import { ConversationChannel } from '../../conversation-channel';
import { ConversationCallbackEvent } from '../conversation-callback-event';
/**
 * When using the Smart Conversations functionality, Machine Learning and Artificial Intelligence analyses are delivered through specific callbacks on the Conversation API.
 */
export interface SmartConversationsEvent extends ConversationCallbackEvent {
    /** Name of the trigger responsible for this event. */
    trigger: 'SMART_CONVERSATIONS';
    /** @see SmartConversationNotification */
    smart_conversation_notification?: SmartConversationNotification;
}
export interface SmartConversationNotification {
    /** The unique ID of the contact that sent the message. */
    contact_id?: string;
    /** The channel-specific identifier for the contact. */
    channel_identity?: string;
    /** @see ConversationChannel */
    channel?: ConversationChannel;
    /** The unique ID of the corresponding message. */
    message_id?: string;
    /** The ID of the conversation the app message is part of. */
    conversation_id?: string;
    /** @see AnalysisResult */
    analysis_results?: AnalysisResult;
}
/**
 * The analysis provided by the Smart Conversations machine learning engine(s). The contents of the object are determined by the functionalities that are enabled for your solution.
 */
export interface AnalysisResult {
    /** An array that contains the analyses of the sentiments of the corresponding messages. */
    ml_sentiment_result?: MachineLearningSentimentResult[];
    /** An array that contains the analyses of the intentions of, and entities within, the corresponding messages. */
    ml_nlu_result?: MachineLearningNLUResult[];
    /** An array that contains the image recognition analyses of the images identified in the corresponding messages. */
    ml_image_recognition_result?: MachineLearningImageRecognitionResult[];
    /** An array that contains the PII analysis of the corresponding messages. */
    ml_pii_result?: MachineLearningPIIResult[];
    /** An array that contains the analyses of the offenses of the corresponding messages. */
    ml_offensive_analysis_result?: OffensiveAnalysis[];
}
export interface MachineLearningSentimentResult {
    /** The message text that was analyzed. */
    message?: string;
    /** An array of JSON objects made up of sentiment and score pairs, where the score represents the likelihood that the message communicates the corresponding sentiment. */
    results?: SentimentResult[];
    /** @see SentimentEnum */
    sentiment?: SentimentEnum;
    /** The likelihood that the assigned sentiment represents the emotional context of the analyzed text. 1 is the maximum value, representing the highest likelihood that the message text matches the sentiment, and 0 is the minimum value, representing the lowest likelihood that the message text matches the sentiment. */
    score?: number;
}
/**
 * The most probable sentiment of the analyzed text.
 */
export type SentimentEnum = 'positive' | 'negative' | 'neutral';
export interface SentimentResult {
    /** @see SentimentEnum */
    sentiment?: SentimentEnum;
    /** The likelihood that the assigned sentiment represents the emotional context of the analyzed text. 1 is the maximum value, representing the highest likelihood that the message text matches the sentiment, and 0 is the minimum value, representing the lowest likelihood that the message text matches the sentiment. */
    score?: number;
}
export interface MachineLearningNLUResult {
    /** The message text that was analyzed. */
    message?: string;
    /** An array of JSON objects made up of intent and score pairs, where the score represents the likelihood that the message has the corresponding intent. */
    results?: IntentResult[];
    /** The most probable intent of the analyzed text. For example, chitchat.greeting, chitchat.bye, chitchat.compliment, chitchat.how_are_you, or general.yes_or_agreed. */
    intent?: string;
    /** The likelihood that the assigned intent represents the purpose of the analyzed text. 1 is the maximum value, representing the highest likelihood that the message text matches the intent, and 0 is the minimum value, representing the lowest likelihood that the message text matches the intent. */
    score?: number;
}
export interface IntentResult {
    /** The most probable intent of the analyzed text. For example, chitchat.greeting, chitchat.bye, chitchat.compliment, chitchat.how_are_you, or general.yes_or_agreed. */
    intent?: string;
    /** The likelihood that the assigned intent represents the purpose of the analyzed text. 1 is the maximum value, representing the highest likelihood that the message text matches the intent, and 0 is the minimum value, representing the lowest likelihood that the message text matches the intent. */
    score?: number;
}
export interface MachineLearningImageRecognitionResult {
    /** The URL of the image that was processed. */
    url?: string;
    /** @see DocumentImageClassification */
    document_image_classification?: DocumentImageClassification;
    /** @see OpticalCharacterRecognition */
    optical_character_recognition?: OpticalCharacterRecognition;
    /** @see DocumentFieldClassification */
    document_field_classification?: DocumentFieldClassification;
}
/**
 * An object that identifies a document type within the image, along with a confidence level for that document type.
 */
export interface DocumentImageClassification {
    /** The document type that the analyzed image most likely contains. */
    doc_type?: string;
    /** The likelihood that the analyzed image contains the assigned document type. 1 is the maximum value, representing the highest likelihood that the analyzed image contains the assigned document type, and 0 is the minimum value, representing the lowest likelihood that the analyzed image contains the assigned document type. */
    confidence?: number;
}
/**
 * An object containing a result array that reports the machine learning engine's character extraction results.
 */
export interface OpticalCharacterRecognition {
    /** The result of the OCR process. */
    result?: OpticalCharacterRecognitionData[];
}
export interface OpticalCharacterRecognitionData {
    /** The data array contains the string(s) identified in one section of an analyzed image. */
    data?: string[];
}
/**
 * An object containing a result object that reports on all identified fields, as well as the values assigned to those fields.
 */
export interface DocumentFieldClassification {
    /** The result of the Document Field Classification process */
    result?: {
        [key: string]: DocumentFieldClassificationData;
    }[];
}
export interface DocumentFieldClassificationData {
    /** The data array contains the string(s) assigned to the corresponding document field. */
    data?: string[];
}
/**
 * An object that contains the PII analysis of the corresponding messages.
 */
export interface MachineLearningPIIResult {
    /** The message text that was analyzed. */
    message?: string;
    /** The redacted message text in which sensitive information was replaced with appropriate masks. A MISC mask is applied to a term that has been identified as PII, but with low confidence regarding which type of mask to assign. */
    masked?: string;
}
export interface OffensiveAnalysis {
    /** Either the message text or the URL of the image that was analyzed. */
    message?: string;
    /** */
    url?: string;
    /** A label, either SAFE or UNSAFE, that classifies the analyzed content. */
    evaluation?: EvaluationEnum;
    /** The likelihood that the assigned evaluation represents the analyzed message correctly. 1 is the maximum value, representing the highest likelihood that the content of the message matches the evaluation. 0 is the minimum value, representing the lowest likelihood that the content of the message matches the evaluation. */
    score?: number;
}
export type EvaluationEnum = 'SAFE' | 'UNSAFE';