# -*- 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