content: /*
   * Licensed to the Apache Software Foundation (ASF) under one
   * or more contributor license agreements.  See the NOTICE file
   * distributed with this work for additional information
   * regarding copyright ownership.  The ASF licenses this file
   * to you under the Apache License, Version 2.0 (the
   * "License"); you may not use this file except in compliance
   * with the License.  You may obtain a copy of the License at
   *
   *     http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  package org.apache.beam.sdk.extensions.ml;
  
  import com.google.api.client.json.GenericJson;
  import com.google.api.gax.rpc.ApiException;
  import com.google.auto.value.AutoValue;
  import com.google.cloud.recommendationengine.v1beta1.PlacementName;
  import com.google.cloud.recommendationengine.v1beta1.PredictResponse;
  import com.google.cloud.recommendationengine.v1beta1.PredictionServiceClient;
  import com.google.cloud.recommendationengine.v1beta1.UserEvent;
  import com.google.protobuf.util.JsonFormat;
  import java.io.IOException;
  import javax.annotation.Nullable;
  import org.apache.beam.sdk.transforms.DoFn;
  import org.apache.beam.sdk.transforms.PTransform;
  import org.apache.beam.sdk.transforms.ParDo;
  import org.apache.beam.sdk.values.PCollection;
  import org.apache.beam.sdk.values.PCollectionTuple;
  import org.apache.beam.sdk.values.TupleTag;
  import org.apache.beam.sdk.values.TupleTagList;
  import org.json.JSONObject;
  
  /**
   * A {@link PTransform} using the Recommendations AI API (https://cloud.google.com/recommendations).
   * Takes an input {@link PCollection} of {@link GenericJson}s and creates {@link
   * PredictResponse.PredictionResult}s.
   *
   * <p>It is possible to provide a catalog name to which you want to add the user event (defaults to
   * "default_catalog"). It is possible to provide a event store to which you want to add the user
   * event (defaults to "default_event_store"). A placement id for the recommendation engine placement
   * to be used.
   */
  @AutoValue
  @SuppressWarnings({"nullness"})
  public abstract class RecommendationAIPredict
      extends PTransform<PCollection<GenericJson>, PCollectionTuple> {
  
    /** @return ID of Google Cloud project to be used for creating catalog items. */
    public abstract @Nullable String projectId();
  
    /** @return Name of the catalog where the catalog items will be created. */
    public abstract @Nullable String catalogName();
  
    /** @return Name of the event store where the user events will be created. */
    public abstract @Nullable String eventStore();
  
    /** @return ID of the recommendation engine placement. */
    public abstract String placementId();
  
    public static final TupleTag<PredictResponse.PredictionResult> SUCCESS_TAG =
        new TupleTag<PredictResponse.PredictionResult>() {};
  
    public static final TupleTag<UserEvent> FAILURE_TAG = new TupleTag<UserEvent>() {};
  
    @AutoValue.Builder
    abstract static class Builder {
      /** @param projectId ID of Google Cloud project to be used for the predictions. */
      public abstract Builder setProjectId(@Nullable String projectId);
  
      /** @param catalogName Name of the catalog to be used for predictions. */
      public abstract Builder setCatalogName(@Nullable String catalogName);
  
      /** @param eventStore Name of the event store to be used for predictions. */
      public abstract Builder setEventStore(@Nullable String eventStore);
  
      /** @param placementId of the recommendation engine placement. */
      public abstract Builder setPlacementId(String placementId);
  
      public abstract RecommendationAIPredict build();
    }
  
    static Builder newBuilder() {
      return new AutoValue_RecommendationAIPredict.Builder()
          .setCatalogName("default_catalog")
          .setEventStore("default_event_store")
          .setPlacementId("recently_viewed_default");
    }
  
    abstract Builder toBuilder();
  
    public RecommendationAIPredict withProjectId(String projectId) {
      return this.toBuilder().setProjectId(projectId).build();
    }
  
    public RecommendationAIPredict withCatalogName(String catalogName) {
      return this.toBuilder().setCatalogName(catalogName).build();
    }
  
    public RecommendationAIPredict withEventStore(String eventStore) {
      return this.toBuilder().setEventStore(eventStore).build();
    }
  
    public RecommendationAIPredict withPlacementId(String placementId) {
      return this.toBuilder().setPlacementId(placementId).build();
    }
  
    @Override
    public PCollectionTuple expand(PCollection<GenericJson> input) {
      return input.apply(
          ParDo.of(new Predict(projectId(), catalogName(), eventStore(), placementId()))
              .withOutputTags(SUCCESS_TAG, TupleTagList.of(FAILURE_TAG)));
    }
  
    private static class Predict extends DoFn<GenericJson, PredictResponse.PredictionResult> {
      private final String projectId;
      private final String catalogName;
      private final String eventStore;
      private final String placementId;
  
      /**
       * @param projectId ID of GCP project to be used for creating catalog items.
       * @param catalogName Catalog name for UserEvent creation.
       * @param eventStore Event store for UserEvent creation.
       * @param placementId ID of the recommendation engine placement.
       */
      private Predict(String projectId, String catalogName, String eventStore, String placementId) {
        this.projectId = projectId;
        this.catalogName = catalogName;
        this.eventStore = eventStore;
        this.placementId = placementId;
      }
  
      @ProcessElement
      public void processElement(ProcessContext context) throws IOException {
        PlacementName name =
            PlacementName.of(projectId, "global", catalogName, eventStore, placementId);
        UserEvent.Builder userEventBuilder = UserEvent.newBuilder();
        JsonFormat.parser().merge(new JSONObject(context.element()).toString(), userEventBuilder);
        UserEvent userEvent = userEventBuilder.build();
        try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
          for (PredictResponse.PredictionResult res :
              predictionServiceClient.predict(name, userEvent).iterateAll()) {
            context.output(SUCCESS_TAG, res);
          }
        } catch (ApiException e) {
          context.output(FAILURE_TAG, userEvent);
        }
      }
    }
  }
  