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Exemple de code
Go
import (
"context"
"fmt"
"io"
automl "cloud.google.com/go/automl/apiv1"
automlpb "google.golang.org/genproto/googleapis/cloud/automl/v1"
)
// translatePredict does a prediction for translate.
func translatePredict(w io.Writer, projectID string, location string, modelID string, content string) error {
// projectID := "my-project-id"
// location := "us-central1"
// modelID := "TRL123456789..."
// content := "text to translate"
ctx := context.Background()
client, err := automl.NewPredictionClient(ctx)
if err != nil {
return fmt.Errorf("NewPredictionClient: %v", err)
}
defer client.Close()
req := &automlpb.PredictRequest{
Name: fmt.Sprintf("projects/%s/locations/%s/models/%s", projectID, location, modelID),
Payload: &automlpb.ExamplePayload{
Payload: &automlpb.ExamplePayload_TextSnippet{
TextSnippet: &automlpb.TextSnippet{
Content: content,
MimeType: "text/plain", // Types: "text/plain", "text/html"
},
},
},
}
resp, err := client.Predict(ctx, req)
if err != nil {
return fmt.Errorf("Predict: %v", err)
}
for _, payload := range resp.GetPayload() {
fmt.Fprintf(w, "Translated content: %v\n", payload.GetTranslation().GetTranslatedContent().GetContent())
}
return nil
}
Java
import com.google.cloud.automl.v1.ExamplePayload;
import com.google.cloud.automl.v1.ModelName;
import com.google.cloud.automl.v1.PredictRequest;
import com.google.cloud.automl.v1.PredictResponse;
import com.google.cloud.automl.v1.PredictionServiceClient;
import com.google.cloud.automl.v1.TextSnippet;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
class TranslatePredict {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
String projectId = "YOUR_PROJECT_ID";
String modelId = "YOUR_MODEL_ID";
String filePath = "path_to_local_file.txt";
predict(projectId, modelId, filePath);
}
static void predict(String projectId, String modelId, String filePath) throws IOException {
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// the "close" method on the client to safely clean up any remaining background resources.
try (PredictionServiceClient client = PredictionServiceClient.create()) {
// Get the full path of the model.
ModelName name = ModelName.of(projectId, "us-central1", modelId);
String content = new String(Files.readAllBytes(Paths.get(filePath)));
TextSnippet textSnippet = TextSnippet.newBuilder().setContent(content).build();
ExamplePayload payload = ExamplePayload.newBuilder().setTextSnippet(textSnippet).build();
PredictRequest predictRequest =
PredictRequest.newBuilder().setName(name.toString()).setPayload(payload).build();
PredictResponse response = client.predict(predictRequest);
TextSnippet translatedContent =
response.getPayload(0).getTranslation().getTranslatedContent();
System.out.format("Translated Content: %s\n", translatedContent.getContent());
}
}
}Node.js
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const modelId = 'YOUR_MODEL_ID';
// const filePath = 'path_to_local_file.txt';
// Imports the Google Cloud AutoML library
const {PredictionServiceClient} = require('@google-cloud/automl').v1;
const fs = require('fs');
// Instantiates a client
const client = new PredictionServiceClient();
// Read the file content for translation.
const content = fs.readFileSync(filePath, 'utf8');
async function predict() {
// Construct request
const request = {
name: client.modelPath(projectId, location, modelId),
payload: {
textSnippet: {
content: content,
},
},
};
const [response] = await client.predict(request);
console.log(
'Translated content: ',
response.payload[0].translation.translatedContent.content
);
}
predict();Python
from google.cloud import automl
# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"
# file_path = "path_to_local_file.txt"
prediction_client = automl.PredictionServiceClient()
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1", model_id)
# Read the file content for translation.
with open(file_path, "rb") as content_file:
content = content_file.read()
content.decode("utf-8")
text_snippet = automl.TextSnippet(content=content)
payload = automl.ExamplePayload(text_snippet=text_snippet)
response = prediction_client.predict(name=model_full_id, payload=payload)
translated_content = response.payload[0].translation.translated_content
print(u"Translated content: {}".format(translated_content.content))