Workflow Predict

Make predictions with your workflows

---

The Workflow Predict API allows you make predictions using one or more models, whether they are Clarifai's pre-built models or custom creations — all in a single API call.

The maximum number of inputs that can be processed at once with any given workflow is 32.

After you're set up, you can initiate predictions under a specific workflow by utilizing the POST /v2/workflows/WORKFLOW_ID_HERE/results endpoint, where WORKFLOW_ID_HERE corresponds to the unique ID you assigned to your workflow.

When crafting the request body, its layout remains consistent with the usual approach for making a prediction call. The response body will include a results object, with each sub-object representing a response from the models, maintaining the same order as specified in the workflow you configured.

You can also use the Workflow Builder in the Clarifai Portal to build your workflows and see the results of their predictions on a given input.

info

The initialization code used in the following example is outlined in detail on the client installation page.

tip

If you want to make a predict call with an external workflow that is outside the scope of your app, you need to use a PAT while specifying the app_id and the user_id associated with the workflow you want to use.

Images

Let's illustrate how you would get predictions from image inputs using Clarifai's Face-Sentiment workflow. The workflow combines these three models:

• A visual detector model that detects bounding box regions in an image;
• An image cropper model that extracts the specific region of interest from an image;
• A visual classifier model that classifies an image into a set of concepts.

Note that the base64 output representation of the image in bytes is already in binary format. It is not encoded, so you do not need to decode it for further downstream tasks.

Python

##############################################################################
# In this section, we set the user authentication, app ID, workflow ID, and
# image URL. Change these strings to run your own example.
##############################################################################

# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = "YOUR_PAT_HERE"
USER_ID = "clarifai"
APP_ID = "main"
# Change these to make your own predictions
WORKFLOW_ID = "Face-Sentiment"
IMAGE_URL = "https://samples.clarifai.com/celebrity.jpeg"
# Or, to use a local text file, assign the location variable
# IMAGE_FILE_LOCATION = "YOUR_IMAGE_FILE_LOCATION_HERE"

##########################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
##########################################################################

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)

metadata = (("authorization", "Key " + PAT),)

userDataObject = resources_pb2.UserAppIDSet(
    user_id=USER_ID, app_id=APP_ID
)  # The userDataObject is required when using a PAT

# To use a local text file, uncomment the following lines
# with open(IMAGE_FILE_LOCATION, "rb") as f:
#    file_bytes = f.read()

post_workflow_results_response = stub.PostWorkflowResults(
    service_pb2.PostWorkflowResultsRequest(
        user_app_id=userDataObject,
        workflow_id=WORKFLOW_ID,
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        url=IMAGE_URL
                      # base64=file_bytes
                    )
                )
            )
        ],
    ),
    metadata=metadata,
)
if post_workflow_results_response.status.code != status_code_pb2.SUCCESS:
    print(post_workflow_results_response.status)
    raise Exception(
        "Post workflow results failed, status: "
        + post_workflow_results_response.status.description
    )

# We'll get one WorkflowResult for each input we used above. Because of one input, we have here one WorkflowResult
results = post_workflow_results_response.results[0]

# Each model we have in the workflow will produce one output.
for output in results.outputs:
    model = output.model

    print("Predicted concepts for the model `%s`" % model.id)

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

# Uncomment this line to print the raw output
# print(results)

JavaScript (REST)

<!--index.html file-->

<script>
  ////////////////////////////////////////////////////////////////////////////////////////
  //  In this section, we set the user authentication, app ID, workflow ID, and
  // image URL. Change these strings to run your own example.
  ///////////////////////////////////////////////////////////////////////////////////////

  // Your PAT (Personal Access Token) can be found in the Account's Security section
  const PAT = "YOUR_PAT_HERE";
  const USER_ID = "clarifai";
  const APP_ID = "main";
  // Change these to make your own predictions
  const WORKFLOW_ID = "Face-Sentiment";
  const IMAGE_URL = "https://samples.clarifai.com/celebrity.jpeg";

  ///////////////////////////////////////////////////////////////////////////////////
  // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
  /////////////////////////////////////////////////////////////////////////////////// 

  const raw = JSON.stringify({
    "user_app_id": {
      "user_id": USER_ID,
      "app_id": APP_ID
    },
    "inputs": [
      {
        "data": {
          "image": {
            "url": IMAGE_URL
          }
        }
      }
    ]
  });

  const requestOptions = {
    method: 'POST',
    headers: {
      'Accept': 'application/json',
      'Authorization': 'Key ' + PAT
    },
    body: raw
  };

  fetch(`https://api.clarifai.com/v2/workflows/${WORKFLOW_ID}/results`, requestOptions)
    .then(response => response.text())
    .then(result => console.log(result))
    .catch(error => console.log('error', error));
</script>

NodeJS

//index.js file

///////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, app ID, workflow ID, and
// image URL. Change these strings to run your own example.
///////////////////////////////////////////////////////////////////////////////////

// Your PAT (Personal Access Token) can be found in the Account's Security section
const PAT = "YOUR_PAT_HERE";
// Specify the correct user_id/app_id pairings
// Since you're making inferences outside your app's scope
const USER_ID = "clarifai";
const APP_ID = "main";
// Change these to make your own predictions
const WORKFLOW_ID = "Face-Sentiment";
const IMAGE_URL = "https://samples.clarifai.com/celebrity.jpeg";
// Or, to use a local text file, assign the location variable
// const IMAGE_FILE_LOCATION = "YOUR_IMAGE_FILE_LOCATION_HERE";

/////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
/////////////////////////////////////////////////////////////////////////////

const { ClarifaiStub, grpc } = require("clarifai-nodejs-grpc");

const stub = ClarifaiStub.grpc();

// This will be used by every Clarifai endpoint call
const metadata = new grpc.Metadata();
metadata.set("authorization", "Key " + PAT);

// To use a local text file, uncomment the following lines
// const fs = require("fs");
// const imageBytes = fs.readFileSync(IMAGE_FILE_LOCATION);

stub.PostWorkflowResults({
        user_app_id: {
            "user_id": USER_ID,
            "app_id": APP_ID,
        },
        workflow_id: WORKFLOW_ID,
        inputs: [{
            data: {
                image: {
                    url: IMAGE_URL,
                    // base64: imageBytes
                }
            }
        }],
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

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

        // We'll get one WorkflowResult for each input we used above. Because of one input, we have here 
        // one WorkflowResult
        const results = response.results[0];

        // Each model we have in the workflow will produce one output.
        for (const output of results.outputs) {
            const model = output.model;

            console.log(`Predicted concepts for the model '${model.id}'`);

            for (const concept of output.data.regions) {
                for (const item of concept.data.concepts) {
                    console.log(`\t${item.name} ${item.value.toFixed(2)}`);
                }
            }
        }
        // Uncomment this line to print the raw output
        // console.log(results);
    }
);

Java

package com.clarifai.example;

import com.clarifai.channel.ClarifaiChannel;
import com.clarifai.credentials.ClarifaiCallCredentials;
import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.StatusCode;

import com.google.protobuf.ByteString;
import java.io.File;
import java.io.IOException;
import java.nio.file.Files;

public class ClarifaiExample {

    ///////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, workflow ID, and
    // image URL. Change these strings to run your own example.
    ///////////////////////////////////////////////////////////////////////////////////
    
    //Your PAT (Personal Access Token) can be found in the portal under Authentication
    static final String PAT = "YOUR_PAT_HERE";
    static final String USER_ID = "clarifai";
    static final String APP_ID = "main";
    // Change these to make your own predictions
    static final String WORKFLOW_ID = "Face-Sentiment";
    static final String IMAGE_URL = "https://samples.clarifai.com/celebrity.jpeg";
    // Or, to use a local text file, assign the location variable
    // static final String IMAGE_FILE_LOCATION = "YOUR_IMAGE_FILE_LOCATION_HERE";

    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////
    
    public static void main(String[] args) throws IOException {

        V2Grpc.V2BlockingStub stub = V2Grpc.newBlockingStub(ClarifaiChannel.INSTANCE.getGrpcChannel())
                .withCallCredentials(new ClarifaiCallCredentials(PAT));

        PostWorkflowResultsResponse postWorkflowResultsResponse = stub.postWorkflowResults(
                PostWorkflowResultsRequest.newBuilder()
                        .setUserAppId(UserAppIDSet.newBuilder().setUserId(USER_ID).setAppId(APP_ID))
                        .setWorkflowId(WORKFLOW_ID)
                        .addInputs(
                                Input.newBuilder().setData(
                                        Data.newBuilder().setImage(
                                                Image.newBuilder().setUrl(IMAGE_URL)
                                                //  To use a local text file, uncomment the following lines
                                                //Image.newBuilder().setBase64(ByteString.copyFrom(Files.readAllBytes(
                                                       // new File(IMAGE_FILE_LOCATION).toPath()
                                                //)))
                                        )
                                )
                        )
                        .build()
        );

        if (postWorkflowResultsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
            throw new RuntimeException("Post workflow results failed, status: " + postWorkflowResultsResponse.getStatus());
        }

        // We'll get one WorkflowResult for each input we used above. Because of one input, we have here
        // one WorkflowResult
        WorkflowResult results = postWorkflowResultsResponse.getResults(0);

        // Each model we have in the workflow will produce its output       
        for (Output output : results.getOutputsList()) {
            Model model = output.getModel();

            System.out.println("Predicted concepts for the model '" + model.getId() + "'");

            for (Region concept : output.getData().getRegionsList()) {
                for (Concept item : concept.getData().getConceptsList()) {
                    System.out.printf("\t%s %.2f%n", item.getName(), item.getValue());
                }
            }
        }

        // Uncomment this line to print the raw output
        // System.out.println(results);
    }

}

PHP

<?php

require __DIR__ . "/vendor/autoload.php";

/////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, app ID, workflow ID, and
// image URL. Change these strings to run your own example.
/////////////////////////////////////////////////////////////////////////////////

// Your PAT (Personal Access Token) can be found in the Account's Security section
$PAT = "YOUR_PAT_HERE";
$USER_ID = "clarifai";
$APP_ID = "main";
// Change these to make your own predictions
$WORKFLOW_ID = "Face-Sentiment";
$IMAGE_URL = "https://samples.clarifai.com/celebrity.jpeg";
# Or, to use a local text file, assign the location variable
// $IMAGE_BYTES_STRING = "YOUR_IMAGE_FILE_LOCATION_HERE";

///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////

use Clarifai\ClarifaiClient;
use Clarifai\Api\Data;
use Clarifai\Api\Image;
use Clarifai\Api\Input;
use Clarifai\Api\PostWorkflowResultsRequest;
use Clarifai\Api\Status\StatusCode;
use Clarifai\Api\UserAppIDSet;

$client = ClarifaiClient::grpc();

$metadata = ["Authorization" => ["Key " . $PAT]];

$userDataObject = new UserAppIDSet([
    "user_id" => $USER_ID,
    "app_id" => $APP_ID,
]);

// To use a local text file, uncomment the following lines
// $imageData = file_get_contents($IMAGE_BYTES_STRING); 

// Let's make a RPC call to the Clarifai platform. It uses the opened gRPC client channel to communicate a
// request and then wait for the response
[$response, $status] = $client
    ->PostWorkflowResults(
        // The request object carries the request along with the request status and other metadata related to the request itself
        new PostWorkflowResultsRequest([
            "user_app_id" => $userDataObject,
            "workflow_id" => $WORKFLOW_ID,
            "inputs" => [
                new Input([
                    // The Input object wraps the Data object in order to meet the API specification
                    "data" => new Data([
                        // The Data object is constructed around the Image object. It offers a container that has additional image independent
                        // metadata. In this particular use case, no other metadata is needed to be specified
                        "image" => new Image([
                            // In the Clarifai platform, an image is defined by a special Image object
                           "url" => $IMAGE_URL,
                            // "base64" => $imageData,
                        ]),
                    ]),
                ]),
            ],
        ]),
        $metadata
    )
    ->wait();

// A response is returned and the first thing we do is check the status of it
// A successful response will have a status code of 0; otherwise, there is some error
if ($status->code !== 0) {
    throw new Exception("Error: {$status->details}");
}
// In addition to the RPC response status, there is a Clarifai API status that reports if the operation was a success or failure
// (not just that the communication was successful)
if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
    throw new Exception(
        "Failure response: " .
            $response->getStatus()->getDescription() .
            " " .
            $response->getStatus()->getDetails()
    );
}

// We'll get one WorkflowResult for each input we used above. Because of one input, we have here one WorkflowResult
$results = $response->getResults()[0];

// Each model we have in the workflow will produce one output
foreach ($results->getOutputs() as $output) {
    $model = $output->getModel();

    echo "Predicted concepts for the model '{$model->getId()}'" . "\n";

    foreach ($output->getData()->getRegions() as $concept) {
        foreach ($concept->getData()->getConcepts() as $item) {
            echo "\t{$item->getName()} {$item->getValue()}" . "\n";
        }
    }
}

// Uncomment this line to print the raw output
// print_r($results);

?>

cURL

curl -X POST "https://api.clarifai.com/v2/users/clarifai/apps/main/workflows/Face-Sentiment/results" \
  -H "authorization: Key YOUR_PAT_HERE" \
  -H "content-type: application/json" \
  -d '{
    "inputs": [
        {
          "data": {
            "image": {
              "url": "https://samples.clarifai.com/celebrity.jpeg"
          }
        }
      }
    ]
}'

Text Output Example

Predicted concepts for the model `face-detection`
        BINARY_POSITIVE 1.00
Predicted concepts for the model `margin-110-image-crop`
Predicted concepts for the model `face-sentiment-recognition`
        happiness 1.00
        disgust 0.00
        fear 0.00
        sadness-contempt 0.00
        surprise 0.00
        anger 0.00
        neutral 0.00

Raw Output Example

status {
  code: SUCCESS
  description: "Ok"
}
input {
  id: "5865e5d55a164beebc6f7a5682269cb4"
  data {
    image {
      url: "https://samples.clarifai.com/celebrity.jpeg"
    }
  }
}
outputs {
  id: "01e76acba82d4453a1d3c10b1777066e"
  status {
    code: SUCCESS
    description: "Ok"
  }
  created_at {
    seconds: 1700656970
    nanos: 361613613
  }
  model {
    id: "face-detection"
    name: "Face"
    created_at {
      seconds: 1606323024
      nanos: 453038000
    }
    modified_at {
      seconds: 1665509418
      nanos: 21257000
    }
    app_id: "main"
    model_version {
      id: "6dc7e46bc9124c5c8824be4822abe105"
      created_at {
        seconds: 1614879626
        nanos: 81729000
      }
      status {
        code: MODEL_TRAINED
        description: "Model is trained and ready"
      }
      visibility {
        gettable: PUBLIC
      }
      app_id: "main"
      user_id: "clarifai"
      metadata {
      }
    }
    user_id: "clarifai"
    model_type_id: "visual-detector"
    visibility {
      gettable: PUBLIC
    }
    workflow_recommended {
    }
  }
  data {
    regions {
      id: "32b383f26ce26a4ff16642447f9317b8"
      region_info {
        bounding_box {
          top_row: 0.151694223
          left_col: 0.285768479
          bottom_row: 0.614028037
          right_col: 0.762517869
        }
      }
      data {
        concepts {
          id: "ai_b1b1b1b1"
          name: "BINARY_POSITIVE"
          value: 0.999997377
          app_id: "main"
        }
      }
      value: 0.999997377
    }
  }
}
outputs {
  id: "a521f59aeaf94d06901da76ab01fd0e0"
  status {
    code: SUCCESS
    description: "Ok"
  }
  created_at {
    seconds: 1700656970
    nanos: 361621134
  }
  model {
    id: "margin-110-image-crop"
    name: "margin-110"
    created_at {
      seconds: 1590505298
      nanos: 387731000
    }
    modified_at {
      seconds: 1634716390
      nanos: 69050000
    }
    app_id: "main"
    model_version {
      id: "b9987421b40a46649566826ef9325303"
      created_at {
        seconds: 1590505298
        nanos: 387731000
      }
      status {
        code: MODEL_TRAINED
        description: "Model is trained and ready"
      }
      visibility {
        gettable: PUBLIC
      }
      app_id: "main"
      user_id: "clarifai"
      metadata {
      }
    }
    display_name: "margin-110-image-crop"
    user_id: "clarifai"
    model_type_id: "image-crop"
    visibility {
      gettable: PUBLIC
    }
    workflow_recommended {
    }
  }
  data {
    regions {
      id: "6b5ea07bdaec9a8e48ff9424bf1f03b7"
      region_info {
        bounding_box {
          top_row: 0.12857753
          left_col: 0.261931
          bottom_row: 0.637144744
          right_col: 0.786355317
        }
      }
      data {
        image {
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          image_info {
            width: 183
            height: 251
            format: "JPEG"
            color_mode: "UnknownColorMode"
          }
        }
      }
    }
  }
}
outputs {
  id: "7ab40bb98cad4133b490d7183095d9b5"
  status {
    code: SUCCESS
    description: "Ok"
  }
  created_at {
    seconds: 1700656970
    nanos: 361626613
  }
  model {
    id: "face-sentiment-recognition"
    name: "face-sentiment"
    created_at {
      seconds: 1620837542
      nanos: 718331000
    }
    modified_at {
      seconds: 1652994708
      nanos: 222496000
    }
    app_id: "main"
    model_version {
      id: "a5d7776f0c064a41b48c3ce039049f65"
      created_at {
        seconds: 1620837542
        nanos: 812738000
      }
      status {
        code: MODEL_TRAINED
        description: "Model is trained and ready"
      }
      visibility {
        gettable: PUBLIC
      }
      app_id: "main"
      user_id: "clarifai"
      metadata {
      }
    }
    user_id: "clarifai"
    model_type_id: "visual-classifier"
    visibility {
      gettable: PUBLIC
    }
    workflow_recommended {
    }
  }
  data {
    regions {
      id: "6b5ea07bdaec9a8e48ff9424bf1f03b7"
      region_info {
        bounding_box {
          top_row: 0.12857753
          left_col: 0.261931
          bottom_row: 0.637144744
          right_col: 0.786355317
        }
      }
      data {
        concepts {
          id: "ai_CrBPDCM6"
          name: "happiness"
          value: 0.999999821
          app_id: "main"
        }
        concepts {
          id: "ai_5fbLSP06"
          name: "disgust"
          value: 3.61372668e-006
          app_id: "main"
        }
        concepts {
          id: "ai_8PZvz0N1"
          name: "fear"
          value: 1.39605234e-007
          app_id: "main"
        }
        concepts {
          id: "ai_KdS5fmgb"
          name: "sadness-contempt"
          value: 4.07719938e-008
          app_id: "main"
        }
        concepts {
          id: "ai_59ZvTKz7"
          name: "surprise"
          value: 1.09932232e-008
          app_id: "main"
        }
        concepts {
          id: "ai_96KLdq72"
          name: "anger"
          value: 6.72241196e-009
          app_id: "main"
        }
        concepts {
          id: "ai_MqGSWdbN"
          name: "neutral"
          value: 4.21860102e-009
          app_id: "main"
        }
      }
    }
  }
}

Videos

When you input a video into the Workflow Predict API, the response includes a list of predicted concepts for each frame of the video. By default, the video is processed at 1 frame per second (FPS), but this rate can be customized in the predict request. This means you’ll receive a set of concepts for every second (1000 milliseconds) of your video.

To adjust the FPS rate, use the sample_ms parameter in your predict request. The sample_ms value specifies the time interval (in milliseconds) between frames selected for inference, determining how frequently frames are processed.

The valid range for sample_ms is between 100 and 60,000 milliseconds.

tip

FPS is calculated as: FPS = 1000 / sample_ms

For example, if sample_ms is set to 1000, the FPS rate will be 1 (the default value).

The Workflow Predict API has size and duration limitations for video inputs:

• Videos uploaded via URL can be up to 100 MB in size or 10 minutes in length.
• Videos sent as byte data are limited to 10 MB in size.

If your video exceeds these limits, you can refer to this tutorial on splitting large videos into smaller segments for processing. Exceeding these limits may cause the process to time out and result in an error response.

Python

######################################################################################################
# In this section, we set the user authentication, user and app ID, workflow ID, video input,
# and sample_ms. Change these strings to run your own example.
######################################################################################################

# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = "YOUR_PAT_HERE"
USER_ID = "YOUR_USER_ID_HERE"
APP_ID = "YOUR_APP_ID_HERE"
# Change these to make your own predictions
WORKFLOW_ID = "YOUR_WORKFLOW_ID_HERE"
VIDEO_URL = "https://samples.clarifai.com/beer.mp4"
# Or, to use a local video file, assign the location variable
# VIDEO_FILE_LOCATION = "YOUR_VIDEO_FILE_LOCATION_HERE"
# Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS) 
SAMPLE_MS = 500

############################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
############################################################################

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)

metadata = (("authorization", "Key " + PAT),)

userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)

# To use a local video file, uncomment the following lines
# with open(VIDEO_FILE_LOCATION, "rb") as f:
   # file_bytes = f.read()

post_workflow_results_response = stub.PostWorkflowResults(
    service_pb2.PostWorkflowResultsRequest(
        user_app_id=userDataObject,
        workflow_id=WORKFLOW_ID,
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    video=resources_pb2.Video(
                        url=VIDEO_URL,
                        # base64=file_bytes
                    )
                )
            )
        ],
        output_config=resources_pb2.OutputConfig(
            sample_ms=SAMPLE_MS
        )
    ),
    metadata=metadata,
)
if post_workflow_results_response.status.code != status_code_pb2.SUCCESS:
    print(post_workflow_results_response.status)
    raise Exception(
        "Post workflow results failed, status: "
        + post_workflow_results_response.status.description
    )

# We'll get one WorkflowResult for each input we used above. Because of one input, we have here one WorkflowResult
results = post_workflow_results_response.results[0]

# Uncomment this line to print the raw output
print(results)

JavaScript (REST)

<!--index.html file-->

<script>
  ////////////////////////////////////////////////////////////////////////////////////////////////////
  // In this section, we set the user authentication, user and app ID, workflow ID, video input,
  // and sample_ms. Change these strings to run your own example.
  ///////////////////////////////////////////////////////////////////////////////////////////////////

  // Your PAT (Personal Access Token) can be found in the Account's Security section
  const PAT = "YOUR_PAT_HERE";
  const USER_ID = "YOUR_USER_ID_HERE";
  const APP_ID = "YOUR_APP_ID_HERE";
  // Change these to make your own predictions
  const WORKFLOW_ID = "YOUR_WORKFLOW_ID_HERE";
  const VIDEO_URL = "https://samples.clarifai.com/beer.mp4";
  // Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS) 
  const SAMPLE_MS = 500;

  ///////////////////////////////////////////////////////////////////////////////////
  // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
  /////////////////////////////////////////////////////////////////////////////////// 

  const raw = JSON.stringify({
    "user_app_id": {
      "user_id": USER_ID,
      "app_id": APP_ID
    },
    "inputs": [
      {
        "data": {
          "video": {            
            "url": VIDEO_URL
          }
        }
      }
    ],
    "output_config": {
      "sample_ms": SAMPLE_MS
    }
  });

  const requestOptions = {
    method: "POST",
    headers: {
      "Accept": "application/json",
      "Authorization": "Key " + PAT
    },
    body: raw
  };

  fetch(`https://api.clarifai.com/v2/workflows/${WORKFLOW_ID}/results`, requestOptions)
    .then(response => response.text())
    .then(result => console.log(result))
    .catch(error => console.log("error", error));

</script>

NodeJS

//index.js file

//////////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, workflow ID, video input,
// and sample_ms. Change these strings to run your own example.
/////////////////////////////////////////////////////////////////////////////////////////////////////

// Your PAT (Personal Access Token) can be found in the Account's Security section
const PAT = "YOUR_PAT_HERE";
const USER_ID = "YOUR_USER_ID_HERE";
const APP_ID = "YOUR_APP_ID_HERE";
// Change these to make your own predictions
const WORKFLOW_ID = "YOUR_WORKFLOW_ID_HERE";
const VIDEO_URL = "https://samples.clarifai.com/beer.mp4";
// Or, to use a local video file, assign the location variable
// const VIDEO_FILE_LOCATION = "YOUR_VIDEO_FILE_LOCATION_HERE"
// Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS) 
const SAMPLE_MS = 500;

/////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
/////////////////////////////////////////////////////////////////////////////

const { ClarifaiStub, grpc } = require("clarifai-nodejs-grpc");

const stub = ClarifaiStub.grpc();

// This will be used by every Clarifai endpoint call
const metadata = new grpc.Metadata();
metadata.set("authorization", "Key " + PAT);

// To use a local text file, uncomment the following lines
// const fs = require("fs");
// const videoBytes = fs.readFileSync(VIDEO_FILE_LOCATION);

stub.PostWorkflowResults({
    user_app_id: {
        "user_id": USER_ID,
        "app_id": APP_ID,
    },
    workflow_id: WORKFLOW_ID,
    inputs: [{
        data: {
            video: {
                url: VIDEO_URL,
                // base64: videoBytes
            }
        }
    }],
    output_config: {
        sample_ms: SAMPLE_MS
      }  
},
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

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

        // We'll get one WorkflowResult for each input we used above. Because of one input, we have here 
        // one WorkflowResult
        const results = response.results[0];

        // Uncomment this line to print the raw output
        console.log(results);
    }
);

Java

package com.clarifai.example;

import com.clarifai.channel.ClarifaiChannel;
import com.clarifai.credentials.ClarifaiCallCredentials;
import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.StatusCode;

import com.google.protobuf.ByteString;
import java.io.File;
import java.io.IOException;
import java.nio.file.Files;

public class ClarifaiExample {

    /////////////////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, user and app ID, workflow ID, video input,
    // and sample_ms. Change these strings to run your own example.
    /////////////////////////////////////////////////////////////////////////////////////////////////////

    //Your PAT (Personal Access Token) can be found in the portal under Authentication
    static final String PAT = "YOUR_PAT_HERE";
    static final String USER_ID = "YOUR_USER_ID_HERE";
    static final String APP_ID = "YOUR_APP_ID_HERE";
    // Change these to make your own predictions
    static final String WORKFLOW_ID = "YOUR_WORKFLOW_ID_HERE";
    static final String VIDEO_URL = "https://samples.clarifai.com/beer.mp4";
    // Or, to use a local video file, assign the location variable
    // static final String VIDEO_FILE_LOCATION = "YOUR_VIDEO_FILE_LOCATION_HERE";
    // Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS) 
    static final int SAMPLE_MS = 500;

    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////

    public static void main(String[] args) throws IOException {

        V2Grpc.V2BlockingStub stub = V2Grpc.newBlockingStub(ClarifaiChannel.INSTANCE.getGrpcChannel())
                .withCallCredentials(new ClarifaiCallCredentials(PAT));

        PostWorkflowResultsResponse postWorkflowResultsResponse = stub.postWorkflowResults(
                PostWorkflowResultsRequest.newBuilder()
                        .setUserAppId(UserAppIDSet.newBuilder().setUserId(USER_ID).setAppId(APP_ID))
                        .setWorkflowId(WORKFLOW_ID)
                        .addInputs(
                                Input.newBuilder().setData(
                                        Data.newBuilder().setVideo(
                                                Video.newBuilder().setUrl(VIDEO_URL)
                                        // Video.newBuilder().setBase64(ByteString.copyFrom(Files.readAllBytes(
                                        // new File(VIDEO_FILE_LOCATION).toPath()
                                        //)))
                                        )
                                )
                        )
                        .setOutputConfig(OutputConfig.newBuilder()
                        	.setSampleMs(SAMPLE_MS)                        		
                        )
                        .build()
        );

        if (postWorkflowResultsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
            throw new RuntimeException("Post workflow results failed, status: " + postWorkflowResultsResponse.getStatus());
        }

        // We'll get one WorkflowResult for each input we used above. Because of one input, we have here
        // one WorkflowResult
        WorkflowResult results = postWorkflowResultsResponse.getResults(0);

        // Uncomment this line to print the raw output
        System.out.println(results);
    }

}

PHP

<?php

require __DIR__ . "/vendor/autoload.php";

/////////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, workflow ID, video input,
// and sample_ms. Change these strings to run your own example.
/////////////////////////////////////////////////////////////////////////////////////////////////////

// Your PAT (Personal Access Token) can be found in the Account's Security section
$PAT = "YOUR_PAT_HERE";
$USER_ID = "YOUR_USER_ID_HERE";
$APP_ID = "YOUR_APP_ID_HERE";
// Change these to make your own predictions
$WORKFLOW_ID = "YOUR_WORKFLOW_ID_HERE";
$VIDEO_URL = "https://samples.clarifai.com/beer.mp4";
# Or, to use a local video file, assign the location variable
# $VIDEO_FILE_LOCATION = "YOUR_VIDEO_FILE_LOCATION_HERE";
# Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS) 
$SAMPLE_MS = 500;

///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////

use Clarifai\ClarifaiClient;
use Clarifai\Api\Data;
use Clarifai\Api\Video;
use Clarifai\Api\Input;
use Clarifai\Api\PostWorkflowResultsRequest;
use Clarifai\Api\Status\StatusCode;
use Clarifai\Api\UserAppIDSet;
use Clarifai\Api\OutputConfig;

$client = ClarifaiClient::grpc();

$metadata = ["Authorization" => ["Key " . $PAT]];

$userDataObject = new UserAppIDSet([
    "user_id" => $USER_ID,
    "app_id" => $APP_ID,
]);

// To use a local text file, uncomment the following lines
// $videoData = file_get_contents($VIDEO_FILE_LOCATION); 

// Let's make a RPC call to the Clarifai platform. It uses the opened gRPC client channel to communicate a
// request and then wait for the response
[$response, $status] = $client
    ->PostWorkflowResults(
        // The request object carries the request along with the request status and other metadata related to the request itself
        new PostWorkflowResultsRequest([
            "user_app_id" => $userDataObject,
            "workflow_id" => $WORKFLOW_ID,
            "inputs" => [
                new Input([
                    // The Input object wraps the Data object in order to meet the API specification
                    "data" => new Data([
                        // The Data object is constructed around the Video object. It offers a container that has additional independent
                        // metadata. In this particular use case, no other metadata is needed to be specified
                        "video" => new Video([
                            // In the Clarifai platform, a Video is defined by a special Video object                            
                            "url" => $VIDEO_URL
                            // "base64" => $videoData 
                        ]),
                    ]),
                ]),
            ],
            "output_config" => new OutputConfig([
                "sample_ms" => $SAMPLE_MS
            ])
        ]),
        $metadata
    )
    ->wait();

// A response is returned and the first thing we do is check the status of it
// A successful response will have a status code of 0; otherwise, there is some error
if ($status->code !== 0) {
    throw new Exception("Error: {$status->details}");
}
// In addition to the RPC response status, there is a Clarifai API status that reports if the operation was a success or failure
// (not just that the communication was successful)
if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
    throw new Exception(
        "Failure response: " .
            $response->getStatus()->getDescription() .
            " " .
            $response->getStatus()->getDetails()
    );
}

// We'll get one WorkflowResult for each input we used above. Because of one input, we have here one WorkflowResult
$results = $response->getResults()[0];

// Uncomment this line to print the raw output
print_r($results);

?>

cURL

curl -X POST "https://api.clarifai.com/v2/users/YOUR_USER_ID_HERE/apps/YOUR_APP_ID_HERE/workflows/YOUR_WORKFLOW_ID_HERE/results" \
  -H "authorization: Key YOUR_PAT_HERE" \
  -H "content-type: application/json" \
  -d '{
    "inputs": [
        {
          "data": {
            "video": {
              "url": "https://samples.clarifai.com/beer.mp4"
          }
        }
      }
    ],
    "output_config": {
        "sample_ms": 500
    }
}'

Text

Let's illustrate how you would produce embeddings and clusters from text inputs using Clarifai's Language-Understanding text workflow.

Python

######################################################################################################
# In this section, we set the user authentication, user and app ID, workflow ID, and the text 
# we want as an input. Change these strings to run your own example.
######################################################################################################

# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = "YOUR_PAT_HERE"
USER_ID = "clarifai"
APP_ID = "main"
# Change these to make your own predictions
WORKFLOW_ID = "Language-Understanding"
RAW_TEXT = "This is a test text for testing"
# To use a hosted text file, assign the URL variable
# TEXT_FILE_URL = "https://samples.clarifai.com/negative_sentence_12.txt"
# Or, to use a local text file, assign the location variable
# TEXT_FILE_LOCATION = "YOUR_TEXT_FILE_LOCATION_HERE"

############################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
############################################################################

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)

metadata = (("authorization", "Key " + PAT),)

userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)

# To use a local text file, uncomment the following lines
# with open(TEXT_FILE_LOCATION, "rb") as f:
#    file_bytes = f.read()

post_workflow_results_response = stub.PostWorkflowResults(
    service_pb2.PostWorkflowResultsRequest(
        user_app_id=userDataObject,
        workflow_id=WORKFLOW_ID,
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    text=resources_pb2.Text(
                        raw=RAW_TEXT
                        # url=TEXT_FILE_URL
                        # raw=file_bytes
                    )
                )
            )
        ],
    ),
    metadata=metadata,
)
if post_workflow_results_response.status.code != status_code_pb2.SUCCESS:
    print(post_workflow_results_response.status)
    raise Exception(
        "Post workflow results failed, status: "
        + post_workflow_results_response.status.description
    )

# We'll get one WorkflowResult for each input we used above. Because of one input, we have here one WorkflowResult
results = post_workflow_results_response.results[0]

# Each model we have in the workflow will produce one output.
for output in results.outputs:
    model = output.model

    print("Predicted concepts for the model `%s`" % model.id)
    print(output.data)

# Uncomment this line to print the raw output
# print(results)

JavaScript (REST)

<!--index.html file-->

<script>
  ////////////////////////////////////////////////////////////////////////////////////////////////////
  // In this section, we set the user authentication, user and app ID, workflow ID, and the text 
  // we want as an input. Change these strings to run your own example.
  ///////////////////////////////////////////////////////////////////////////////////////////////////

  // Your PAT (Personal Access Token) can be found in the Account's Security section
  const PAT = "YOUR_PAT_HERE";
  const USER_ID = "clarifai";
  const APP_ID = "main";
  // Change these to make your own predictions
  const WORKFLOW_ID = "Language-Understanding";
  const RAW_TEXT = "This is a test text for testing";
  // To use a hosted text file, assign the URL variable
  // const TEXT_FILE_URL = "https://samples.clarifai.com/negative_sentence_12.txt";

  ///////////////////////////////////////////////////////////////////////////////////
  // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
  /////////////////////////////////////////////////////////////////////////////////// 

  const raw = JSON.stringify({
    "user_app_id": {
      "user_id": USER_ID,
      "app_id": APP_ID
    },
    "inputs": [
      {
        "data": {
          "text": {
            "raw": RAW_TEXT
            // "url": TEXT_FILE_URL
          }
        }
      }
    ]
  });

  const requestOptions = {
    method: "POST",
    headers: {
      "Accept": "application/json",
      "Authorization": "Key " + PAT
    },
    body: raw
  };

  fetch(`https://api.clarifai.com/v2/workflows/${WORKFLOW_ID}/results`, requestOptions)
    .then(response => response.text())
    .then(result => console.log(result))
    .catch(error => console.log("error", error));

</script>

NodeJS

//index.js file

//////////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, workflow ID, and the text 
// we want as an input. Change these strings to run your own example.
/////////////////////////////////////////////////////////////////////////////////////////////////////

// Your PAT (Personal Access Token) can be found in the Account's Security section
const PAT = "YOUR_PAT_HERE";
const USER_ID = "clarifai";
const APP_ID = "main";
// Change these to make your own predictions
const WORKFLOW_ID = "Language-Understanding";
const RAW_TEXT = "This is a test text for testing";
// To use a hosted text file, assign the URL variable
// const TEXT_FILE_URL = "https://samples.clarifai.com/negative_sentence_12.txt"
// Or, to use a local text file, assign the location variable
// TEXT_FILE_LOCATION = "YOUR_TEXT_FILE_LOCATION_HERE"

/////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
/////////////////////////////////////////////////////////////////////////////

const { ClarifaiStub, grpc } = require("clarifai-nodejs-grpc");

const stub = ClarifaiStub.grpc();

// This will be used by every Clarifai endpoint call
const metadata = new grpc.Metadata();
metadata.set("authorization", "Key " + PAT);

// To use a local text file, uncomment the following lines
// const fs = require("fs");
// const fileBytes = fs.readFileSync(TEXT_FILE_LOCATION);

stub.PostWorkflowResults({
    user_app_id: {
        "user_id": USER_ID,
        "app_id": APP_ID,
    },
    workflow_id: WORKFLOW_ID,
    inputs: [{
        data: {
            text: {
                raw: RAW_TEXT
                // url: TEXT_FILE_URL,
                // raw: fileBytes
            }
        }
    }],
},
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

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

        // We'll get one WorkflowResult for each input we used above. Because of one input, we have here 
        // one WorkflowResult
        const results = response.results[0];

        // Each model we have in the workflow will produce one output.
        for (const output of results.outputs) {
            const model = output.model;

            console.log(`Predicted concepts for the model '${model.id}'`);
            console.log(output.data);

        }
        // Uncomment this line to print the raw output
        // console.log(results);
    }
);

Java

package com.clarifai.example;

import com.clarifai.channel.ClarifaiChannel;
import com.clarifai.credentials.ClarifaiCallCredentials;
import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.StatusCode;

import com.google.protobuf.ByteString;
import java.io.File;
import java.io.IOException;
import java.nio.file.Files;

public class ClarifaiExample {

    /////////////////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, user and app ID, workflow ID, and the text 
    // we want as an input. Change these strings to run your own example.
    /////////////////////////////////////////////////////////////////////////////////////////////////////
    
    //Your PAT (Personal Access Token) can be found in the portal under Authentication
    static final String PAT = "YOUR_PAT_HERE";
    static final String USER_ID = "clarifai";
    static final String APP_ID = "main";
    // Change these to make your own predictions
    static final String WORKFLOW_ID = "Language-Understanding";
    static final String RAW_TEXT = "This is a test text for testing";    
    // To use a hosted text file, assign the URL variable
    // static final String TEXT_FILE_URL = "https://samples.clarifai.com/negative_sentence_12.txt";
    // Or, to use a local text file, assign the location variable
    // static final String TEXT_FILE_LOCATION = "YOUR_TEXT_FILE_LOCATION_HERE";

    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////
    
    public static void main(String[] args) throws IOException {

        V2Grpc.V2BlockingStub stub = V2Grpc.newBlockingStub(ClarifaiChannel.INSTANCE.getGrpcChannel())
                .withCallCredentials(new ClarifaiCallCredentials(PAT));

        PostWorkflowResultsResponse postWorkflowResultsResponse = stub.postWorkflowResults(
                PostWorkflowResultsRequest.newBuilder()
                        .setUserAppId(UserAppIDSet.newBuilder().setUserId(USER_ID).setAppId(APP_ID))
                        .setWorkflowId(WORKFLOW_ID)
                        .addInputs(
                                Input.newBuilder().setData(
                                        Data.newBuilder().setText(
                                                Text.newBuilder().setRaw(RAW_TEXT)
                                                // Text.newBuilder().setUrl(TEXT_FILE_URL)
                                                // Text.newBuilder().setRawBytes(ByteString.copyFrom(Files.readAllBytes(
                                                       // new File(TEXT_FILE_LOCATION).toPath()
                                                //)))
                                        )
                                )
                        )
                        .build()
        );

        if (postWorkflowResultsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
            throw new RuntimeException("Post workflow results failed, status: " + postWorkflowResultsResponse.getStatus());
        }

        // We'll get one WorkflowResult for each input we used above. Because of one input, we have here
        // one WorkflowResult
        WorkflowResult results = postWorkflowResultsResponse.getResults(0);

        // Each model we have in the workflow will produce its output       
        for (Output output : results.getOutputsList()) {
            Model model = output.getModel();

            System.out.println("Predicted concepts for the model '" + model.getId() + "'");
            
            System.out.println(output.getData());
 
        }

        // Uncomment this line to print the raw output
        // System.out.println(results);
    }

}

PHP

<?php

require __DIR__ . "/vendor/autoload.php";

/////////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, workflow ID, and the text 
// we want as an input. Change these strings to run your own example.
/////////////////////////////////////////////////////////////////////////////////////////////////////

// Your PAT (Personal Access Token) can be found in the Account's Security section
$PAT = "YOUR_PAT_HERE";
$USER_ID = "clarifai";
$APP_ID = "main";
// Change these to make your own predictions
$WORKFLOW_ID = "Language-Understanding";
$RAW_TEXT = "This is a test text for testing";
// To use a hosted text file, assign the URL variable
// $TEXT_FILE_URL = "https://samples.clarifai.com/negative_sentence_12.txt";
// Or, to use a local text file, assign the location variable
// $TEXT_FILE_LOCATION = "YOUR_TEXT_FILE_LOCATION_HERE";

///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////

use Clarifai\ClarifaiClient;
use Clarifai\Api\Data;
use Clarifai\Api\Text;
use Clarifai\Api\Input;
use Clarifai\Api\PostWorkflowResultsRequest;
use Clarifai\Api\Status\StatusCode;
use Clarifai\Api\UserAppIDSet;

$client = ClarifaiClient::grpc();

$metadata = ["Authorization" => ["Key " . $PAT]];

$userDataObject = new UserAppIDSet([
    "user_id" => $USER_ID,
    "app_id" => $APP_ID,
]);

// To use a local text file, uncomment the following lines
// $textData = file_get_contents($TEXT_FILE_LOCATION); 

// Let's make a RPC call to the Clarifai platform. It uses the opened gRPC client channel to communicate a
// request and then wait for the response
[$response, $status] = $client
    ->PostWorkflowResults(
        // The request object carries the request along with the request status and other metadata related to the request itself
        new PostWorkflowResultsRequest([
            "user_app_id" => $userDataObject,
            "workflow_id" => $WORKFLOW_ID,
            "inputs" => [
                new Input([
                    // The Input object wraps the Data object in order to meet the API specification
                    "data" => new Data([
                        // The Data object is constructed around the Image object. It offers a container that has additional image independent
                        // metadata. In this particular use case, no other metadata is needed to be specified
                        "text" => new Text([
                            // In the Clarifai platform, a text is defined by a special Text object
                            "raw" => $RAW_TEXT
                            // "url" => $TEXT_FILE_URL 
                            // "raw" => $textData 
                        ]),
                    ]),
                ]),
            ],
        ]),
        $metadata
    )
    ->wait();

// A response is returned and the first thing we do is check the status of it
// A successful response will have a status code of 0; otherwise, there is some error
if ($status->code !== 0) {
    throw new Exception("Error: {$status->details}");
}
// In addition to the RPC response status, there is a Clarifai API status that reports if the operation was a success or failure
// (not just that the communication was successful)
if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
    throw new Exception(
        "Failure response: " .
            $response->getStatus()->getDescription() .
            " " .
            $response->getStatus()->getDetails()
    );
}

// We'll get one WorkflowResult for each input we used above. Because of one input, we have here one WorkflowResult
$results = $response->getResults()[0];

// Each model we have in the workflow will produce one output
foreach ($results->getOutputs() as $output) {
    $model = $output->getModel();

    echo "Predicted concepts for the model '{$model->getId()}'" . "\n";

    $convertDataToJSONString = $output->getData()->serializeToJsonString();

    echo  $convertDataToJSONString . "\n";
}

// Uncomment this line to print the raw output
// print_r($results);

?>

cURL

curl -X POST "https://api.clarifai.com/v2/users/clarifai/apps/main/workflows/Language-Understanding/results" \
  -H "authorization: Key YOUR_PAT_HERE" \
  -H "content-type: application/json" \
  -d '{
    "inputs": [
        {
          "data": {
            "text": {
              "raw": "This is a test text for testing"
          }
        }
      }
    ]
}'

Text Output Example

Predicted concepts for the model `multilingual-text-embedding`
embeddings {
  vector: 0.0255603846
  vector: 0.0256410129
  vector: 0.0107929539
  vector: 0.030718796
  vector: 0.0150386961
  vector: -0.0226166341
  vector: -0.0263089519
  vector: 0.00326520158
  vector: 0.0102104917
  vector: -0.044386141
  vector: -0.0195556302
  vector: 0.00893216766
  vector: 0.015003683
  vector: -0.0130465208
  vector: -0.0104514267
  vector: 0.0217112433
  vector: -0.0362542719
  vector: -0.00232617464
  vector: 0.0566842183
  vector: -0.0483675748
  vector: -0.010495048
  vector: 0.0236451961
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  num_dimensions: 768
}

Predicted concepts for the model `multilingual-text-clustering`
clusters {
  id: "9_17"
  projection: 0.210836634
  projection: -0.248410583
}

Raw Output Example

status {
  code: SUCCESS
  description: "Ok"
}
input {
  id: "f58574ce6a304c39ba298ecb4f4eef5e"
  data {
    text {
      raw: "This is a test text for testing"
      url: "https://samples.clarifai.com/placeholder.gif"
    }
  }
}
outputs {
  id: "0ff24540c786470e912984865c03efc0"
  status {
    code: SUCCESS
    description: "Ok"
  }
  created_at {
    seconds: 1700723375
    nanos: 640229252
  }
  model {
    id: "multilingual-text-embedding"
    name: "multilingual-text-embedding"
    created_at {
      seconds: 1581694729
      nanos: 522174000
    }
    modified_at {
      seconds: 1655211303
      nanos: 454205000
    }
    app_id: "main"
    model_version {
      id: "9b33adf15280465b857163ddaaacdcb1"
      created_at {
        seconds: 1606747915
        nanos: 848030000
      }
      status {
        code: MODEL_TRAINED
        description: "Model is trained and ready"
      }
      visibility {
        gettable: PUBLIC
      }
      app_id: "main"
      user_id: "clarifai"
      metadata {
      }
    }
    user_id: "clarifai"
    model_type_id: "text-embedder"
    visibility {
      gettable: PUBLIC
    }
    workflow_recommended {
    }
  }
  data {
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      vector: -0.0236418
      vector: 0.00602063863
      vector: -0.0117947338
      vector: -0.0382487215
      vector: -0.0253913198
      vector: 0.0540902093
      vector: -0.00069479784
      vector: -0.0030842768
      vector: 0.00678593758
      vector: -0.0158268567
      vector: -0.0301091801
      vector: -0.047721222
      vector: 0.0486889854
      vector: -0.0031820375
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      vector: -0.0473188572
      vector: -0.0377694
      vector: 0.00300174044
      vector: 0.00725157047
      vector: 0.0334912054
      vector: -0.0190392211
      vector: -0.00786152
      vector: -0.00393901
      vector: 0.0346906558
      vector: -0.0048984047
      vector: -0.0289619621
      vector: -0.0132993627
      vector: -0.0260517057
      vector: -0.0194860194
      vector: -0.00903460104
      vector: -0.0720684156
      vector: -0.0326718502
      vector: -0.0178757478
      vector: 0.0116493832
      vector: -0.0249003451
      vector: -0.012765849
      vector: -0.0367143154
      vector: 0.0253556
      vector: -0.000750295934
      vector: 0.00800141
      vector: -0.0399938263
      vector: -0.0156591497
      vector: 0.00355092739
      vector: 0.00527952937
      vector: -0.00371489814
      vector: 0.00979584455
      vector: 0.0128817623
      vector: -0.0257944819
      vector: 0.00532698631
      vector: -0.0054745716
      vector: -0.0402425155
      vector: 0.0023749
      vector: -0.0191466119
      vector: -0.000185457975
      vector: 0.0123729743
      vector: -0.0285796933
      vector: 0.027248418
      vector: -0.00682593649
      vector: -0.027824128
      vector: -0.0483085215
      vector: 0.0509723
      vector: 0.0150561249
      vector: -0.0671003759
      vector: -0.0302604195
      vector: 0.0335228
      vector: -0.00811030716
      vector: -0.0476540774
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      vector: 0.0257905629
      vector: 0.0216702223
      vector: 0.0756625161
      vector: -0.0114407977
      vector: 0.0250642728
      vector: 0.0492128
      vector: 0.0104585467
      vector: 0.0354731493
      vector: 0.0342974328
      vector: -0.0315436497
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      vector: 0.0251945127
      vector: 0.0550534874
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      vector: 0.0629401803
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      vector: 0.0277529527
      vector: 0.00905152876
      vector: -0.0214481559
      vector: -0.0336390026
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      vector: 0.0371279158
      vector: -0.0259519368
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      vector: 0.0480304547
      vector: 0.0241338
      vector: 0.00995781645
      vector: -0.0145506114
      vector: -0.0132244434
      vector: 0.0104087936
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      vector: 0.0418220572
      vector: 0.00906385668
      vector: -0.0608912781
      vector: 0.0269323979
      vector: 0.0267148074
      vector: 0.0114678359
      vector: -0.00542484596
      vector: 0.0605565161
      vector: 0.00729974685
      vector: 0.0284503475
      vector: 0.000257256615
      vector: -0.029183466
      vector: 0.029569421
      vector: 0.000915852608
      vector: 0.0109864781
      vector: 0.0160177927
      vector: -0.0311278421
      vector: -0.00568914367
      vector: -0.045658119
      vector: -0.0380534567
      vector: 0.00769931311
      vector: -0.0169286355
      vector: -0.0414473452
      vector: 0.0346236937
      vector: 0.00468268059
      vector: -0.000171230145
      vector: -0.0555126853
      vector: -0.0213408619
      vector: -0.00562082976
      vector: -0.042139709
      vector: 0.00932554808
      vector: 0.0544001274
      vector: 0.0186972376
      vector: 0.0127014797
      vector: 0.0039651487
      vector: 0.0317394622
      vector: 0.00775914779
      vector: -0.0223883521
      vector: -0.0120635182
      vector: 0.0196055751
      vector: 0.0291586574
      vector: 0.0337944
      num_dimensions: 768
    }
  }
}
outputs {
  id: "ce36cd3d9aa645338238341bfbdfcf45"
  status {
    code: SUCCESS
    description: "Ok"
  }
  created_at {
    seconds: 1700723375
    nanos: 640238148
  }
  model {
    id: "multilingual-text-clustering"
    name: "multilingual-text-clustering"
    created_at {
      seconds: 1607379316
      nanos: 936028000
    }
    modified_at {
      seconds: 1657111032
      nanos: 332505000
    }
    app_id: "main"
    model_version {
      id: "f3f0dbe5e9ec4072ae4aa2794021982b"
      created_at {
        seconds: 1607365607
        nanos: 249885000
      }
      status {
        code: MODEL_TRAINED
        description: "Model is trained and ready"
      }
      visibility {
        gettable: PUBLIC
      }
      app_id: "main"
      user_id: "clarifai"
      metadata {
      }
    }
    user_id: "clarifai"
    model_type_id: "clusterer"
    visibility {
      gettable: PUBLIC
    }
    workflow_recommended {
    }
  }
  data {
    clusters {
      id: "9_17"
      projection: 0.210836634
      projection: -0.248410583
    }
  }
}

Audio

Let's illustrate how you would get the sentiment of an audio input using Clarifai's asr-sentiment workflow.

Python

###########################################################################################
# In this section, we set the user authentication, user and app ID, workflow ID, and
# audio URL. Change these strings to run your own example.
##########################################################################################

# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = "YOUR_PAT_HERE"
USER_ID = "clarifai"
APP_ID = "main"
# Change these to make your own predictions
WORKFLOW_ID = "asr-sentiment"
AUDIO_URL = "https://samples.clarifai.com/negative_sentence_1.wav"
# Or, to use a local audio file, assign the location variable
# AUDIO_FILE_LOCATION = "YOUR_AUDIO_FILE_LOCATION_HERE"

##########################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
##########################################################################

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)

metadata = (("authorization", "Key " + PAT),)

userDataObject = resources_pb2.UserAppIDSet(
    user_id=USER_ID, app_id=APP_ID
)  # The userDataObject is required when using a PAT

# To use a local video file, uncomment the following lines
# with open(AUDIO_FILE_LOCATION, "rb") as f:
# audio_bytes = f.read()

post_workflow_results_response = stub.PostWorkflowResults(
    service_pb2.PostWorkflowResultsRequest(
        user_app_id=userDataObject,
        workflow_id=WORKFLOW_ID,
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    audio=resources_pb2.Audio(
                        url=AUDIO_URL,
                        # base64=audio_bytes
                    )
                )
            )
        ],
    ),
    metadata=metadata,
)
if post_workflow_results_response.status.code != status_code_pb2.SUCCESS:
    print(post_workflow_results_response.status)
    raise Exception(
        "Post workflow results failed, status: "
        + post_workflow_results_response.status.description
    )

# We'll get one WorkflowResult for each input we used above. Because of one input, we have here one WorkflowResult
results = post_workflow_results_response.results[0]

# Each model we have in the workflow will produce its output
for output in results.outputs:
    model = output.model
    print("Output for the model: `%s`" % model.id)
    for concept in output.data.concepts:
        print("\t%s %.2f" % (concept.name, concept.value))
    print(output.data.text.raw)

# Uncomment this line to print the raw output
# print(results)

JavaScript (REST)

<!--index.html file-->

<script>
  ////////////////////////////////////////////////////////////////////////////////////////////////////
  // In this section, we set the user authentication, user and app ID, workflow ID, and
  // audio URL. Change these strings to run your own example.
  ///////////////////////////////////////////////////////////////////////////////////////////////////

  // Your PAT (Personal Access Token) can be found in the Account's Security section
  const PAT = "YOUR_PAT_HERE";
  const USER_ID = "clarifai";
  const APP_ID = "main";
  // Change these to make your own predictions
  const WORKFLOW_ID = "asr-sentiment";
  const AUDIO_URL = "https://samples.clarifai.com/negative_sentence_1.wav";

  ///////////////////////////////////////////////////////////////////////////////////
  // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
  /////////////////////////////////////////////////////////////////////////////////// 

  const raw = JSON.stringify({
    "user_app_id": {
      "user_id": USER_ID,
      "app_id": APP_ID
    },
    "inputs": [
      {
        "data": {
          "audio": {            
            "url": AUDIO_URL
          }
        }
      }
    ]
  });

  const requestOptions = {
    method: "POST",
    headers: {
      "Accept": "application/json",
      "Authorization": "Key " + PAT
    },
    body: raw
  };

  fetch(`https://api.clarifai.com/v2/workflows/${WORKFLOW_ID}/results`, requestOptions)
    .then(response => response.text())
    .then(result => console.log(result))
    .catch(error => console.log("error", error));

</script>

NodeJS

//index.js file

//////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, workflow ID, and
// audio URL. Change these strings to run your own example.
/////////////////////////////////////////////////////////////////////////////////////////

// Your PAT (Personal Access Token) can be found in the Account's Security section
const PAT = "YOUR_PAT_HERE";
const USER_ID = "clarifai";
const APP_ID = "main";
// Change these to make your own predictions
const WORKFLOW_ID = "asr-sentiment";
const AUDIO_URL = "https://samples.clarifai.com/negative_sentence_1.wav";
// Or, to use a local audio file, assign the location variable
// const AUDIO_FILE_LOCATION = "YOUR_AUDIO_FILE_LOCATION_HERE";

/////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
/////////////////////////////////////////////////////////////////////////////

const { ClarifaiStub, grpc } = require("clarifai-nodejs-grpc");

const stub = ClarifaiStub.grpc();

// This will be used by every Clarifai endpoint call
const metadata = new grpc.Metadata();
metadata.set("authorization", "Key " + PAT);

// To use a local text file, uncomment the following lines
// const fs = require("fs");
// const audioBytes = fs.readFileSync(AUDIO_FILE_LOCATION);

stub.PostWorkflowResults(
    {
        user_app_id: {
            "user_id": USER_ID,
            "app_id": APP_ID,
        },
        workflow_id: WORKFLOW_ID,
        inputs: [{
            data: {
                audio: {
                    url: AUDIO_URL,
                    // base64: audioBytes
                }
            }
        }],
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

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

        // We'll get one WorkflowResult for each input we used above. Because of one input, we have here 
        // one WorkflowResult
        const results = response.results[0];

        // Each model we have in the workflow will produce its output   
        for (const output of results.outputs) {
            const model = output.model;
            console.log("Output for the model: `" + model.id + "`");
            for (const concept of output.data.concepts) {
                console.log("\t" + concept.name + " " + concept.value);
            }
            if (output.data.text) {
                console.log(output.data.text.raw);
            }
        }
    }
);

Java

package com.clarifai.example;

import com.clarifai.channel.ClarifaiChannel;
import com.clarifai.credentials.ClarifaiCallCredentials;
import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.StatusCode;

import com.google.protobuf.ByteString;
import java.io.File;
import java.io.IOException;
import java.nio.file.Files;

public class ClarifaiExample {

    ///////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, workflow ID, and
    // audio URL. Change these strings to run your own example.
    ///////////////////////////////////////////////////////////////////////////////////

    //Your PAT (Personal Access Token) can be found in the portal under Authentication
    static final String PAT = "YOUR_PAT_HERE";
    static final String USER_ID = "clarifai";
    static final String APP_ID = "main";
    // Change these to make your own predictions
    static final String WORKFLOW_ID = "asr-sentiment";
    static final String AUDIO_URL = "https://samples.clarifai.com/negative_sentence_1.wav";
    // Or, to use a local audio file, assign the location variable
    // static final String AUDIO_FILE_LOCATION = "YOUR_IMAGE_FILE_LOCATION_HERE";

    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////

    public static void main(String[] args) throws IOException {

        V2Grpc.V2BlockingStub stub = V2Grpc.newBlockingStub(ClarifaiChannel.INSTANCE.getGrpcChannel())
                .withCallCredentials(new ClarifaiCallCredentials(PAT));

        PostWorkflowResultsResponse postWorkflowResultsResponse = stub.postWorkflowResults(
                PostWorkflowResultsRequest.newBuilder()
                        .setUserAppId(UserAppIDSet.newBuilder().setUserId(USER_ID).setAppId(APP_ID))
                        .setWorkflowId(WORKFLOW_ID)
                        .addInputs(
                                Input.newBuilder().setData(
                                        Data.newBuilder().setAudio(
                                                Audio.newBuilder().setUrl(AUDIO_URL)
                                        //  To use a local text file, uncomment the following lines
                                        //Audio.newBuilder().setBase64(ByteString.copyFrom(Files.readAllBytes(
                                        // new File(AUDIO_FILE_LOCATION).toPath()
                                        //)))
                                        )
                                )
                        )
                        .build()
        );

        if (postWorkflowResultsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
            throw new RuntimeException("Post workflow results failed, status: " + postWorkflowResultsResponse.getStatus());
        }

        // We'll get one WorkflowResult for each input we used above. Because of one input, we have here
        // one WorkflowResult
        WorkflowResult results = postWorkflowResultsResponse.getResults(0);

        // Each model we have in the workflow will produce its output
        for (Output output : results.getOutputsList()) {
            Model model = output.getModel();
            System.out.println("Output for the model: `" + model.getId() + "`");
            for (Concept concept : output.getData().getConceptsList()) {
                System.out.printf("%s %.2f%n", concept.getName(), concept.getValue());
            }
            System.out.println(output.getData().getText().getRaw());
        }

    }

}

PHP

<?php

require __DIR__ . "/vendor/autoload.php";

///////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, workflow ID, and
// audio URL. Change these strings to run your own example.
////////////////////////////////////////////////////////////////////////////////////////////

// Your PAT (Personal Access Token) can be found in the Account's Security section
$PAT = "YOUR_PAT_HERE";
$USER_ID = "clarifai";
$APP_ID = "main";
// Change these to make your own predictions
$WORKFLOW_ID = "asr-sentiment";
$AUDIO_URL = "https://samples.clarifai.com/negative_sentence_1.wav";
# Or, to use a local audio file, assign the location variable
# $AUDIO_FILE_LOCATION = "YOUR_AUDIO_FILE_LOCATION_HERE";

///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////

use Clarifai\ClarifaiClient;
use Clarifai\Api\Data;
use Clarifai\Api\Audio;
use Clarifai\Api\Input;
use Clarifai\Api\PostWorkflowResultsRequest;
use Clarifai\Api\Status\StatusCode;
use Clarifai\Api\UserAppIDSet;

$client = ClarifaiClient::grpc();

$metadata = ["Authorization" => ["Key " . $PAT]];

$userDataObject = new UserAppIDSet([
    "user_id" => $USER_ID,
    "app_id" => $APP_ID,
]);

// To use a local text file, uncomment the following lines
//$audioData = file_get_contents($AUDIO_FILE_LOCATION);

// Let's make a RPC call to the Clarifai platform. It uses the opened gRPC client channel to communicate a
// request and then wait for the response
[$response, $status] = $client
    ->PostWorkflowResults(
        // The request object carries the request along with the request status and other metadata related to the request itself
        new PostWorkflowResultsRequest([
            "user_app_id" => $userDataObject,
            "workflow_id" => $WORKFLOW_ID,
            "inputs" => [
                new Input([
                    // The Input object wraps the Data object in order to meet the API specification
                    "data" => new Data([
                        // The Data object is constructed around the Audio object. It offers a container that has additional independent
                        // metadata. In this particular use case, no other metadata is needed to be specified
                        "audio" => new Audio([
                            // In the Clarifai platform, a audio is defined by a special Audio object
                            "url" => $AUDIO_URL,
                            //"base64" => $audioData
                        ]),
                    ]),
                ]),
            ],
        ]),
        $metadata
    )
    ->wait();

// A response is returned and the first thing we do is check the status of it
// A successful response will have a status code of 0; otherwise, there is some error
if ($status->code !== 0) {
    throw new Exception("Error: {$status->details}");
}
// In addition to the RPC response status, there is a Clarifai API status that reports if the operation was a success or failure
// (not just that the communication was successful)
if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
    throw new Exception(
        "Failure response: " .
            $response->getStatus()->getDescription() .
            " " .
            $response->getStatus()->getDetails()
    );
}

// We'll get one WorkflowResult for each input we used above. Because of one input, we have here one WorkflowResult
$results = $response->getResults()[0];

// Each model we have in the workflow will produce its output
foreach ($results->getOutputs() as $output) {
    $model = $output->getModel();
    echo "Output for the model: `" . $model->getId() . "`" . "<br>";
    foreach ($output->getData()->getConcepts() as $concept) {
        echo $concept->getName() .
            " " .
            number_format($concept->getValue(), 2) .
            "<br>";
    }
    $textData = $output->getData()->getText();

    if ($textData !== null) {
        echo $textData->getRaw() . "<br>";
    }
}

// Uncomment this line to print the raw output
// echo $results->serializeToJsonString();

cURL

curl -X POST "https://api.clarifai.com/v2/users/clarifai/apps/main/workflows/asr-sentiment/results" \
  -H "authorization: Key YOUR_PAT_HERE" \
  -H "content-type: application/json" \
  -d '{
    "inputs": [
        {
          "data": {
            "audio": {
              "url": "https://samples.clarifai.com/negative_sentence_1.wav"
          }
        }
      }
    ]
}'

Text Output Example

Output for the model: `asr-wav2vec2-large-robust-ft-swbd-300h-english`
I AM NOT FLYING TO ENGLAND
Output for the model: `sentiment-analysis-twitter-roberta-base`
        LABEL_0 0.92
        LABEL_1 0.07
        LABEL_2 0.01

Raw Output Example

status {
  code: SUCCESS
  description: "Ok"
}
input {
  id: "c7b258c785614694bc1d9982e847e327"
  data {
    audio {
      url: "https://samples.clarifai.com/negative_sentence_1.wav"
    }
  }
}
outputs {
  id: "b562c938bb4545199a6908eabd8f6295"
  status {
    code: SUCCESS
    description: "Ok"
  }
  created_at {
    seconds: 1700762370
    nanos: 800729365
  }
  model {
    id: "asr-wav2vec2-large-robust-ft-swbd-300h-english"
    name: "wav2vec2-large-robust-ft-swbd-300"
    created_at {
      seconds: 1636021464
      nanos: 884891000
    }
    modified_at {
      seconds: 1659644487
      nanos: 107647000
    }
    app_id: "asr"
    model_version {
      id: "7adce5efc90744ed986fbd0bdc40000f"
      created_at {
        seconds: 1638786626
        nanos: 104602000
      }
      status {
        code: MODEL_TRAINED
        description: "Model is trained and ready"
      }
      visibility {
        gettable: PUBLIC
      }
      app_id: "asr"
      user_id: "facebook"
      metadata {
      }
      license: "Apache-2.0"
    }
    user_id: "facebook"
    model_type_id: "audio-to-text"
    visibility {
      gettable: PUBLIC
    }
    workflow_recommended {
    }
  }
  data {
    text {
      raw: "I AM NOT FLYING TO ENGLAND"
      text_info {
        encoding: "UnknownTextEnc"
      }
    }
  }
}
outputs {
  id: "1a3815e9c46b425e8247a4c491cfa0f2"
  status {
    code: SUCCESS
    description: "Ok"
  }
  created_at {
    seconds: 1700762370
    nanos: 800742640
  }
  model {
    id: "sentiment-analysis-twitter-roberta-base"
    name: "sentiment-analysis-twitter-roberta-base"
    created_at {
      seconds: 1656525158
      nanos: 299847000
    }
    modified_at {
      seconds: 1659564125
      nanos: 82152000
    }
    app_id: "text-classification"
    model_version {
      id: "f7f3df02b79d4080a0233ec1fb6404bd"
      created_at {
        seconds: 1656525158
        nanos: 310142000
      }
      status {
        code: MODEL_TRAINED
        description: "Model is trained and ready"
      }
      visibility {
        gettable: PUBLIC
      }
      app_id: "text-classification"
      user_id: "erfan"
      metadata {
        fields {
          key: "Model version logs zipped"
          value {
            string_value: "https://s3.amazonaws.com/clarifai-temp/prod/f7f3df02b79d4080a0233ec1fb6404bd.zip"
          }
        }
      }
    }
    user_id: "erfan"
    model_type_id: "text-classifier"
    task: "text-classification"
    visibility {
      gettable: PUBLIC
    }
    workflow_recommended {
    }
  }
  data {
    concepts {
      id: "LABEL_0"
      name: "LABEL_0"
      value: 0.91823113
      app_id: "text-classification"
    }
    concepts {
      id: "LABEL_1"
      name: "LABEL_1"
      value: 0.0743510351
      app_id: "text-classification"
    }
    concepts {
      id: "LABEL_2"
      name: "LABEL_2"
      value: 0.00741776684
      app_id: "text-classification"
    }
  }
}