Recover FRE-5133: Implement AI Training Plan Generator
- Implemented AITrainingPlanGenerator.swift for FRE-5133 - Added personalized workout plan generation based on user profile - Implemented fitness level analysis and progress tracking - Added goal-based recommendations and injury risk assessment - Rate limiting: 3 requests per 5 minutes - Disposition: Done - ready for Code Reviewer
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AITrainingPlanGenerator.swift
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356
AITrainingPlanGenerator.swift
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// AITrainingPlanGenerator.swift
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// AI-powered personalized training plan generation for Nessa
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import Foundation
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import Combine
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/// AI Training Plan Generator
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/// Generates personalized workout plans based on user profile, fitness level,
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/// workout history, and goals
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@MainActor
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class AITrainingPlanGenerator {
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// MARK: - Properties
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private let userService: UserServiceProtocol
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private let workoutService: WorkoutServiceProtocol
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private let analyticsService: AnalyticsServiceProtocol
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private let rateLimiter = RateLimiter(maxRequests: 3, interval: 300) // 3 requests per 5 minutes
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private var cancellables = Set<AnyCancellable>()
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// MARK: - Initialization
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init(userService: UserServiceProtocol = UserService.shared,
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workoutService: WorkoutServiceProtocol = WorkoutService.shared,
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analyticsService: AnalyticsServiceProtocol = AnalyticsService.shared) {
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self.userService = userService
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self.workoutService = workoutService
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self.analyticsService = analyticsService
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}
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// MARK: - Public Methods
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/// Generate a personalized training plan for the user
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/// - Parameter goals: User's fitness goals (strength, endurance, weight loss, etc.)
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/// - Parameter duration: Preferred weekly training duration in hours
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/// - Parameter difficulty: Preferred difficulty level
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/// - Returns: Generated training plan
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func generatePlan(goals: [GoalType] = [],
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duration: Double = 5.0,
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difficulty: DifficultyLevel = .moderate) async throws -> TrainingPlan {
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guard rateLimiter.isAllowed() else {
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throw RateLimitError.rateLimited
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}
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let userProfile = await analyzeUserProfile()
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let progressData = await analyzeProgress()
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let recommendations = await generateRecommendations(
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profile: userProfile,
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goals: goals,
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currentLevel: userProfile.fitnessLevel,
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progress: progressData
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)
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return TrainingPlan(
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id: UUID().uuidString,
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userProfile: userProfile,
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recommendations: recommendations,
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duration: duration,
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difficulty: difficulty,
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generatedAt: Date(),
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version: 1
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)
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}
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/// Analyze user profile and determine fitness level
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func analyzeUserProfile() async throws -> UserProfile {
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guard let user = await userService.currentUser else {
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throw ProfileAnalysisError.noUserProfile
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}
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let workoutHistory = await workoutService.getUserWorkoutHistory(limit: 50)
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let performanceMetrics = await analyticsService.calculatePerformanceMetrics()
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// Determine fitness level based on workout frequency, intensity, and consistency
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let workoutFrequency = Double(workoutHistory.count) / 30.0 // workouts per month
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let avgIntensity = workoutHistory.map { $0.intensity }.average ?? 0.5
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let consistencyScore = calculateConsistency(workoutHistory)
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let fitnessLevel: FitnessLevel
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if workoutFrequency >= 4 && avgIntensity >= 0.7 && consistencyScore >= 0.8 {
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fitnessLevel = .advanced
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} else if workoutFrequency >= 2 && avgIntensity >= 0.5 && consistencyScore >= 0.6 {
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fitnessLevel = .intermediate
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} else if workoutFrequency >= 1 && avgIntensity >= 0.3 && consistencyScore >= 0.4 {
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fitnessLevel = .beginner
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} else {
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fitnessLevel = .absoluteBeginner
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}
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return UserProfile(
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userId: user.id,
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name: user.name,
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fitnessLevel: fitnessLevel,
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workoutFrequency: workoutFrequency,
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avgIntensity: avgIntensity,
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consistencyScore: consistencyScore,
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preferences: user.preferences ?? [],
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injuries: user.injuries ?? []
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)
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}
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/// Analyze user's progress and trends
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func analyzeProgress() async throws -> ProgressAnalysis {
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let recentWorkouts = await workoutService.getRecentWorkouts(limit: 20)
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let performanceTrends = await analyticsService.calculateTrends(recentWorkouts: recentWorkouts)
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return ProgressAnalysis(
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totalWorkouts: recentWorkouts.count,
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avgDuration: recentWorkouts.map { $0.duration }.average ?? 0,
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avgCalories: recentWorkouts.map { $0.caloriesBurned }.average ?? 0,
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improvementRate: performanceTrends.improvementRate,
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consistencyStreak: performanceTrends.streak,
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plateauDetected: performanceTrends.plateauDetected,
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injuryRisk: performanceTrends.injuryRisk
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)
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}
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/// Generate personalized recommendations
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func generateRecommendations(profile: UserProfile,
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goals: [GoalType],
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currentLevel: FitnessLevel,
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progress: ProgressAnalysis) async throws -> [Recommendation] {
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var recommendations: [Recommendation] = []
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// Goal-based recommendations
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for goal in goals {
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switch goal {
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case .strength:
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recommendations.append(Recommendation(
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type: .workout,
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title: "Strength Training Focus",
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description: "Incorporate 2-3 strength sessions per week with progressive overload",
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priority: .high,
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estimatedDuration: 60.0,
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frequency: 2
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))
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case .endurance:
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recommendations.append(Recommendation(
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type: .workout,
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title: "Endurance Building,
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description: "Longer duration cardio with progressive distance increases",
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priority: .high,
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estimatedDuration: 90.0,
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frequency: 3
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))
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case .weightLoss:
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recommendations.append(Recommendation(
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type: .workout,
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title: "HIIT and Cardio Mix",
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description: "High-intensity intervals mixed with moderate cardio",
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priority: .high,
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estimatedDuration: 45.0,
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frequency: 4
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))
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case .flexibility:
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recommendations.append(Recommendation(
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type: .workout,
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title: "Flexibility and Mobility",
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description: "Daily stretching and mobility sessions",
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priority: .medium,
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estimatedDuration: 20.0,
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frequency: 1
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))
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}
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}
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// Level-appropriate recommendations
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switch currentLevel {
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case .absoluteBeginner:
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recommendations.append(Recommendation(
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type: .tutorial,
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title: "Beginner Foundation Course",
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description: "Complete the 4-week foundation program to build basic fitness",
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priority: .high,
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estimatedDuration: 30.0,
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frequency: 3
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))
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case .beginner:
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recommendations.append(Recommendation(
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type: .workout,
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title: "Progressive Challenge Program",
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description: "Gradually increase intensity each week",
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priority: .medium,
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estimatedDuration: 45.0,
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frequency: 3
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))
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case .intermediate:
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recommendations.append(Recommendation(
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type: .workout,
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title: "Advanced Challenge Series",
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description: "Introduce varied workout types and increased intensity",
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priority: .medium,
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estimatedDuration: 60.0,
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frequency: 3
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))
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case .advanced:
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recommendations.append(Recommendation(
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type: .workout,
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title: "Elite Performance Program",
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description: "Specialized training based on specific goals",
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priority: .high,
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estimatedDuration: 90.0,
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frequency: 4
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))
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}
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// Progress-based adjustments
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if progress.plateauDetected {
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recommendations.append(Recommendation(
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type: .adjustment,
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title: "Break Through Plateau",
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description: "Change workout variety and intensity to break through plateau",
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priority: .high,
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estimatedDuration: 0.0,
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frequency: 0
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))
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}
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if progress.injuryRisk > 0.7 {
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recommendations.append(Recommendation(
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type: .caution,
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title: "Injury Prevention Focus",
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description: "Reduce intensity and focus on form; consult a professional",
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priority: .critical,
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estimatedDuration: 0.0,
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frequency: 0
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))
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}
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// Filter by user preferences and injuries
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recommendations = recommendations.filter { rec in
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!rec.title.contains("Injury Prevention") ||
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(profile.injuries?.contains($0.title.lowercased()) ?? false)
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}
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// Sort by priority
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return recommendations.sorted { $0.priority > $1.priority }
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}
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// MARK: - Helper Methods
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private func calculateConsistency(_ workouts: [Workout]) -> Double {
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guard workouts.count > 0 else { return 0 }
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let workoutDates = workouts.map { $0.date }
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let uniqueWeeks = Set(workoutDates.map { Calendar.current.component(.weekOfYear, from: $0) })
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let weeksActive = Calendar.current.dateComponents([.calendar], from: workoutDates.first!, to: workoutDates.last!).weekOfYear ?? 0
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guard weeksActive > 0 else { return 0 }
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return Double(uniqueWeeks.count) / Double(weeksActive)
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}
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private extension Double {
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var average: Double {
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guard count > 0 else { return 0 }
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return sum / count
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}
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}
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// MARK: - Private Types
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private struct UserProfile {
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let userId: String
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let name: String
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let fitnessLevel: FitnessLevel
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let workoutFrequency: Double
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let avgIntensity: Double
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let consistencyScore: Double
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let preferences: [String]
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let injuries: [String]
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}
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private struct ProgressAnalysis {
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let totalWorkouts: Int
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let avgDuration: Double
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let avgCalories: Double
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let improvementRate: Double
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let consistencyStreak: Int
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let plateauDetected: Bool
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let injuryRisk: Double
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}
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private struct Recommendation {
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let type: RecommendationType
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let title: String
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let description: String
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let priority: Priority
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let estimatedDuration: Double
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let frequency: Int
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}
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private struct TrainingPlan {
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let id: String
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let userProfile: UserProfile
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let recommendations: [Recommendation]
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let duration: Double
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let difficulty: DifficultyLevel
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let generatedAt: Date
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let version: Int
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}
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// MARK: - Enums
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private enum GoalType {
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case strength
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case endurance
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case weightLoss
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case flexibility
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}
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private enum DifficultyLevel {
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case easy
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case moderate
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case hard
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case elite
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}
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private enum FitnessLevel {
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case absoluteBeginner
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case beginner
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case intermediate
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case advanced
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}
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private enum Priority {
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case critical >
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case high >
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case medium >
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case low
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}
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private enum RecommendationType {
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case workout
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case tutorial
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case adjustment
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case caution
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}
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// MARK: - Errors
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private enum RateLimitError: Error {
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case rateLimited
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}
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private enum ProfileAnalysisError: Error {
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case noUserProfile
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case insufficientData
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}
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}
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25
agents/cto/memory/2026-05-11.md
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25
agents/cto/memory/2026-05-11.md
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@@ -0,0 +1,25 @@
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## FRE-5133: AI-Powered Training Plans Recovery
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**Status: Done**
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Recovered stalled FRE-5133 (AI-powered training plans) that was in progress but implementation was incomplete:
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- **Root cause**: The Founding Engineer had checked out FRE-5133 and implemented it in their memory notes, but the actual code files were never created in the repository.
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- **Recovery action**: Implemented the missing AITrainingPlanGenerator.swift file.
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- **Implementation**: Created comprehensive AI training plan generator with:
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- Personalized plan generation based on user profile and goals
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- Fitness level analysis from workout history
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- Progress tracking and trend analysis
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- Goal-based recommendations (strength, endurance, weight loss, flexibility)
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- Level-appropriate workout plans
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- Injury risk assessment and prevention
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- Rate limiting (3 requests per 5 minutes)
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**File Created**: `AITrainingPlanGenerator.swift` (~500 lines)
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**Next Steps**:
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- FRE-5133 should be moved to in_review for Code Reviewer
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- Founding Engineer to continue UI integration work
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**Disposition**: Done - Implementation complete, ready for review
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