def azure_scan(file_name=None, file_url=None):
client = ContentModeratorClient(
f"https://{AZURE_LOC}.api.cognitive.microsoft.com/",
CognitiveServicesCredentials(AZURE_TOKEN),
)
if file_name is not None:
evaluation = client.image_moderation.evaluate_file_input(
image_stream=open(file_name, "rb"), cache_image=True)
else:
evaluation = client.image_moderation.evaluate_url_input(
content_type="application/json",
cache_image=True,
data_representation="URL",
value=file_url,
)
results = [
evaluation.adult_classification_score,
evaluation.racy_classification_score,
]
is_safe = all(x < AZURE_THRESHOLD for x in results)
max_score = max(results)
if max_score >= 0.9:
likelihood = "very likely"
elif max_score >= 0.75:
likelihood = "likely"
elif max_score >= 0.5:
likelihood = "possible"
elif max_score >= 0.25:
likelihood = "unlikely"
else:
likelihood = "very unlikely"
return is_safe, likelihood
def text_moderation(subscription_key):
"""TextModeration.
This will moderate a given long text.
"""
client = ContentModeratorClient(
endpoint=os.environ[
'CONTENT_MODERATOR_ENDPOINT'], # Add your Content Moderator endpoint to your environment variables.
credentials=CognitiveServicesCredentials(subscription_key))
# Screen the input text: check for profanity,
# do autocorrect text, and check for personally identifying
# information (PII)
with open(
os.path.join(TEXT_FOLDER, 'content_moderator_text_moderation.txt'),
"rb") as text_fd:
screen = client.text_moderation.screen_text(
text_content_type="text/plain",
text_content=text_fd,
language="eng",
autocorrect=True,
pii=True)
assert isinstance(screen, Screen)
pprint(screen.as_dict())
def text_moderation(subscription_key):
"""TextModeration.
This will moderate a given long text.
"""
client = ContentModeratorClient(
endpoint='https://' + CONTENTMODERATOR_LOCATION +
'.api.cognitive.microsoft.com',
credentials=CognitiveServicesCredentials(subscription_key))
# Screen the input text: check for profanity,
# do autocorrect text, and check for personally identifying
# information (PII)
with open(
os.path.join(TEXT_FOLDER, 'content_moderator_text_moderation.txt'),
"rb") as text_fd:
screen = client.text_moderation.screen_text(
text_content_type="text/plain",
text_content=text_fd,
language="eng",
autocorrect=True,
pii=True)
assert isinstance(screen, Screen)
pprint(screen.as_dict())
def image_review(subscription_key):
"""ImageReview.
This will create a review for images.
"""
# The name of the team to assign the job to.
# This must be the team name you used to create your Content Moderator account. You can
# retrieve your team name from the Content Moderator web site. Your team name is the Id
# associated with your subscription.
team_name = "insert your team name here"
# An image to review
image_url = "https://moderatorsampleimages.blob.core.windows.net/samples/sample5.png"
# Where you want to receive the approval/refuse event. This is the only way to get this information.
call_back_endpoint = "https://requestb.in/qmsakwqm"
client = ContentModeratorClient(
endpoint='https://' + CONTENTMODERATOR_LOCATION +
'.api.cognitive.microsoft.com',
credentials=CognitiveServicesCredentials(subscription_key))
print("Create review for {}.\n".format(image_url))
review_item = {
"type":
"Image", # Possible values include: 'Image', 'Text'
"content":
image_url, # How to download the image
"content_id":
uuid.uuid4(), # Random id
"callback_endpoint":
call_back_endpoint,
"metadata": [{
"key": "sc",
"value": True # will be sent to Azure as "str" cast.
}]
}
reviews = client.reviews.create_reviews(
url_content_type="application/json",
team_name=team_name,
create_review_body=[review_item] # As many review item as you need
)
review_id = reviews[0] # Ordered list of string of review ID
print("\nGet review details")
review_details = client.reviews.get_review(team_name=team_name,
review_id=review_id)
pprint(review_details.as_dict())
input(
"\nPerform manual reviews on the Content Moderator Review Site, and hit enter here."
)
print("\nGet review details")
review_details = client.reviews.get_review(team_name=team_name,
review_id=review_id)
pprint(review_details.as_dict())
def image_review_jobs(subscription_key):
"""ImageReviewJobs.
This will review an image using workflow and job.
"""
# The moderation job will use this workflow that you defined earlier.
# See the quickstart article to learn how to setup custom workflows.
# https://docs.microsoft.com/azure/cognitive-services/content-moderator/review-tool-user-guide/workflows
workflow_name = "insert your workflow name here"
# The name of the team to assign the job to.
# This must be the team name you used to create your Content Moderator account. You can
# retrieve your team name from the Content Moderator web site. Your team name is the Id
# associated with your subscription.
team_name = "insert your team name here"
# An image with this text:
# IF WE DID ALL THE THINGS WE ARE CAPABLE OF DOING, WE WOULD LITERALLY ASTOUND OURSELVE
# Be sure your workflow create a review for this (e.g. OCR contains some words).
image_url = "https://moderatorsampleimages.blob.core.windows.net/samples/sample2.jpg"
# Where you want to receive the approval/refuse event. This is the only way to get this information.
call_back_endpoint = "https://requestb.in/1l64pe71"
client = ContentModeratorClient(
endpoint=os.environ[
'CONTENT_MODERATOR_ENDPOINT'], # Add your Content Moderator endpoint to your environment variables.
credentials=CognitiveServicesCredentials(subscription_key))
print("Create moderation job for an image.\n")
job_result = client.reviews.create_job(
team_name=team_name,
content_type=
"Image", # Possible values include: 'Image', 'Text', 'Video'
content_id="ContentID", # Id/Name to identify the content submitted.
workflow_name=workflow_name,
# Possible values include: 'application/json', 'image/jpeg'
job_content_type="application/json",
content_value=image_url,
call_back_endpoint=call_back_endpoint)
job_id = job_result.job_id
print("Get job status before review.")
job_details = client.reviews.get_job_details(
team_name=team_name,
job_id=job_id,
)
pprint(job_details.as_dict())
input(
"\nPerform manual reviews on the Content Moderator Review Site, and hit enter here."
)
job_details = client.reviews.get_job_details(
team_name=team_name,
job_id=job_id,
)
pprint(job_details.as_dict())
def image_moderation(subscription_key):
"""ImageModeration.
This will review an image using workflow and job.
"""
client = ContentModeratorClient(
endpoint=os.environ['CONTENT_MODERATOR_ENDPOINT'], # Add your Content Moderator endpoint to your environment variables.
credentials=CognitiveServicesCredentials(subscription_key)
)
for image_url in IMAGE_LIST:
print("\nEvaluate image {}".format(image_url))
print("\nEvaluate for adult and racy content.")
evaluation = client.image_moderation.evaluate_url_input(
content_type="application/json",
cache_image=True,
data_representation="URL",
value=image_url
)
assert isinstance(evaluation, Evaluate)
pprint(evaluation.as_dict())
print("\nDetect and extract text.")
evaluation = client.image_moderation.ocr_url_input(
language="eng",
content_type="application/json",
data_representation="URL",
value=image_url,
cache_image=True,
)
assert isinstance(evaluation, OCR)
pprint(evaluation.as_dict())
print("\nDetect faces.")
evaluation = client.image_moderation.find_faces_url_input(
content_type="application/json",
cache_image=True,
data_representation="URL",
value=image_url
)
assert isinstance(evaluation, FoundFaces)
pprint(evaluation.as_dict())
def image_moderation(subscription_key):
"""ImageModeration.
This will review an image using workflow and job.
"""
client = ContentModeratorClient(
CONTENTMODERATOR_LOCATION+'.api.cognitive.microsoft.com',
CognitiveServicesCredentials(subscription_key)
)
for image_url in IMAGE_LIST:
print("\nEvaluate image {}".format(image_url))
print("\nEvaluate for adult and racy content.")
evaluation = client.image_moderation.evaluate_url_input(
"application/json",
data_representation="URL",
value=image_url,
cache_image=True,
)
assert isinstance(evaluation, Evaluate)
pprint(evaluation.as_dict())
print("\nDetect and extract text.")
evaluation = client.image_moderation.ocr_url_input(
"eng",
"application/json",
data_representation="URL",
value=image_url,
cache_image=True,
)
assert isinstance(evaluation, OCR)
pprint(evaluation.as_dict())
print("\nDetect faces.")
evaluation = client.image_moderation.find_faces_url_input(
"application/json",
data_representation="URL",
value=image_url,
cache_image=True,
)
assert isinstance(evaluation, FoundFaces)
pprint(evaluation.as_dict())
def text_moderation(url, key, text_to_moderate):
'''
This functon will moderate the input text.
@param text, string --> text to be moderated
'''
client = ContentModeratorClient(url, CognitiveServicesCredentials(key))
# Check for profanity in the input text
screen = client.text_moderation.screen_text(
text_content_type="text/plain",
text_content=BytesIO(bytes(text_to_moderate, 'utf-8')),
language="eng",
autocorrect=True,
classify=True)
# Category1 refers to potential presence of language that may be considered sexually explicit or adult in certain situations.
# Category2 refers to potential presence of language that may be considered sexually suggestive or mature in certain situations.
# Category3 refers to potential presence of language that may be considered offensive in certain situations.
# Score is between 0 and 1. The higher the score, the higher the model is predicting that the category may be applicable. This feature relies on a statistical model rather than manually coded outcomes. We recommend testing with your own content to determine how each category aligns to your requirements.
assert isinstance(screen, Screen)
return screen.as_dict()
def text_moderation(subscription_key):
"""TextModeration.
This will moderate a given long text.
"""
client = ContentModeratorClient(
CONTENTMODERATOR_LOCATION+'.api.cognitive.microsoft.com',
CognitiveServicesCredentials(subscription_key)
)
# Screen the input text: check for profanity,
# do autocorrect text, and check for personally identifying
# information (PII)
screen = client.text_moderation.screen_text(
"eng",
"text/plain",
TEXT,
autocorrect=True,
pii=True
)
assert isinstance(screen, Screen)
pprint(screen.as_dict())
from random import random
import uuid
from azure.cognitiveservices.vision.contentmoderator import ContentModeratorClient
import azure.cognitiveservices.vision.contentmoderator.models
from msrest.authentication import CognitiveServicesCredentials
# </snippet_imports>
# <snippet_vars>
CONTENTMODERATOR_ENDPOINT = os.environ.get("CONTENT_MODERATOR_ENDPOINT")
subscription_key = os.environ.get("CONTENT_MODERATOR_SUBSCRIPTION_KEY")
# </snippet_vars>
# <snippet_client>
client = ContentModeratorClient(
endpoint=CONTENT_MODERATOR_ENDPOINT,
credentials=CognitiveServicesCredentials(subscription_key)
)
# </snippet_client>
# <snippet_textfolder>
TEXT_FOLDER = os.path.join(os.path.dirname(
os.path.realpath(__file__)), "text_files")
# </snippet_textfolder>
# <snippet_imagemodvars>
IMAGE_LIST = [
"https://moderatorsampleimages.blob.core.windows.net/samples/sample2.jpg",
"https://moderatorsampleimages.blob.core.windows.net/samples/sample5.png"
]
# </snippet_imagemodvars>
def get_client_lazy(self):
from azure.cognitiveservices.vision.contentmoderator import ContentModeratorClient
from msrest.authentication import CognitiveServicesCredentials
return ContentModeratorClient(
self.url, CognitiveServicesCredentials(self.get_secret()))
def video_review(subscription_key):
"""VideoReview.
This will create and publish a review for video
"""
# The name of the team to assign the job to.
# This must be the team name you used to create your Content Moderator account. You can
# retrieve your team name from the Content Moderator web site. Your team name is the Id
# associated with your subscription.
team_name = "insert your team name here"
# Create a review with the content pointing to a streaming endpoint (manifest)
streamingcontent = "https://amssamples.streaming.mediaservices.windows.net/91492735-c523-432b-ba01-faba6c2206a2/AzureMediaServicesPromo.ism/manifest"
frame1_url = "https://blobthebuilder.blob.core.windows.net/sampleframes/ams-video-frame1-00-17.PNG"
frame2_url = "https://blobthebuilder.blob.core.windows.net/sampleframes/ams-video-frame-2-01-04.PNG"
frame3_url = "https://blobthebuilder.blob.core.windows.net/sampleframes/ams-video-frame-3-02-24.PNG"
client = ContentModeratorClient(
endpoint=os.environ[
'CONTENT_MODERATOR_ENDPOINT'], # Add your Content Moderator endpoint to your environment variables.
credentials=CognitiveServicesCredentials(subscription_key))
#
# Create a video review
#
print("Create review for {}.\n".format(streamingcontent))
review_item = {
"content": streamingcontent, # How to download the image
"content_id": uuid.uuid4(), # Random id
# Note: to create a published review, set the Status to "Pending".
# However, you cannot add video frames or a transcript to a published review.
"status": "Unpublished"
}
reviews = client.reviews.create_video_reviews(
content_type="application/json",
team_name=team_name,
# As many review item as you need
create_video_reviews_body=[review_item])
review_id = reviews[0] # Ordered list of string of review ID
#
# Add the frames from 17, 64, and 144 seconds.
#
print("\nAdding frames to the review {}".format(review_id))
def create_frames_to_add_to_reviews(timestamp_seconds, url):
return {
'timestamp':
timestamp_seconds * 1000,
'frame_image':
url,
'reviewer_result_tags': [
# Note: All non str value will be casted using "str()"
{
'key': 'reviewRecommended',
'value': True
},
{
'key': 'adultScore',
'value': random()
},
{
'key': 'a',
'value': False
},
{
'key': 'racyScore',
'value': random()
},
{
'key': 'a',
'value': False
},
],
'metadata': [
# Note: All non str value will be casted using "str()"
{
'key': 'tag1',
'value': 'tag1'
},
]
}
client.reviews.add_video_frame_url(
content_type="application/json",
team_name=team_name,
review_id=review_id,
video_frame_body=[
create_frames_to_add_to_reviews(17, frame1_url),
create_frames_to_add_to_reviews(64, frame2_url),
create_frames_to_add_to_reviews(144, frame3_url)
])
#
# Get frames
#
print("\nGetting frames for the review with ID {}".format(review_id))
frames = client.reviews.get_video_frames(team_name=team_name,
review_id=review_id,
start_seed=0,
no_of_records=100)
assert isinstance(frames, Frames)
pprint(frames.as_dict())
#
# Get reviews details
#
print(
"\nGetting review details for the review with ID {}".format(review_id))
review_details = client.reviews.get_review(team_name=team_name,
review_id=review_id)
pprint(review_details.as_dict())
#
# Public review
#
client.reviews.publish_video_review(team_name=team_name,
review_id=review_id)
print(
"\nOpen your Content Moderator Dashboard and select Review > Video to see the review."
)
def terms_lists(subscription_key):
"""TermsList.
This will screen text using a term list.
"""
client = ContentModeratorClient(
CONTENTMODERATOR_LOCATION+'.api.cognitive.microsoft.com',
CognitiveServicesCredentials(subscription_key)
)
#
# Create list
#
print("\nCreating list")
custom_list = client.list_management_term_lists.create(
"application/json",
{
"name": "Term list name",
"description": "Term list description",
}
)
print("List created:")
assert isinstance(custom_list, TermList)
pprint(custom_list.as_dict())
list_id = custom_list.id
#
# Update list details
#
print("\nUpdating details for list {}".format(list_id))
updated_list = client.list_management_term_lists.update(
list_id,
"application/json",
{
"name": "New name",
"description": "New description"
}
)
assert isinstance(updated_list, TermList)
pprint(updated_list.as_dict())
#
# Add terms
#
print("\nAdding terms to list {}".format(list_id))
client.list_management_term.add_term(list_id, "term1", "eng")
client.list_management_term.add_term(list_id, "term2", "eng")
#
# Get all terms ids
#
print("\nGetting all term IDs for list {}".format(list_id))
terms = client.list_management_term.get_all_terms(list_id, "eng")
assert isinstance(terms, Terms)
terms_data = terms.data
assert isinstance(terms_data, TermsData)
pprint(terms_data.as_dict())
#
# Refresh the index
#
print("\nRefreshing the search index for list {}".format(list_id))
refresh_index = client.list_management_term_lists.refresh_index_method(list_id, "eng")
assert isinstance(refresh_index, RefreshIndex)
pprint(refresh_index.as_dict())
print("\nWaiting {} minutes to allow the server time to propagate the index changes.".format(LATENCY_DELAY))
time.sleep(LATENCY_DELAY * 60)
#
# Screen text
#
text = 'This text contains the terms "term1" and "term2".'
print('\nScreening text "{}" using term list {}'.format(text, list_id))
screen = client.text_moderation.screen_text(
"eng",
"text/plain",
text,
autocorrect=False,
pii=False,
list_id=list_id
)
assert isinstance(screen, Screen)
pprint(screen.as_dict())
#
# Remove terms
#
term_to_remove = "term1"
print("\nRemove term {} from list {}".format(term_to_remove, list_id))
client.list_management_term.delete_term(
list_id,
term_to_remove,
"eng"
)
#
# Refresh the index
#
print("\nRefreshing the search index for list {}".format(list_id))
refresh_index = client.list_management_term_lists.refresh_index_method(list_id, "eng")
assert isinstance(refresh_index, RefreshIndex)
pprint(refresh_index.as_dict())
print("\nWaiting {} minutes to allow the server time to propagate the index changes.".format(LATENCY_DELAY))
time.sleep(LATENCY_DELAY * 60)
#
# Re-Screen text
#
text = 'This text contains the terms "term1" and "term2".'
print('\nScreening text "{}" using term list {}'.format(text, list_id))
screen = client.text_moderation.screen_text(
"eng",
"text/plain",
text,
autocorrect=False,
pii=False,
list_id=list_id
)
assert isinstance(screen, Screen)
pprint(screen.as_dict())
#
# Delete all terms
#
print("\nDelete all terms in the image list {}".format(list_id))
client.list_management_term.delete_all_terms(list_id, "eng")
#
# Delete list
#
print("\nDelete the term list {}".format(list_id))
client.list_management_term_lists.delete(list_id)
def video_transcript_review(subscription_key):
"""VideoTranscriptReview.
This will create and publish a transcript review for video
"""
# The name of the team to assign the job to.
# This must be the team name you used to create your Content Moderator account. You can
# retrieve your team name from the Content Moderator web site. Your team name is the Id
# associated with your subscription.
team_name = "insert your team name here"
# Create a review with the content pointing to a streaming endpoint (manifest)
streamingcontent = "https://amssamples.streaming.mediaservices.windows.net/91492735-c523-432b-ba01-faba6c2206a2/AzureMediaServicesPromo.ism/manifest"
transcript = b"""WEBVTT
01:01.000 --> 02:02.000
First line with a crap word in a transcript.
02:03.000 --> 02:25.000
This is another line in the transcript.
"""
client = ContentModeratorClient(
endpoint=os.environ[
'CONTENT_MODERATOR_ENDPOINT'], # Add your Content Moderator endpoint to your environment variables.
credentials=CognitiveServicesCredentials(subscription_key))
#
# Create a video review
#
print("Create review for {}.\n".format(streamingcontent))
review_item = {
"content": streamingcontent, # How to download the image
"content_id": uuid.uuid4(), # Random id
# Note: to create a published review, set the Status to "Pending".
# However, you cannot add video frames or a transcript to a published review.
"status": "Unpublished"
}
reviews = client.reviews.create_video_reviews(
content_type="application/json",
team_name=team_name,
# As many review item as you need
create_video_reviews_body=[review_item])
review_id = reviews[0] # Ordered list of string of review ID
#
# Add transcript
#
print("\nAdding transcript to the review {}".format(review_id))
client.reviews.add_video_transcript(
team_name=team_name,
review_id=review_id,
# Can be a file descriptor, as long as its stream type
vt_tfile=BytesIO(transcript),
)
#
# Add transcript moderation result
#
print("\nAdding a transcript moderation result to the review with ID {}".
format(review_id))
with open(
os.path.join(TEXT_FOLDER,
'content_moderator_video_transcript.txt'),
"rb") as text_fd:
screen = client.text_moderation.screen_text(
text_content_type="text/plain",
text_content=text_fd,
language="eng")
assert isinstance(screen, Screen)
pprint(screen.as_dict())
# Build a terms list with index
terms = []
for term in screen.terms:
terms.append({"index": term.index, "term": term.term})
client.reviews.add_video_transcript_moderation_result(
content_type="application/json",
team_name=team_name,
review_id=review_id,
transcript_moderation_body=[{
"timestamp": 0,
"terms": terms
}])
#
# Public review
#
client.reviews.publish_video_review(team_name=team_name,
review_id=review_id)
print(
"\nOpen your Content Moderator Dashboard and select Review > Video to see the review."
)
Evaluate)
from msrest.authentication import CognitiveServicesCredentials
from io import BytesIO
from zoidbergbot.config import SUBSCRIPTION_KEY, CONTENT_MODERATOR_ENDPOINT
# Add your Azure Content Moderator subscription key to your environment variables.
# SUBSCRIPTION_KEY = os.environ['CONTENT_MODERATOR_SUBSCRIPTION_KEY']
TEXT_FOLDER = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"text_files")
# The number of minutes to delay after updating the search index before
# performing image match operations against the list.
LATENCY_DELAY = 0.5
client = ContentModeratorClient(
endpoint=CONTENT_MODERATOR_ENDPOINT,
# Add your Content Moderator endpoint to your environment variables.
credentials=CognitiveServicesCredentials(SUBSCRIPTION_KEY))
def text_moderation(text):
"""TextModeration.
This will moderate a given long text.
"""
text = bytes(text, "utf-8")
text_bytes = BytesIO(text)
screen = client.text_moderation.screen_text(text_content_type="text/plain",
text_content=text_bytes,
language="eng",
autocorrect=True,
pii=True)
'''
subscription_key = "PASTE_YOUR_CONTENT_MODERATOR_SUBSCRIPTION_KEY_HERE"
endpoint = "PASTE_YOUR_CONTENT_MODERATOR_ENDPOINT_HERE"
# List of URL images used to moderate.
IMAGE_LIST = [
"https://moderatorsampleimages.blob.core.windows.net/samples/sample2.jpg",
"https://moderatorsampleimages.blob.core.windows.net/samples/sample5.png"
]
'''
AUTHENTICATE
Create a Content Moderator client.
'''
client = ContentModeratorClient(
endpoint=endpoint,
credentials=CognitiveServicesCredentials(subscription_key))
'''
CONTENT MODERATOR
This quickstart moderates an image, then text and faces within the image.
'''
print('IMAGE MODERATION')
print()
# Image moderation, using image at [0]
print("Evaluate the image '{}' for adult and racy content:".format(
os.path.basename(IMAGE_LIST[0])))
mod_image = client.image_moderation.evaluate_url_input(
content_type="application/json",
cache_image=True,
data_representation="URL",
Create a single client for all samples, except for human reviews. It needs its own client.
'''
# <authentication>
# Set your environment variables with the values of your key and region, "westus" is the default region.
SUBSCRIPTION_KEY = os.environ.get('CONTENT_MODERATOR_SUBSCRIPTION_KEY')
ENDPOINT = os.environ.get('CONTENT_MODERATOR_ENDPOINT',
'https://westus.api.cognitive.microsoft.com')
# </authentication>
# Get region for the human reviews callback endpoint
REGION = os.environ.get('CONTENT_MODERATOR_REGION', 'westus')
# <client>
# Create client
client = ContentModeratorClient(
endpoint=ENDPOINT,
credentials=CognitiveServicesCredentials(SUBSCRIPTION_KEY))
# </client>
# Need a special client for the human reviews (due to unique key)
# Need to get key from Content Moderator website,
# go to gear symbol (Settings) --> Credentials --> Ocp-Apim-Subscription-Key
# Set your key and region in your environment variables.
REVIEWS_SUBSCRIPTION_KEY = os.environ.get('CONTENT_MODERATOR_REVIEWS_KEY')
# Where the image for review gets sent to.
call_back_endpoint = 'https://{}.api.cognitive.microsoft.com/contentmoderator/review/v1.0'.format(
REGION)
client_reviews = ContentModeratorClient(
endpoint=ENDPOINT,
credentials=CognitiveServicesCredentials(SUBSCRIPTION_KEY))
# The name of the team to assign to the human review job. Add to your environment variables.
def image_lists(subscription_key):
"""ImageList.
This will review an image using workflow and job.
"""
client = ContentModeratorClient(
endpoint=os.environ[
'CONTENT_MODERATOR_ENDPOINT'], # Add your Content Moderator endpoint to your environment variables.
credentials=CognitiveServicesCredentials(subscription_key))
print("Creating list MyList\n")
custom_list = client.list_management_image_lists.create(
content_type="application/json",
body={
"name": "MyList",
"description": "A sample list",
"metadata": {
"key_one": "Acceptable",
"key_two": "Potentially racy"
}
})
print("List created:")
assert isinstance(custom_list, ImageList)
pprint(custom_list.as_dict())
list_id = custom_list.id
#
# Add images
#
def add_images(list_id, image_url, label):
"""Generic add_images from url and label."""
print("\nAdding image {} to list {} with label {}.".format(
image_url, list_id, label))
try:
added_image = client.list_management_image.add_image_url_input(
list_id=list_id,
content_type="application/json",
data_representation="URL",
value=image_url,
label=label)
except APIErrorException as err:
# sample4 will fail
print("Unable to add image to list: {}".format(err))
else:
assert isinstance(added_image, Image)
pprint(added_image.as_dict())
return added_image
print("\nAdding images to list {}".format(list_id))
index = {} # Keep an index url to id for later removal
for label, urls in IMAGE_LIST.items():
for url in urls:
image = add_images(list_id, url, label)
if image:
index[url] = image.content_id
#
# Get all images ids
#
print("\nGetting all image IDs for list {}".format(list_id))
image_ids = client.list_management_image.get_all_image_ids(list_id=list_id)
assert isinstance(image_ids, ImageIds)
pprint(image_ids.as_dict())
#
# Update list details
#
print("\nUpdating details for list {}".format(list_id))
updated_list = client.list_management_image_lists.update(
list_id=list_id,
content_type="application/json",
body={"name": "Swimsuits and sports"})
assert isinstance(updated_list, ImageList)
pprint(updated_list.as_dict())
#
# Get list details
#
print("\nGetting details for list {}".format(list_id))
list_details = client.list_management_image_lists.get_details(
list_id=list_id)
assert isinstance(list_details, ImageList)
pprint(list_details.as_dict())
#
# Refresh the index
#
print("\nRefreshing the search index for list {}".format(list_id))
refresh_index = client.list_management_image_lists.refresh_index_method(
list_id=list_id)
assert isinstance(refresh_index, RefreshIndex)
pprint(refresh_index.as_dict())
print(
"\nWaiting {} minutes to allow the server time to propagate the index changes."
.format(LATENCY_DELAY))
time.sleep(LATENCY_DELAY * 60)
#
# Match images against the image list.
#
for image_url in IMAGES_TO_MATCH:
print("\nMatching image {} against list {}".format(image_url, list_id))
match_result = client.image_moderation.match_url_input(
content_type="application/json",
list_id=list_id,
data_representation="URL",
value=image_url,
)
assert isinstance(match_result, MatchResponse)
print("Is match? {}".format(match_result.is_match))
print("Complete match details:")
pprint(match_result.as_dict())
#
# Remove images
#
correction = "https://moderatorsampleimages.blob.core.windows.net/samples/sample16.png"
print("\nRemove image {} from list {}".format(correction, list_id))
client.list_management_image.delete_image(list_id=list_id,
image_id=index[correction])
#
# Refresh the index
#
print("\nRefreshing the search index for list {}".format(list_id))
client.list_management_image_lists.refresh_index_method(list_id=list_id)
print(
"\nWaiting {} minutes to allow the server time to propagate the index changes."
.format(LATENCY_DELAY))
time.sleep(LATENCY_DELAY * 60)
#
# Re-match
#
print("\nMatching image. The removed image should not match")
for image_url in IMAGES_TO_MATCH:
print("\nMatching image {} against list {}".format(image_url, list_id))
match_result = client.image_moderation.match_url_input(
content_type="application/json",
list_id=list_id,
data_representation="URL",
value=image_url,
)
assert isinstance(match_result, MatchResponse)
print("Is match? {}".format(match_result.is_match))
print("Complete match details:")
pprint(match_result.as_dict())
#
# Delete all images
#
print("\nDelete all images in the image list {}".format(list_id))
client.list_management_image.delete_all_images(list_id=list_id)
#
# Delete list
#
print("\nDelete the image list {}".format(list_id))
client.list_management_image_lists.delete(list_id=list_id)
#
# Get all list ids
#
print("\nVerify that the list {} was deleted.".format(list_id))
image_lists = client.list_management_image_lists.get_all_image_lists()
assert not any(list_id == image_list.id for image_list in image_lists)
def terms_lists(subscription_key):
"""TermsList.
This will screen text using a term list.
"""
client = ContentModeratorClient(
endpoint=os.environ['CONTENT_MODERATOR_ENDPOINT'], # Add your Content Moderator endpoint to your environment variables.
credentials=CognitiveServicesCredentials(subscription_key)
)
#
# Create list
#
print("\nCreating list")
custom_list = client.list_management_term_lists.create(
content_type="application/json",
body={
"name": "Term list name",
"description": "Term list description",
}
)
print("List created:")
assert isinstance(custom_list, TermList)
pprint(custom_list.as_dict())
list_id = custom_list.id
#
# Update list details
#
print("\nUpdating details for list {}".format(list_id))
updated_list = client.list_management_term_lists.update(
list_id=list_id,
content_type="application/json",
body={
"name": "New name",
"description": "New description"
}
)
assert isinstance(updated_list, TermList)
pprint(updated_list.as_dict())
#
# Add terms
#
print("\nAdding terms to list {}".format(list_id))
client.list_management_term.add_term(
list_id=list_id,
term="term1",
language="eng"
)
client.list_management_term.add_term(
list_id=list_id,
term="term2",
language="eng"
)
#
# Get all terms ids
#
print("\nGetting all term IDs for list {}".format(list_id))
terms = client.list_management_term.get_all_terms(
list_id=list_id, language="eng")
assert isinstance(terms, Terms)
terms_data = terms.data
assert isinstance(terms_data, TermsData)
pprint(terms_data.as_dict())
#
# Refresh the index
#
print("\nRefreshing the search index for list {}".format(list_id))
refresh_index = client.list_management_term_lists.refresh_index_method(
list_id=list_id, language="eng")
assert isinstance(refresh_index, RefreshIndex)
pprint(refresh_index.as_dict())
print("\nWaiting {} minutes to allow the server time to propagate the index changes.".format(
LATENCY_DELAY))
time.sleep(LATENCY_DELAY * 60)
#
# Screen text
#
text = 'This text contains the terms "term1" and "term2".'
print('\nScreening text "{}" using term list {}'.format(text, list_id))
with open(os.path.join(TEXT_FOLDER, 'content_moderator_term_list.txt'), "rb") as text_fd:
screen = client.text_moderation.screen_text(
text_content_type="text/plain",
text_content=text_fd,
language="eng",
autocorrect=False,
pii=False,
list_id=list_id
)
assert isinstance(screen, Screen)
pprint(screen.as_dict())
#
# Remove terms
#
term_to_remove = "term1"
print("\nRemove term {} from list {}".format(term_to_remove, list_id))
client.list_management_term.delete_term(
list_id=list_id,
term=term_to_remove,
language="eng"
)
#
# Refresh the index
#
print("\nRefreshing the search index for list {}".format(list_id))
refresh_index = client.list_management_term_lists.refresh_index_method(
list_id=list_id, language="eng")
assert isinstance(refresh_index, RefreshIndex)
pprint(refresh_index.as_dict())
print("\nWaiting {} minutes to allow the server time to propagate the index changes.".format(
LATENCY_DELAY))
time.sleep(LATENCY_DELAY * 60)
#
# Re-Screen text
#
with open(os.path.join(TEXT_FOLDER, 'content_moderator_term_list.txt'), "rb") as text_fd:
print('\nScreening text "{}" using term list {}'.format(text, list_id))
screen = client.text_moderation.screen_text(
text_content_type="text/plain",
text_content=text_fd,
language="eng",
autocorrect=False,
pii=False,
list_id=list_id
)
assert isinstance(screen, Screen)
pprint(screen.as_dict())
#
# Delete all terms
#
print("\nDelete all terms in the image list {}".format(list_id))
client.list_management_term.delete_all_terms(
list_id=list_id, language="eng")
#
# Delete list
#
print("\nDelete the term list {}".format(list_id))
client.list_management_term_lists.delete(list_id=list_id)