# Images

## Via URL

To get predictions for an input, you need to supply an image and the model you'd like to get predictions from. You can supply an image either with a publicly accessible URL or by directly sending bytes. You can send up to 128 images in one API call. You specify the model you'd like to use with the `{model-id}` parameter.

Below is an example of how you would send image URLs and receive back predictions from the `general` model.

You can learn all about the different [Clarifai Models](/guide/v6.3/api-guide/predict/images.md) available later in the guide.

{% 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("{THE_MODEL_ID}")
        .setVersionId("{THE_MODEL_VERSION_ID")  // This is optional. Defaults to the latest model version.
        .addInputs(
            Input.newBuilder().setData(
                Data.newBuilder().setImage(
                    Image.newBuilder().setUrl("https://samples.clarifai.com/metro-north.jpg")
                )
            )
        )
        .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: "{THE_MODEL_ID}",
        version_id: "{THE_MODEL_VERSION_ID}",  // This is optional. Defaults to the latest model version.
        inputs: [
            {data: {image: {url: "https://samples.clarifai.com/metro-north.jpg"}}}
        ]
    },
    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="{THE_MODEL_ID}",
        version_id="{THE_MODEL_VERSION_ID}",  # This is optional. Defaults to the latest model version.
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        url="https://samples.clarifai.com/metro-north.jpg"
                    )
                )
            )
        ]
    ),
    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("%s %.2f" % (concept.name, concept.value))
```

{% endtab %}

{% tab title="js" %}

```javascript
app.models.initModel({id: Clarifai.GENERAL_MODEL, version: "aa7f35c01e0642fda5cf400f543e7c40"})
      .then(generalModel => {
        return generalModel.predict("@@sampleTrain");
      })
      .then(response => {
        var concepts = response['outputs'][0]['data']['concepts']
      })
```

{% endtab %}

{% tab title="python" %}

```python
from clarifai.rest import ClarifaiApp

app = ClarifaiApp(api_key='YOUR_API_KEY')
model = app.public_models.general_model
response = model.predict_by_url('@@sampleTrain')
```

{% endtab %}

{% tab title="java" %}

```java
ConceptModel model = client.getDefaultModels().generalModel();
    ModelVersion modelVersion = model.getVersionByID("the-version").executeSync().get();

    ClarifaiResponse<List<ClarifaiOutput<Prediction>>> response = client.predict(model.id())
        .withInputs(ClarifaiInput.forImage("@@sampleTrain"))
        .withVersion("aa7f35c01e0642fda5cf400f543e7c40")
        .executeSync();
```

{% endtab %}

{% tab title="csharp" %}

```csharp
using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.DTOs.Inputs;

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

             var response = await Client.Predict<Concept>(
                    Client.PublicModels.GeneralModel.ModelID,
                    new List<IClarifaiInput>
                    {
                        new ClarifaiURLImage("@@sampleTrain"),
                        new ClarifaiURLImage("the-url-2")
                    },
                    "aa7f35c01e0642fda5cf400f543e7c40")
                .ExecuteAsync();
        }
    }
}
```

{% endtab %}

{% tab title="objective-c" %}

```
ClarifaiImage *image = [[ClarifaiImage alloc] initWithURL:@"@@sampleTrain"];
[_app getModelByName:@"general-v1.3" completion:^(ClarifaiModel *model, NSError *error) {
    [model predictOnImages:@[image]
                completion:^(NSArray<ClarifaiSearchResult *> *outputs, NSError *error) {
                    NSLog(@"outputs: %@", outputs);
                }];
}];
```

{% endtab %}

{% tab title="php" %}

```php
use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiURLImage;
use Clarifai\DTOs\Outputs\ClarifaiOutput;
use Clarifai\DTOs\Predictions\Concept;

$client = new ClarifaiClient('YOUR_API_KEY');

$model = $client->publicModels()->generalModel();

$input = new ClarifaiURLImage("@@sampleTrain");
$response = $model->predict($input)
    ->withModelVersionID("aa7f35c01e0642fda5cf400f543e7c40")
    ->executeSync();

if ($response->isSuccessful()) {
    /** @var ClarifaiOutput $output */
    $output = $response->get();

    echo "Predicted concepts:\n";
    /** @var Concept $concept */
    foreach ($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"
            }
          }
        }
      ]
    }'
    https://api.clarifai.com/v2/models/{THE_MODEL_ID}/versions/{THE_MODEL_VERSION_ID}/outputs
```

{% endtab %}
{% endtabs %}

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

```javascript
{
  "status": {
    "code": 10000,
    "description": "Ok"
  },
  "outputs": [
    {
      "id": "ea68cac87c304b28a8046557062f34a0",
      "status": {
        "code": 10000,
        "description": "Ok"
      },
      "created_at": "2016-11-22T16:50:25Z",
      "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": "ea68cac87c304b28a8046557062f34a0",
        "data": {
          "image": {
            "url": "https://samples.clarifai.com/metro-north.jpg"
          }
        }
      },
      "data": {
        "concepts": [
          {
            "id": "ai_HLmqFqBf",
            "name": "train",
            "app_id": null,
            "value": 0.9989112
          },
          {
            "id": "ai_fvlBqXZR",
            "name": "railway",
            "app_id": null,
            "value": 0.9975532
          },
          {
            "id": "ai_Xxjc3MhT",
            "name": "transportation system",
            "app_id": null,
            "value": 0.9959158
          },
          {
            "id": "ai_6kTjGfF6",
            "name": "station",
            "app_id": null,
            "value": 0.992573
          },
          {
            "id": "ai_RRXLczch",
            "name": "locomotive",
            "app_id": null,
            "value": 0.992556
          },
          {
            "id": "ai_VRmbGVWh",
            "name": "travel",
            "app_id": null,
            "value": 0.98789215
          },
          {
            "id": "ai_SHNDcmJ3",
            "name": "subway system",
            "app_id": null,
            "value": 0.9816359
          },
          {
            "id": "ai_jlb9q33b",
            "name": "commuter",
            "app_id": null,
            "value": 0.9712483
          },
          {
            "id": "ai_46lGZ4Gm",
            "name": "railroad track",
            "app_id": null,
            "value": 0.9690325
          },
          {
            "id": "ai_tr0MBp64",
            "name": "traffic",
            "app_id": null,
            "value": 0.9687052
          },
          {
            "id": "ai_l4WckcJN",
            "name": "blur",
            "app_id": null,
            "value": 0.9667078
          },
          {
            "id": "ai_2gkfMDsM",
            "name": "platform",
            "app_id": null,
            "value": 0.9624243
          },
          {
            "id": "ai_CpFBRWzD",
            "name": "urban",
            "app_id": null,
            "value": 0.960752
          },
          {
            "id": "ai_786Zr311",
            "name": "no person",
            "app_id": null,
            "value": 0.95864904
          },
          {
            "id": "ai_6lhccv44",
            "name": "business",
            "app_id": null,
            "value": 0.95720303
          },
          {
            "id": "ai_971KsJkn",
            "name": "track",
            "app_id": null,
            "value": 0.9494642
          },
          {
            "id": "ai_WBQfVV0p",
            "name": "city",
            "app_id": null,
            "value": 0.94089437
          },
          {
            "id": "ai_dSCKh8xv",
            "name": "fast",
            "app_id": null,
            "value": 0.9399334
          },
          {
            "id": "ai_TZ3C79C6",
            "name": "road",
            "app_id": null,
            "value": 0.93121606
          },
          {
            "id": "ai_VSVscs9k",
            "name": "terminal",
            "app_id": null,
            "value": 0.9230834
          }
        ]
      }
    }
  ]
}
```

{% endtab %}
{% endtabs %}

## Via bytes

Below is an example of how you would send the bytes of an image and receive back predictions from the `general` model.

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

```java
import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;
import com.google.protobuf.ByteString;
import java.io.File;
import java.nio.file.Files;

// 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("{THE_MODEL_ID}")
        .setVersionId("{THE_MODEL_VERSION_ID")  // This is optional. Defaults to the latest model version.
        .addInputs(
            Input.newBuilder().setData(
                Data.newBuilder().setImage(
                    Image.newBuilder()
                        .setBase64(ByteString.copyFrom(Files.readAllBytes(
                            new File("{YOUR_IMAGE_FILE_LOCATION}").toPath()
                        )))
                )
            )
        )
        .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

const fs = require("fs");
const imageBytes = fs.readFileSync("{YOUR_IMAGE_FILE_LOCATION}");

stub.PostModelOutputs(
    {
        model_id: "{THE_MODEL_ID}",
        version_id: "{THE_MODEL_VERSION_ID}",  // This is optional. Defaults to the latest model version.
        inputs: [
            {data: {image: {base64: imageBytes}}}
        ]
    },
    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

with open("{YOUR_IMAGE_FILE_LOCATION}", "rb") as f:
    file_bytes = f.read()

post_model_outputs_response = stub.PostModelOutputs(
    service_pb2.PostModelOutputsRequest(
        model_id="{THE_MODEL_ID}",
        version_id="{THE_MODEL_VERSION_ID}",  # This is optional. Defaults to the latest model version.
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        base64=file_bytes
                    )
                )
            )
        ]
    ),
    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("%s %.2f" % (concept.name, concept.value))
```

{% endtab %}

{% tab title="js" %}

```javascript
app.models.predict(Clarifai.GENERAL_MODEL, {base64: "G7p3m95uAl..."}).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')

model = app.public_models.general_model
response = model.predict_by_filename('/home/user/image.jpeg')
# You could also use model.predict_by_bytes or model.predict_by_base64
```

{% endtab %}

{% tab title="java" %}

```java
client.getDefaultModels().generalModel().predict()
    .withInputs(ClarifaiInput.forImage(new File("/home/user/image.jpeg")))
    .executeSync();
```

{% endtab %}

{% tab title="csharp" %}

```csharp
using System.IO;
using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.DTOs.Inputs;

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

            await client.PublicModels.GeneralModel.Predict(
                    new ClarifaiFileImage(File.ReadAllBytes("/home/user/image.jpeg")))
                .ExecuteAsync();
        }
    }
}
```

{% endtab %}

{% tab title="obj-c" %}

```
UIImage *image = [UIImage imageNamed:@"dress.jpg"];
ClarifaiImage *clarifaiImage = [[ClarifaiImage alloc] initWithImage:image];
[_app getModelByName:@"general-v1.3" completion:^(ClarifaiModel *model, NSError *error) {
    [model predictOnImages:@[clarifaiImage]
                completion:^(NSArray<ClarifaiSearchResult *> *outputs, NSError *error) {
                    NSLog(@"outputs: %@", outputs);
                }];
}];
```

{% endtab %}

{% tab title="php" %}

```php
use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiFileImage;
use Clarifai\DTOs\Outputs\ClarifaiOutput;
use Clarifai\DTOs\Predictions\Concept;

$client = new ClarifaiClient('YOUR_API_KEY');

$response = $client->publicModels()->generalModel()->predict(
        new ClarifaiFileImage(file_get_contents('/home/user/image.jpeg')))
    ->executeSync();

if ($response->isSuccessful()) {
    /** @var ClarifaiOutput $output */
    $output = $response->get();

    echo "Predicted concepts:\n";
    /** @var Concept $concept */
    foreach ($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" %}

```
// Smaller files (195 KB or less)

curl -X POST \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
    "inputs": [
      {
        "data": {
          "image": {
            "base64": "'"$(base64 /home/user/image.jpeg)"'"
          }
        }
      }
    ]
  }'\
  https://api.clarifai.com/v2/models/{THE_MODEL_ID}/outputs

// Larger Files (Greater than 195 KB)

curl -X POST \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d @- https://api.clarifai.com/v2/models/{model-id}/outputs << FILEIN
  {
    "inputs": [
      {
        "data": {
          "image": {
            "base64": "$(base64 /home/user/image.png)"
          }
        }
      }
    ]
  }
FILEIN
```

{% endtab %}
{% endtabs %}

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

```javascript
{
  "status": {
    "code": 10000,
    "description": "Ok"
  },
  "outputs": [
    {
      "id": "e1cf385843b94c6791bbd9f2654db5c0",
      "status": {
        "code": 10000,
        "description": "Ok"
      },
      "created_at": "2016-11-22T16:59:23Z",
      "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": "e1cf385843b94c6791bbd9f2654db5c0",
        "data": {
          "image": {
            "url": "https://s3.amazonaws.com/clarifai-api/img/prod/b749af061d564b829fb816215f6dc832/e11c81745d6d42a78ef712236023df1c.jpeg"
          }
        }
      },
      "data": {
        "concepts": [
          {
            "id": "ai_l4WckcJN",
            "name": "blur",
            "app_id": null,
            "value": 0.9973569
          },
          {
            "id": "ai_786Zr311",
            "name": "no person",
            "app_id": null,
            "value": 0.98865616
          },
          {
            "id": "ai_JBPqff8z",
            "name": "art",
            "app_id": null,
            "value": 0.986006
          },
          {
            "id": "ai_5rD7vW4j",
            "name": "wallpaper",
            "app_id": null,
            "value": 0.9722556
          },
          {
            "id": "ai_sTjX6dqC",
            "name": "abstract",
            "app_id": null,
            "value": 0.96476805
          },
          {
            "id": "ai_Dm5GLXnB",
            "name": "illustration",
            "app_id": null,
            "value": 0.922542
          },
          {
            "id": "ai_5xjvC0Tj",
            "name": "background",
            "app_id": null,
            "value": 0.8775655
          },
          {
            "id": "ai_tBcWlsCp",
            "name": "nature",
            "app_id": null,
            "value": 0.87474406
          },
          {
            "id": "ai_rJGvwlP0",
            "name": "insubstantial",
            "app_id": null,
            "value": 0.8196385
          },
          {
            "id": "ai_2Bh4VMrb",
            "name": "artistic",
            "app_id": null,
            "value": 0.8142488
          },
          {
            "id": "ai_mKzmkKDG",
            "name": "Christmas",
            "app_id": null,
            "value": 0.7996079
          },
          {
            "id": "ai_RQccV41p",
            "name": "woman",
            "app_id": null,
            "value": 0.7955615
          },
          {
            "id": "ai_20SCBBZ0",
            "name": "vector",
            "app_id": null,
            "value": 0.7775099
          },
          {
            "id": "ai_4sJLn6nX",
            "name": "dark",
            "app_id": null,
            "value": 0.7715479
          },
          {
            "id": "ai_5Kp5FMJw",
            "name": "still life",
            "app_id": null,
            "value": 0.7657637
          },
          {
            "id": "ai_LM64MDHs",
            "name": "shining",
            "app_id": null,
            "value": 0.7542407
          },
          {
            "id": "ai_swtdphX8",
            "name": "love",
            "app_id": null,
            "value": 0.74926054
          },
          {
            "id": "ai_h45ZTxZl",
            "name": "square",
            "app_id": null,
            "value": 0.7449074
          },
          {
            "id": "ai_cMfj16kJ",
            "name": "design",
            "app_id": null,
            "value": 0.73926914
          },
          {
            "id": "ai_LxrzLJmf",
            "name": "bright",
            "app_id": null,
            "value": 0.73790145
          }
        ]
      }
    }
  ]
}
```

{% 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/v6.3/api-guide/predict/images.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.