# Workflow Predict 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](/files/Sebir4dt2DSt78UtbP1P) You can also use the Explorer in Clarifai Portal to see the results of your workflow's predictions on a given input. {% tabs %} {% tab title="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/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()); } } ``` {% endtab %} {% tab title="NodeJS" %} ```javascript // 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); } } } ); ``` {% endtab %} {% tab title="Python" %} ```python # 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( user_app_id=userDataObject, # The userDataObject is created in the overview and is required when using a PAT 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)) ``` {% 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/workflows/{YOUR_WORKFLOW_ID}/results ``` {% endtab %} {% endtabs %} {% tabs %} {% tab title="Response JSON" %} ```javascript { "status": { "code": 10000, "description": "Ok" }, "workflow": { "id": "my-workflow", "app_id": "c54b7637df12407aa9c57dfd6d5c057f", "created_at": "2017-07-10T01:45:05.672880Z" }, "results": [ { "status": { "code": 10000, "description": "Ok" }, "input": { "id": "c88aeed9d04c471cace6f8e4801f1a1c", "data": { "image": { "url": "https://samples.clarifai.com/metro-north.jpg" } } }, "outputs": [ { "id": "feae971167a04d1bbebb7ea49d6ba0f7", "status": { "code": 10000, "description": "Ok" }, "created_at": "2017-07-10T12:01:44.929928529Z", "model": { "id": "d16f390eb32cad478c7ae150069bd2c6", "name": "moderation", "created_at": "2017-05-12T21:28:00.471607Z", "app_id": "main", "output_info": { "message": "Show output_info with: GET /models/{model_id}/output_info", "type": "concept", "type_ext": "concept" }, "model_version": { "id": "b42ac907ac93483484483a0040a386be", "created_at": "2017-05-12T21:28:00.471607Z", "status": { "code": 21100, "description": "Model trained successfully" } } }, "data": { "concepts": [ { "id": "ai_QD1zClSd", "name": "safe", "value": 0.99999714, "app_id": "main" }, { "id": "ai_kBBGf7r8", "name": "gore", "value": 3.7771046e-05, "app_id": "main" }, { "id": "ai_8QQwMjQR", "name": "drug", "value": 1.0449563e-05, "app_id": "main" }, { "id": "ai_V76bvrtj", "name": "explicit", "value": 5.2887003e-06, "app_id": "main" }, { "id": "ai_RtXh5qkR", "name": "suggestive", "value": 4.7939684e-06, "app_id": "main" } ] } }, { "id": "f635b40cbeee47ddb7b348a981e14faf", "status": { "code": 10000, "description": "Ok" }, "created_at": "2017-07-10T12:01:44.929941126Z", "model": { "id": "aaa03c23b3724a16a56b629203edc62c", "name": "general-v1.3", "created_at": "2016-02-26T23:38:40.086101Z", "app_id": "main", "output_info": { "message": "Show output_info with: GET /models/{model_id}/output_info", "type": "concept", "type_ext": "concept" }, "model_version": { "id": "aa9ca48295b37401f8af92ad1af0d91d", "created_at": "2016-07-13T00:58:55.915745Z", "status": { "code": 21100, "description": "Model trained successfully" } } }, "data": { "concepts": [ { "id": "ai_HLmqFqBf", "name": "train", "value": 0.9989112, "app_id": "main" }, { "id": "ai_fvlBqXZR", "name": "railway", "value": 0.9975532, "app_id": "main" }, { "id": "ai_Xxjc3MhT", "name": "transportation system", "value": 0.9959158, "app_id": "main" }, { "id": "ai_6kTjGfF6", "name": "station", "value": 0.992573, "app_id": "main" }, { "id": "ai_RRXLczch", "name": "locomotive", "value": 0.992556, "app_id": "main" }, { "id": "ai_VRmbGVWh", "name": "travel", "value": 0.98789215, "app_id": "main" }, { "id": "ai_SHNDcmJ3", "name": "subway system", "value": 0.9816359, "app_id": "main" }, { "id": "ai_jlb9q33b", "name": "commuter", "value": 0.9712483, "app_id": "main" }, { "id": "ai_46lGZ4Gm", "name": "railroad track", "value": 0.9690325, "app_id": "main" }, { "id": "ai_tr0MBp64", "name": "traffic", "value": 0.9687052, "app_id": "main" }, { "id": "ai_l4WckcJN", "name": "blur", "value": 0.9667078, "app_id": "main" }, { "id": "ai_2gkfMDsM", "name": "platform", "value": 0.9624243, "app_id": "main" }, { "id": "ai_CpFBRWzD", "name": "urban", "value": 0.960752, "app_id": "main" }, { "id": "ai_786Zr311", "name": "no person", "value": 0.95864904, "app_id": "main" }, { "id": "ai_6lhccv44", "name": "business", "value": 0.95720303, "app_id": "main" }, { "id": "ai_971KsJkn", "name": "track", "value": 0.9494642, "app_id": "main" }, { "id": "ai_WBQfVV0p", "name": "city", "value": 0.94089437, "app_id": "main" }, { "id": "ai_dSCKh8xv", "name": "fast", "value": 0.9399334, "app_id": "main" }, { "id": "ai_TZ3C79C6", "name": "road", "value": 0.93121606, "app_id": "main" }, { "id": "ai_VSVscs9k", "name": "terminal", "value": 0.9230834, "app_id": "main" } ] } } ] } ] } ``` {% 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/api-guide/workflows/common-workflows/workflow-predict.md?ask= ``` 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.