# Multilingual Classification

The Clarifai API supports [many languages in addition to English](/guide/v6.4/api-guide/concepts/languages.md). When making a [predict api request](/guide/v6.4/api-guide/predict.md), 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:

{% tabs %}
{% tab title="gRPC Java" %}

```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());
}
```

{% endtab %}

{% tab title="gRPC NodeJS" %}

```javascript
// 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);
        }
    }
);
```

{% endtab %}

{% tab title="gRPC Python" %}

```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))
```

{% endtab %}

{% tab title="js" %}

```javascript
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
  }
);
```

{% endtab %}

{% tab title="python" %}

```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')
```

{% endtab %}

{% tab title="java" %}

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

{% endtab %}

{% tab title="csharp" %}

```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();
        }
    }
}
```

{% endtab %}

{% tab title="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);
    }
  }];
}];
```

{% endtab %}

{% tab title="php" %}

```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();
}
```

{% endtab %}

{% tab title="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.
```

{% endtab %}
{% endtabs %}

{% tabs %}
{% tab title="Response JSON" %}

```javascript
{
  "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
          }
        ]
      }
    }
  ]
}
```

{% endtab %}
{% endtabs %}

## 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'.

{% tabs %}
{% tab title="gRPC Java" %}

```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());
}
```

{% endtab %}

{% tab title="gRPC NodeJS" %}

```javascript
// 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);
        }
    }
);
```

{% endtab %}

{% tab title="gRPC Python" %}

```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))
```

{% endtab %}

{% tab title="js" %}

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

{% endtab %}

{% tab title="python" %}

```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')
```

{% endtab %}

{% tab title="java" %}

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

{% endtab %}

{% tab title="csharp" %}

```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();
        }
    }
}
```

{% endtab %}

{% tab title="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]);
  }
}];
```

{% endtab %}

{% tab title="php" %}

```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();
}
```

{% endtab %}

{% tab title="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
```

{% endtab %}
{% endtabs %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://old-docs.clarifai.com/guide/v6.4/api-guide/predict/multilingual-classification.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.