSHY COUNTRY ROADS AND SPEAKING TO DAMP AUDIENCES IN DRAUGHTY SCHOOLROOMS DAY AFTER DAY FOR A FORTNIGHT HE'LL HAVE TO PUT IN AN APPEARANCE AT SOME PLACE OF WORSHIP ON SUNDAY MORNING AND HE CAN COME TO US IMMEDIATELY AFTERWARDS") + + let data = try Data(contentsOf: Bundle.module.url(forResource: "sample1", withExtension: "flac")!) + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.automaticSpeechRecognition( + data, + model: "facebook/wav2vec2-large-960h-lv60-self" + ) + XCTAssertEqual(expected.text, actual.text) + } + + func testTextToSpeech() async throws { +// let sessionConfig = URLSessionConfiguration.default +// sessionConfig.timeoutIntervalForRequest = 230.0 +// sessionConfig.timeoutIntervalForResource = 260.0 +// let session = URLSession(configuration: sessionConfig) +// +// let hf = HfInference( +// session: session, +// logLevel: logLevel +// ) +// let actual = try await hf.textToSpeech( +// inputs: "Hello World!", +// model: "facebook/fastspeech2-en-ljspeech" +// ) +// let filename = UUID().uuidString +// try actual.write(to: URL(filePath: "/tmp/\(filename)")) + let expected = Data([0xFF, 0xAA] as [UInt8]) + let hf = HfInference( + fetch: TestFetch(result: [ + expected, + ]), + logLevel: logLevel + ) + let actual = try await hf.textToSpeech( + inputs: "Hello World!", + model: "facebook/fastspeech2-en-ljspeech" + ) + + XCTAssertEqual(expected, actual) + } + + func testAudioToAudio() async throws { + let expected = [AudioToAudioOutput( + label: "label_0", + blob: "BASE64ENCODED_AUDIO", + contentType: "audio/flac" + )] + + let data = try Data(contentsOf: Bundle.module.url(forResource: "sample1", withExtension: "flac")!) + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.audioToAudio( + data, + model: "speechbrain/sepformer-wham" + ) + XCTAssertEqual(expected[0].label, actual[0].label) + } + + func testVisualQuestionAnswering() async throws { + let expected = [ + VisualQuestionAnsweringOutput(answer: "2", score: 0.8440957069396973), + VisualQuestionAnsweringOutput(answer: "3", score: 0.331712931394577), + VisualQuestionAnsweringOutput(answer: "1", score: 0.05015309900045395), + VisualQuestionAnsweringOutput(answer: "4", score: 0.03215616196393967), + VisualQuestionAnsweringOutput(answer: "0", score: 0.006209534592926502), + ] + + let data = try Data(contentsOf: Bundle.module.url(forResource: "elephants", withExtension: "png")!) + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.visualQuestionAnswering( + image: data, + question: "How many animals are in this image?", + model: "dandelin/vilt-b32-finetuned-vqa" + ) + XCTAssertEqual(expected[0].answer, actual[0].answer) + XCTAssertEqual(expected[1].answer, actual[1].answer) + XCTAssertEqual(expected[2].answer, actual[2].answer) + XCTAssertEqual(expected[3].answer, actual[3].answer) + XCTAssertEqual(expected[4].answer, actual[4].answer) + } + + func testDocumentQuestionAnswering() async throws { + let expected = [ + DocumentQuestionAnsweringOutput(answer: "$154.06", start: 75, end: 75, score: 0.5467308163642883), + ] + + let data = try Data(contentsOf: Bundle.module.url(forResource: "invoice", withExtension: "png")!) + let hf = HfInference( + fetch: TestFetch(result: [ + try! encoder.encode(expected), + ]), + logLevel: logLevel + ) + let actual = try await hf.documentQuestionAnswering( + image: data, + question: "What is the invoice total amount?", + model: "impira/layoutlm-document-qa" + ) + XCTAssertEqual(expected[0].answer, actual[0].answer) + } +} diff --git a/Tests/Huggingface.Swift.Tests/TestFetch.swift b/Tests/Huggingface.Swift.Tests/TestFetch.swift new file mode 100644 index 0000000..8f0eadf --- /dev/null +++ b/Tests/Huggingface.Swift.Tests/TestFetch.swift @@ -0,0 +1,62 @@ +// +// TestFetch.swift +// + +import Foundation +@testable import Huggingface_Inference + +class TestFetch: FetchProtocol { + private let result: [Result] + var index: Int = 0 + + init( + result: [Result] + ) { + self.result = result + } + + convenience init( + result: String + ) { + self.init(result: [.success(RequestResponse( + data: result.data(using: .utf8)!, + response: HTTPURLResponse()) + )]) + } + + convenience init( + result: [Data] + ) { + self.init(result: result.map { + .success(RequestResponse( + data: $0, + response: HTTPURLResponse()) + ) + }) + } + + convenience init( + result: [String] + ) { + self.init(result: result.map { + .success(RequestResponse( + data: $0.data(using: .utf8)!, + response: HTTPURLResponse()) + ) + }) + } + + convenience init( + error: RequestError + ) { + self.init(result: [.failure(error)]) + } + + func perform( + _ request: Request + ) async -> Result { + let output = result[index] + index += 1 + return output + } +} diff --git a/Tests/Huggingface.Swift.Tests/assets/elephants.png b/Tests/Huggingface.Swift.Tests/assets/elephants.png new file mode 100644 index 0000000000000000000000000000000000000000..04e848d55ddc1e7905a0edcdd2e7c45af0528e65 GIT binary patch literal 788390 zcmZ^K19T-_)9#5+l8H0%#I|kQwr$(a1QRA008hO7WT~m z;%kj>B(5j}0CgeZ~_3(Nd6fh z5{krEUo|^eD62cG%Sdw?+1bz<7~2_|(7M~${}Ttm?auin+L$;S;Je#c+d6T&^AP^a zgY!%NM@>hF|1TG3D;`318F_pmJ4X|IR$6*mdO}_pe0+RvM`Ke?MPbqZfPbxc2+f_H z?K$b_+}zw~-I!?Y9L?w$I5;@y=o#r48EL*eXq-H3oekV+Y@LYy9ppdb2%9(=Ia=5| zTiDs+{}b21(9Xq~hmi1}ME`aC`#eqDE&e-`tRE9rr)MI8~fX9EI#`Y)ovOdH?&0bN?&T|7HB|^ZYkZ-oo9)T0_|43+eP# z6E6z~!+)UuU(5fBRR3=z8$0{|ME(!U|BDo|v$k_ovNtd?;r-8K|3mdZy#FbfQ_;l9 z&f4Xl3$9{o;mpg(P4~a4{{zKM_s>oFA2;dWJK$g1uPwj}!%g>J+ld#(gbc+D0N@8m 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