Swiftpack.co - Package - Clarifai/clarifai-swift-grpc

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Clarifai Swift gRPC Client

This is the official Clarifai gRPC Swift client for interacting with our powerful recognition API. The Clarifai API offers image and video recognition as a service. Whether you have one image or billions, you are only steps away from using artificial intelligence to recognize your visual content.

  • Try the Clarifai demo at: https://clarifai.com/demo
  • Sign up for a free account at: https://portal.clarifai.com/signup
  • Read the documentation at: https://docs.clarifai.com/

Build GitHub release (latest by date)


Add the library to the dependencies list in your project's Package.swift file.

    dependencies: [
        // ... Your other dependencies.
        .package(name: "ClarifaiGrpc", url: "https://github.com/Clarifai/clarifai-swift-grpc", from: "0.0.1")

Use the dependency in your target:

    targets: [
            name: "YOUR-TARGET",
            dependencies: ["ClarifaiGrpc"]),

Getting started

Construct the client and setup your API key or Personal Access Token in the metadata variable.

import NIO
import GRPC
import ClarifaiGrpc

let group = MultiThreadedEventLoopGroup(numberOfThreads: 1)
let channel = ClientConnection.secure(group: group).connect(host: "api.clarifai.com", port: 443)

var authHeaders: HPACKHeaders = ["Authorization": "Key MY_CLARIFAI_API_KEY_OR_PAT"]
let client = Clarifai_Api_V2Client(channel: channel, defaultCallOptions: CallOptions(customMetadata: authHeaders))

Predict concepts in an image:

let response = try client.postModelOutputs(
    Clarifai_Api_PostModelOutputsRequest.with {
        // This is the ID of the publicly available General model.
        $0.modelID = "aaa03c23b3724a16a56b629203edc62c";
        $0.inputs = [
            Clarifai_Api_Input.with {
                $0.data = Clarifai_Api_Data.with {
                    $0.image = Clarifai_Api_Image.with {
                        $0.url = "https://samples.clarifai.com/dog2.jpeg"

if response.status.code != Clarifai_Api_Status_StatusCode.success {
    print("Request failed, response: \(response)")
} else {
    print("Predicted concepts")
    for concept in response.outputs[0].data.concepts {
        print("\(concept.name): \(concept.value)")


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Release 0.0.2 - 2020-09-27 18:39:22

Release 0.0.1 - 2020-09-27 18:13:33