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‎samples/job_with_embedded_input_sample.js‎

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@@ -6,110 +6,112 @@ const fs = require('fs');
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logger.level = "info";
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// The system admin can provide the right base API URL, the API key can be downloaded from your profile page on Modzy.
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// You can config those params as is described in the readme file (as environment variables, or by using the .env file), or you
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// or you can just update the BASE_URL and API_KEY vars and use this sample code (not recommended for production environments).
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// You can configure those params as described in the README file (as environment variables, or by using the .env file),
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// or you can just update the BASE_URL and API_KEY variables and use this sample code (not recommended for production environments).
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// The MODZY_BASE_URL should point to the API services route which may be different from the Modzy page URL.
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// (ie: https://modzy.example.com/api).
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const BASE_URL = process.env.MODZY_BASE_URL;
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// The MODZY_API_KEY is your own personal API key. It is composed by a public part, a dot character and a private part
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// The MODZY_API_KEY is your own personal API key. It is composed by a public part, a dot character, and a private part
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// (ie: AzQBJ3h4B1z60xNmhAJF.uQyQh8putLIRDi1nOldh).
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const API_KEY = process.env.MODZY_API_KEY;
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const API_KEY = process.env.MODZY_API_KEY;
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// Client initialization
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// Client initialization:
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// Initialize the ApiClient instance with the BASE_URL and the API_KEY to store those arguments
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// for the following API calls.
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const modzyClient = new modzy.ModzyClient(BASE_URL, API_KEY);
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// Create a Job with an embedded input, wait, and retrieve results:
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async function createJobWithEmbeddedInput(){
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try {
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// Get the model object:
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// If you already know the model identifier (i.e.: you got from the URL of the model details page or the input sample),
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// you can skip this step. If you don't you can find the model identifier by using its name as follows:
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async function createJobWithEmbeddedInput() {
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try {
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// Get the model object:
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// If you already know the model identifier (i.e.: you got it from the URL of the model details page or the input sample),
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// you can skip this step. If you don't, you can find the model identifier by using its name as follows:
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let model = await modzyClient.getModelByName("Multi-Language OCR");
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// Or if you already know the model id and want to know more about the model, you can use this instead:
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//let model = await modzyClient.getModel("c60c8dbd79");
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// The model identifier is under the modelId key. You can take a look at the other keys by uncommenting the following line
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//let model = await modzyClient.getModel("c60c8dbd79");
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//You can find more information about how to query the models on the model_samples.js file.
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// The model identifier is under the modelId key. You can take a look at the other keys by uncommenting the following line
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logger.info(Object.keys(model).toString().replace('\n', ' '));
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// Or just log the model identifier and the latest version
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logger.info(`The model identifier is ${model.modelId} and the latest version is ${model.latestVersion}`);
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// Get the model version object:
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// If you already know the model version and the input key(s) of the model version you can skip this step. Also, you can
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// use the following code block to know about the inputs keys and skip the call on future job submissions.
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// If you already know the model version and the input key(s) of the model version, you can skip this step. Also, you can
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// use the following code block to know about the input keys and skip the call on future job submissions.
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let modelVersion = await modzyClient.getModelVersion(model.modelId, model.latestVersion);
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// The info stored in modelVersion provides insights about the amount of time that the model can spend processing, the inputs, and
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// The info stored in modelVersion provides insights about the amount of time that the model can spend processing, the input, and
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// output keys of the model.
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logger.info(`Ths model version is ${modelVersion.version}`);
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logger.info(`This model version is ${modelVersion.version}`);
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logger.info(` timeouts: status ${modelVersion.timeout.status}ms, run ${modelVersion.timeout.run}ms `);
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logger.info(" inputs: ");
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for(key in modelVersion.inputs){
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for (key in modelVersion.inputs) {
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let input = modelVersion.inputs[key];
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logger.info(` key ${input.name}, type ${input.acceptedMediaTypes}, description: ${input.description}`);
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}
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logger.info(" outputs: ")
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for(key in modelVersion.outputs){
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for (key in modelVersion.outputs) {
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let output = modelVersion.outputs[key];
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logger.info(` key ${output.name}, type ${output.mediaType}, description: ${output.description}`);
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}
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// Send the job:
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// An embedded input is a byte array encoded as a string in Base64, that's very handy for small to middle size files, for
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// bigger files can be a memory issue because you need to load the file in memory (load + encode).
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const imageBytes = fs.readFileSync('samples/image.png');
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let configBytes = fs.readFileSync('samples/config.json');
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// With the info about the model (identifier), the model version (version string, input/output keys), you are ready to
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// submit the job. Just prepare the source object:
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let sources = {"source-key": {"input": imageBytes, "config.json": configBytes}};
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// An inference job groups input data that you send to a model. You can send any amount of inputs to
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// process and you can identify and refer to a specific input by the key that you assign, for example we can add:
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sources["second-key"] = {"input": imageBytes, "config.json":configBytes}
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// You don't need to load all the inputs from files, just convert to bytes as follows:
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configBytes = Buffer.from(JSON.stringify({"languages":["spa"]}));
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sources["another-key"] = {"input": imageBytes, "config.json":configBytes}
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// If you send a wrong input key, the model fails to process the input.
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sources["wrong-key"] = {"a.wrong.key": imageBytes, "config.json":configBytes}
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// If you send a correct input key, but some wrong values, the model fails to process the input.
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sources["wrong-value"] = {"input": configBytes, "config.json":imageBytes}
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// When you have all your inputs ready, you can use our helper method to submit the job as follows:
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let job = await modzyClient.submitJobEmbedded(model.modelId,modelVersion.version, "application/octet-stream", sources);
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// Send the job:
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// An embedded input is a byte array encoded as a string in Base64. This input type comes very handy for small to middle size files. However,
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// it requires to load and encode files in memory which can be an issue for larger files.
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const imageBytes = fs.readFileSync('samples/image.png');
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let configBytes = fs.readFileSync('samples/config.json');
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// With the info about the model (identifier) and the model version (version string, input/output keys), you are ready to
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// submit the job. Just prepare the source object:
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let sources = { "source-key": { "input": imageBytes, "config.json": configBytes } };
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// An inference job groups input data sent to a model. You can send any amount of inputs to
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// process and you can identify and refer to a specific input by the key assigned. For example we can add:
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sources["second-key"] = { "input": imageBytes, "config.json": configBytes }
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// You don't need to load all the inputs from files, just convert to bytes as follows:
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configBytes = Buffer.from(JSON.stringify({ "languages": ["spa"] }));
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sources["another-key"] = { "input": imageBytes, "config.json": configBytes }
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// If you send a wrong input key, the model fails to process the input.
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sources["wrong-key"] = { "a.wrong.key": imageBytes, "config.json": configBytes }
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// If you send a correct input key, but some wrong values, the model fails to process the input.
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sources["wrong-value"] = { "input": configBytes, "config.json": imageBytes }
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// When you have all your inputs ready, you can use our helper method to submit the job as follows:
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let job = await modzyClient.submitJobEmbedded(model.modelId, modelVersion.version, "application/octet-stream", sources);
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// Modzy creates the job and queue for processing. The job object contains all the info that you need to keep track
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// of the process, the most important being the job identifier and the job status.
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logger.info("job: "+job.jobIdentifier+" "+job.status);
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logger.info("job: " + job.jobIdentifier + " " + job.status);
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// The job moves to SUBMITTED, meaning that Modzy acknowledged the job and sent it to the queue to be processed.
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// We provide a helper method to listen until the job finishes processing. it will listen until the job finishes
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// We provide a helper method to listen until the job finishes processing. It listens until the job finishes
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// and moves to COMPLETED, CANCELED, or TIMEOUT.
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job = await modzyClient.blockUntilComplete(job);
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// Get the results:
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// Check the status of the job. Jobs may be canceled or may reach a timeout.
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if( job.status === "COMPLETED" ){
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if (job.status === "COMPLETED") {
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// A completed job means that all the inputs were processed by the model. Check the results for each
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// input keys provided in the source object to see the model output.
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// input key provided in the source object to see the model output.
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let result = await modzyClient.getResult(job.jobIdentifier);
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// The result object has some useful info:
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logger.info(`Result: finished: ${result.finished}, total: ${result.total}, completed: ${result.completed}, failed: ${result.failed}`);
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// Notice that we are iterating through the same input sources keys
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for( key in sources ){
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// The result object has the individual results of each job input. In this case the output key is called
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// Notice that we are iterating through the same input source keys
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for (key in sources) {
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// The results object has the individual results of each job input. In this case the output key is called
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// results.json, so we can get the results as follows:
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if( result.results[key] ){
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if (result.results[key]) {
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let model_res = result.results[key]["results.json"];
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// The output for this model comes in a JSON format, so we can directly log the model results:
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logger.info(` ${key}: ${JSON.stringify(model_res)}`);
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}
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else{
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else {
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// If the model raises an error, we can get the specific error message:
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logger.warn(` ${key}: failure ${result.failures[key]['error']}`);
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}
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}
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}
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else{
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else {
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log.warn(`The job ends with status ${job.status}`);
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}
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}
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catch(error){
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logger.warn(error);
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}
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}
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}
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catch (error) {
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logger.warn(error);
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}
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}
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