The Workflow Predict API allows you to predict using 1 or more model(s), regardless of them being Clarifai or custom, within a single API call. The max number of inputs processed at once with any given workflow is 32.
Now that you have that all set up, you will be able to predict under a workflow using the POST /v2/workflows/{workflow_id}/results endpoint. Your {workflow-id} currently is whatever you set as your ID. Then as far as your request body, nothing has changed with how you would normally do a predict. In the response body, you will see a results object and each object will be the response from the models in the same ordering from the workflow you set up.
Image showing the Portal's workflow prediction results
You can also use the Explorer in Clarifai Portal to see the results of your workflow's predictions on a given input.
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/api-clients#client-installation-instructions
PostWorkflowResultsResponse postWorkflowResultsResponse = stub.postWorkflowResults(
PostWorkflowResultsRequest.newBuilder()
.setWorkflowId("{YOUR_WORKFLOW_ID}")
.addInputs(
Input.newBuilder().setData(
Data.newBuilder().setImage(
Image.newBuilder().setUrl(
"https://samples.clarifai.com/metro-north.jpg"
)
)
)
)
.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 one output.
for (Output output : results.getOutputsList()) {
Model model = output.getModel();
System.out.println("Predicted concepts for the model `" + model.getName() + "`:");
for (Concept concept : output.getData().getConceptsList()) {
System.out.printf("\t%s %.2f%n", concept.getName(), concept.getValue());
}
}
// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
stub.PostWorkflowResults(
{
workflow_id: "{YOUR_WORKFLOW_ID}",
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 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.name + "`:");
for (const concept of output.data.concepts) {
console.log("\t" + concept.name + " " + concept.value);
}
}
}
);
# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
post_workflow_results_response = stub.PostWorkflowResults(
service_pb2.PostWorkflowResultsRequest(
workflow_id="{YOUR_WORKFLOW_ID}",
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(
url="https://samples.clarifai.com/metro-north.jpg"
)
)
)
]
),
metadata=metadata
)
if post_workflow_results_response.status.code != status_code_pb2.SUCCESS:
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.name)
for concept in output.data.concepts:
print("\t%s %.2f" % (concept.name, concept.value))
app.workflow.predict('{workflow-id}', "https://samples.clarifai.com/metro-north.jpg").then(
function(response){
// Do something with response
},
function(err){
// There was an error
}
);
from clarifai.rest import ClarifaiApp
from clarifai.rest import Workflow
app = ClarifaiApp(api_key='YOUR_API_KEY')
workflow = Workflow(app.api, workflow_id="YOUR_WORKFLOW_ID")
response = workflow.predict_by_url('https://samples.clarifai.com/metro-north.jpg')
using System.Collections.Generic;
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.WorkflowPredict(
"{workflow-id}",
new List<IClarifaiInput>
{
new ClarifaiURLImage("https://samples.clarifai.com/puppy.jpeg")
})
.ExecuteAsync();
}
}
}
// Coming Soon
use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiURLImage;
use Clarifai\DTOs\Predictions\Concept;
use Clarifai\DTOs\Workflows\WorkflowPredictResult;
$client = new ClarifaiClient('YOUR_API_KEY');
$response = $client->workflowPredict('your-workflow-id',
new ClarifaiURLImage('https://samples.clarifai.com/puppy.jpeg'))
->executeSync();
if ($response-> isSuccessful()) {
echo "Response is successful.\n";
/** @var WorkflowPredictResult $workflowResult */
$workflowResult = $response->get();
echo "Predicted concepts:\n";
/** @var Concept $concept */
foreach ($workflowResult->workflowResult()->predictions() as $output) {
echo 'Predictions for output ' . $output->id() . "\n";
/** @var Concept $concept */
foreach ($output->data() as $concept) {
echo "\t" . $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();
}
