Multilingual Classification

The Clarifai API supports many languages in addition to English. When making a predict api request, you can pass in the language you would like the concepts returned in. When you create a new Application, you must specify a default language which will be the language of the returned concepts if not specified in the predict request.

Example Predict API Request

You can predict concepts in a language other then the Application's default, by explicitly passing in the language. Here is how you predict concepts in Chinese:

gRPC Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview

MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
    PostModelOutputsRequest.newBuilder()
        .setModelId("aaa03c23b3724a16a56b629203edc62c")  // This is model ID of the publicly available General model.
        .addInputs(
            Input.newBuilder().setData(
                Data.newBuilder().setImage(
                    Image.newBuilder().setUrl("https://samples.clarifai.com/metro-north.jpg")
                )
            )
        )
        .setModel(
            Model.newBuilder().setOutputInfo(
                OutputInfo.newBuilder().setOutputConfig(
                    OutputConfig.newBuilder().setLanguage("zh")  // Chinese
                )
            )
        )
        .build()
);

if (postModelOutputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
  throw new RuntimeException("Post model outputs failed, status: " + postModelOutputsResponse.getStatus());
}

// Since we have one input, one output will exist here.
Output output = postModelOutputsResponse.getOutputs(0);

System.out.println("Predicted concepts:");
for (Concept concept : output.getData().getConceptsList()) {
    System.out.printf("%s %.2f%n", concept.getName(), concept.getValue());
}

gRPC NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview

stub.PostModelOutputs(
    {
        model_id: "aaa03c23b3724a16a56b629203edc62c",
        inputs: [
            {data: {image: {url: "https://samples.clarifai.com/metro-north.jpg"}}}
        ],
        model: {output_info: {output_config: {language: "zh"}}}
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Post model outputs failed, status: " + response.status.description);
        }

        // Since we have one input, one output will exist here.
        const output = response.outputs[0];

        console.log("Predicted concepts:");
        for (const concept of output.data.concepts) {
            console.log(concept.name + " " + concept.value);
        }
    }
);

gRPC Python

from clarifai_grpc.grpc.api import service_pb2, resources_pb2
from clarifai_grpc.grpc.api.status import status_code_pb2

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview

post_model_outputs_response = stub.PostModelOutputs(
    service_pb2.PostModelOutputsRequest(
        model_id="aaa03c23b3724a16a56b629203edc62c",  # This is model ID of the publicly available General model.
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        url="https://samples.clarifai.com/metro-north.jpg"
                    )
                )
            )
        ],
        model=resources_pb2.Model(
            output_info=resources_pb2.OutputInfo(
                output_config=resources_pb2.OutputConfig(
                    language="zh"  # Chinese
                )
            )
        )
    ),
    metadata=metadata
)

if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)

# Since we have one input, one output will exist here.
output = post_model_outputs_response.outputs[0]

print("Predicted concepts:")
for concept in output.data.concepts:
    print("\t%s %.2f" % (concept.name, concept.value))

js

app.models.predict(Clarifai.GENERAL_MODEL, "https://samples.clarifai.com/metro-north.jpg", {language: 'zh'}).then(
  function(response) {
    // do something with response
  },
  function(err) {
    // there was an error
  }
);

python

from clarifai.rest import ClarifaiApp
app = ClarifaiApp(api_key='YOUR_API_KEY')

m = app.models.get('general-v1.3')

# predict labels in simplified Chinese
m.predict_by_url('https://samples.clarifai.com/metro-north.jpg', lang='zh')

# predict labels in Japanese
m.predict_by_url('https://samples.clarifai.com/metro-north.jpg', lang='ja')

java

client.predict(client.getDefaultModels().generalModel().id())
    .withInputs(ClarifaiInput.forImage("https://samples.clarifai.com/metro-north.jpg"))
    .withLanguage("zh")
    .executeSync();

csharp

using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.DTOs.Inputs;
using Clarifai.DTOs.Predictions;

namespace YourNamespace
{
    public class YourClassName
    {
        public static async Task Main()
        {
            var client = new ClarifaiClient("YOUR_API_KEY");

            await client.Predict<Concept>(
                    client.PublicModels.GeneralModel.ModelID,
                    input: new ClarifaiURLImage("https://samples.clarifai.com/metro-north.jpg"),
                    language: "zh")
                .ExecuteAsync();
        }
    }
}

objective-c

// first get the general model.
[app getModelByName:@"general-v1.3" completion:^(ClarifaiModel *model, NSError *error) {
  // create input to predict on.
  ClarifaiImage *input = [[ClarifaiImage alloc] initWithURL:@"https://samples.clarifai.com/metro-north.jpg"];

  // predict with the general model in Chinese.
  [model predictOnImages:@[input] withLanguage:@"zh" completion:^(NSArray<ClarifaiOutput *> *outputs, NSError *error) {
    for (ClarifaiConcept *concept in outputs[0].concepts) {
      NSLog(@"tag: %@", concept.conceptName);
      NSLog(@"probability: %f", concept.score);
    }
  }];
}];

php

use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiURLImage;
use Clarifai\DTOs\Models\ModelType;
use Clarifai\DTOs\Outputs\ClarifaiOutput;
use Clarifai\DTOs\Predictions\Concept;

$client = new ClarifaiClient();

$response = $client->predict(ModelType::concept(),
        $client->publicModels()->generalModel()->modelID(),
        new ClarifaiURLImage('https://samples.clarifai.com/metro-north.jpg'))
    ->withLanguage('zh')
    ->executeSync();

if ($response-> isSuccessful()) {
    $output = $response->get();

    echo "Predicted concepts:\n";

    foreach ($output->data() as $concept) {
        echo $concept->name() . ': ' . $concept->value() . "\n";
    }
} else {
    echo "Response is not successful. Reason: \n";
    echo $response->status()->description() . "\n";
    echo $response->status()->errorDetails() . "\n";
    echo "Status code: " . $response->status()->statusCode();
}

cURL

curl -X POST \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
  "inputs": [
    {
      "data": {
        "image": {
          "url": "https://samples.clarifai.com/metro-north.jpg"
        }
      }
    }
  ],
  "model":{
    "output_info":{
      "output_config":{
        "language":"zh"
      }
    }
  }
}'\
  https://api.clarifai.com/v2/models/aaa03c23b3724a16a56b629203edc62c/outputs

# Above is model ID of the publicly available General model.

Response JSON

{
  "status": {
    "code": 10000,
    "description": "Ok"
  },
  "outputs": [
    {
      "id": "b9f3c12f1534440fa984dc463e491780",
      "status": {
        "code": 10000,
        "description": "Ok"
      },
      "created_at": "2017-01-31T20:59:27Z",
      "model": {
        "name": "general-v1.3",
        "id": "aaa03c23b3724a16a56b629203edc62c",
        "created_at": "2016-03-09T17:11:39Z",
        "app_id": null,
        "output_info": {
          "message": "Show output_info with: GET /models/{model_id}/output_info",
          "type": "concept"
        },
        "model_version": {
          "id": "aa9ca48295b37401f8af92ad1af0d91d",
          "created_at": "2016-07-13T01:19:12Z",
          "status": {
            "code": 21100,
            "description": "Model trained successfully"
          }
        }
      },
      "input": {
        "id": "b9f3c12f1534440fa984dc463e491780",
        "data": {
          "image": {
            "url": "https://samples.clarifai.com/metro-north.jpg"
          }
        }
      },
      "data": {
        "concepts": [
          {
            "id": "ai_HLmqFqBf",
            "name": "铁路列车",
            "app_id": null,
            "value": 0.9989112
          },
          {
            "id": "ai_fvlBqXZR",
            "name": "铁路",
            "app_id": null,
            "value": 0.9975532
          },
          {
            "id": "ai_Xxjc3MhT",
            "name": "运输系统",
            "app_id": null,
            "value": 0.9959158
          },
          {
            "id": "ai_6kTjGfF6",
            "name": "站",
            "app_id": null,
            "value": 0.992573
          },
          {
            "id": "ai_RRXLczch",
            "name": "火车",
            "app_id": null,
            "value": 0.992556
          },
          {
            "id": "ai_VRmbGVWh",
            "name": "旅游",
            "app_id": null,
            "value": 0.98789215
          },
          {
            "id": "ai_SHNDcmJ3",
            "name": "地铁",
            "app_id": null,
            "value": 0.9816359
          },
          {
            "id": "ai_jlb9q33b",
            "name": "通勤",
            "app_id": null,
            "value": 0.9712483
          },
          {
            "id": "ai_46lGZ4Gm",
            "name": "铁路",
            "app_id": null,
            "value": 0.9690325
          },
          {
            "id": "ai_tr0MBp64",
            "name": "交通",
            "app_id": null,
            "value": 0.9687052
          },
          {
            "id": "ai_l4WckcJN",
            "name": "模煳",
            "app_id": null,
            "value": 0.9667078
          },
          {
            "id": "ai_2gkfMDsM",
            "name": "平台",
            "app_id": null,
            "value": 0.9624243
          },
          {
            "id": "ai_CpFBRWzD",
            "name": "城市的",
            "app_id": null,
            "value": 0.960752
          },
          {
            "id": "ai_786Zr311",
            "name": "沒有人",
            "app_id": null,
            "value": 0.95864904
          },
          {
            "id": "ai_6lhccv44",
            "name": "商业",
            "app_id": null,
            "value": 0.95720303
          },
          {
            "id": "ai_971KsJkn",
            "name": "跑道",
            "app_id": null,
            "value": 0.9494642
          },
          {
            "id": "ai_WBQfVV0p",
            "name": "城市",
            "app_id": null,
            "value": 0.94089437
          },
          {
            "id": "ai_dSCKh8xv",
            "name": "快速的",
            "app_id": null,
            "value": 0.9399334
          },
          {
            "id": "ai_TZ3C79C6",
            "name": "马路",
            "app_id": null,
            "value": 0.93121606
          },
          {
            "id": "ai_VSVscs9k",
            "name": "终点站",
            "app_id": null,
            "value": 0.9230834
          }
        ]
      }
    }
  ]
}

Example Search By Tag API Request

You can search for concepts in other languages even if the default language of your application is English. When you add inputs to your application, concepts are predicted for every language. Here is an example of searching for '人' which is simplified Chinese for 'people'.

gRPC Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview

MultiConceptResponse postConceptsSearchesResponse = stub.postConceptsSearches(
    PostConceptsSearchesRequest.newBuilder()
        .setConceptQuery(
            ConceptQuery.newBuilder()
                .setName("人")
                .setLanguage("zh")
        )
        .build()
);

if (postConceptsSearchesResponse.getStatus().getCode() != StatusCode.SUCCESS) {
  throw new RuntimeException("Post concepts searches failed, status: " + postConceptsSearchesResponse.getStatus());
}

System.out.println("Found concepts:");
for (Concept concept : postConceptsSearchesResponse.getConceptsList()) {
    System.out.printf("\t%s %.2f%n", concept.getName(), concept.getValue());
}

gRPC NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview

stub.PostConceptsSearches(
    {
        concept_query: {name: "人", language: "zh"}
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Post concepts searches failed, status: " + response.status.description);
        }

        console.log("Found concepts:");
        for (const concept of response.concepts) {
            console.log("\t" + concept.name + " " + concept.value);
        }
    }
);

gRPC Python

from clarifai_grpc.grpc.api import service_pb2, resources_pb2
from clarifai_grpc.grpc.api.status import status_code_pb2

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview

post_concepts_searches_response = stub.PostConceptsSearches(
    service_pb2.PostConceptsSearchesRequest(
        concept_query=resources_pb2.ConceptQuery(
            name="人",
            language="zh"
        )
    ),
    metadata=metadata
)

if post_concepts_searches_response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Post concepts searches failed, status: " + post_concepts_searches_response.status.description)

print("Found concepts:")
for concept in post_concepts_searches_response.concepts:
    print("\t%s %.2f" % (concept.name, concept.value))

js

app.inputs.search({
  concept: {
    name: '人'
  },
  language: 'ja'
}).then(
  function(response) {
    // do something with response
  },
  function(err) {
    // there was an error
  }
);

python

from clarifai.rest import ClarifaiApp

app = ClarifaiApp(api_key='YOUR_API_KEY')

# search '人' in simplified Chinese
app.inputs.search_by_predicted_concepts(u'人', lang='zh')

java

client.searchInputs(
    SearchClause.matchImageURL(ClarifaiImage.of("https://samples.clarifai.com/metro-north.jpg")))
    .withLanguage("zh")
    .getPage(1)
    .executeSync();

csharp

using System.Collections.Generic;
using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.DTOs.Searches;

namespace YourNamespace
{
    public class YourClassName
    {
        public static async Task Main()
        {
            var client = new ClarifaiClient("YOUR_API_KEY");

            await client.SearchInputs(
                    new List<SearchBy>
                    {
                        SearchBy.ImageURL("https://samples.clarifai.com/metro-north.jpg")
                    },
                    language: "zh")
                .Page(1)
                .ExecuteAsync();
        }
    }
}

objective-c

// create search term with concept you want to search predicted inputs with.
ClarifaiConcept *concept1 = [[ClarifaiConcept alloc] initWithConceptName:@"人"];
ClarifaiSearchTerm *searchTerm = [[ClarifaiSearchTerm alloc] initWithSearchItem:concept1 isInput:NO];

// search will find inputs predicted to be associated with the given concept.
[_app search:@[searchTerm] page:@1 perPage:@20 language:@"zh" completion:^(NSArray<ClarifaiSearchResult *> *results, NSError *error) {
  for (ClarifaiSearchResult *result in results) {
    NSLog(@"image url: %@", result.mediaURL);
    NSLog(@"probability: %f", [result.score floatValue]);
  }
}];

php

use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Searches\SearchBy;
use Clarifai\DTOs\Searches\SearchInputsResult;

$client = new ClarifaiClient();

$response = $client->searchInputs(
        SearchBy::imageURL('https://samples.clarifai.com/metro-north.jpg'))
    ->withLanguage('zh')
    ->executeSync();

if ($response-> isSuccessful()) {
    echo "Response is successful.\n";

    /** @var SearchInputsResult $result */
    $result = $response->get();
    foreach ($result->searchHits() as $searchHit) {
        echo $searchHit->input()->id() . ' ' . $searchHit->score() . "\n";
    }
} else {
    echo "Response is not successful. Reason: \n";
    echo $response->status()->description() . "\n";
    echo $response->status()->errorDetails() . "\n";
    echo "Status code: " . $response->status()->statusCode();
}

cURL

curl -X POST \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
    "query": {
      "ands": [
        {
          "output": {
            "data": {
              "concepts": [
                {
                  "name": "人"
                }
              ]
            }
          }
        }
      ],
      "language": "zh"
    }
  }'\
  https://api.clarifai.com/v2/searches