green-20220302/alibabacloud_green20220302/models.py (6,111 lines of code) (raw):
# -*- coding: utf-8 -*-
# This file is auto-generated, don't edit it. Thanks.
from Tea.model import TeaModel
from typing import List, Dict, Any
class DescribeFileModerationResultRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The type of the moderation service.
self.service = service
# The parameters required by the moderation service. The value is a JSON string.
#
# * taskId: required. The URL of the object that you want to moderate. Make sure that the URL can be accessed over the Internet.
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class DescribeFileModerationResultResponseBodyDataPageResultImageResultLabelResult(TeaModel):
def __init__(
self,
confidence: float = None,
description: str = None,
label: str = None,
):
# The score of the confidence level. Valid values: 0 to 100. The value is accurate to two decimal places.
self.confidence = confidence
# The description.
self.description = description
# The details of the labels.
self.label = label
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
return self
class DescribeFileModerationResultResponseBodyDataPageResultImageResultLocation(TeaModel):
def __init__(
self,
h: int = None,
w: int = None,
x: int = None,
y: int = None,
):
# The H value of the coordinate point.
self.h = h
# The W value of the coordinate point.
self.w = w
# The X value of the coordinate point.
self.x = x
# The Y value of the coordinate point.
self.y = y
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.h is not None:
result['H'] = self.h
if self.w is not None:
result['W'] = self.w
if self.x is not None:
result['X'] = self.x
if self.y is not None:
result['Y'] = self.y
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('H') is not None:
self.h = m.get('H')
if m.get('W') is not None:
self.w = m.get('W')
if m.get('X') is not None:
self.x = m.get('X')
if m.get('Y') is not None:
self.y = m.get('Y')
return self
class DescribeFileModerationResultResponseBodyDataPageResultImageResult(TeaModel):
def __init__(
self,
description: str = None,
label_result: List[DescribeFileModerationResultResponseBodyDataPageResultImageResultLabelResult] = None,
location: DescribeFileModerationResultResponseBodyDataPageResultImageResultLocation = None,
risk_level: str = None,
service: str = None,
):
# The description.
self.description = description
# The label information.
self.label_result = label_result
# The location information
self.location = location
# Risk Level
self.risk_level = risk_level
# The moderation service.
self.service = service
def validate(self):
if self.label_result:
for k in self.label_result:
if k:
k.validate()
if self.location:
self.location.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.description is not None:
result['Description'] = self.description
result['LabelResult'] = []
if self.label_result is not None:
for k in self.label_result:
result['LabelResult'].append(k.to_map() if k else None)
if self.location is not None:
result['Location'] = self.location.to_map()
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
if self.service is not None:
result['Service'] = self.service
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Description') is not None:
self.description = m.get('Description')
self.label_result = []
if m.get('LabelResult') is not None:
for k in m.get('LabelResult'):
temp_model = DescribeFileModerationResultResponseBodyDataPageResultImageResultLabelResult()
self.label_result.append(temp_model.from_map(k))
if m.get('Location') is not None:
temp_model = DescribeFileModerationResultResponseBodyDataPageResultImageResultLocation()
self.location = temp_model.from_map(m['Location'])
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
if m.get('Service') is not None:
self.service = m.get('Service')
return self
class DescribeFileModerationResultResponseBodyDataPageResultTextResult(TeaModel):
def __init__(
self,
description: str = None,
descriptions: str = None,
labels: str = None,
risk_level: str = None,
risk_tips: str = None,
risk_words: str = None,
service: str = None,
text: str = None,
text_segment: str = None,
):
# The description.
self.description = description
self.descriptions = descriptions
# The details of the labels.
self.labels = labels
# Risk Level
self.risk_level = risk_level
# The risk details that are hit.
self.risk_tips = risk_tips
# The risk words that are hit.
self.risk_words = risk_words
# The moderation service.
self.service = service
# The text content.
self.text = text
# The text segmentation information.
self.text_segment = text_segment
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.description is not None:
result['Description'] = self.description
if self.descriptions is not None:
result['Descriptions'] = self.descriptions
if self.labels is not None:
result['Labels'] = self.labels
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
if self.risk_tips is not None:
result['RiskTips'] = self.risk_tips
if self.risk_words is not None:
result['RiskWords'] = self.risk_words
if self.service is not None:
result['Service'] = self.service
if self.text is not None:
result['Text'] = self.text
if self.text_segment is not None:
result['TextSegment'] = self.text_segment
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Descriptions') is not None:
self.descriptions = m.get('Descriptions')
if m.get('Labels') is not None:
self.labels = m.get('Labels')
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
if m.get('RiskTips') is not None:
self.risk_tips = m.get('RiskTips')
if m.get('RiskWords') is not None:
self.risk_words = m.get('RiskWords')
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('Text') is not None:
self.text = m.get('Text')
if m.get('TextSegment') is not None:
self.text_segment = m.get('TextSegment')
return self
class DescribeFileModerationResultResponseBodyDataPageResult(TeaModel):
def __init__(
self,
image_result: List[DescribeFileModerationResultResponseBodyDataPageResultImageResult] = None,
image_url: str = None,
page_num: int = None,
text_result: List[DescribeFileModerationResultResponseBodyDataPageResultTextResult] = None,
text_url: str = None,
):
# The image moderation results.
self.image_result = image_result
# The image URL.
self.image_url = image_url
# The page number.
self.page_num = page_num
# The text moderation results.
self.text_result = text_result
# The text URL.
self.text_url = text_url
def validate(self):
if self.image_result:
for k in self.image_result:
if k:
k.validate()
if self.text_result:
for k in self.text_result:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
result['ImageResult'] = []
if self.image_result is not None:
for k in self.image_result:
result['ImageResult'].append(k.to_map() if k else None)
if self.image_url is not None:
result['ImageUrl'] = self.image_url
if self.page_num is not None:
result['PageNum'] = self.page_num
result['TextResult'] = []
if self.text_result is not None:
for k in self.text_result:
result['TextResult'].append(k.to_map() if k else None)
if self.text_url is not None:
result['TextUrl'] = self.text_url
return result
def from_map(self, m: dict = None):
m = m or dict()
self.image_result = []
if m.get('ImageResult') is not None:
for k in m.get('ImageResult'):
temp_model = DescribeFileModerationResultResponseBodyDataPageResultImageResult()
self.image_result.append(temp_model.from_map(k))
if m.get('ImageUrl') is not None:
self.image_url = m.get('ImageUrl')
if m.get('PageNum') is not None:
self.page_num = m.get('PageNum')
self.text_result = []
if m.get('TextResult') is not None:
for k in m.get('TextResult'):
temp_model = DescribeFileModerationResultResponseBodyDataPageResultTextResult()
self.text_result.append(temp_model.from_map(k))
if m.get('TextUrl') is not None:
self.text_url = m.get('TextUrl')
return self
class DescribeFileModerationResultResponseBodyDataPageSummaryImageSummaryImageLabels(TeaModel):
def __init__(
self,
description: str = None,
label: str = None,
label_sum: int = None,
):
# The description.
self.description = description
# The details of the labels.
self.label = label
# The number of times that the label is matched.
self.label_sum = label_sum
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
if self.label_sum is not None:
result['LabelSum'] = self.label_sum
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
if m.get('LabelSum') is not None:
self.label_sum = m.get('LabelSum')
return self
class DescribeFileModerationResultResponseBodyDataPageSummaryImageSummary(TeaModel):
def __init__(
self,
image_labels: List[DescribeFileModerationResultResponseBodyDataPageSummaryImageSummaryImageLabels] = None,
risk_level: str = None,
):
# Image Label
self.image_labels = image_labels
# Risk Level
self.risk_level = risk_level
def validate(self):
if self.image_labels:
for k in self.image_labels:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
result['ImageLabels'] = []
if self.image_labels is not None:
for k in self.image_labels:
result['ImageLabels'].append(k.to_map() if k else None)
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
return result
def from_map(self, m: dict = None):
m = m or dict()
self.image_labels = []
if m.get('ImageLabels') is not None:
for k in m.get('ImageLabels'):
temp_model = DescribeFileModerationResultResponseBodyDataPageSummaryImageSummaryImageLabels()
self.image_labels.append(temp_model.from_map(k))
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
return self
class DescribeFileModerationResultResponseBodyDataPageSummaryTextSummaryTextLabels(TeaModel):
def __init__(
self,
description: str = None,
label: str = None,
label_sum: int = None,
):
self.description = description
# The details of the labels.
self.label = label
# The number of times that the label is matched.
self.label_sum = label_sum
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
if self.label_sum is not None:
result['LabelSum'] = self.label_sum
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
if m.get('LabelSum') is not None:
self.label_sum = m.get('LabelSum')
return self
class DescribeFileModerationResultResponseBodyDataPageSummaryTextSummary(TeaModel):
def __init__(
self,
risk_level: str = None,
text_labels: List[DescribeFileModerationResultResponseBodyDataPageSummaryTextSummaryTextLabels] = None,
):
# Risk Level
self.risk_level = risk_level
# Text Label
self.text_labels = text_labels
def validate(self):
if self.text_labels:
for k in self.text_labels:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
result['TextLabels'] = []
if self.text_labels is not None:
for k in self.text_labels:
result['TextLabels'].append(k.to_map() if k else None)
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
self.text_labels = []
if m.get('TextLabels') is not None:
for k in m.get('TextLabels'):
temp_model = DescribeFileModerationResultResponseBodyDataPageSummaryTextSummaryTextLabels()
self.text_labels.append(temp_model.from_map(k))
return self
class DescribeFileModerationResultResponseBodyDataPageSummary(TeaModel):
def __init__(
self,
image_summary: DescribeFileModerationResultResponseBodyDataPageSummaryImageSummary = None,
page_sum: int = None,
text_summary: DescribeFileModerationResultResponseBodyDataPageSummaryTextSummary = None,
):
# Image Results Summary
self.image_summary = image_summary
# Number of pages
self.page_sum = page_sum
# Text Results Summary
self.text_summary = text_summary
def validate(self):
if self.image_summary:
self.image_summary.validate()
if self.text_summary:
self.text_summary.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.image_summary is not None:
result['ImageSummary'] = self.image_summary.to_map()
if self.page_sum is not None:
result['PageSum'] = self.page_sum
if self.text_summary is not None:
result['TextSummary'] = self.text_summary.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('ImageSummary') is not None:
temp_model = DescribeFileModerationResultResponseBodyDataPageSummaryImageSummary()
self.image_summary = temp_model.from_map(m['ImageSummary'])
if m.get('PageSum') is not None:
self.page_sum = m.get('PageSum')
if m.get('TextSummary') is not None:
temp_model = DescribeFileModerationResultResponseBodyDataPageSummaryTextSummary()
self.text_summary = temp_model.from_map(m['TextSummary'])
return self
class DescribeFileModerationResultResponseBodyData(TeaModel):
def __init__(
self,
data_id: str = None,
doc_type: str = None,
page_result: List[DescribeFileModerationResultResponseBodyDataPageResult] = None,
page_summary: DescribeFileModerationResultResponseBodyDataPageSummary = None,
risk_level: str = None,
url: str = None,
):
# The ID of the moderated object.
self.data_id = data_id
# Optional. The document type.
self.doc_type = doc_type
# An array that consists of the moderation results.
self.page_result = page_result
# Summary of results
self.page_summary = page_summary
# Risk Level
self.risk_level = risk_level
# The URL of the moderated object.
self.url = url
def validate(self):
if self.page_result:
for k in self.page_result:
if k:
k.validate()
if self.page_summary:
self.page_summary.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.data_id is not None:
result['DataId'] = self.data_id
if self.doc_type is not None:
result['DocType'] = self.doc_type
result['PageResult'] = []
if self.page_result is not None:
for k in self.page_result:
result['PageResult'].append(k.to_map() if k else None)
if self.page_summary is not None:
result['PageSummary'] = self.page_summary.to_map()
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
if self.url is not None:
result['Url'] = self.url
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
if m.get('DocType') is not None:
self.doc_type = m.get('DocType')
self.page_result = []
if m.get('PageResult') is not None:
for k in m.get('PageResult'):
temp_model = DescribeFileModerationResultResponseBodyDataPageResult()
self.page_result.append(temp_model.from_map(k))
if m.get('PageSummary') is not None:
temp_model = DescribeFileModerationResultResponseBodyDataPageSummary()
self.page_summary = temp_model.from_map(m['PageSummary'])
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
if m.get('Url') is not None:
self.url = m.get('Url')
return self
class DescribeFileModerationResultResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: DescribeFileModerationResultResponseBodyData = None,
message: str = None,
request_id: str = None,
):
# The returned HTTP status code. The status code 200 indicates that the request was successful.
self.code = code
# The data returned.
self.data = data
# The message that is returned in response to the request.
self.message = message
# Id of the request
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.message is not None:
result['Message'] = self.message
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = DescribeFileModerationResultResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Message') is not None:
self.message = m.get('Message')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class DescribeFileModerationResultResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: DescribeFileModerationResultResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = DescribeFileModerationResultResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class DescribeImageModerationResultRequest(TeaModel):
def __init__(
self,
req_id: str = None,
):
# The ReqId field returned by the asynchronous Image Moderation 2.0 API.
self.req_id = req_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.req_id is not None:
result['ReqId'] = self.req_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('ReqId') is not None:
self.req_id = m.get('ReqId')
return self
class DescribeImageModerationResultResponseBodyDataResult(TeaModel):
def __init__(
self,
confidence: float = None,
description: str = None,
label: str = None,
risk_level: str = None,
):
# The score of the confidence level. Valid values: 0 to 100. The value is accurate to two decimal places.
self.confidence = confidence
# The description of the result.
self.description = description
# The labels returned after the image moderation.
self.label = label
self.risk_level = risk_level
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
return self
class DescribeImageModerationResultResponseBodyData(TeaModel):
def __init__(
self,
data_id: str = None,
frame: str = None,
frame_num: int = None,
req_id: str = None,
result: List[DescribeImageModerationResultResponseBodyDataResult] = None,
risk_level: str = None,
):
# The value of dataId that is specified in the API request. If this parameter is not specified in the API request, this field is not available in the response.
self.data_id = data_id
# The information about the captured frames.
self.frame = frame
# The number of frames.
self.frame_num = frame_num
# The reqId field returned by the Image Async Moderation API.
self.req_id = req_id
# The results of image moderation parameters such as the label parameter and the confidence parameter.
self.result = result
# Risk Level.
self.risk_level = risk_level
def validate(self):
if self.result:
for k in self.result:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.data_id is not None:
result['DataId'] = self.data_id
if self.frame is not None:
result['Frame'] = self.frame
if self.frame_num is not None:
result['FrameNum'] = self.frame_num
if self.req_id is not None:
result['ReqId'] = self.req_id
result['Result'] = []
if self.result is not None:
for k in self.result:
result['Result'].append(k.to_map() if k else None)
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
if m.get('Frame') is not None:
self.frame = m.get('Frame')
if m.get('FrameNum') is not None:
self.frame_num = m.get('FrameNum')
if m.get('ReqId') is not None:
self.req_id = m.get('ReqId')
self.result = []
if m.get('Result') is not None:
for k in m.get('Result'):
temp_model = DescribeImageModerationResultResponseBodyDataResult()
self.result.append(temp_model.from_map(k))
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
return self
class DescribeImageModerationResultResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: DescribeImageModerationResultResponseBodyData = None,
msg: str = None,
request_id: str = None,
):
# The returned HTTP status code.
self.code = code
# The image moderation results.
self.data = data
# The message that is returned in response to the request.
self.msg = msg
# The request ID, which is used to locate and troubleshoot issues.
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.msg is not None:
result['Msg'] = self.msg
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = DescribeImageModerationResultResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Msg') is not None:
self.msg = m.get('Msg')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class DescribeImageModerationResultResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: DescribeImageModerationResultResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = DescribeImageModerationResultResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class DescribeImageResultExtRequest(TeaModel):
def __init__(
self,
info_type: str = None,
req_id: str = None,
):
# The content of the information to be obtained. Multiple values are separated by commas.
self.info_type = info_type
# The reqId field returned by the Url Async Moderation API.
self.req_id = req_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.info_type is not None:
result['InfoType'] = self.info_type
if self.req_id is not None:
result['ReqId'] = self.req_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('InfoType') is not None:
self.info_type = m.get('InfoType')
if m.get('ReqId') is not None:
self.req_id = m.get('ReqId')
return self
class DescribeImageResultExtResponseBodyDataCustomImage(TeaModel):
def __init__(
self,
image_id: str = None,
lib_id: str = None,
lib_name: str = None,
):
# The image ID.
self.image_id = image_id
# The image library ID.
self.lib_id = lib_id
# The image library name.
self.lib_name = lib_name
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.image_id is not None:
result['ImageId'] = self.image_id
if self.lib_id is not None:
result['LibId'] = self.lib_id
if self.lib_name is not None:
result['LibName'] = self.lib_name
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('ImageId') is not None:
self.image_id = m.get('ImageId')
if m.get('LibId') is not None:
self.lib_id = m.get('LibId')
if m.get('LibName') is not None:
self.lib_name = m.get('LibName')
return self
class DescribeImageResultExtResponseBodyDataPublicFigure(TeaModel):
def __init__(
self,
figure_id: str = None,
):
# Identified person coding information.
self.figure_id = figure_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.figure_id is not None:
result['FigureId'] = self.figure_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('FigureId') is not None:
self.figure_id = m.get('FigureId')
return self
class DescribeImageResultExtResponseBodyDataTextInImageCustomTexts(TeaModel):
def __init__(
self,
key_words: str = None,
lib_id: str = None,
lib_name: str = None,
):
# Custom words, multiple words separated by commas.
self.key_words = key_words
# Custom library ID.
self.lib_id = lib_id
# Custom library name.
self.lib_name = lib_name
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.key_words is not None:
result['KeyWords'] = self.key_words
if self.lib_id is not None:
result['LibId'] = self.lib_id
if self.lib_name is not None:
result['LibName'] = self.lib_name
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('KeyWords') is not None:
self.key_words = m.get('KeyWords')
if m.get('LibId') is not None:
self.lib_id = m.get('LibId')
if m.get('LibName') is not None:
self.lib_name = m.get('LibName')
return self
class DescribeImageResultExtResponseBodyDataTextInImage(TeaModel):
def __init__(
self,
custom_texts: List[DescribeImageResultExtResponseBodyDataTextInImageCustomTexts] = None,
ocr_datas: List[str] = None,
risk_words: List[str] = None,
):
# When a custom text library is hit, the custom library ID, custom library name, and custom word are returned.
self.custom_texts = custom_texts
# Returns the text information in the recognized image.
self.ocr_datas = ocr_datas
# The risk words that are hit. Multiple words are separated by commas (,).
self.risk_words = risk_words
def validate(self):
if self.custom_texts:
for k in self.custom_texts:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
result['CustomTexts'] = []
if self.custom_texts is not None:
for k in self.custom_texts:
result['CustomTexts'].append(k.to_map() if k else None)
if self.ocr_datas is not None:
result['OcrDatas'] = self.ocr_datas
if self.risk_words is not None:
result['RiskWords'] = self.risk_words
return result
def from_map(self, m: dict = None):
m = m or dict()
self.custom_texts = []
if m.get('CustomTexts') is not None:
for k in m.get('CustomTexts'):
temp_model = DescribeImageResultExtResponseBodyDataTextInImageCustomTexts()
self.custom_texts.append(temp_model.from_map(k))
if m.get('OcrDatas') is not None:
self.ocr_datas = m.get('OcrDatas')
if m.get('RiskWords') is not None:
self.risk_words = m.get('RiskWords')
return self
class DescribeImageResultExtResponseBodyData(TeaModel):
def __init__(
self,
custom_image: List[DescribeImageResultExtResponseBodyDataCustomImage] = None,
public_figure: List[DescribeImageResultExtResponseBodyDataPublicFigure] = None,
text_in_image: DescribeImageResultExtResponseBodyDataTextInImage = None,
):
# If a custom image library is hit, information about the hit custom image library is returned.
self.custom_image = custom_image
# Person information list.
self.public_figure = public_figure
# Returns the text information in the hit image.
self.text_in_image = text_in_image
def validate(self):
if self.custom_image:
for k in self.custom_image:
if k:
k.validate()
if self.public_figure:
for k in self.public_figure:
if k:
k.validate()
if self.text_in_image:
self.text_in_image.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
result['CustomImage'] = []
if self.custom_image is not None:
for k in self.custom_image:
result['CustomImage'].append(k.to_map() if k else None)
result['PublicFigure'] = []
if self.public_figure is not None:
for k in self.public_figure:
result['PublicFigure'].append(k.to_map() if k else None)
if self.text_in_image is not None:
result['TextInImage'] = self.text_in_image.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
self.custom_image = []
if m.get('CustomImage') is not None:
for k in m.get('CustomImage'):
temp_model = DescribeImageResultExtResponseBodyDataCustomImage()
self.custom_image.append(temp_model.from_map(k))
self.public_figure = []
if m.get('PublicFigure') is not None:
for k in m.get('PublicFigure'):
temp_model = DescribeImageResultExtResponseBodyDataPublicFigure()
self.public_figure.append(temp_model.from_map(k))
if m.get('TextInImage') is not None:
temp_model = DescribeImageResultExtResponseBodyDataTextInImage()
self.text_in_image = temp_model.from_map(m['TextInImage'])
return self
class DescribeImageResultExtResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: DescribeImageResultExtResponseBodyData = None,
msg: str = None,
request_id: str = None,
):
# The returned HTTP status code.
self.code = code
# The data returned.
self.data = data
# The message that is returned in response to the request.
self.msg = msg
# The request ID.
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.msg is not None:
result['Msg'] = self.msg
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = DescribeImageResultExtResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Msg') is not None:
self.msg = m.get('Msg')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class DescribeImageResultExtResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: DescribeImageResultExtResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = DescribeImageResultExtResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class DescribeUploadTokenResponseBodyData(TeaModel):
def __init__(
self,
access_key_id: str = None,
access_key_secret: str = None,
bucket_name: str = None,
expiration: int = None,
file_name_prefix: str = None,
oss_internal_end_point: str = None,
oss_internet_end_point: str = None,
security_token: str = None,
):
# The AccessKey ID.
self.access_key_id = access_key_id
# The AccessKey secret.
self.access_key_secret = access_key_secret
# The bucket name.
self.bucket_name = bucket_name
# The time when the file sharing link expires.
self.expiration = expiration
# The file prefix.
self.file_name_prefix = file_name_prefix
# the oss intranet point.
self.oss_internal_end_point = oss_internal_end_point
# the oss internet point.
self.oss_internet_end_point = oss_internet_end_point
# The security token.
self.security_token = security_token
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.access_key_id is not None:
result['AccessKeyId'] = self.access_key_id
if self.access_key_secret is not None:
result['AccessKeySecret'] = self.access_key_secret
if self.bucket_name is not None:
result['BucketName'] = self.bucket_name
if self.expiration is not None:
result['Expiration'] = self.expiration
if self.file_name_prefix is not None:
result['FileNamePrefix'] = self.file_name_prefix
if self.oss_internal_end_point is not None:
result['OssInternalEndPoint'] = self.oss_internal_end_point
if self.oss_internet_end_point is not None:
result['OssInternetEndPoint'] = self.oss_internet_end_point
if self.security_token is not None:
result['SecurityToken'] = self.security_token
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('AccessKeyId') is not None:
self.access_key_id = m.get('AccessKeyId')
if m.get('AccessKeySecret') is not None:
self.access_key_secret = m.get('AccessKeySecret')
if m.get('BucketName') is not None:
self.bucket_name = m.get('BucketName')
if m.get('Expiration') is not None:
self.expiration = m.get('Expiration')
if m.get('FileNamePrefix') is not None:
self.file_name_prefix = m.get('FileNamePrefix')
if m.get('OssInternalEndPoint') is not None:
self.oss_internal_end_point = m.get('OssInternalEndPoint')
if m.get('OssInternetEndPoint') is not None:
self.oss_internet_end_point = m.get('OssInternetEndPoint')
if m.get('SecurityToken') is not None:
self.security_token = m.get('SecurityToken')
return self
class DescribeUploadTokenResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: DescribeUploadTokenResponseBodyData = None,
msg: str = None,
request_id: str = None,
):
# The returned HTTP status code.
self.code = code
# The data returned.
self.data = data
# The message that is returned in response to the request.
self.msg = msg
# The request ID.
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.msg is not None:
result['Msg'] = self.msg
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = DescribeUploadTokenResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Msg') is not None:
self.msg = m.get('Msg')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class DescribeUploadTokenResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: DescribeUploadTokenResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = DescribeUploadTokenResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class DescribeUrlModerationResultRequest(TeaModel):
def __init__(
self,
req_id: str = None,
):
# The ReqId field returned by an asynchronous URL moderation operation.
self.req_id = req_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.req_id is not None:
result['ReqId'] = self.req_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('ReqId') is not None:
self.req_id = m.get('ReqId')
return self
class DescribeUrlModerationResultResponseBodyDataExtraInfo(TeaModel):
def __init__(
self,
icp_no: str = None,
icp_type: str = None,
site_type: str = None,
):
# The ICP number.
self.icp_no = icp_no
# The type of the ICP filing.
self.icp_type = icp_type
# The type of site
self.site_type = site_type
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.icp_no is not None:
result['IcpNo'] = self.icp_no
if self.icp_type is not None:
result['IcpType'] = self.icp_type
if self.site_type is not None:
result['SiteType'] = self.site_type
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('IcpNo') is not None:
self.icp_no = m.get('IcpNo')
if m.get('IcpType') is not None:
self.icp_type = m.get('IcpType')
if m.get('SiteType') is not None:
self.site_type = m.get('SiteType')
return self
class DescribeUrlModerationResultResponseBodyDataResult(TeaModel):
def __init__(
self,
confidence: float = None,
label: str = None,
):
# The score of the confidence level. Valid values: 0 to 100. The value is accurate to two decimal places.
self.confidence = confidence
# The labels returned after the asynchronous URL moderation.
self.label = label
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.label is not None:
result['Label'] = self.label
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Label') is not None:
self.label = m.get('Label')
return self
class DescribeUrlModerationResultResponseBodyData(TeaModel):
def __init__(
self,
data_id: str = None,
extra_info: DescribeUrlModerationResultResponseBodyDataExtraInfo = None,
req_id: str = None,
result: List[DescribeUrlModerationResultResponseBodyDataResult] = None,
):
# The value of dataId that is specified in the API request. If this parameter is not specified in the API request, this field is not available in the response.
self.data_id = data_id
# The supplementary information.
self.extra_info = extra_info
# The ReqId field returned by an asynchronous URL moderation operation.
self.req_id = req_id
# The returned results.
self.result = result
def validate(self):
if self.extra_info:
self.extra_info.validate()
if self.result:
for k in self.result:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.data_id is not None:
result['DataId'] = self.data_id
if self.extra_info is not None:
result['ExtraInfo'] = self.extra_info.to_map()
if self.req_id is not None:
result['ReqId'] = self.req_id
result['Result'] = []
if self.result is not None:
for k in self.result:
result['Result'].append(k.to_map() if k else None)
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
if m.get('ExtraInfo') is not None:
temp_model = DescribeUrlModerationResultResponseBodyDataExtraInfo()
self.extra_info = temp_model.from_map(m['ExtraInfo'])
if m.get('ReqId') is not None:
self.req_id = m.get('ReqId')
self.result = []
if m.get('Result') is not None:
for k in m.get('Result'):
temp_model = DescribeUrlModerationResultResponseBodyDataResult()
self.result.append(temp_model.from_map(k))
return self
class DescribeUrlModerationResultResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: DescribeUrlModerationResultResponseBodyData = None,
msg: str = None,
request_id: str = None,
):
# The returned HTTP status code. The status code 200 indicates that the request was successful.
self.code = code
# The data returned.
self.data = data
# The message that is returned in response to the request.
self.msg = msg
# The request ID.
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.msg is not None:
result['Msg'] = self.msg
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = DescribeUrlModerationResultResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Msg') is not None:
self.msg = m.get('Msg')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class DescribeUrlModerationResultResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: DescribeUrlModerationResultResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = DescribeUrlModerationResultResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class FileModerationRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The type of the moderation service.
self.service = service
# The parameters required by the moderation service. The value is a JSON string.
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class FileModerationResponseBodyData(TeaModel):
def __init__(
self,
task_id: str = None,
):
# The task ID.
self.task_id = task_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.task_id is not None:
result['TaskId'] = self.task_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('TaskId') is not None:
self.task_id = m.get('TaskId')
return self
class FileModerationResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: FileModerationResponseBodyData = None,
message: str = None,
request_id: str = None,
):
# The returned HTTP status code.
self.code = code
# The data returned.
self.data = data
# The message that is returned in response to the request.
self.message = message
# The request ID.
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.message is not None:
result['Message'] = self.message
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = FileModerationResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Message') is not None:
self.message = m.get('Message')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class FileModerationResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: FileModerationResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = FileModerationResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class ImageAsyncModerationRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The type of the moderation service.
self.service = service
# The parameters required by the moderation service. The value is a JSON string.
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class ImageAsyncModerationResponseBodyData(TeaModel):
def __init__(
self,
data_id: str = None,
req_id: str = None,
):
# The ID of the moderated object.
self.data_id = data_id
# The reqId field returned by the Image Async Moderation API. You can use this field to query the detection results.
self.req_id = req_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.data_id is not None:
result['DataId'] = self.data_id
if self.req_id is not None:
result['ReqId'] = self.req_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
if m.get('ReqId') is not None:
self.req_id = m.get('ReqId')
return self
class ImageAsyncModerationResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: ImageAsyncModerationResponseBodyData = None,
msg: str = None,
request_id: str = None,
):
# The returned HTTP status code.
self.code = code
# The data returned.
self.data = data
# The message that is returned in response to the request.
self.msg = msg
# The request ID.
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.msg is not None:
result['Msg'] = self.msg
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = ImageAsyncModerationResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Msg') is not None:
self.msg = m.get('Msg')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class ImageAsyncModerationResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: ImageAsyncModerationResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = ImageAsyncModerationResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class ImageBatchModerationRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The types of detection supported by the enhanced image review, separated by English commas. Values:
#
# - baselineCheck：General Baseline Detection
# - baselineCheck_pro：General Baseline Detection_Pro Edition
# - tonalityImprove：Content governance monitoring
# - aigcCheck：AIGC image detection
self.service = service
# The set of relevant parameters for content detection objects.
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class ImageBatchModerationResponseBodyDataResult(TeaModel):
def __init__(
self,
confidence: float = None,
description: str = None,
label: str = None,
):
# Confidence score, ranging from 0 to 100, retained to two decimal places. Some labels do not have a confidence score.
self.confidence = confidence
# Description.
self.description = description
# The labels returned after image content detection. A single image may be associated with multiple labels and corresponding scores.
self.label = label
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
return self
class ImageBatchModerationResponseBodyDataResultsExtCustomImage(TeaModel):
def __init__(
self,
image_id: str = None,
lib_id: str = None,
lib_name: str = None,
):
# The ID of the hit custom image.
self.image_id = image_id
# Custom library ID
self.lib_id = lib_id
# The name of the hit custom gallery.
self.lib_name = lib_name
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.image_id is not None:
result['ImageId'] = self.image_id
if self.lib_id is not None:
result['LibId'] = self.lib_id
if self.lib_name is not None:
result['LibName'] = self.lib_name
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('ImageId') is not None:
self.image_id = m.get('ImageId')
if m.get('LibId') is not None:
self.lib_id = m.get('LibId')
if m.get('LibName') is not None:
self.lib_name = m.get('LibName')
return self
class ImageBatchModerationResponseBodyDataResultsExtLogoDataLocation(TeaModel):
def __init__(
self,
h: int = None,
w: int = None,
x: int = None,
y: int = None,
):
# The width of the text area, in pixels.
self.h = h
# The height of the text area, in pixels.
self.w = w
# The distance from the top-left corner of the text area to the y-axis, with the top-left corner of the image as the origin, in pixels.
self.x = x
# The distance from the top-left corner of the text area to the x-axis, with the top-left corner of the image as the origin, in pixels.
self.y = y
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.h is not None:
result['H'] = self.h
if self.w is not None:
result['W'] = self.w
if self.x is not None:
result['X'] = self.x
if self.y is not None:
result['Y'] = self.y
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('H') is not None:
self.h = m.get('H')
if m.get('W') is not None:
self.w = m.get('W')
if m.get('X') is not None:
self.x = m.get('X')
if m.get('Y') is not None:
self.y = m.get('Y')
return self
class ImageBatchModerationResponseBodyDataResultsExtLogoDataLogo(TeaModel):
def __init__(
self,
confidence: float = None,
label: str = None,
name: str = None,
):
# Confidence score, from 0 to 100, rounded to two decimal places.
self.confidence = confidence
# Identify the category.
self.label = label
# identifier name.
self.name = name
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.label is not None:
result['Label'] = self.label
if self.name is not None:
result['Name'] = self.name
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Label') is not None:
self.label = m.get('Label')
if m.get('Name') is not None:
self.name = m.get('Name')
return self
class ImageBatchModerationResponseBodyDataResultsExtLogoData(TeaModel):
def __init__(
self,
location: ImageBatchModerationResponseBodyDataResultsExtLogoDataLocation = None,
logo: List[ImageBatchModerationResponseBodyDataResultsExtLogoDataLogo] = None,
):
# The location information of the identifier.
self.location = location
# identification information
self.logo = logo
def validate(self):
if self.location:
self.location.validate()
if self.logo:
for k in self.logo:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.location is not None:
result['Location'] = self.location.to_map()
result['Logo'] = []
if self.logo is not None:
for k in self.logo:
result['Logo'].append(k.to_map() if k else None)
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Location') is not None:
temp_model = ImageBatchModerationResponseBodyDataResultsExtLogoDataLocation()
self.location = temp_model.from_map(m['Location'])
self.logo = []
if m.get('Logo') is not None:
for k in m.get('Logo'):
temp_model = ImageBatchModerationResponseBodyDataResultsExtLogoDataLogo()
self.logo.append(temp_model.from_map(k))
return self
class ImageBatchModerationResponseBodyDataResultsExtPublicFigureLocation(TeaModel):
def __init__(
self,
h: int = None,
w: int = None,
x: int = None,
y: int = None,
):
# The width of the text area, in pixels.
self.h = h
# The height of the text area, in pixels.
self.w = w
# The distance from the top-left corner of the text area to the y-axis, with the top-left corner of the image as the origin, in pixels.
self.x = x
# The distance from the top-left corner of the text area to the x-axis, with the top-left corner of the image as the origin, in pixels.
self.y = y
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.h is not None:
result['H'] = self.h
if self.w is not None:
result['W'] = self.w
if self.x is not None:
result['X'] = self.x
if self.y is not None:
result['Y'] = self.y
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('H') is not None:
self.h = m.get('H')
if m.get('W') is not None:
self.w = m.get('W')
if m.get('X') is not None:
self.x = m.get('X')
if m.get('Y') is not None:
self.y = m.get('Y')
return self
class ImageBatchModerationResponseBodyDataResultsExtPublicFigure(TeaModel):
def __init__(
self,
figure_id: str = None,
figure_name: str = None,
location: List[ImageBatchModerationResponseBodyDataResultsExtPublicFigureLocation] = None,
):
# Identify the encoded information of the person.
self.figure_id = figure_id
# The identified person\\"s name information.
self.figure_name = figure_name
# The location information of the identifier.
self.location = location
def validate(self):
if self.location:
for k in self.location:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.figure_id is not None:
result['FigureId'] = self.figure_id
if self.figure_name is not None:
result['FigureName'] = self.figure_name
result['Location'] = []
if self.location is not None:
for k in self.location:
result['Location'].append(k.to_map() if k else None)
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('FigureId') is not None:
self.figure_id = m.get('FigureId')
if m.get('FigureName') is not None:
self.figure_name = m.get('FigureName')
self.location = []
if m.get('Location') is not None:
for k in m.get('Location'):
temp_model = ImageBatchModerationResponseBodyDataResultsExtPublicFigureLocation()
self.location.append(temp_model.from_map(k))
return self
class ImageBatchModerationResponseBodyDataResultsExtTextInImageCustomText(TeaModel):
def __init__(
self,
key_words: str = None,
lib_id: str = None,
lib_name: str = None,
):
# Custom words, separate multiple words with commas.
self.key_words = key_words
# Custom library ID.
self.lib_id = lib_id
# Custom library name.
self.lib_name = lib_name
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.key_words is not None:
result['KeyWords'] = self.key_words
if self.lib_id is not None:
result['LibId'] = self.lib_id
if self.lib_name is not None:
result['LibName'] = self.lib_name
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('KeyWords') is not None:
self.key_words = m.get('KeyWords')
if m.get('LibId') is not None:
self.lib_id = m.get('LibId')
if m.get('LibName') is not None:
self.lib_name = m.get('LibName')
return self
class ImageBatchModerationResponseBodyDataResultsExtTextInImageOcrResultLocation(TeaModel):
def __init__(
self,
h: int = None,
w: int = None,
x: int = None,
y: int = None,
):
# The height of the text area, in pixels.
self.h = h
# The width of the text area, in pixels.
self.w = w
# The distance from the top-left corner of the text area to the y-axis, with the top-left corner of the image as the origin, in pixels.
self.x = x
# The distance from the top-left corner of the text area to the x-axis, with the top-left corner of the image as the origin, in pixels.
self.y = y
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.h is not None:
result['H'] = self.h
if self.w is not None:
result['W'] = self.w
if self.x is not None:
result['X'] = self.x
if self.y is not None:
result['Y'] = self.y
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('H') is not None:
self.h = m.get('H')
if m.get('W') is not None:
self.w = m.get('W')
if m.get('X') is not None:
self.x = m.get('X')
if m.get('Y') is not None:
self.y = m.get('Y')
return self
class ImageBatchModerationResponseBodyDataResultsExtTextInImageOcrResult(TeaModel):
def __init__(
self,
location: ImageBatchModerationResponseBodyDataResultsExtTextInImageOcrResultLocation = None,
text: str = None,
):
# Text line coordinate information.
self.location = location
# Text information.
self.text = text
def validate(self):
if self.location:
self.location.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.location is not None:
result['Location'] = self.location.to_map()
if self.text is not None:
result['Text'] = self.text
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Location') is not None:
temp_model = ImageBatchModerationResponseBodyDataResultsExtTextInImageOcrResultLocation()
self.location = temp_model.from_map(m['Location'])
if m.get('Text') is not None:
self.text = m.get('Text')
return self
class ImageBatchModerationResponseBodyDataResultsExtTextInImage(TeaModel):
def __init__(
self,
custom_text: List[ImageBatchModerationResponseBodyDataResultsExtTextInImageCustomText] = None,
ocr_result: List[ImageBatchModerationResponseBodyDataResultsExtTextInImageOcrResult] = None,
risk_word: List[str] = None,
):
# When a custom text library is matched, return the custom library ID, custom library name, and custom words.
self.custom_text = custom_text
# Return the text information of each line recognized in the image.
self.ocr_result = ocr_result
# hit risk keywords
self.risk_word = risk_word
def validate(self):
if self.custom_text:
for k in self.custom_text:
if k:
k.validate()
if self.ocr_result:
for k in self.ocr_result:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
result['CustomText'] = []
if self.custom_text is not None:
for k in self.custom_text:
result['CustomText'].append(k.to_map() if k else None)
result['OcrResult'] = []
if self.ocr_result is not None:
for k in self.ocr_result:
result['OcrResult'].append(k.to_map() if k else None)
if self.risk_word is not None:
result['RiskWord'] = self.risk_word
return result
def from_map(self, m: dict = None):
m = m or dict()
self.custom_text = []
if m.get('CustomText') is not None:
for k in m.get('CustomText'):
temp_model = ImageBatchModerationResponseBodyDataResultsExtTextInImageCustomText()
self.custom_text.append(temp_model.from_map(k))
self.ocr_result = []
if m.get('OcrResult') is not None:
for k in m.get('OcrResult'):
temp_model = ImageBatchModerationResponseBodyDataResultsExtTextInImageOcrResult()
self.ocr_result.append(temp_model.from_map(k))
if m.get('RiskWord') is not None:
self.risk_word = m.get('RiskWord')
return self
class ImageBatchModerationResponseBodyDataResultsExt(TeaModel):
def __init__(
self,
custom_image: List[ImageBatchModerationResponseBodyDataResultsExtCustomImage] = None,
logo_data: ImageBatchModerationResponseBodyDataResultsExtLogoData = None,
public_figure: List[ImageBatchModerationResponseBodyDataResultsExtPublicFigure] = None,
text_in_image: ImageBatchModerationResponseBodyDataResultsExtTextInImage = None,
):
# Custom image library hit information list.
self.custom_image = custom_image
# Logo identification information.
self.logo_data = logo_data
# List of character information.
self.public_figure = public_figure
# Return the text information from the recognized images.
self.text_in_image = text_in_image
def validate(self):
if self.custom_image:
for k in self.custom_image:
if k:
k.validate()
if self.logo_data:
self.logo_data.validate()
if self.public_figure:
for k in self.public_figure:
if k:
k.validate()
if self.text_in_image:
self.text_in_image.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
result['CustomImage'] = []
if self.custom_image is not None:
for k in self.custom_image:
result['CustomImage'].append(k.to_map() if k else None)
if self.logo_data is not None:
result['LogoData'] = self.logo_data.to_map()
result['PublicFigure'] = []
if self.public_figure is not None:
for k in self.public_figure:
result['PublicFigure'].append(k.to_map() if k else None)
if self.text_in_image is not None:
result['TextInImage'] = self.text_in_image.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
self.custom_image = []
if m.get('CustomImage') is not None:
for k in m.get('CustomImage'):
temp_model = ImageBatchModerationResponseBodyDataResultsExtCustomImage()
self.custom_image.append(temp_model.from_map(k))
if m.get('LogoData') is not None:
temp_model = ImageBatchModerationResponseBodyDataResultsExtLogoData()
self.logo_data = temp_model.from_map(m['LogoData'])
self.public_figure = []
if m.get('PublicFigure') is not None:
for k in m.get('PublicFigure'):
temp_model = ImageBatchModerationResponseBodyDataResultsExtPublicFigure()
self.public_figure.append(temp_model.from_map(k))
if m.get('TextInImage') is not None:
temp_model = ImageBatchModerationResponseBodyDataResultsExtTextInImage()
self.text_in_image = temp_model.from_map(m['TextInImage'])
return self
class ImageBatchModerationResponseBodyDataResultsResult(TeaModel):
def __init__(
self,
confidence: float = None,
description: str = None,
label: str = None,
):
# Confidence score, ranging from 0 to 100, rounded to two decimal places. Some labels do not have a confidence score.
self.confidence = confidence
# Description.
self.description = description
# The labels returned after image content detection. A single image may have multiple labels and corresponding scores detected.
self.label = label
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
return self
class ImageBatchModerationResponseBodyDataResults(TeaModel):
def __init__(
self,
ext: ImageBatchModerationResponseBodyDataResultsExt = None,
result: List[ImageBatchModerationResponseBodyDataResultsResult] = None,
risk_level: str = None,
service: str = None,
):
# Image supplementary reference information.
self.ext = ext
# The risk labels, confidence scores, and other parameters of image detection results, in an array structure.
self.result = result
# Risk level.
self.risk_level = risk_level
# The enhanced image detection service supports various detection services.
self.service = service
def validate(self):
if self.ext:
self.ext.validate()
if self.result:
for k in self.result:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.ext is not None:
result['Ext'] = self.ext.to_map()
result['Result'] = []
if self.result is not None:
for k in self.result:
result['Result'].append(k.to_map() if k else None)
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
if self.service is not None:
result['Service'] = self.service
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Ext') is not None:
temp_model = ImageBatchModerationResponseBodyDataResultsExt()
self.ext = temp_model.from_map(m['Ext'])
self.result = []
if m.get('Result') is not None:
for k in m.get('Result'):
temp_model = ImageBatchModerationResponseBodyDataResultsResult()
self.result.append(temp_model.from_map(k))
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
if m.get('Service') is not None:
self.service = m.get('Service')
return self
class ImageBatchModerationResponseBodyData(TeaModel):
def __init__(
self,
data_id: str = None,
result: List[ImageBatchModerationResponseBodyDataResult] = None,
results: List[ImageBatchModerationResponseBodyDataResults] = None,
risk_level: str = None,
):
# To detect the data ID corresponding to the object.
self.data_id = data_id
# The risk labels, confidence scores, and other parameters of image detection results, in an array structure.
self.result = result
# The risk labels, confidence scores, and other parameters for each service\\"s image detection, in an array structure.
self.results = results
# Risk level.
self.risk_level = risk_level
def validate(self):
if self.result:
for k in self.result:
if k:
k.validate()
if self.results:
for k in self.results:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.data_id is not None:
result['DataId'] = self.data_id
result['Result'] = []
if self.result is not None:
for k in self.result:
result['Result'].append(k.to_map() if k else None)
result['Results'] = []
if self.results is not None:
for k in self.results:
result['Results'].append(k.to_map() if k else None)
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
self.result = []
if m.get('Result') is not None:
for k in m.get('Result'):
temp_model = ImageBatchModerationResponseBodyDataResult()
self.result.append(temp_model.from_map(k))
self.results = []
if m.get('Results') is not None:
for k in m.get('Results'):
temp_model = ImageBatchModerationResponseBodyDataResults()
self.results.append(temp_model.from_map(k))
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
return self
class ImageBatchModerationResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: ImageBatchModerationResponseBodyData = None,
msg: str = None,
request_id: str = None,
):
# Return code. A return of 200 represents success.
self.code = code
# The result of image content detection.
self.data = data
# The response message for this request.
self.msg = msg
# The ID of this invocation request, generated by Alibaba Cloud as a unique identifier for the request, can be used for troubleshooting and pinpointing issues.
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.msg is not None:
result['Msg'] = self.msg
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = ImageBatchModerationResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Msg') is not None:
self.msg = m.get('Msg')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class ImageBatchModerationResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: ImageBatchModerationResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = ImageBatchModerationResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class ImageModerationRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The moderation services supported by Image Moderation 2.0. Valid values:
#
# * baselineCheck: common baseline moderation
# * baselineCheck_pro: common baseline moderation_Professional
# * baselineCheck_cb: common baseline moderation_For regions outside the Chinese mainland
# * tonalityImprove: content governance moderation
# * aigcCheck: AI-generated image identification
# * profilePhotoCheck: avatar image moderation
# * advertisingCheck: marketing material identification
# * liveStreamCheck: moderation of screenshots of videos and live streams
#
# Valid values:
#
# * liveStreamCheck: moderation of screenshots of videos and live streams
# * baselineCheck: common baseline moderation
# * aigcCheck: AI-generated image identification
# * baselineCheck_pro: common baseline moderation_Professional
# * advertisingCheck: marketing material identification
# * baselineCheck_cb: common baseline moderation_For regions outside the Chinese mainland
# * tonalityImprove: content governance moderation
# * profilePhotoCheck: avatar image moderation
self.service = service
# The parameters required by the moderation service. The value is a JSON string.
#
# * imageUrl: the URL of the object that you want to moderate. This parameter is required.
# * dataId: the ID of the object that you want to moderate. This parameter is optional.
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class ImageModerationResponseBodyDataExtCustomImage(TeaModel):
def __init__(
self,
image_id: str = None,
lib_id: str = None,
lib_name: str = None,
):
# The image ID.
self.image_id = image_id
# The image library ID.
self.lib_id = lib_id
# The image library name.
self.lib_name = lib_name
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.image_id is not None:
result['ImageId'] = self.image_id
if self.lib_id is not None:
result['LibId'] = self.lib_id
if self.lib_name is not None:
result['LibName'] = self.lib_name
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('ImageId') is not None:
self.image_id = m.get('ImageId')
if m.get('LibId') is not None:
self.lib_id = m.get('LibId')
if m.get('LibName') is not None:
self.lib_name = m.get('LibName')
return self
class ImageModerationResponseBodyDataExtFaceDataBang(TeaModel):
def __init__(
self,
confidence: float = None,
value: str = None,
):
# The confidence level of the bang recognition result. Valid values: 0 to 100. A higher value indicates a more credible result.
self.confidence = confidence
# Indicates whether the recognition result of bangs is available.
self.value = value
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.value is not None:
result['Value'] = self.value
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Value') is not None:
self.value = m.get('Value')
return self
class ImageModerationResponseBodyDataExtFaceDataGender(TeaModel):
def __init__(
self,
confidence: float = None,
value: str = None,
):
# The confidence level of the gender recognition result. Valid values: 0 to 100. A higher value indicates a more credible result.
self.confidence = confidence
# The gender recognition result. Valid values:
#
# - Male
#
# - FeMale
self.value = value
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.value is not None:
result['Value'] = self.value
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Value') is not None:
self.value = m.get('Value')
return self
class ImageModerationResponseBodyDataExtFaceDataHairstyle(TeaModel):
def __init__(
self,
confidence: float = None,
value: str = None,
):
# The confidence level of the hairstyle recognition result. Valid values: 0 to 100. A higher value indicates a more credible result.
self.confidence = confidence
# The hairstyle recognition result. Valid values:
#
# - Bald: bald head.
#
# - Long: Long hair.
#
# - Short: Short hair.
self.value = value
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.value is not None:
result['Value'] = self.value
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Value') is not None:
self.value = m.get('Value')
return self
class ImageModerationResponseBodyDataExtFaceDataHat(TeaModel):
def __init__(
self,
confidence: float = None,
value: str = None,
):
# The confidence level of the result of wearing the hat. Valid values: 0 to 100. A higher value indicates a more credible result.
self.confidence = confidence
# The recognition result of whether to wear the hat. Valid values:
#
# - Wear: Wear a hat.
#
# - None: No hat.
self.value = value
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.value is not None:
result['Value'] = self.value
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Value') is not None:
self.value = m.get('Value')
return self
class ImageModerationResponseBodyDataExtFaceDataLocation(TeaModel):
def __init__(
self,
h: int = None,
w: int = None,
x: int = None,
y: int = None,
):
# The height of the face area. Unit: pixels.
self.h = h
# The width of the face area. Unit: pixels.
self.w = w
# The distance from the upper-left corner of the face area to the y-axis with the upper-left corner of the image as the coordinate origin. Unit: pixels.
self.x = x
# The distance from the upper-left corner of the face area to the x-axis with the upper-left corner of the image as the coordinate origin. Unit: pixels.
self.y = y
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.h is not None:
result['H'] = self.h
if self.w is not None:
result['W'] = self.w
if self.x is not None:
result['X'] = self.x
if self.y is not None:
result['Y'] = self.y
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('H') is not None:
self.h = m.get('H')
if m.get('W') is not None:
self.w = m.get('W')
if m.get('X') is not None:
self.x = m.get('X')
if m.get('Y') is not None:
self.y = m.get('Y')
return self
class ImageModerationResponseBodyDataExtFaceDataMask(TeaModel):
def __init__(
self,
confidence: float = None,
value: str = None,
):
# The confidence level of the result of wearing the mask. Valid values: 0 to 100. A higher value indicates a more credible result.
self.confidence = confidence
# The recognition result of whether to wear a mask. Valid values:
#
# - Wear a mask.
#
# - None: No mask.
self.value = value
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.value is not None:
result['Value'] = self.value
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Value') is not None:
self.value = m.get('Value')
return self
class ImageModerationResponseBodyDataExtFaceDataMustache(TeaModel):
def __init__(
self,
confidence: float = None,
value: str = None,
):
# The confidence level of the result of the beard. Valid values: 0 to 100. A higher value indicates a more credible result.
self.confidence = confidence
# The identification result of whether there is a beard.Valid values:
#
# - Has:have a beard.
#
# - None:No beard.
self.value = value
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.value is not None:
result['Value'] = self.value
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Value') is not None:
self.value = m.get('Value')
return self
class ImageModerationResponseBodyDataExtFaceDataQuality(TeaModel):
def __init__(
self,
blur: float = None,
integrity: float = None,
pitch: float = None,
roll: float = None,
yaw: float = None,
):
# The blur of the face image. Valid values: 0 to 100. The higher the score, the more fuzzy it is.
# Recommended values: 0 to 25.
self.blur = blur
# The integrity of the human face. Recommended values:80 to 100.
self.integrity = integrity
# The head-up or head-down angle of the face.
# Recommended values:-30 to 30.
self.pitch = pitch
# The plane rotation angle of the face.
# Recommended values:-30 to 30.
self.roll = roll
# The left and right shaking angle of the human face.
# Recommended values:-30 to 30.
self.yaw = yaw
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.blur is not None:
result['Blur'] = self.blur
if self.integrity is not None:
result['Integrity'] = self.integrity
if self.pitch is not None:
result['Pitch'] = self.pitch
if self.roll is not None:
result['Roll'] = self.roll
if self.yaw is not None:
result['Yaw'] = self.yaw
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Blur') is not None:
self.blur = m.get('Blur')
if m.get('Integrity') is not None:
self.integrity = m.get('Integrity')
if m.get('Pitch') is not None:
self.pitch = m.get('Pitch')
if m.get('Roll') is not None:
self.roll = m.get('Roll')
if m.get('Yaw') is not None:
self.yaw = m.get('Yaw')
return self
class ImageModerationResponseBodyDataExtFaceData(TeaModel):
def __init__(
self,
age: int = None,
bang: ImageModerationResponseBodyDataExtFaceDataBang = None,
gender: ImageModerationResponseBodyDataExtFaceDataGender = None,
glasses: str = None,
hairstyle: ImageModerationResponseBodyDataExtFaceDataHairstyle = None,
hat: ImageModerationResponseBodyDataExtFaceDataHat = None,
location: ImageModerationResponseBodyDataExtFaceDataLocation = None,
mask: ImageModerationResponseBodyDataExtFaceDataMask = None,
mustache: ImageModerationResponseBodyDataExtFaceDataMustache = None,
quality: ImageModerationResponseBodyDataExtFaceDataQuality = None,
smile: float = None,
):
# The age recognition result.
self.age = age
# Indicates whether the recognition result of bangs is available.
self.bang = bang
# The gender recognition result.
self.gender = gender
# The recognition result of whether to wear glasses.
#
# - None: No glasses.
#
# - Wear: Wear glasses.
#
# - Sunglass: Wear sunglasses.
self.glasses = glasses
# The hairstyle recognition result.
self.hairstyle = hairstyle
# The recognition result of whether to wear a hat.
self.hat = hat
# The location of the face.
self.location = location
# The recognition result of whether to wear a mask.
self.mask = mask
# The identification result of whether there is a beard.
self.mustache = mustache
# The quality information of the face image.
self.quality = quality
# The smiling degree of the face.
self.smile = smile
def validate(self):
if self.bang:
self.bang.validate()
if self.gender:
self.gender.validate()
if self.hairstyle:
self.hairstyle.validate()
if self.hat:
self.hat.validate()
if self.location:
self.location.validate()
if self.mask:
self.mask.validate()
if self.mustache:
self.mustache.validate()
if self.quality:
self.quality.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.age is not None:
result['Age'] = self.age
if self.bang is not None:
result['Bang'] = self.bang.to_map()
if self.gender is not None:
result['Gender'] = self.gender.to_map()
if self.glasses is not None:
result['Glasses'] = self.glasses
if self.hairstyle is not None:
result['Hairstyle'] = self.hairstyle.to_map()
if self.hat is not None:
result['Hat'] = self.hat.to_map()
if self.location is not None:
result['Location'] = self.location.to_map()
if self.mask is not None:
result['Mask'] = self.mask.to_map()
if self.mustache is not None:
result['Mustache'] = self.mustache.to_map()
if self.quality is not None:
result['Quality'] = self.quality.to_map()
if self.smile is not None:
result['Smile'] = self.smile
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Age') is not None:
self.age = m.get('Age')
if m.get('Bang') is not None:
temp_model = ImageModerationResponseBodyDataExtFaceDataBang()
self.bang = temp_model.from_map(m['Bang'])
if m.get('Gender') is not None:
temp_model = ImageModerationResponseBodyDataExtFaceDataGender()
self.gender = temp_model.from_map(m['Gender'])
if m.get('Glasses') is not None:
self.glasses = m.get('Glasses')
if m.get('Hairstyle') is not None:
temp_model = ImageModerationResponseBodyDataExtFaceDataHairstyle()
self.hairstyle = temp_model.from_map(m['Hairstyle'])
if m.get('Hat') is not None:
temp_model = ImageModerationResponseBodyDataExtFaceDataHat()
self.hat = temp_model.from_map(m['Hat'])
if m.get('Location') is not None:
temp_model = ImageModerationResponseBodyDataExtFaceDataLocation()
self.location = temp_model.from_map(m['Location'])
if m.get('Mask') is not None:
temp_model = ImageModerationResponseBodyDataExtFaceDataMask()
self.mask = temp_model.from_map(m['Mask'])
if m.get('Mustache') is not None:
temp_model = ImageModerationResponseBodyDataExtFaceDataMustache()
self.mustache = temp_model.from_map(m['Mustache'])
if m.get('Quality') is not None:
temp_model = ImageModerationResponseBodyDataExtFaceDataQuality()
self.quality = temp_model.from_map(m['Quality'])
if m.get('Smile') is not None:
self.smile = m.get('Smile')
return self
class ImageModerationResponseBodyDataExtLogoDataLocation(TeaModel):
def __init__(
self,
h: int = None,
w: int = None,
x: int = None,
y: int = None,
):
# The height of the text area, in pixels.
self.h = h
# The width of the text area, in pixels.
self.w = w
# The distance between the upper-left corner of the text area and the y-axis, using the upper-left corner of the image as the coordinate origin, in pixels.
self.x = x
# The distance between the upper left corner of the text area and the x-axis, with the upper left corner of the image as the coordinate origin, in pixels.
self.y = y
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.h is not None:
result['H'] = self.h
if self.w is not None:
result['W'] = self.w
if self.x is not None:
result['X'] = self.x
if self.y is not None:
result['Y'] = self.y
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('H') is not None:
self.h = m.get('H')
if m.get('W') is not None:
self.w = m.get('W')
if m.get('X') is not None:
self.x = m.get('X')
if m.get('Y') is not None:
self.y = m.get('Y')
return self
class ImageModerationResponseBodyDataExtLogoDataLogo(TeaModel):
def __init__(
self,
confidence: float = None,
label: str = None,
name: str = None,
):
# The score of the confidence level. Valid values: 0 to 100. The value is accurate to two decimal places. Some labels do not have scores of confidence levels.
self.confidence = confidence
# Logo category.
self.label = label
# Logo name.
self.name = name
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.label is not None:
result['Label'] = self.label
if self.name is not None:
result['Name'] = self.name
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Label') is not None:
self.label = m.get('Label')
if m.get('Name') is not None:
self.name = m.get('Name')
return self
class ImageModerationResponseBodyDataExtLogoData(TeaModel):
def __init__(
self,
location: ImageModerationResponseBodyDataExtLogoDataLocation = None,
logo: List[ImageModerationResponseBodyDataExtLogoDataLogo] = None,
):
# Location information.
self.location = location
# Logo information.
self.logo = logo
def validate(self):
if self.location:
self.location.validate()
if self.logo:
for k in self.logo:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.location is not None:
result['Location'] = self.location.to_map()
result['Logo'] = []
if self.logo is not None:
for k in self.logo:
result['Logo'].append(k.to_map() if k else None)
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Location') is not None:
temp_model = ImageModerationResponseBodyDataExtLogoDataLocation()
self.location = temp_model.from_map(m['Location'])
self.logo = []
if m.get('Logo') is not None:
for k in m.get('Logo'):
temp_model = ImageModerationResponseBodyDataExtLogoDataLogo()
self.logo.append(temp_model.from_map(k))
return self
class ImageModerationResponseBodyDataExtOcrResultLocation(TeaModel):
def __init__(
self,
h: int = None,
w: int = None,
x: int = None,
y: int = None,
):
# The height of the text area, in pixels.
self.h = h
# The width of the text area, in pixels.
self.w = w
# The distance between the upper-left corner of the text area and the y-axis, using the upper-left corner of the image as the coordinate origin, in pixels.
self.x = x
# The distance between the upper left corner of the text area and the x-axis, with the upper left corner of the image as the coordinate origin, in pixels.
self.y = y
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.h is not None:
result['H'] = self.h
if self.w is not None:
result['W'] = self.w
if self.x is not None:
result['X'] = self.x
if self.y is not None:
result['Y'] = self.y
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('H') is not None:
self.h = m.get('H')
if m.get('W') is not None:
self.w = m.get('W')
if m.get('X') is not None:
self.x = m.get('X')
if m.get('Y') is not None:
self.y = m.get('Y')
return self
class ImageModerationResponseBodyDataExtOcrResult(TeaModel):
def __init__(
self,
location: ImageModerationResponseBodyDataExtOcrResultLocation = None,
text: str = None,
):
# Location information.
self.location = location
# The text information in the recognized image.
self.text = text
def validate(self):
if self.location:
self.location.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.location is not None:
result['Location'] = self.location.to_map()
if self.text is not None:
result['Text'] = self.text
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Location') is not None:
temp_model = ImageModerationResponseBodyDataExtOcrResultLocation()
self.location = temp_model.from_map(m['Location'])
if m.get('Text') is not None:
self.text = m.get('Text')
return self
class ImageModerationResponseBodyDataExtPublicFigureLocation(TeaModel):
def __init__(
self,
h: int = None,
w: int = None,
x: int = None,
y: int = None,
):
# The height
self.h = h
# The weight
self.w = w
# X coordinate
self.x = x
# Y coordinate
self.y = y
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.h is not None:
result['H'] = self.h
if self.w is not None:
result['W'] = self.w
if self.x is not None:
result['X'] = self.x
if self.y is not None:
result['Y'] = self.y
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('H') is not None:
self.h = m.get('H')
if m.get('W') is not None:
self.w = m.get('W')
if m.get('X') is not None:
self.x = m.get('X')
if m.get('Y') is not None:
self.y = m.get('Y')
return self
class ImageModerationResponseBodyDataExtPublicFigure(TeaModel):
def __init__(
self,
figure_id: str = None,
figure_name: str = None,
location: List[ImageModerationResponseBodyDataExtPublicFigureLocation] = None,
):
# Identified person coding information.
self.figure_id = figure_id
# Identified person name information.
self.figure_name = figure_name
# the data array of location info
self.location = location
def validate(self):
if self.location:
for k in self.location:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.figure_id is not None:
result['FigureId'] = self.figure_id
if self.figure_name is not None:
result['FigureName'] = self.figure_name
result['Location'] = []
if self.location is not None:
for k in self.location:
result['Location'].append(k.to_map() if k else None)
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('FigureId') is not None:
self.figure_id = m.get('FigureId')
if m.get('FigureName') is not None:
self.figure_name = m.get('FigureName')
self.location = []
if m.get('Location') is not None:
for k in m.get('Location'):
temp_model = ImageModerationResponseBodyDataExtPublicFigureLocation()
self.location.append(temp_model.from_map(k))
return self
class ImageModerationResponseBodyDataExtRecognition(TeaModel):
def __init__(
self,
classification: str = None,
confidence: float = None,
):
# The category of image recognition.
self.classification = classification
# The score of the confidence level. Valid values: 0 to 100. The value is accurate to two decimal places. Some labels do not have scores of confidence levels.
self.confidence = confidence
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.classification is not None:
result['Classification'] = self.classification
if self.confidence is not None:
result['Confidence'] = self.confidence
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Classification') is not None:
self.classification = m.get('Classification')
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
return self
class ImageModerationResponseBodyDataExtTextInImageCustomText(TeaModel):
def __init__(
self,
key_words: str = None,
lib_id: str = None,
lib_name: str = None,
):
# Custom words, multiple words separated by commas.
self.key_words = key_words
# Custom library ID.
self.lib_id = lib_id
# Custom library name.
self.lib_name = lib_name
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.key_words is not None:
result['KeyWords'] = self.key_words
if self.lib_id is not None:
result['LibId'] = self.lib_id
if self.lib_name is not None:
result['LibName'] = self.lib_name
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('KeyWords') is not None:
self.key_words = m.get('KeyWords')
if m.get('LibId') is not None:
self.lib_id = m.get('LibId')
if m.get('LibName') is not None:
self.lib_name = m.get('LibName')
return self
class ImageModerationResponseBodyDataExtTextInImageOcrResultLocation(TeaModel):
def __init__(
self,
h: int = None,
w: int = None,
x: int = None,
y: int = None,
):
# The height of the text area, in pixels.
self.h = h
# The width of the text area, in pixels.
self.w = w
# The distance between the upper-left corner of the text area and the y-axis, using the upper-left corner of the image as the coordinate origin, in pixels.
self.x = x
# The distance between the upper left corner of the text area and the x-axis, with the upper left corner of the image as the coordinate origin, in pixels.
self.y = y
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.h is not None:
result['H'] = self.h
if self.w is not None:
result['W'] = self.w
if self.x is not None:
result['X'] = self.x
if self.y is not None:
result['Y'] = self.y
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('H') is not None:
self.h = m.get('H')
if m.get('W') is not None:
self.w = m.get('W')
if m.get('X') is not None:
self.x = m.get('X')
if m.get('Y') is not None:
self.y = m.get('Y')
return self
class ImageModerationResponseBodyDataExtTextInImageOcrResult(TeaModel):
def __init__(
self,
location: ImageModerationResponseBodyDataExtTextInImageOcrResultLocation = None,
text: str = None,
):
# Location information.
self.location = location
# The text information in the recognized image.
self.text = text
def validate(self):
if self.location:
self.location.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.location is not None:
result['Location'] = self.location.to_map()
if self.text is not None:
result['Text'] = self.text
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Location') is not None:
temp_model = ImageModerationResponseBodyDataExtTextInImageOcrResultLocation()
self.location = temp_model.from_map(m['Location'])
if m.get('Text') is not None:
self.text = m.get('Text')
return self
class ImageModerationResponseBodyDataExtTextInImage(TeaModel):
def __init__(
self,
custom_text: List[ImageModerationResponseBodyDataExtTextInImageCustomText] = None,
ocr_result: List[ImageModerationResponseBodyDataExtTextInImageOcrResult] = None,
risk_word: List[str] = None,
):
# When a custom text library is hit, the custom library ID, custom library name, and custom word are returned.
self.custom_text = custom_text
# Returns the text information in the recognized image.
self.ocr_result = ocr_result
# The risk words that are hit. Multiple words are separated by commas (,).
self.risk_word = risk_word
def validate(self):
if self.custom_text:
for k in self.custom_text:
if k:
k.validate()
if self.ocr_result:
for k in self.ocr_result:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
result['CustomText'] = []
if self.custom_text is not None:
for k in self.custom_text:
result['CustomText'].append(k.to_map() if k else None)
result['OcrResult'] = []
if self.ocr_result is not None:
for k in self.ocr_result:
result['OcrResult'].append(k.to_map() if k else None)
if self.risk_word is not None:
result['RiskWord'] = self.risk_word
return result
def from_map(self, m: dict = None):
m = m or dict()
self.custom_text = []
if m.get('CustomText') is not None:
for k in m.get('CustomText'):
temp_model = ImageModerationResponseBodyDataExtTextInImageCustomText()
self.custom_text.append(temp_model.from_map(k))
self.ocr_result = []
if m.get('OcrResult') is not None:
for k in m.get('OcrResult'):
temp_model = ImageModerationResponseBodyDataExtTextInImageOcrResult()
self.ocr_result.append(temp_model.from_map(k))
if m.get('RiskWord') is not None:
self.risk_word = m.get('RiskWord')
return self
class ImageModerationResponseBodyDataExtVlContent(TeaModel):
def __init__(
self,
output_text: str = None,
):
# the vl output content
self.output_text = output_text
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.output_text is not None:
result['OutputText'] = self.output_text
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('OutputText') is not None:
self.output_text = m.get('OutputText')
return self
class ImageModerationResponseBodyDataExt(TeaModel):
def __init__(
self,
custom_image: List[ImageModerationResponseBodyDataExtCustomImage] = None,
face_data: List[ImageModerationResponseBodyDataExtFaceData] = None,
logo_data: List[ImageModerationResponseBodyDataExtLogoData] = None,
ocr_result: List[ImageModerationResponseBodyDataExtOcrResult] = None,
public_figure: List[ImageModerationResponseBodyDataExtPublicFigure] = None,
recognition: List[ImageModerationResponseBodyDataExtRecognition] = None,
text_in_image: ImageModerationResponseBodyDataExtTextInImage = None,
vl_content: ImageModerationResponseBodyDataExtVlContent = None,
):
# If a custom image library is hit, information about the hit custom image library is returned.
self.custom_image = custom_image
# The returned face attribute information
self.face_data = face_data
# Logo information.
self.logo_data = logo_data
# Returns the text information in the recognized image.
self.ocr_result = ocr_result
# Person information list.
self.public_figure = public_figure
# The result of image recognition.
self.recognition = recognition
# Returns the text information in the hit image.
self.text_in_image = text_in_image
# the vl output content
self.vl_content = vl_content
def validate(self):
if self.custom_image:
for k in self.custom_image:
if k:
k.validate()
if self.face_data:
for k in self.face_data:
if k:
k.validate()
if self.logo_data:
for k in self.logo_data:
if k:
k.validate()
if self.ocr_result:
for k in self.ocr_result:
if k:
k.validate()
if self.public_figure:
for k in self.public_figure:
if k:
k.validate()
if self.recognition:
for k in self.recognition:
if k:
k.validate()
if self.text_in_image:
self.text_in_image.validate()
if self.vl_content:
self.vl_content.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
result['CustomImage'] = []
if self.custom_image is not None:
for k in self.custom_image:
result['CustomImage'].append(k.to_map() if k else None)
result['FaceData'] = []
if self.face_data is not None:
for k in self.face_data:
result['FaceData'].append(k.to_map() if k else None)
result['LogoData'] = []
if self.logo_data is not None:
for k in self.logo_data:
result['LogoData'].append(k.to_map() if k else None)
result['OcrResult'] = []
if self.ocr_result is not None:
for k in self.ocr_result:
result['OcrResult'].append(k.to_map() if k else None)
result['PublicFigure'] = []
if self.public_figure is not None:
for k in self.public_figure:
result['PublicFigure'].append(k.to_map() if k else None)
result['Recognition'] = []
if self.recognition is not None:
for k in self.recognition:
result['Recognition'].append(k.to_map() if k else None)
if self.text_in_image is not None:
result['TextInImage'] = self.text_in_image.to_map()
if self.vl_content is not None:
result['VlContent'] = self.vl_content.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
self.custom_image = []
if m.get('CustomImage') is not None:
for k in m.get('CustomImage'):
temp_model = ImageModerationResponseBodyDataExtCustomImage()
self.custom_image.append(temp_model.from_map(k))
self.face_data = []
if m.get('FaceData') is not None:
for k in m.get('FaceData'):
temp_model = ImageModerationResponseBodyDataExtFaceData()
self.face_data.append(temp_model.from_map(k))
self.logo_data = []
if m.get('LogoData') is not None:
for k in m.get('LogoData'):
temp_model = ImageModerationResponseBodyDataExtLogoData()
self.logo_data.append(temp_model.from_map(k))
self.ocr_result = []
if m.get('OcrResult') is not None:
for k in m.get('OcrResult'):
temp_model = ImageModerationResponseBodyDataExtOcrResult()
self.ocr_result.append(temp_model.from_map(k))
self.public_figure = []
if m.get('PublicFigure') is not None:
for k in m.get('PublicFigure'):
temp_model = ImageModerationResponseBodyDataExtPublicFigure()
self.public_figure.append(temp_model.from_map(k))
self.recognition = []
if m.get('Recognition') is not None:
for k in m.get('Recognition'):
temp_model = ImageModerationResponseBodyDataExtRecognition()
self.recognition.append(temp_model.from_map(k))
if m.get('TextInImage') is not None:
temp_model = ImageModerationResponseBodyDataExtTextInImage()
self.text_in_image = temp_model.from_map(m['TextInImage'])
if m.get('VlContent') is not None:
temp_model = ImageModerationResponseBodyDataExtVlContent()
self.vl_content = temp_model.from_map(m['VlContent'])
return self
class ImageModerationResponseBodyDataResult(TeaModel):
def __init__(
self,
confidence: float = None,
description: str = None,
label: str = None,
risk_level: str = None,
):
# The score of the confidence level. Valid values: 0 to 100. The value is accurate to two decimal places. Some labels do not have scores of confidence levels.
self.confidence = confidence
# The description of the result.
self.description = description
# The labels returned after the image moderation. Multiple risk labels and the corresponding scores of confidence levels may be returned for an image.
self.label = label
self.risk_level = risk_level
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
return self
class ImageModerationResponseBodyData(TeaModel):
def __init__(
self,
data_id: str = None,
ext: ImageModerationResponseBodyDataExt = None,
result: List[ImageModerationResponseBodyDataResult] = None,
risk_level: str = None,
):
# The ID of the moderated object.
#
# > If you specify the dataId parameter in the request, the value of the dataId parameter is returned in the response.
self.data_id = data_id
# Auxiliary reference information.
self.ext = ext
# The results of image moderation parameters such as the label parameter and the confidence parameter, which are an array structure.
self.result = result
# Risk Level.
self.risk_level = risk_level
def validate(self):
if self.ext:
self.ext.validate()
if self.result:
for k in self.result:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.data_id is not None:
result['DataId'] = self.data_id
if self.ext is not None:
result['Ext'] = self.ext.to_map()
result['Result'] = []
if self.result is not None:
for k in self.result:
result['Result'].append(k.to_map() if k else None)
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
if m.get('Ext') is not None:
temp_model = ImageModerationResponseBodyDataExt()
self.ext = temp_model.from_map(m['Ext'])
self.result = []
if m.get('Result') is not None:
for k in m.get('Result'):
temp_model = ImageModerationResponseBodyDataResult()
self.result.append(temp_model.from_map(k))
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
return self
class ImageModerationResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: ImageModerationResponseBodyData = None,
msg: str = None,
request_id: str = None,
):
# The returned HTTP status code. The status code 200 indicates that the request was successful.
self.code = code
# The moderation results.
self.data = data
# The message that is returned in response to the request.
self.msg = msg
# The request ID, which is used to locate and troubleshoot issues.
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.msg is not None:
result['Msg'] = self.msg
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = ImageModerationResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Msg') is not None:
self.msg = m.get('Msg')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class ImageModerationResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: ImageModerationResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = ImageModerationResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class ManualCallbackRequest(TeaModel):
def __init__(
self,
channel: str = None,
checksum: str = None,
code: str = None,
data: str = None,
msg: str = None,
req_id: str = None,
):
self.channel = channel
self.checksum = checksum
self.code = code
self.data = data
self.msg = msg
self.req_id = req_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.channel is not None:
result['Channel'] = self.channel
if self.checksum is not None:
result['Checksum'] = self.checksum
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data
if self.msg is not None:
result['Msg'] = self.msg
if self.req_id is not None:
result['ReqId'] = self.req_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Channel') is not None:
self.channel = m.get('Channel')
if m.get('Checksum') is not None:
self.checksum = m.get('Checksum')
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
self.data = m.get('Data')
if m.get('Msg') is not None:
self.msg = m.get('Msg')
if m.get('ReqId') is not None:
self.req_id = m.get('ReqId')
return self
class ManualCallbackResponseBody(TeaModel):
def __init__(
self,
code: int = None,
message: str = None,
request_id: str = None,
):
self.code = code
self.message = message
# Id of the request
self.request_id = request_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.message is not None:
result['Message'] = self.message
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Message') is not None:
self.message = m.get('Message')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class ManualCallbackResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: ManualCallbackResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = ManualCallbackResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class ManualModerationRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
self.service = service
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class ManualModerationResponseBodyData(TeaModel):
def __init__(
self,
data_id: str = None,
task_id: str = None,
):
self.data_id = data_id
self.task_id = task_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.data_id is not None:
result['DataId'] = self.data_id
if self.task_id is not None:
result['TaskId'] = self.task_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
if m.get('TaskId') is not None:
self.task_id = m.get('TaskId')
return self
class ManualModerationResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: ManualModerationResponseBodyData = None,
message: str = None,
request_id: str = None,
):
self.code = code
self.data = data
self.message = message
# Id of the request
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.message is not None:
result['Message'] = self.message
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = ManualModerationResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Message') is not None:
self.message = m.get('Message')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class ManualModerationResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: ManualModerationResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = ManualModerationResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class ManualModerationResultRequest(TeaModel):
def __init__(
self,
service_parameters: str = None,
):
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class ManualModerationResultResponseBodyDataResult(TeaModel):
def __init__(
self,
description: str = None,
label: str = None,
):
self.description = description
self.label = label
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
return self
class ManualModerationResultResponseBodyData(TeaModel):
def __init__(
self,
data_id: str = None,
result: List[ManualModerationResultResponseBodyDataResult] = None,
risk_level: str = None,
task_id: str = None,
):
self.data_id = data_id
self.result = result
self.risk_level = risk_level
self.task_id = task_id
def validate(self):
if self.result:
for k in self.result:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.data_id is not None:
result['DataId'] = self.data_id
result['Result'] = []
if self.result is not None:
for k in self.result:
result['Result'].append(k.to_map() if k else None)
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
if self.task_id is not None:
result['TaskId'] = self.task_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
self.result = []
if m.get('Result') is not None:
for k in m.get('Result'):
temp_model = ManualModerationResultResponseBodyDataResult()
self.result.append(temp_model.from_map(k))
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
if m.get('TaskId') is not None:
self.task_id = m.get('TaskId')
return self
class ManualModerationResultResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: ManualModerationResultResponseBodyData = None,
message: str = None,
request_id: str = None,
):
self.code = code
self.data = data
self.message = message
# Id of the request
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.message is not None:
result['Message'] = self.message
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = ManualModerationResultResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Message') is not None:
self.message = m.get('Message')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class ManualModerationResultResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: ManualModerationResultResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = ManualModerationResultResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class TextModerationRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The type of the moderation service.
self.service = service
# The parameters required by the moderation service. The value is a JSON string.
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class TextModerationResponseBodyData(TeaModel):
def __init__(
self,
account_id: str = None,
data_id: str = None,
descriptions: str = None,
device_id: str = None,
labels: str = None,
reason: str = None,
):
# The ID of the Alibaba Cloud account.
self.account_id = account_id
self.data_id = data_id
self.descriptions = descriptions
# The device ID.
self.device_id = device_id
# Labels.
self.labels = labels
# The JSON string used to locate the cause.
self.reason = reason
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.account_id is not None:
result['accountId'] = self.account_id
if self.data_id is not None:
result['dataId'] = self.data_id
if self.descriptions is not None:
result['descriptions'] = self.descriptions
if self.device_id is not None:
result['deviceId'] = self.device_id
if self.labels is not None:
result['labels'] = self.labels
if self.reason is not None:
result['reason'] = self.reason
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('accountId') is not None:
self.account_id = m.get('accountId')
if m.get('dataId') is not None:
self.data_id = m.get('dataId')
if m.get('descriptions') is not None:
self.descriptions = m.get('descriptions')
if m.get('deviceId') is not None:
self.device_id = m.get('deviceId')
if m.get('labels') is not None:
self.labels = m.get('labels')
if m.get('reason') is not None:
self.reason = m.get('reason')
return self
class TextModerationResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: TextModerationResponseBodyData = None,
message: str = None,
request_id: str = None,
):
# The returned HTTP status code.
self.code = code
# The moderation results.
self.data = data
# The message that is returned in response to the request.
self.message = message
# The request ID.
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.message is not None:
result['Message'] = self.message
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = TextModerationResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Message') is not None:
self.message = m.get('Message')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class TextModerationResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: TextModerationResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = TextModerationResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class TextModerationPlusRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The moderation service.
#
# Valid values:
#
# * chat_detection_pro: moderation of interactive content of private chats_Professional
# * llm_response_moderation: moderation of text generated by LLMs
# * llm_query_moderation: moderation of input text of LLMs
# * nickname_detection_pro: moderation of user nicknames_Professional
# * comment_detection_pro: moderation of comment content of public chats_Professional
self.service = service
# The parameters required by the moderation service. The value is a JSON string.
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class TextModerationPlusResponseBodyDataAdvice(TeaModel):
def __init__(
self,
answer: str = None,
hit_label: str = None,
hit_lib_name: str = None,
):
# The answer.
self.answer = answer
# Hit Label
self.hit_label = hit_label
# Hit Library Name
self.hit_lib_name = hit_lib_name
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.answer is not None:
result['Answer'] = self.answer
if self.hit_label is not None:
result['HitLabel'] = self.hit_label
if self.hit_lib_name is not None:
result['HitLibName'] = self.hit_lib_name
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Answer') is not None:
self.answer = m.get('Answer')
if m.get('HitLabel') is not None:
self.hit_label = m.get('HitLabel')
if m.get('HitLibName') is not None:
self.hit_lib_name = m.get('HitLibName')
return self
class TextModerationPlusResponseBodyDataAttackResult(TeaModel):
def __init__(
self,
attack_level: str = None,
confidence: float = None,
description: str = None,
label: str = None,
):
# The level of prompt attack
self.attack_level = attack_level
# The confidence
self.confidence = confidence
# Description
self.description = description
# The label
self.label = label
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.attack_level is not None:
result['AttackLevel'] = self.attack_level
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('AttackLevel') is not None:
self.attack_level = m.get('AttackLevel')
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
return self
class TextModerationPlusResponseBodyDataResultCustomizedHit(TeaModel):
def __init__(
self,
key_words: str = None,
lib_name: str = None,
):
# The terms that are hit. Multiple terms are separated by commas (,).
self.key_words = key_words
# The library name.
self.lib_name = lib_name
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.key_words is not None:
result['KeyWords'] = self.key_words
if self.lib_name is not None:
result['LibName'] = self.lib_name
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('KeyWords') is not None:
self.key_words = m.get('KeyWords')
if m.get('LibName') is not None:
self.lib_name = m.get('LibName')
return self
class TextModerationPlusResponseBodyDataResult(TeaModel):
def __init__(
self,
confidence: float = None,
customized_hit: List[TextModerationPlusResponseBodyDataResultCustomizedHit] = None,
description: str = None,
label: str = None,
risk_words: str = None,
):
# The score of the confidence level. Valid values: 0 to 100. The value is accurate to two decimal places.
self.confidence = confidence
# The custom term hit by the moderated content.
self.customized_hit = customized_hit
# The description of the label.
self.description = description
# The label.
self.label = label
# The term hit by the moderated content.
self.risk_words = risk_words
def validate(self):
if self.customized_hit:
for k in self.customized_hit:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
result['CustomizedHit'] = []
if self.customized_hit is not None:
for k in self.customized_hit:
result['CustomizedHit'].append(k.to_map() if k else None)
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
if self.risk_words is not None:
result['RiskWords'] = self.risk_words
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
self.customized_hit = []
if m.get('CustomizedHit') is not None:
for k in m.get('CustomizedHit'):
temp_model = TextModerationPlusResponseBodyDataResultCustomizedHit()
self.customized_hit.append(temp_model.from_map(k))
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
if m.get('RiskWords') is not None:
self.risk_words = m.get('RiskWords')
return self
class TextModerationPlusResponseBodyDataSensitiveResult(TeaModel):
def __init__(
self,
description: str = None,
label: str = None,
sensitive_data: List[str] = None,
sensitive_level: str = None,
):
# Description
self.description = description
# The label
self.label = label
# The sensitive data.
self.sensitive_data = sensitive_data
# The level of sensitivity data
self.sensitive_level = sensitive_level
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
if self.sensitive_data is not None:
result['SensitiveData'] = self.sensitive_data
if self.sensitive_level is not None:
result['SensitiveLevel'] = self.sensitive_level
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
if m.get('SensitiveData') is not None:
self.sensitive_data = m.get('SensitiveData')
if m.get('SensitiveLevel') is not None:
self.sensitive_level = m.get('SensitiveLevel')
return self
class TextModerationPlusResponseBodyData(TeaModel):
def __init__(
self,
advice: List[TextModerationPlusResponseBodyDataAdvice] = None,
attack_level: str = None,
attack_result: List[TextModerationPlusResponseBodyDataAttackResult] = None,
data_id: str = None,
result: List[TextModerationPlusResponseBodyDataResult] = None,
risk_level: str = None,
score: float = None,
sensitive_level: str = None,
sensitive_result: List[TextModerationPlusResponseBodyDataSensitiveResult] = None,
):
# The suggestion.
self.advice = advice
# The level of prompt attack
self.attack_level = attack_level
# The result of prompt attack detect
self.attack_result = attack_result
# The id of data
self.data_id = data_id
# The results.
self.result = result
# Risk Level
self.risk_level = risk_level
# The score.
self.score = score
# The level of sensitivity data
self.sensitive_level = sensitive_level
# The result of sensitivity data detect
self.sensitive_result = sensitive_result
def validate(self):
if self.advice:
for k in self.advice:
if k:
k.validate()
if self.attack_result:
for k in self.attack_result:
if k:
k.validate()
if self.result:
for k in self.result:
if k:
k.validate()
if self.sensitive_result:
for k in self.sensitive_result:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
result['Advice'] = []
if self.advice is not None:
for k in self.advice:
result['Advice'].append(k.to_map() if k else None)
if self.attack_level is not None:
result['AttackLevel'] = self.attack_level
result['AttackResult'] = []
if self.attack_result is not None:
for k in self.attack_result:
result['AttackResult'].append(k.to_map() if k else None)
if self.data_id is not None:
result['DataId'] = self.data_id
result['Result'] = []
if self.result is not None:
for k in self.result:
result['Result'].append(k.to_map() if k else None)
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
if self.score is not None:
result['Score'] = self.score
if self.sensitive_level is not None:
result['SensitiveLevel'] = self.sensitive_level
result['SensitiveResult'] = []
if self.sensitive_result is not None:
for k in self.sensitive_result:
result['SensitiveResult'].append(k.to_map() if k else None)
return result
def from_map(self, m: dict = None):
m = m or dict()
self.advice = []
if m.get('Advice') is not None:
for k in m.get('Advice'):
temp_model = TextModerationPlusResponseBodyDataAdvice()
self.advice.append(temp_model.from_map(k))
if m.get('AttackLevel') is not None:
self.attack_level = m.get('AttackLevel')
self.attack_result = []
if m.get('AttackResult') is not None:
for k in m.get('AttackResult'):
temp_model = TextModerationPlusResponseBodyDataAttackResult()
self.attack_result.append(temp_model.from_map(k))
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
self.result = []
if m.get('Result') is not None:
for k in m.get('Result'):
temp_model = TextModerationPlusResponseBodyDataResult()
self.result.append(temp_model.from_map(k))
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
if m.get('Score') is not None:
self.score = m.get('Score')
if m.get('SensitiveLevel') is not None:
self.sensitive_level = m.get('SensitiveLevel')
self.sensitive_result = []
if m.get('SensitiveResult') is not None:
for k in m.get('SensitiveResult'):
temp_model = TextModerationPlusResponseBodyDataSensitiveResult()
self.sensitive_result.append(temp_model.from_map(k))
return self
class TextModerationPlusResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: TextModerationPlusResponseBodyData = None,
message: str = None,
request_id: str = None,
):
# The returned HTTP status code. The status code 200 indicates that the request was successful.
self.code = code
# The moderation results.
self.data = data
# The message that is returned in response to the request.
self.message = message
# Id of the request
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.message is not None:
result['Message'] = self.message
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = TextModerationPlusResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Message') is not None:
self.message = m.get('Message')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class TextModerationPlusResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: TextModerationPlusResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = TextModerationPlusResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class UrlAsyncModerationRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The type of the moderation service.
self.service = service
# The parameters required by the moderation service. The value is a JSON string.
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class UrlAsyncModerationResponseBodyData(TeaModel):
def __init__(
self,
data_id: str = None,
req_id: str = None,
):
# The ID of the moderated object.
self.data_id = data_id
# The reqId field returned by the Url Async Moderation API.
self.req_id = req_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.data_id is not None:
result['DataId'] = self.data_id
if self.req_id is not None:
result['ReqId'] = self.req_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
if m.get('ReqId') is not None:
self.req_id = m.get('ReqId')
return self
class UrlAsyncModerationResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: UrlAsyncModerationResponseBodyData = None,
msg: str = None,
request_id: str = None,
):
# The returned HTTP status code.
self.code = code
# The data returned.
self.data = data
# The message that is returned in response to the request.
self.msg = msg
# Id of the request
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.msg is not None:
result['Msg'] = self.msg
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = UrlAsyncModerationResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Msg') is not None:
self.msg = m.get('Msg')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class UrlAsyncModerationResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: UrlAsyncModerationResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = UrlAsyncModerationResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class VideoModerationRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The type of the moderation service.
self.service = service
# The parameters required by the moderation service. The value is a JSON string.
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class VideoModerationResponseBodyData(TeaModel):
def __init__(
self,
data_id: str = None,
task_id: str = None,
):
# The ID of the moderated object.
self.data_id = data_id
# The task ID.
self.task_id = task_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.data_id is not None:
result['DataId'] = self.data_id
if self.task_id is not None:
result['TaskId'] = self.task_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
if m.get('TaskId') is not None:
self.task_id = m.get('TaskId')
return self
class VideoModerationResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: VideoModerationResponseBodyData = None,
message: str = None,
request_id: str = None,
):
# The returned HTTP status code.
self.code = code
# The data returned.
self.data = data
# The message that is returned in response to the request.
self.message = message
# The request ID.
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.message is not None:
result['Message'] = self.message
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = VideoModerationResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Message') is not None:
self.message = m.get('Message')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class VideoModerationResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: VideoModerationResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = VideoModerationResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class VideoModerationCancelRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The type of the moderation service.
self.service = service
# The parameters required by the moderation service. The value is a JSON string.
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class VideoModerationCancelResponseBody(TeaModel):
def __init__(
self,
code: int = None,
message: str = None,
request_id: str = None,
):
# The returned HTTP status code.
self.code = code
# The message that is returned in response to the request.
self.message = message
# The request ID.
self.request_id = request_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.message is not None:
result['Message'] = self.message
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Message') is not None:
self.message = m.get('Message')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class VideoModerationCancelResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: VideoModerationCancelResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = VideoModerationCancelResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class VideoModerationResultRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The type of the moderation service.
#
# Valid values:
#
# * liveStreamDetection: live stream moderation
# * videoDetection: video file moderation
# * liveStreamDetection_cb: live stream moderation_For regions outside the Chinese mainland
# * videoDetection_cb: video file moderation_For regions outside the Chinese mainland.
self.service = service
# The parameters required by the moderation service. The ID of the task that you want to query. You can specify one task ID at a time.
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class VideoModerationResultResponseBodyDataAudioResultAudioSummarys(TeaModel):
def __init__(
self,
description: str = None,
label: str = None,
label_sum: int = None,
):
self.description = description
# The voice label.
self.label = label
# The number of times that the label is matched.
self.label_sum = label_sum
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
if self.label_sum is not None:
result['LabelSum'] = self.label_sum
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
if m.get('LabelSum') is not None:
self.label_sum = m.get('LabelSum')
return self
class VideoModerationResultResponseBodyDataAudioResultSliceDetails(TeaModel):
def __init__(
self,
descriptions: str = None,
end_time: int = None,
end_timestamp: int = None,
extend: str = None,
labels: str = None,
risk_level: str = None,
risk_tips: str = None,
risk_words: str = None,
score: float = None,
start_time: int = None,
start_timestamp: int = None,
text: str = None,
url: str = None,
):
self.descriptions = descriptions
# The end time of the text after voice-to-text conversion. Unit: seconds.
self.end_time = end_time
# The end timestamp of the segment. Unit: milliseconds.
self.end_timestamp = end_timestamp
# A reserved parameter.
self.extend = extend
# The details of the labels.
self.labels = labels
# Risk Level.
self.risk_level = risk_level
# Subcategory labels. Multiple labels are separated by commas (,).
self.risk_tips = risk_tips
# The risk words that are hit. Multiple words are separated by commas (,).
self.risk_words = risk_words
# The risk score. Default range: 0 to 99.
self.score = score
# The start time of the text after voice-to-text conversion. Unit: seconds.
self.start_time = start_time
# The start timestamp of the segment. Unit: milliseconds.
self.start_timestamp = start_timestamp
# The text converted from voice.
self.text = text
# If the moderation object is a voice stream, this parameter indicates the temporary access URL of the voice stream to which the text entry corresponds. The validity period of the URL is 30 minutes. You must prepare another URL to store the voice stream at the earliest opportunity.
self.url = url
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.descriptions is not None:
result['Descriptions'] = self.descriptions
if self.end_time is not None:
result['EndTime'] = self.end_time
if self.end_timestamp is not None:
result['EndTimestamp'] = self.end_timestamp
if self.extend is not None:
result['Extend'] = self.extend
if self.labels is not None:
result['Labels'] = self.labels
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
if self.risk_tips is not None:
result['RiskTips'] = self.risk_tips
if self.risk_words is not None:
result['RiskWords'] = self.risk_words
if self.score is not None:
result['Score'] = self.score
if self.start_time is not None:
result['StartTime'] = self.start_time
if self.start_timestamp is not None:
result['StartTimestamp'] = self.start_timestamp
if self.text is not None:
result['Text'] = self.text
if self.url is not None:
result['Url'] = self.url
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Descriptions') is not None:
self.descriptions = m.get('Descriptions')
if m.get('EndTime') is not None:
self.end_time = m.get('EndTime')
if m.get('EndTimestamp') is not None:
self.end_timestamp = m.get('EndTimestamp')
if m.get('Extend') is not None:
self.extend = m.get('Extend')
if m.get('Labels') is not None:
self.labels = m.get('Labels')
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
if m.get('RiskTips') is not None:
self.risk_tips = m.get('RiskTips')
if m.get('RiskWords') is not None:
self.risk_words = m.get('RiskWords')
if m.get('Score') is not None:
self.score = m.get('Score')
if m.get('StartTime') is not None:
self.start_time = m.get('StartTime')
if m.get('StartTimestamp') is not None:
self.start_timestamp = m.get('StartTimestamp')
if m.get('Text') is not None:
self.text = m.get('Text')
if m.get('Url') is not None:
self.url = m.get('Url')
return self
class VideoModerationResultResponseBodyDataAudioResult(TeaModel):
def __init__(
self,
audio_summarys: List[VideoModerationResultResponseBodyDataAudioResultAudioSummarys] = None,
risk_level: str = None,
slice_details: List[VideoModerationResultResponseBodyDataAudioResultSliceDetails] = None,
):
# Summary of voice labels.
self.audio_summarys = audio_summarys
# Risk Level.
self.risk_level = risk_level
# The details about the text in the moderated voice. The value is a JSON array that contains one or more elements. Each element corresponds to a text entry.
self.slice_details = slice_details
def validate(self):
if self.audio_summarys:
for k in self.audio_summarys:
if k:
k.validate()
if self.slice_details:
for k in self.slice_details:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
result['AudioSummarys'] = []
if self.audio_summarys is not None:
for k in self.audio_summarys:
result['AudioSummarys'].append(k.to_map() if k else None)
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
result['SliceDetails'] = []
if self.slice_details is not None:
for k in self.slice_details:
result['SliceDetails'].append(k.to_map() if k else None)
return result
def from_map(self, m: dict = None):
m = m or dict()
self.audio_summarys = []
if m.get('AudioSummarys') is not None:
for k in m.get('AudioSummarys'):
temp_model = VideoModerationResultResponseBodyDataAudioResultAudioSummarys()
self.audio_summarys.append(temp_model.from_map(k))
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
self.slice_details = []
if m.get('SliceDetails') is not None:
for k in m.get('SliceDetails'):
temp_model = VideoModerationResultResponseBodyDataAudioResultSliceDetails()
self.slice_details.append(temp_model.from_map(k))
return self
class VideoModerationResultResponseBodyDataFrameResultFrameSummarys(TeaModel):
def __init__(
self,
description: str = None,
label: str = None,
label_sum: int = None,
):
# The description of the result.
self.description = description
# The label against which a captured frame is matched.
self.label = label
# The number of times that the label is matched.
self.label_sum = label_sum
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
if self.label_sum is not None:
result['LabelSum'] = self.label_sum
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
if m.get('LabelSum') is not None:
self.label_sum = m.get('LabelSum')
return self
class VideoModerationResultResponseBodyDataFrameResultFramesResultsCustomImage(TeaModel):
def __init__(
self,
image_id: str = None,
lib_id: str = None,
):
# The ID of the custom image that is hit.
self.image_id = image_id
# The ID of the custom image library that is hit.
self.lib_id = lib_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.image_id is not None:
result['ImageId'] = self.image_id
if self.lib_id is not None:
result['LibId'] = self.lib_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('ImageId') is not None:
self.image_id = m.get('ImageId')
if m.get('LibId') is not None:
self.lib_id = m.get('LibId')
return self
class VideoModerationResultResponseBodyDataFrameResultFramesResultsLogoDataLocation(TeaModel):
def __init__(
self,
h: int = None,
w: int = None,
x: int = None,
y: int = None,
):
self.h = h
self.w = w
self.x = x
self.y = y
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.h is not None:
result['H'] = self.h
if self.w is not None:
result['W'] = self.w
if self.x is not None:
result['X'] = self.x
if self.y is not None:
result['Y'] = self.y
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('H') is not None:
self.h = m.get('H')
if m.get('W') is not None:
self.w = m.get('W')
if m.get('X') is not None:
self.x = m.get('X')
if m.get('Y') is not None:
self.y = m.get('Y')
return self
class VideoModerationResultResponseBodyDataFrameResultFramesResultsLogoDataLogo(TeaModel):
def __init__(
self,
confidence: int = None,
label: str = None,
name: str = None,
):
self.confidence = confidence
self.label = label
self.name = name
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['confidence'] = self.confidence
if self.label is not None:
result['label'] = self.label
if self.name is not None:
result['name'] = self.name
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('confidence') is not None:
self.confidence = m.get('confidence')
if m.get('label') is not None:
self.label = m.get('label')
if m.get('name') is not None:
self.name = m.get('name')
return self
class VideoModerationResultResponseBodyDataFrameResultFramesResultsLogoData(TeaModel):
def __init__(
self,
location: VideoModerationResultResponseBodyDataFrameResultFramesResultsLogoDataLocation = None,
logo: List[VideoModerationResultResponseBodyDataFrameResultFramesResultsLogoDataLogo] = None,
):
self.location = location
self.logo = logo
def validate(self):
if self.location:
self.location.validate()
if self.logo:
for k in self.logo:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.location is not None:
result['Location'] = self.location.to_map()
result['Logo'] = []
if self.logo is not None:
for k in self.logo:
result['Logo'].append(k.to_map() if k else None)
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Location') is not None:
temp_model = VideoModerationResultResponseBodyDataFrameResultFramesResultsLogoDataLocation()
self.location = temp_model.from_map(m['Location'])
self.logo = []
if m.get('Logo') is not None:
for k in m.get('Logo'):
temp_model = VideoModerationResultResponseBodyDataFrameResultFramesResultsLogoDataLogo()
self.logo.append(temp_model.from_map(k))
return self
class VideoModerationResultResponseBodyDataFrameResultFramesResultsPublicFigure(TeaModel):
def __init__(
self,
figure_id: str = None,
):
# The information about the code of the identified figure.
self.figure_id = figure_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.figure_id is not None:
result['FigureId'] = self.figure_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('FigureId') is not None:
self.figure_id = m.get('FigureId')
return self
class VideoModerationResultResponseBodyDataFrameResultFramesResultsResult(TeaModel):
def __init__(
self,
confidence: float = None,
description: str = None,
label: str = None,
):
# The score of the confidence level. Valid values: 0 to 100. The value is accurate to two decimal places.
self.confidence = confidence
# The description of the result.
self.description = description
# The label returned after a frame is moderated. Multiple risk labels and the corresponding scores of confidence levels may be returned for a frame.
self.label = label
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.confidence is not None:
result['Confidence'] = self.confidence
if self.description is not None:
result['Description'] = self.description
if self.label is not None:
result['Label'] = self.label
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Confidence') is not None:
self.confidence = m.get('Confidence')
if m.get('Description') is not None:
self.description = m.get('Description')
if m.get('Label') is not None:
self.label = m.get('Label')
return self
class VideoModerationResultResponseBodyDataFrameResultFramesResults(TeaModel):
def __init__(
self,
custom_image: List[VideoModerationResultResponseBodyDataFrameResultFramesResultsCustomImage] = None,
logo_data: List[VideoModerationResultResponseBodyDataFrameResultFramesResultsLogoData] = None,
public_figure: List[VideoModerationResultResponseBodyDataFrameResultFramesResultsPublicFigure] = None,
result: List[VideoModerationResultResponseBodyDataFrameResultFramesResultsResult] = None,
service: str = None,
text_in_image: Dict[str, Any] = None,
):
# If a custom image library is hit, information about the custom image library is returned.
self.custom_image = custom_image
self.logo_data = logo_data
# If the video contains a specific figure, the code of the identified figure is returned.
self.public_figure = public_figure
# The results of frame moderation parameters such as the label parameter and the confidence parameter.
self.result = result
# The moderation service that is called.
self.service = service
# The information about the text hit in the image is returned.
self.text_in_image = text_in_image
def validate(self):
if self.custom_image:
for k in self.custom_image:
if k:
k.validate()
if self.logo_data:
for k in self.logo_data:
if k:
k.validate()
if self.public_figure:
for k in self.public_figure:
if k:
k.validate()
if self.result:
for k in self.result:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
result['CustomImage'] = []
if self.custom_image is not None:
for k in self.custom_image:
result['CustomImage'].append(k.to_map() if k else None)
result['LogoData'] = []
if self.logo_data is not None:
for k in self.logo_data:
result['LogoData'].append(k.to_map() if k else None)
result['PublicFigure'] = []
if self.public_figure is not None:
for k in self.public_figure:
result['PublicFigure'].append(k.to_map() if k else None)
result['Result'] = []
if self.result is not None:
for k in self.result:
result['Result'].append(k.to_map() if k else None)
if self.service is not None:
result['Service'] = self.service
if self.text_in_image is not None:
result['TextInImage'] = self.text_in_image
return result
def from_map(self, m: dict = None):
m = m or dict()
self.custom_image = []
if m.get('CustomImage') is not None:
for k in m.get('CustomImage'):
temp_model = VideoModerationResultResponseBodyDataFrameResultFramesResultsCustomImage()
self.custom_image.append(temp_model.from_map(k))
self.logo_data = []
if m.get('LogoData') is not None:
for k in m.get('LogoData'):
temp_model = VideoModerationResultResponseBodyDataFrameResultFramesResultsLogoData()
self.logo_data.append(temp_model.from_map(k))
self.public_figure = []
if m.get('PublicFigure') is not None:
for k in m.get('PublicFigure'):
temp_model = VideoModerationResultResponseBodyDataFrameResultFramesResultsPublicFigure()
self.public_figure.append(temp_model.from_map(k))
self.result = []
if m.get('Result') is not None:
for k in m.get('Result'):
temp_model = VideoModerationResultResponseBodyDataFrameResultFramesResultsResult()
self.result.append(temp_model.from_map(k))
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('TextInImage') is not None:
self.text_in_image = m.get('TextInImage')
return self
class VideoModerationResultResponseBodyDataFrameResultFrames(TeaModel):
def __init__(
self,
offset: float = None,
results: List[VideoModerationResultResponseBodyDataFrameResultFramesResults] = None,
risk_level: str = None,
temp_url: str = None,
timestamp: int = None,
):
# The interval between the start of the video file and the captured frame. Unit: seconds.
self.offset = offset
# The results of frame moderation parameters such as the label parameter and the confidence parameter.
self.results = results
# Risk Level.
self.risk_level = risk_level
# The temporary URL of a captured frame.
self.temp_url = temp_url
# The absolute timestamp. Unit: milliseconds.
self.timestamp = timestamp
def validate(self):
if self.results:
for k in self.results:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.offset is not None:
result['Offset'] = self.offset
result['Results'] = []
if self.results is not None:
for k in self.results:
result['Results'].append(k.to_map() if k else None)
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
if self.temp_url is not None:
result['TempUrl'] = self.temp_url
if self.timestamp is not None:
result['Timestamp'] = self.timestamp
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Offset') is not None:
self.offset = m.get('Offset')
self.results = []
if m.get('Results') is not None:
for k in m.get('Results'):
temp_model = VideoModerationResultResponseBodyDataFrameResultFramesResults()
self.results.append(temp_model.from_map(k))
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
if m.get('TempUrl') is not None:
self.temp_url = m.get('TempUrl')
if m.get('Timestamp') is not None:
self.timestamp = m.get('Timestamp')
return self
class VideoModerationResultResponseBodyDataFrameResult(TeaModel):
def __init__(
self,
frame_num: int = None,
frame_summarys: List[VideoModerationResultResponseBodyDataFrameResultFrameSummarys] = None,
frames: List[VideoModerationResultResponseBodyDataFrameResultFrames] = None,
risk_level: str = None,
):
# The number of captured frames that are returned for the video file.
self.frame_num = frame_num
# The summary of the labels against which captured frames are matched.
self.frame_summarys = frame_summarys
# The information about the frames that match the labels.
self.frames = frames
# Risk Level.
self.risk_level = risk_level
def validate(self):
if self.frame_summarys:
for k in self.frame_summarys:
if k:
k.validate()
if self.frames:
for k in self.frames:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.frame_num is not None:
result['FrameNum'] = self.frame_num
result['FrameSummarys'] = []
if self.frame_summarys is not None:
for k in self.frame_summarys:
result['FrameSummarys'].append(k.to_map() if k else None)
result['Frames'] = []
if self.frames is not None:
for k in self.frames:
result['Frames'].append(k.to_map() if k else None)
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('FrameNum') is not None:
self.frame_num = m.get('FrameNum')
self.frame_summarys = []
if m.get('FrameSummarys') is not None:
for k in m.get('FrameSummarys'):
temp_model = VideoModerationResultResponseBodyDataFrameResultFrameSummarys()
self.frame_summarys.append(temp_model.from_map(k))
self.frames = []
if m.get('Frames') is not None:
for k in m.get('Frames'):
temp_model = VideoModerationResultResponseBodyDataFrameResultFrames()
self.frames.append(temp_model.from_map(k))
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
return self
class VideoModerationResultResponseBodyData(TeaModel):
def __init__(
self,
audio_result: VideoModerationResultResponseBodyDataAudioResult = None,
data_id: str = None,
frame_result: VideoModerationResultResponseBodyDataFrameResult = None,
live_id: str = None,
risk_level: str = None,
task_id: str = None,
):
# The voice moderation results. The moderation results contain a structure.
self.audio_result = audio_result
# The value of dataId that is specified in the API request. If this parameter is not specified in the API request, the dataId field is not available in the response.
self.data_id = data_id
# The image moderation results. If the call is successful, the HTTP status code 200 and moderation results are returned. The moderation results contain a structure.
self.frame_result = frame_result
# The unique ID of the live stream.
self.live_id = live_id
# Risk Level.
self.risk_level = risk_level
# The task ID.
self.task_id = task_id
def validate(self):
if self.audio_result:
self.audio_result.validate()
if self.frame_result:
self.frame_result.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.audio_result is not None:
result['AudioResult'] = self.audio_result.to_map()
if self.data_id is not None:
result['DataId'] = self.data_id
if self.frame_result is not None:
result['FrameResult'] = self.frame_result.to_map()
if self.live_id is not None:
result['LiveId'] = self.live_id
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
if self.task_id is not None:
result['TaskId'] = self.task_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('AudioResult') is not None:
temp_model = VideoModerationResultResponseBodyDataAudioResult()
self.audio_result = temp_model.from_map(m['AudioResult'])
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
if m.get('FrameResult') is not None:
temp_model = VideoModerationResultResponseBodyDataFrameResult()
self.frame_result = temp_model.from_map(m['FrameResult'])
if m.get('LiveId') is not None:
self.live_id = m.get('LiveId')
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
if m.get('TaskId') is not None:
self.task_id = m.get('TaskId')
return self
class VideoModerationResultResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: VideoModerationResultResponseBodyData = None,
message: str = None,
request_id: str = None,
):
# The returned HTTP status code. The status code 200 indicates that the request was successful.
self.code = code
# The moderation results.
self.data = data
# The message that is returned in response to the request.
self.message = message
# Id of the request
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.message is not None:
result['Message'] = self.message
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = VideoModerationResultResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Message') is not None:
self.message = m.get('Message')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class VideoModerationResultResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: VideoModerationResultResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = VideoModerationResultResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class VoiceModerationRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The type of the moderation service.
self.service = service
# The parameters required by the moderation service. The value is a JSON string.
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class VoiceModerationResponseBodyData(TeaModel):
def __init__(
self,
data_id: str = None,
task_id: str = None,
):
# The ID of the moderated object.
self.data_id = data_id
# The task ID.
self.task_id = task_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.data_id is not None:
result['DataId'] = self.data_id
if self.task_id is not None:
result['TaskId'] = self.task_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
if m.get('TaskId') is not None:
self.task_id = m.get('TaskId')
return self
class VoiceModerationResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: VoiceModerationResponseBodyData = None,
message: str = None,
request_id: str = None,
):
# The returned HTTP status code.
self.code = code
# The data returned.
self.data = data
# The message that is returned in response to the request.
self.message = message
# The request ID.
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.message is not None:
result['Message'] = self.message
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = VoiceModerationResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Message') is not None:
self.message = m.get('Message')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class VoiceModerationResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: VoiceModerationResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = VoiceModerationResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class VoiceModerationCancelRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The type of the moderation service.
self.service = service
# The parameters required by the moderation service. The value is a JSON string.
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class VoiceModerationCancelResponseBody(TeaModel):
def __init__(
self,
code: int = None,
message: str = None,
request_id: str = None,
):
# The returned HTTP status code.
self.code = code
# The message that is returned in response to the request.
self.message = message
# The request ID.
self.request_id = request_id
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.message is not None:
result['Message'] = self.message
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Message') is not None:
self.message = m.get('Message')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class VoiceModerationCancelResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: VoiceModerationCancelResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = VoiceModerationCancelResponseBody()
self.body = temp_model.from_map(m['body'])
return self
class VoiceModerationResultRequest(TeaModel):
def __init__(
self,
service: str = None,
service_parameters: str = None,
):
# The type of the moderation service. Valid values: nickname_detection: user nickname
self.service = service
# The parameters of API requests that are sent from API Gateway to the backend service.
#
# For more information, see [ServiceParameter](https://help.aliyun.com/document_detail/43988.html).
self.service_parameters = service_parameters
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.service is not None:
result['Service'] = self.service
if self.service_parameters is not None:
result['ServiceParameters'] = self.service_parameters
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Service') is not None:
self.service = m.get('Service')
if m.get('ServiceParameters') is not None:
self.service_parameters = m.get('ServiceParameters')
return self
class VoiceModerationResultResponseBodyDataSliceDetails(TeaModel):
def __init__(
self,
descriptions: str = None,
end_time: int = None,
end_timestamp: int = None,
extend: str = None,
labels: str = None,
origin_algo_result: Dict[str, Any] = None,
risk_level: str = None,
risk_tips: str = None,
risk_words: str = None,
score: float = None,
start_time: int = None,
start_timestamp: int = None,
text: str = None,
url: str = None,
):
self.descriptions = descriptions
# The end time of the audio segment in seconds.
self.end_time = end_time
# The end timestamp of the segment. Unit: milliseconds.
self.end_timestamp = end_timestamp
# Extended fields.
self.extend = extend
# The details of the labels.
self.labels = labels
# Reserved parameter.
self.origin_algo_result = origin_algo_result
# Risk Level.
self.risk_level = risk_level
# The details of the risky content.
self.risk_tips = risk_tips
# The term hit by the risky content.
self.risk_words = risk_words
# The risk score. Default range: 0 to 99.
self.score = score
# The start time of the audio segment in seconds.
self.start_time = start_time
# The start timestamp of the segment. Unit: milliseconds.
self.start_timestamp = start_timestamp
# The text converted from the audio segment.
self.text = text
# The temporary URL of the audio segment.
self.url = url
def validate(self):
pass
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.descriptions is not None:
result['Descriptions'] = self.descriptions
if self.end_time is not None:
result['EndTime'] = self.end_time
if self.end_timestamp is not None:
result['EndTimestamp'] = self.end_timestamp
if self.extend is not None:
result['Extend'] = self.extend
if self.labels is not None:
result['Labels'] = self.labels
if self.origin_algo_result is not None:
result['OriginAlgoResult'] = self.origin_algo_result
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
if self.risk_tips is not None:
result['RiskTips'] = self.risk_tips
if self.risk_words is not None:
result['RiskWords'] = self.risk_words
if self.score is not None:
result['Score'] = self.score
if self.start_time is not None:
result['StartTime'] = self.start_time
if self.start_timestamp is not None:
result['StartTimestamp'] = self.start_timestamp
if self.text is not None:
result['Text'] = self.text
if self.url is not None:
result['Url'] = self.url
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Descriptions') is not None:
self.descriptions = m.get('Descriptions')
if m.get('EndTime') is not None:
self.end_time = m.get('EndTime')
if m.get('EndTimestamp') is not None:
self.end_timestamp = m.get('EndTimestamp')
if m.get('Extend') is not None:
self.extend = m.get('Extend')
if m.get('Labels') is not None:
self.labels = m.get('Labels')
if m.get('OriginAlgoResult') is not None:
self.origin_algo_result = m.get('OriginAlgoResult')
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
if m.get('RiskTips') is not None:
self.risk_tips = m.get('RiskTips')
if m.get('RiskWords') is not None:
self.risk_words = m.get('RiskWords')
if m.get('Score') is not None:
self.score = m.get('Score')
if m.get('StartTime') is not None:
self.start_time = m.get('StartTime')
if m.get('StartTimestamp') is not None:
self.start_timestamp = m.get('StartTimestamp')
if m.get('Text') is not None:
self.text = m.get('Text')
if m.get('Url') is not None:
self.url = m.get('Url')
return self
class VoiceModerationResultResponseBodyData(TeaModel):
def __init__(
self,
data_id: str = None,
live_id: str = None,
risk_level: str = None,
slice_details: List[VoiceModerationResultResponseBodyDataSliceDetails] = None,
task_id: str = None,
url: str = None,
):
# The ID of the moderated object.
self.data_id = data_id
# The unique ID of the live stream.
self.live_id = live_id
# Risk Level.
self.risk_level = risk_level
# The moderation results of audio segments.
self.slice_details = slice_details
# The task ID.
self.task_id = task_id
# The URL of the moderated content.
self.url = url
def validate(self):
if self.slice_details:
for k in self.slice_details:
if k:
k.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.data_id is not None:
result['DataId'] = self.data_id
if self.live_id is not None:
result['LiveId'] = self.live_id
if self.risk_level is not None:
result['RiskLevel'] = self.risk_level
result['SliceDetails'] = []
if self.slice_details is not None:
for k in self.slice_details:
result['SliceDetails'].append(k.to_map() if k else None)
if self.task_id is not None:
result['TaskId'] = self.task_id
if self.url is not None:
result['Url'] = self.url
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('DataId') is not None:
self.data_id = m.get('DataId')
if m.get('LiveId') is not None:
self.live_id = m.get('LiveId')
if m.get('RiskLevel') is not None:
self.risk_level = m.get('RiskLevel')
self.slice_details = []
if m.get('SliceDetails') is not None:
for k in m.get('SliceDetails'):
temp_model = VoiceModerationResultResponseBodyDataSliceDetails()
self.slice_details.append(temp_model.from_map(k))
if m.get('TaskId') is not None:
self.task_id = m.get('TaskId')
if m.get('Url') is not None:
self.url = m.get('Url')
return self
class VoiceModerationResultResponseBody(TeaModel):
def __init__(
self,
code: int = None,
data: VoiceModerationResultResponseBodyData = None,
message: str = None,
request_id: str = None,
):
# The returned HTTP status code.
self.code = code
# The data returned.
self.data = data
# The message that is returned in response to the request.
self.message = message
# Id of the request
self.request_id = request_id
def validate(self):
if self.data:
self.data.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.code is not None:
result['Code'] = self.code
if self.data is not None:
result['Data'] = self.data.to_map()
if self.message is not None:
result['Message'] = self.message
if self.request_id is not None:
result['RequestId'] = self.request_id
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('Code') is not None:
self.code = m.get('Code')
if m.get('Data') is not None:
temp_model = VoiceModerationResultResponseBodyData()
self.data = temp_model.from_map(m['Data'])
if m.get('Message') is not None:
self.message = m.get('Message')
if m.get('RequestId') is not None:
self.request_id = m.get('RequestId')
return self
class VoiceModerationResultResponse(TeaModel):
def __init__(
self,
headers: Dict[str, str] = None,
status_code: int = None,
body: VoiceModerationResultResponseBody = None,
):
self.headers = headers
self.status_code = status_code
self.body = body
def validate(self):
if self.body:
self.body.validate()
def to_map(self):
_map = super().to_map()
if _map is not None:
return _map
result = dict()
if self.headers is not None:
result['headers'] = self.headers
if self.status_code is not None:
result['statusCode'] = self.status_code
if self.body is not None:
result['body'] = self.body.to_map()
return result
def from_map(self, m: dict = None):
m = m or dict()
if m.get('headers') is not None:
self.headers = m.get('headers')
if m.get('statusCode') is not None:
self.status_code = m.get('statusCode')
if m.get('body') is not None:
temp_model = VoiceModerationResultResponseBody()
self.body = temp_model.from_map(m['body'])
return self