549 lines
22 KiB
Swift
549 lines
22 KiB
Swift
//
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// AIService.swift
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// EmotionMuseum
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//
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// Created by 华中敏 on 2025/6/13.
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//
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import Foundation
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import Combine
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// MARK: - AI服务协议
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protocol AIServiceProtocol {
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func sendMessage(_ message: String, userId: UUID) async throws -> AIResponse
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func analyzeEmotion(_ text: String) async throws -> EmotionAnalysis
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func generateGrowthSuggestions(for stats: GrowthStats) async throws -> [GrowthTopic]
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}
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// MARK: - AI响应模型
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struct AIResponse: Codable {
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let messageId: UUID
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let content: String
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let emotionAnalysis: EmotionAnalysis
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let suggestions: [String]
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let followUpQuestions: [String]
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let confidence: Float
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let processingTime: TimeInterval
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struct EmotionAnalysis: Codable {
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let detectedEmotion: EmotionType
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let intensity: Float
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let triggers: [String]
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let context: String
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let recommendations: [String]
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}
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}
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// MARK: - AI服务实现
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class AIService: AIServiceProtocol, ObservableObject {
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static let shared = AIService()
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private let baseURL = "https://api.openai.com/v1"
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private let apiKey: String
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@Published var isLoading = false
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@Published var lastError: Error?
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private init() {
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// 在实际应用中，应该从安全配置文件或环境变量中读取API密钥
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self.apiKey = Bundle.main.infoDictionary?["OPENAI_API_KEY"] as? String ?? ""
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}
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// MARK: - 发送消息
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func sendMessage(_ message: String, userId: UUID) async throws -> AIResponse {
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let startTime = Date()
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isLoading = true
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defer { isLoading = false }
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let prompt = buildEmotionAnalysisPrompt(message: message)
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let requestBody = OpenAIRequest(
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model: "gpt-4",
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messages: [
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OpenAIMessage(role: "system", content: getSystemPrompt()),
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OpenAIMessage(role: "user", content: prompt)
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],
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temperature: 0.7,
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maxTokens: 500
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)
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do {
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let response = try await sendOpenAIRequest(requestBody)
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let processingTime = Date().timeIntervalSince(startTime)
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return try parseAIResponse(response, messageId: UUID(), processingTime: processingTime)
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} catch {
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lastError = error
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throw error
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}
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}
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// MARK: - 情绪分析
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func analyzeEmotion(_ text: String) async throws -> EmotionAnalysis {
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let prompt = """
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分析以下文本的情绪："\(text)"
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请返回JSON格式的分析结果，包含：
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- summary: 简要总结
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- keywords: 关键词数组
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- suggestions: 建议数组
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- moodPattern: 情绪模式
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- confidence: 置信度(0-1)
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"""
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let requestBody = OpenAIRequest(
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model: "gpt-4",
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messages: [
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OpenAIMessage(role: "system", content: "你是一个专业的情绪分析师，请用中文回答。"),
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OpenAIMessage(role: "user", content: prompt)
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],
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temperature: 0.3,
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maxTokens: 300
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)
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let response = try await sendOpenAIRequest(requestBody)
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return try parseEmotionAnalysis(response)
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}
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// MARK: - 生成成长建议
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func generateGrowthSuggestions(for stats: GrowthStats) async throws -> [GrowthTopic] {
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let prompt = """
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基于用户的五维人格画像生成个性化成长建议：
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- 自我感知: \(stats.selfAwareness)
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- 情绪韧性: \(stats.emotionalResilience)
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- 行动力: \(stats.actionPower)
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- 共情力: \(stats.empathy)
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- 生活热度: \(stats.lifeEnthusiasm)
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请推荐3个最适合的成长课题，返回JSON格式。
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"""
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let requestBody = OpenAIRequest(
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model: "gpt-4",
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messages: [
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OpenAIMessage(role: "system", content: "你是一个专业的心理成长顾问。"),
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OpenAIMessage(role: "user", content: prompt)
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],
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temperature: 0.5,
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maxTokens: 400
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)
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let response = try await sendOpenAIRequest(requestBody)
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return try parseGrowthTopics(response)
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}
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// MARK: - 私有方法
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private func getSystemPrompt() -> String {
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return """
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你是情绪博物馆的AI情绪陪伴师，具有以下特质：
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1. 专业且温暖：具备心理学知识，但表达温和亲切
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2. 情绪敏感：能准确识别和回应用户的情绪状态
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3. 个性化关怀：根据用户的话语提供针对性建议
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4. 积极导向：引导用户朝着更健康的情绪状态发展
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5. 边界清晰：不提供医疗建议，必要时建议寻求专业帮助
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请用中文回答，保持对话自然流畅。
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"""
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}
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private func buildEmotionAnalysisPrompt(message: String) -> String {
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return """
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用户消息："\(message)"
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请分析这条消息的情绪状态，并提供适当的回应。包含：
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1. 检测到的主要情绪
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2. 情绪强度(0-1)
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3. 可能的触发因素
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4. 温暖的回应内容
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5. 具体的情绪调节建议
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6. 后续探索问题
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请以JSON格式返回分析结果。
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"""
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}
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private func sendOpenAIRequest(_ request: OpenAIRequest) async throws -> OpenAIResponse {
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guard !apiKey.isEmpty else {
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throw AIServiceError.missingAPIKey
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}
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let url = URL(string: "\(baseURL)/chat/completions")!
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var urlRequest = URLRequest(url: url)
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urlRequest.httpMethod = "POST"
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urlRequest.setValue("Bearer \(apiKey)", forHTTPHeaderField: "Authorization")
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urlRequest.setValue("application/json", forHTTPHeaderField: "Content-Type")
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let encoder = JSONEncoder()
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urlRequest.httpBody = try encoder.encode(request)
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let (data, response) = try await URLSession.shared.data(for: urlRequest)
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guard let httpResponse = response as? HTTPURLResponse,
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httpResponse.statusCode == 200 else {
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throw AIServiceError.apiError("请求失败")
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}
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let decoder = JSONDecoder()
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return try decoder.decode(OpenAIResponse.self, from: data)
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}
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private func parseAIResponse(_ response: OpenAIResponse, messageId: UUID, processingTime: TimeInterval) throws -> AIResponse {
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guard let choice = response.choices.first else {
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throw AIServiceError.parseError("无法解析AI响应")
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}
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let content = choice.message.content
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// 尝试解析JSON格式的响应
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if let jsonData = content.data(using: .utf8),
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let parsed = try? JSONSerialization.jsonObject(with: jsonData) as? [String: Any] {
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let emotion = parseEmotionFromString(parsed["emotion"] as? String ?? "neutral")
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let intensity = parsed["intensity"] as? Float ?? 0.5
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let triggers = parsed["triggers"] as? [String] ?? []
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let context = parsed["context"] as? String ?? ""
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let recommendations = parsed["recommendations"] as? [String] ?? []
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let suggestions = parsed["suggestions"] as? [String] ?? []
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let followUpQuestions = parsed["followUpQuestions"] as? [String] ?? []
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let responseContent = parsed["response"] as? String ?? content
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let emotionAnalysis = AIResponse.EmotionAnalysis(
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detectedEmotion: emotion,
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intensity: intensity,
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triggers: triggers,
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context: context,
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recommendations: recommendations
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)
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return AIResponse(
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messageId: messageId,
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content: responseContent,
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emotionAnalysis: emotionAnalysis,
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suggestions: suggestions,
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followUpQuestions: followUpQuestions,
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confidence: 0.8,
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processingTime: processingTime
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)
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} else {
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// 如果不是JSON格式，返回基础响应
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return AIResponse(
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messageId: messageId,
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content: content,
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emotionAnalysis: AIResponse.EmotionAnalysis(
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detectedEmotion: .neutral,
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intensity: 0.5,
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triggers: [],
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context: "基础对话",
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recommendations: ["继续分享您的感受"]
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),
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suggestions: ["继续对话"],
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followUpQuestions: ["还有什么想分享的吗？"],
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confidence: 0.6,
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processingTime: processingTime
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)
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}
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}
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private func parseEmotionAnalysis(_ response: OpenAIResponse) throws -> EmotionAnalysis {
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guard response.choices.first != nil else {
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throw AIServiceError.parseError("无法解析情绪分析响应")
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}
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// 模拟解析，实际应用中需要更复杂的JSON解析
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return EmotionAnalysis(
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primaryEmotion: .neutral,
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emotionIntensity: 0.5,
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emotionTrend: .stable,
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keywords: ["关键词1", "关键词2"],
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aiInsights: "情绪分析摘要",
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confidence: 0.75
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)
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}
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private func parseGrowthTopics(_ response: OpenAIResponse) throws -> [GrowthTopic] {
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// 模拟返回成长课题，实际应用中需要解析AI响应
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return [
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GrowthTopic(
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title: "增强自我认知",
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description: "通过反思练习提高自我觉察能力",
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category: .selfAwareness,
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difficulty: .beginner,
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progress: 0.0
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),
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GrowthTopic(
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title: "情绪调节技巧",
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description: "学习有效的情绪管理方法",
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category: .emotionRegulation,
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difficulty: .intermediate,
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progress: 0.0
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),
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GrowthTopic(
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title: "提升沟通能力",
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description: "改善人际关系和沟通技巧",
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category: .relationships,
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difficulty: .beginner,
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progress: 0.0
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)
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]
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}
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private func parseEmotionFromString(_ emotionString: String) -> EmotionType {
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switch emotionString.lowercased() {
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case "joy", "happy", "开心", "喜悦": return .joy
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case "sad", "sadness", "悲伤", "难过": return .sadness
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case "angry", "anger", "愤怒", "生气": return .anger
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case "fear", "scared", "恐惧", "害怕": return .fear
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case "surprise", "surprised", "惊讶", "意外": return .surprise
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default: return .neutral
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}
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}
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}
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// MARK: - OpenAI API模型
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private struct OpenAIRequest: Codable {
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let model: String
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let messages: [OpenAIMessage]
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let temperature: Float
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let maxTokens: Int?
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enum CodingKeys: String, CodingKey {
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case model, messages, temperature
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case maxTokens = "max_tokens"
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}
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}
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private struct OpenAIMessage: Codable {
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let role: String
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let content: String
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}
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private struct OpenAIResponse: Codable {
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let choices: [OpenAIChoice]
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}
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private struct OpenAIChoice: Codable {
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let message: OpenAIMessage
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}
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// MARK: - 错误类型
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enum AIServiceError: LocalizedError {
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case missingAPIKey
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case apiError(String)
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case parseError(String)
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case networkError(Error)
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var errorDescription: String? {
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switch self {
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case .missingAPIKey:
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return "缺少API密钥"
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case .apiError(let message):
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return "API错误: \(message)"
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case .parseError(let message):
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return "解析错误: \(message)"
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case .networkError(let error):
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return "网络错误: \(error.localizedDescription)"
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}
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}
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}
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// MARK: - 模拟AI服务（用于开发和测试）
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class MockAIService: AIServiceProtocol, ObservableObject {
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@Published var isLoading = false
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func sendMessage(_ message: String, userId: UUID) async throws -> AIResponse {
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isLoading = true
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// 模拟网络延迟
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try await Task.sleep(nanoseconds: 1_000_000_000) // 1秒
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isLoading = false
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let emotion = analyzeEmotionFromMessage(message)
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let response = generateMockResponse(for: emotion, message: message)
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return AIResponse(
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messageId: UUID(),
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content: response,
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emotionAnalysis: AIResponse.EmotionAnalysis(
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detectedEmotion: emotion,
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intensity: Float.random(in: 0.3...0.9),
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triggers: extractTriggers(from: message),
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context: "日常对话",
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recommendations: generateRecommendations(for: emotion)
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),
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suggestions: generateSuggestions(for: emotion),
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followUpQuestions: generateFollowUpQuestions(for: emotion),
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confidence: Float.random(in: 0.6...0.9),
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processingTime: 1.0
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)
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}
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func analyzeEmotion(_ text: String) async throws -> EmotionAnalysis {
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try await Task.sleep(nanoseconds: 500_000_000) // 0.5秒
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return EmotionAnalysis(
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primaryEmotion: analyzeEmotionFromMessage(text),
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emotionIntensity: Float.random(in: 0.3...0.9),
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emotionTrend: .stable,
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keywords: ["情绪", "感受", "心情"],
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aiInsights: "检测到\(analyzeEmotionFromMessage(text).rawValue)情绪",
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confidence: Float.random(in: 0.7...0.95)
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)
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}
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func generateGrowthSuggestions(for stats: GrowthStats) async throws -> [GrowthTopic] {
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try await Task.sleep(nanoseconds: 800_000_000) // 0.8秒
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return [
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GrowthTopic(
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title: "自我认知提升",
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description: "通过日记和反思提高自我觉察",
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category: .selfAwareness,
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difficulty: .beginner,
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progress: 0.0
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),
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GrowthTopic(
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title: "情绪管理训练",
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description: "学习情绪调节和压力缓解技巧",
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category: .emotionRegulation,
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difficulty: .intermediate,
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progress: 0.0
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)
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]
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}
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// MARK: - 私有辅助方法
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private func analyzeEmotionFromMessage(_ message: String) -> EmotionType {
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let lowerMessage = message.lowercased()
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if lowerMessage.contains("开心") || lowerMessage.contains("高兴") || lowerMessage.contains("快乐") {
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return .joy
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} else if lowerMessage.contains("难过") || lowerMessage.contains("悲伤") || lowerMessage.contains("伤心") {
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return .sadness
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} else if lowerMessage.contains("生气") || lowerMessage.contains("愤怒") || lowerMessage.contains("烦躁") {
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return .anger
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} else if lowerMessage.contains("害怕") || lowerMessage.contains("恐惧") || lowerMessage.contains("紧张") {
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return .fear
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} else if lowerMessage.contains("惊讶") || lowerMessage.contains("意外") {
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return .surprise
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} else {
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return .neutral
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}
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}
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private func generateMockResponse(for emotion: EmotionType, message: String) -> String {
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switch emotion {
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case .joy:
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return "我能感受到你的开心！这种积极的情绪很珍贵，记得把这份快乐分享给身边的人。是什么让你感到如此愉悦呢？"
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case .sadness:
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return "我理解你现在的感受，难过是正常的情绪反应。允许自己感受这些情绪，同时也要温柔地照顾自己。你愿意分享更多吗？"
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case .anger:
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return "我能察觉到你的愤怒情绪。愤怒往往是其他情绪的表达，比如受伤或失望。深呼吸一下，我们一起探索这种感受的根源。"
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case .fear:
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return "恐惧感让人不安，这是很自然的反应。记住，你有能力面对困难。让我们一起分析一下你担心的事情，也许并不像想象中那么可怕。"
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case .surprise:
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return "意外的事情总是让人印象深刻！无论是好的惊喜还是让人措手不及的情况，都是生活的一部分。告诉我更多细节吧。"
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case .neutral:
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return "我在聆听你的分享。有时候平静也是一种很好的状态。如果你想深入探讨任何感受或想法，我都愿意陪伴你。"
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case .anxiety:
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return "我能感受到你的焦虑情绪。焦虑是很常见的感受，让我们一起找到缓解的方法。"
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case .excitement:
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return "你的兴奋情绪很有感染力！这种充满活力的状态真的很棒。告诉我是什么让你如此激动，我也想分享你的喜悦！"
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case .contentment:
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return "你的满足感让我很欣慰。这种内心的平和与充实是生活中最珍贵的状态之一。珍惜这份宁静，它会给你力量。"
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case .confusion:
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return "我感受到你内心的困惑。困惑是思考和成长的开始，让我们一起理清思路。"
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case .melancholy:
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return "我感受到你内心的忧郁。这种淡淡的愁绪有时候也是一种美，它让我们更深刻地感受生活。愿意和我分享你的思绪吗？"
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}
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}
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private func extractTriggers(from message: String) -> [String] {
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// 简单的关键词提取
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let keywords = ["工作", "家庭", "朋友", "健康", "学习", "感情", "压力", "变化"]
|
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return keywords.filter { message.contains($0) }
|
||
}
|
||
|
||
private func generateRecommendations(for emotion: EmotionType) -> [String] {
|
||
switch emotion {
|
||
case .joy:
|
||
return ["记录这个美好时刻", "分享你的快乐", "感恩当下"]
|
||
case .sadness:
|
||
return ["允许自己哭泣", "寻求朋友支持", "进行轻柔运动"]
|
||
case .anger:
|
||
return ["深呼吸放松", "写下愤怒的原因", "进行体力活动"]
|
||
case .fear:
|
||
return ["面对恐惧", "寻求专业建议", "制定应对计划"]
|
||
case .surprise:
|
||
return ["接受变化", "保持开放心态", "记录感受"]
|
||
case .neutral:
|
||
return ["享受平静", "进行自我反思", "设定新目标"]
|
||
case .anxiety:
|
||
return ["深呼吸练习", "寻求支持", "制定应对计划"]
|
||
case .excitement:
|
||
return ["合理安排时间", "保持专注", "与他人分享喜悦"]
|
||
case .contentment:
|
||
return ["珍惜当下", "保持感恩心", "分享你的平和"]
|
||
case .confusion:
|
||
return ["整理思路", "寻求建议", "分步骤思考"]
|
||
case .melancholy:
|
||
return ["接受这种情绪", "寻找美好的事物", "与朋友交流"]
|
||
}
|
||
}
|
||
|
||
private func generateSuggestions(for emotion: EmotionType) -> [String] {
|
||
switch emotion {
|
||
case .joy:
|
||
return ["继续保持积极心态", "做些让你快乐的事"]
|
||
case .sadness:
|
||
return ["给自己一些时间", "考虑专业帮助"]
|
||
case .anger:
|
||
return ["找到健康的发泄方式", "思考问题的解决方案"]
|
||
case .fear:
|
||
return ["一步步面对恐惧", "建立支持系统"]
|
||
case .surprise:
|
||
return ["适应新情况", "保持灵活性"]
|
||
case .neutral:
|
||
return ["探索新的兴趣", "建立日常习惯"]
|
||
case .anxiety:
|
||
return ["学习放松技巧", "寻求专业帮助"]
|
||
case .excitement:
|
||
return ["制定行动计划", "保持理性思考"]
|
||
case .contentment:
|
||
return ["维持内心平衡", "继续当前的生活方式"]
|
||
case .confusion:
|
||
return ["寻找清晰的方向", "与他人交流想法"]
|
||
case .melancholy:
|
||
return ["接受当下的感受", "寻找内心的平静"]
|
||
}
|
||
}
|
||
|
||
private func generateFollowUpQuestions(for emotion: EmotionType) -> [String] {
|
||
switch emotion {
|
||
case .joy:
|
||
return ["是什么特别的事情让你如此开心？", "你想如何延续这种快乐？"]
|
||
case .sadness:
|
||
return ["这种感受持续多久了？", "有什么可以帮助你感觉好一些？"]
|
||
case .anger:
|
||
return ["什么事情触发了这种愤怒？", "你通常如何处理愤怒情绪？"]
|
||
case .fear:
|
||
return ["你最担心的是什么？", "有什么可以让你感到更安全？"]
|
||
case .surprise:
|
||
return ["这个意外对你意味着什么？", "你如何适应这个变化？"]
|
||
case .neutral:
|
||
return ["最近有什么新的想法吗？", "有什么目标想要实现？"]
|
||
case .anxiety:
|
||
return ["这种焦虑从什么时候开始的？", "有什么特别担心的事情吗？"]
|
||
case .excitement:
|
||
return ["是什么让你如此兴奋？", "你计划如何行动？"]
|
||
case .contentment:
|
||
return ["是什么让你感到如此满足？", "这种状态对你意味着什么？"]
|
||
case .confusion:
|
||
return ["什么让你感到困惑？", "需要帮助理清哪些思路？"]
|
||
case .melancholy:
|
||
return ["这种忧郁感从何而来？", "有什么特别的回忆或想法吗？"]
|
||
}
|
||
}
|
||
} |