dalequark

    input_configs = [{"gcs_source": gcs_source, "mime_type": "text/plain"}]
    output_config = {"gcs_destination": {"output_uri_prefix": output_uri}}
    
    operation = client.batch_translate_text(
        parent=parent,
        source_language_code="en",
        target_language_codes="es",
        input_configs=input_configs,
        output_config=output_config)

dalequark

MODEL_PATH = 'projects/project_num/locations/us-central1/models/your_model_id'
response = client.translate_text(
    ["Let's try the Translation API on Google Cloud Platform."],
    target_language_code="en",
    source_language_code="ja",
    parent=parent,
    model=MODEL_PATH,
    mime_type='text/plain'  # mime types: text/plain, text/html
)

dalequark

from google.cloud import translate_v3 as translate

client = translate.TranslationServiceClient()

PROJECT_ID = "your_project_id"
LOCATION = "us-central1"
GLOSSARY_ID = "my_first_glossary"

client = translate.TranslationServiceClient()

dalequark

import os
from google.cloud import translate_v3 as translate

client = translate.TranslationServiceClient()

PROJECT_ID = "misc-230215"
# Set location so that all operations happen in a single zone.
# For now, this must be us-central1
LOCATION = "us-central1"
# This URI should take the form gs://bucket_name/file_name

dalequark

from google.cloud import translate_v3

client = translate.TranslationServiceClient()

PROJECT_ID = "your_project_id_here"
LOCATION = "us-central1"

response = client.translate_text(
    parent=client.location_path(PROJECT_ID, LOCATION),
    contents=["Let's try the Translation API on Google Cloud Platform."],

dalequark

import microphone_stream
import dialogflow_v2 as dialogflow
import pyaudio
import re
PROJECT_ID = "talking-machines"
# Audio recording parameters
STREAMING_LIMIT = 10000
SAMPLE_RATE = 44100
CHUNK_SIZE = int(SAMPLE_RATE / 10)  # 100ms
OUTPUT_BUFFER = 1000

dalequark

const { TranslationServiceClient } = require("@google-cloud/translate").v3beta1;

const projectId = "your_project_id";
const location = "us-central1";

async function translateText(srcText, targetLang, srcLang) {
  // Translates text srcText to language targetLang.
  // srcLang is optional and will be auto-detected if left empty.

  // Instantiates a client

dalequark

async function translateText(srcText, targetLang, srcLang) {
  // Translates text srcText to language targetLang.
  // srcLang is optional and will be auto-detected if left empty.

  // Instantiates a client
  const translationClient = new TranslationServiceClient();

  // Constructs a request
  const request = {
    parent: translationClient.locationPath(projectId, location),

dalequark

const {TranslationServiceClient} = require('@google-cloud/translate').v3beta1;

const projectId = "your-project-id-230215";
const location = "us-central1";

dalequark

def getPrediction(in_sentences):
  labels = ["Negative", "Positive"]
  input_examples = [run_classifier.InputExample(guid="", text_a = x, text_b = None, label = 0) for x in in_sentences] # here, "" is just a dummy label
  input_features = run_classifier.convert_examples_to_features(input_examples, label_list, MAX_SEQ_LENGTH, tokenizer)
  predict_input_fn = run_classifier.input_fn_builder(features=input_features, seq_length=MAX_SEQ_LENGTH, is_training=False, drop_remainder=False)
  predictions = estimator.predict(predict_input_fn)
  return [(sentence, prediction['probabilities'], labels[prediction['labels']]) for sentence, prediction in zip(in_sentences, predictions)]
  
  pred_sentences = [
  "That movie was absolutely awful",