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Create a professional cloud architecture diagram for "TuneAcademy", an AI-powered two-sided music instruction marketplace mobile app. The diagram should be clean, modern, and use a sleek black and white color palette with subtle musical accents to match the app's minimal aesthetic. Use official cloud service icons where possible. Layout should flow top to bottom or left to right with clearly labeled swimlanes or sections. Include the following layers and components: CLIENT LAYER - Mobile App (React Native / Expo) for both iOS and Android - Learner Dashboard (4 tabs: Home, Musireels, Instructors, Analyze) - Instructor Dashboard (3 tabs: Dashboard, Musireels, Profile) - Audio Recorder (Expo AV - captures WAV/MP3 for AI analysis) - Musireels Feed (TikTok-style vertical video feed) - Session Request and Scheduling UI - Onboarding Flow (Instructor profile setup) - Static Chatbot UI (AI Assistant placeholder) API GATEWAY LAYER - REST API Gateway - Firebase Authentication (email/password, JWT tokens) - Rate Limiter / Request Validator APPLICATION LAYER (Backend Services) - User Profile Service (stores role, name, avatar, joined date) - Instructor Profile Service (specialties, bio, hourly rate, experience, rating) - Session Management Service (handles booking requests, confirmations, 1-on-1 and 3-on-1 session types, scheduling) - Reel Management Service (upload, like, comment, recruit flow) - Invitation Service (instructor to learner recruitment messages) - Messaging / Chat Service (chat threads between users) - Review and Rating Service (post-session student ratings) - Report History Service (stores all past analysis reports per user for progress tracking over time) - Notification Service (session confirmations, invitations, new messages) AI / ML LAYER - FastAPI Python Backend (containerized via Docker) - Audio Preprocessing Module (resample to 44.1kHz, mono, normalize) - Essentia Feature Extraction Engine: Shared: PredominantPitchMelodia, RhythmExtractor2013, MFCC, OnsetRate Voice: vibrato irregularity, pitch drift Guitar: inharmonicity, string buzz, noise floor Piano: log attack time, velocity evenness Saxophone: spectral centroid, amplitude envelope - Golden Reference Comparator (loads precomputed reference_features.json, computes feature deltas) - Scoring Engine (converts deltas to 0-100 dimension scores: pitch centre, pitch stability, rhythm, tone quality, note attack) - Weakness Mapper (threshold rules to plain-English feedback strings per instrument) - Instructor Matching Engine (maps weakness dimensions to instructor specialties for suggested matches) EXTERNAL INTEGRATIONS LAYER - Firebase Storage (reel video uploads, user avatars, audio temp files) - Expo AV (in-app audio recording on device) - Expo Image Picker (video selection for Musireels) - Expo Video Thumbnails (auto-generates reel thumbnail on upload) - (Future) Stripe API for instructor payments shown as a dashed future component DATA LAYER - Cloud Firestore Collections: /users (role, name, avatar, createdAt) /instructors (specialties, bio, rating, hourlyRate) /reports (instrument, overallScore, dimensionScores, weaknesses, createdAt) /sessions (learnerId, instructorId, scheduledDate, status: pending/confirmed/declined/completed) /reviews (rating, comment, reviewerId, instructorId) /reels (videoUrl, thumbnailUrl, likesCount, commentsCount, likedBy) /reels/{reelId}/comments (subcollection) /invitations (instructorId, learnerId, message, status) /messages/{chatId}/messages (subcollection) - Redis Cache (fast reel feed loading, session state) - Reference Features Store (reference_features.json bundled with Python backend) INFRASTRUCTURE LAYER (Google Cloud Platform) - Cloud Run (containerized FastAPI Python backend + Essentia model runtime via Docker) - Firebase Hosting (web admin panel if needed) - Cloud Firestore (real-time NoSQL database) - Firebase Storage (video, audio, and image assets) - Firebase Authentication (identity management) - Cloud Pub/Sub (event streaming: new session requests, new invitations, new messages) - Cloud CDN (fast global video and asset delivery for Musireels feed) - Cloud Storage Bucket (golden reference audio files and precomputed JSON) - IAM and Security (role-based access control for learner vs instructor permissions) Show data flow arrows between all layers. Highlight the AI/ML layer with a distinct border or accent color to emphasize it as the core differentiator of the platform. Draw a clear arrow from the Audio Recorder on the client, through the API Gateway, into the FastAPI backend, through each stage of the AI pipeline, and back to the client as a Report JSON response to visually tell the story of the analysis flow. Add a dashed box around future roadmap components (Stripe payments, advanced ML model with thousands of golden references, any-song analysis mode) and label it "Future Roadmap". The overall diagram should look polished enough to present at a hackathon to a panel of senior engineers and investors.