DSC-Workshop-2-Setup

Introduction

Have you ever wondered if memes are funnier in French rather than English? In this workshop we will be learning how to translate memes from different languages into English. We will use the Vision API, Translation API, and the Text-to-speech API of GCP to achieve our objectives:

1. Pass text recognized by the Cloud Vision API to the Cloud Translation API.
2. Create and use Cloud Translation glossaries to personalize Cloud Translation API translations.
3. Create an audio representation of translated text using the Text-to-Speech API.

Setup - DO THIS BEFORE WORKSHOP!!

1. Install Anaconda from here which provides a virtual environment to run your python scripts and libraries. Make sure to add anaconda to your path.
2. After downloading it, create an environment called 'visionAI' by running these commands:

conda create --name visionAI

activate visionAI

3. Once you're in the env, install the python libraries necessary for GCP to work:

pip install --upgrade google-cloud-storage

4. Follow the installation instructions here to install the Cloud SDK.
5. Make sure to add the SDK bin file to your path (environment variables). It should be of this file path pattern assuming you installed it at the default location:
   C:\Users\{your_name}\AppData\Local\Google\Cloud SDK\google-cloud-sdk\bin
6. Create a free account on GCP here and navigate to your console.

Code Snippets

We will be using these code snippets during the workshop. You don't have to worry what each line means right now! Everything will be explained during the workshop.

Code snippet #1

import html
import io
import os


from google.api_core.exceptions import AlreadyExists
from google.cloud import texttospeech
from google.cloud import translate_v3beta1 as translate
from google.cloud import vision

Code snippet #2

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = r'{insert your own json key}'
PROJECT_ID = os.environ["GOOGLE_CLOUD_PROJECT"]

Code snippet #3

def pic_to_text(infile):
    
   
    client = vision.ImageAnnotatorClient()

    
    with io.open(infile, "rb") as image_file:
        content = image_file.read()

    image = vision.Image(content=content)

    
    response = client.document_text_detection(image=image)
    text = response.full_text_annotation.text
    print("Detected text: {}".format(text))

    return text

Code snippet #4

def create_glossary(languages, project_id, glossary_name, glossary_uri):
    
    
    client = translate.TranslationServiceClient()

    
    location = "us-central1"

    
    name = client.glossary_path(project_id, location, glossary_name)

    
    language_codes_set = translate.types.Glossary.LanguageCodesSet(
        language_codes=languages
    )

    gcs_source = translate.types.GcsSource(input_uri=glossary_uri)

    input_config = translate.types.GlossaryInputConfig(gcs_source=gcs_source)

    
    glossary = translate.types.Glossary(
        name=name, language_codes_set=language_codes_set, input_config=input_config
    )

    parent = f"projects/{project_id}/locations/{location}"

    
    try:
        operation = client.create_glossary(parent=parent, glossary=glossary)
        operation.result(timeout=90)
        print("Created glossary " + glossary_name + ".")
    except AlreadyExists:
        print(
            "The glossary "
            + glossary_name
            + " already exists. No new glossary was created."
        )    

Code snippet #5

def translate_text(
    text, source_language_code, target_language_code, project_id, glossary_name
):
    

    
    client = translate.TranslationServiceClient()

    
    location = "us-central1"

    glossary = client.glossary_path(project_id, location, glossary_name)

    glossary_config = translate.types.TranslateTextGlossaryConfig(glossary=glossary)

    parent = f"projects/{project_id}/locations/{location}"

    result = client.translate_text(
        request={
            "parent": parent,
            "contents": [text],
            "mime_type": "text/plain",  # mime types: text/plain, text/html
            "source_language_code": source_language_code,
            "target_language_code": target_language_code,
            "glossary_config": glossary_config,
        }
    )

   
    return result.glossary_translations[0].translated_text   

Code snippet #6

def text_to_speech(text, outfile):
    

    
    escaped_lines = html.escape(text)

    
    ssml = "<speak>{}</speak>".format(
        escaped_lines.replace("\n", '\n<break time="2s"/>')
    )

    
    client = texttospeech.TextToSpeechClient()

    
    synthesis_input = texttospeech.SynthesisInput(ssml=ssml)

    
    voice = texttospeech.VoiceSelectionParams(
        language_code="en-US", ssml_gender=texttospeech.SsmlVoiceGender.MALE
    )

    
    audio_config = texttospeech.AudioConfig(
        audio_encoding=texttospeech.AudioEncoding.MP3
    )

   

    request = texttospeech.SynthesizeSpeechRequest(
        input=synthesis_input, voice=voice, audio_config=audio_config
    )

    response = client.synthesize_speech(request=request)

    
    with open(outfile, "wb") as out:
        out.write(response.audio_content)
        print("Audio content written to file " + outfile) 

Code snippet #7

def main():

    
    infile = "resources/example.png"
    
    outfile = "resources/example.mp3"

    
    glossary_langs = ["fr", "en"]
    
    glossary_name = "bistro-glossary"
    
    glossary_uri = "gs://cloud-samples-data/translation/bistro_glossary.csv"

    create_glossary(glossary_langs, PROJECT_ID, glossary_name, glossary_uri)

    
    text_to_translate = pic_to_text(infile)
    
    text_to_speak = translate_text(
        text_to_translate, "fr", "en", PROJECT_ID, glossary_name
    )
    
    text_to_speech(text_to_speak, outfile)

if __name__ == "__main__":
    main()