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+ const logger = require ( 'log4js' ) . getLogger ( "modzy" ) ;
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+ const modzy = require ( 'modzy-sdk' ) ;
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+ const fs = require ( 'fs' ) ;
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+
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+ // Always configure the logger level (ie: all, trace, debug, info, warn, error, fatal)
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+ logger . level = "info" ;
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+
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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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+ // 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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+ // (ie: AzQBJ3h4B1z60xNmhAJF.uQyQh8putLIRDi1nOldh).
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+ const API_KEY = process . env . MODZY_API_KEY ;
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+
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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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+
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+ // Create a Job with an embedded input, wait, and retrieve results:
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+
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+ async function createJobWithFileInput ( ) {
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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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+ 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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+
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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 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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+ // output keys of the model.
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+ logger . info ( `Ths 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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+ 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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+ 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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+ // A file input can be a byte array or any file path. This input type fits files of any size.
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+ const imagePath = 'samples/image.png' ;
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+ const configPath = '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" : imagePath , "config.json" : configPath } } ;
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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" : imagePath , "config.json" : configPath }
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+ // You don’t need to load all the inputs from the files, just convert to bytes as follows:
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+ const configBytes = Buffer . from ( JSON . stringify ( { "languages" :[ "spa" ] } ) ) ;
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+ sources [ "another-key" ] = { "input" : imagePath , "config.json" :configBytes }
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+ // If you send an incorrect input key, the model fails to process the input.
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+ sources [ "wrong-key" ] = { "a.wrong.key" : imagePath , "config.json" :configPath }
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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" : configPath , "config.json" :imagePath }
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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 . submitJobFiles ( model . modelId , modelVersion . version , 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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+ // 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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+ // 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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+ // A completed job means that all the inputs were processed by the model. Check the results for each
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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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+ // results.json, so we can get the results as follows:
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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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+ 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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+ 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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+
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+ createJobWithFileInput ( ) ;
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