Class v1beta1.PredictionServiceClient (3.1.2)

   
 /** 
 * This snippet has been automatically generated and should be regarded as a code template only. 
 * It will require modifications to work. 
 * It may require correct/in-range values for request initialization. 
 * TODO(developer): Uncomment these variables before running the sample. 
 */ 
  
 /** 
 *  Required. Name of the model requested to serve the batch prediction. 
 */ 
  
 // const name = 'abc123' 
  
 /** 
 *  Required. The input configuration for batch prediction. 
 */ 
  
 // const inputConfig = {} 
  
 /** 
 *  Required. The Configuration specifying where output predictions should 
 *  be written. 
 */ 
  
 // const outputConfig = {} 
  
 /** 
 *  Required. Additional domain-specific parameters for the predictions, any string must 
 *  be up to 25000 characters long. 
 *  *  For Text Classification: 
 *     `score_threshold` - (float) A value from 0.0 to 1.0. When the model 
 *          makes predictions for a text snippet, it will only produce results 
 *          that have at least this confidence score. The default is 0.5. 
 *  *  For Image Classification: 
 *     `score_threshold` - (float) A value from 0.0 to 1.0. When the model 
 *          makes predictions for an image, it will only produce results that 
 *          have at least this confidence score. The default is 0.5. 
 *  *  For Image Object Detection: 
 *     `score_threshold` - (float) When Model detects objects on the image, 
 *         it will only produce bounding boxes which have at least this 
 *         confidence score. Value in 0 to 1 range, default is 0.5. 
 *     `max_bounding_box_count` - (int64) No more than this number of bounding 
 *         boxes will be produced per image. Default is 100, the 
 *         requested value may be limited by server. 
 *  *  For Video Classification : 
 *     `score_threshold` - (float) A value from 0.0 to 1.0. When the model 
 *         makes predictions for a video, it will only produce results that 
 *         have at least this confidence score. The default is 0.5. 
 *     `segment_classification` - (boolean) Set to true to request 
 *         segment-level classification. AutoML Video Intelligence returns 
 *         labels and their confidence scores for the entire segment of the 
 *         video that user specified in the request configuration. 
 *         The default is "true". 
 *     `shot_classification` - (boolean) Set to true to request shot-level 
 *         classification. AutoML Video Intelligence determines the boundaries 
 *         for each camera shot in the entire segment of the video that user 
 *         specified in the request configuration. AutoML Video Intelligence 
 *         then returns labels and their confidence scores for each detected 
 *         shot, along with the start and end time of the shot. 
 *         WARNING: Model evaluation is not done for this classification type, 
 *         the quality of it depends on training data, but there are no metrics 
 *         provided to describe that quality. The default is "false". 
 *     `1s_interval_classification` - (boolean) Set to true to request 
 *         classification for a video at one-second intervals. AutoML Video 
 *         Intelligence returns labels and their confidence scores for each 
 *         second of the entire segment of the video that user specified in the 
 *         request configuration. 
 *         WARNING: Model evaluation is not done for this classification 
 *         type, the quality of it depends on training data, but there are no 
 *         metrics provided to describe that quality. The default is 
 *         "false". 
 *  *  For Tables: 
 *     feature_imp ortan 
ce - (boolean) Whether feature importance 
 *         should be populated in the returned TablesAnnotations. The 
 *         default is false. 
 *  *  For Video Object Tracking: 
 *     `score_threshold` - (float) When Model detects objects on video frames, 
 *         it will only produce bounding boxes which have at least this 
 *         confidence score. Value in 0 to 1 range, default is 0.5. 
 *     `max_bounding_box_count` - (int64) No more than this number of bounding 
 *         boxes will be returned per frame. Default is 100, the requested 
 *         value may be limited by server. 
 *     `min_bounding_box_size` - (float) Only bounding boxes with shortest edge 
 *       at least that long as a relative value of video frame size will be 
 *       returned. Value in 0 to 1 range. Default is 0. 
 */ 
  
 // const params = 1234 
  
 // Imports the Automl library 
  
 const 
  
 { 
 PredictionServiceClient 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/automl 
' 
 ). 
 v1beta1 
 ; 
  
 // Instantiates a client 
  
 const 
  
 automlClient 
  
 = 
  
 new 
  
  PredictionServiceClient 
 
 (); 
  
 async 
  
 function 
  
 callBatchPredict 
 () 
  
 { 
  
 // Construct request 
  
 const 
  
 request 
  
 = 
  
 { 
  
 name 
 , 
  
 inputConfig 
 , 
  
 outputConfig 
 , 
  
 params 
 , 
  
 }; 
  
 // Run request 
  
 const 
  
 [ 
 operation 
 ] 
  
 = 
  
 await 
  
 automlClient 
 . 
 batchPredict 
 ( 
 request 
 ); 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 operation 
 . 
 promise 
 (); 
  
 console 
 . 
 log 
 ( 
 response 
 ); 
  
 } 
  
 callBatchPredict 
 (); 
 

   
 /** 
 * This snippet has been automatically generated and should be regarded as a code template only. 
 * It will require modifications to work. 
 * It may require correct/in-range values for request initialization. 
 * TODO(developer): Uncomment these variables before running the sample. 
 */ 
  
 /** 
 *  Required. Name of the model requested to serve the batch prediction. 
 */ 
  
 // const name = 'abc123' 
  
 /** 
 *  Required. The input configuration for batch prediction. 
 */ 
  
 // const inputConfig = {} 
  
 /** 
 *  Required. The Configuration specifying where output predictions should 
 *  be written. 
 */ 
  
 // const outputConfig = {} 
  
 /** 
 *  Required. Additional domain-specific parameters for the predictions, any string must 
 *  be up to 25000 characters long. 
 *  *  For Text Classification: 
 *     `score_threshold` - (float) A value from 0.0 to 1.0. When the model 
 *          makes predictions for a text snippet, it will only produce results 
 *          that have at least this confidence score. The default is 0.5. 
 *  *  For Image Classification: 
 *     `score_threshold` - (float) A value from 0.0 to 1.0. When the model 
 *          makes predictions for an image, it will only produce results that 
 *          have at least this confidence score. The default is 0.5. 
 *  *  For Image Object Detection: 
 *     `score_threshold` - (float) When Model detects objects on the image, 
 *         it will only produce bounding boxes which have at least this 
 *         confidence score. Value in 0 to 1 range, default is 0.5. 
 *     `max_bounding_box_count` - (int64) No more than this number of bounding 
 *         boxes will be produced per image. Default is 100, the 
 *         requested value may be limited by server. 
 *  *  For Video Classification : 
 *     `score_threshold` - (float) A value from 0.0 to 1.0. When the model 
 *         makes predictions for a video, it will only produce results that 
 *         have at least this confidence score. The default is 0.5. 
 *     `segment_classification` - (boolean) Set to true to request 
 *         segment-level classification. AutoML Video Intelligence returns 
 *         labels and their confidence scores for the entire segment of the 
 *         video that user specified in the request configuration. 
 *         The default is "true". 
 *     `shot_classification` - (boolean) Set to true to request shot-level 
 *         classification. AutoML Video Intelligence determines the boundaries 
 *         for each camera shot in the entire segment of the video that user 
 *         specified in the request configuration. AutoML Video Intelligence 
 *         then returns labels and their confidence scores for each detected 
 *         shot, along with the start and end time of the shot. 
 *         WARNING: Model evaluation is not done for this classification type, 
 *         the quality of it depends on training data, but there are no metrics 
 *         provided to describe that quality. The default is "false". 
 *     `1s_interval_classification` - (boolean) Set to true to request 
 *         classification for a video at one-second intervals. AutoML Video 
 *         Intelligence returns labels and their confidence scores for each 
 *         second of the entire segment of the video that user specified in the 
 *         request configuration. 
 *         WARNING: Model evaluation is not done for this classification 
 *         type, the quality of it depends on training data, but there are no 
 *         metrics provided to describe that quality. The default is 
 *         "false". 
 *  *  For Tables: 
 *     feature_imp ortan 
ce - (boolean) Whether feature importance 
 *         should be populated in the returned TablesAnnotations. The 
 *         default is false. 
 *  *  For Video Object Tracking: 
 *     `score_threshold` - (float) When Model detects objects on video frames, 
 *         it will only produce bounding boxes which have at least this 
 *         confidence score. Value in 0 to 1 range, default is 0.5. 
 *     `max_bounding_box_count` - (int64) No more than this number of bounding 
 *         boxes will be returned per frame. Default is 100, the requested 
 *         value may be limited by server. 
 *     `min_bounding_box_size` - (float) Only bounding boxes with shortest edge 
 *       at least that long as a relative value of video frame size will be 
 *       returned. Value in 0 to 1 range. Default is 0. 
 */ 
  
 // const params = 1234 
  
 // Imports the Automl library 
  
 const 
  
 { 
 PredictionServiceClient 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/automl 
' 
 ). 
 v1beta1 
 ; 
  
 // Instantiates a client 
  
 const 
  
 automlClient 
  
 = 
  
 new 
  
  PredictionServiceClient 
 
 (); 
  
 async 
  
 function 
  
 callBatchPredict 
 () 
  
 { 
  
 // Construct request 
  
 const 
  
 request 
  
 = 
  
 { 
  
 name 
 , 
  
 inputConfig 
 , 
  
 outputConfig 
 , 
  
 params 
 , 
  
 }; 
  
 // Run request 
  
 const 
  
 [ 
 operation 
 ] 
  
 = 
  
 await 
  
 automlClient 
 . 
 batchPredict 
 ( 
 request 
 ); 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 operation 
 . 
 promise 
 (); 
  
 console 
 . 
 log 
 ( 
 response 
 ); 
  
 } 
  
 callBatchPredict 
 (); 
 

   
 /** 
 * This snippet has been automatically generated and should be regarded as a code template only. 
 * It will require modifications to work. 
 * It may require correct/in-range values for request initialization. 
 * TODO(developer): Uncomment these variables before running the sample. 
 */ 
  
 /** 
 *  Required. Name of the model requested to serve the prediction. 
 */ 
  
 // const name = 'abc123' 
  
 /** 
 *  Required. Payload to perform a prediction on. The payload must match the 
 *  problem type that the model was trained to solve. 
 */ 
  
 // const payload = {} 
  
 /** 
 *  Additional domain-specific parameters, any string must be up to 25000 
 *  characters long. 
 *  *  For Image Classification: 
 *     `score_threshold` - (float) A value from 0.0 to 1.0. When the model 
 *      makes predictions for an image, it will only produce results that have 
 *      at least this confidence score. The default is 0.5. 
 *   *  For Image Object Detection: 
 *     `score_threshold` - (float) When Model detects objects on the image, 
 *         it will only produce bounding boxes which have at least this 
 *         confidence score. Value in 0 to 1 range, default is 0.5. 
 *     `max_bounding_box_count` - (int64) No more than this number of bounding 
 *         boxes will be returned in the response. Default is 100, the 
 *         requested value may be limited by server. 
 *  *  For Tables: 
 *     feature_imp ortan 
ce - (boolean) Whether feature importance 
 *         should be populated in the returned TablesAnnotation. 
 *         The default is false. 
 */ 
  
 // const params = 1234 
  
 // Imports the Automl library 
  
 const 
  
 { 
 PredictionServiceClient 
 } 
  
 = 
  
 require 
 ( 
 ' @google-cloud/automl 
' 
 ). 
 v1beta1 
 ; 
  
 // Instantiates a client 
  
 const 
  
 automlClient 
  
 = 
  
 new 
  
  PredictionServiceClient 
 
 (); 
  
 async 
  
 function 
  
 callPredict 
 () 
  
 { 
  
 // Construct request 
  
 const 
  
 request 
  
 = 
  
 { 
  
 name 
 , 
  
 payload 
 , 
  
 }; 
  
 // Run request 
  
 const 
  
 response 
  
 = 
  
 await 
  
 automlClient 
 . 
 predict 
 ( 
 request 
 ); 
  
 console 
 . 
 log 
 ( 
 response 
 ); 
  
 } 
  
 callPredict 
 ();