Auralytics

This repository, focused on multimodal analytics (Auralytics), demonstrates a sophisticated blend of modern full-stack TypeScript (Next.js, Prisma) and scalable Python MLOps (SageMaker). ### Detailed Assessment 1. **Code Quality & Patterns** * The architecture successfully decouples the API layer (`route.ts`) from the inference engine (SageMaker), establishing a solid microservice pattern for ML serving. * The API key validation (File 1) combines authentication, authorization, and critical business logic (quota checking) into a single, high-complexity function, demanding careful maintenance. * The use of the SageMaker Python SDK (File 2) is professional, clearly defining training environments (GPU instances, TensorBoard integration) suitable for production MLOps workflows. 2. **Language-Specific Observations** * **TypeScript:** Effective use of `NextResponse` for standardized API responses and structured error handling in `route.ts`. * **Next.js/React:** The implementation correctly leverages server-side rendering and `react/cache` (File 3) to optimize data fetching for authenticated sessions, enhancing application speed. * **Python:** The model utilities (File 5) confirm the project's complexity, defining trainable parameters across specialized modalities (text, audio, video encoders, fusion). 3. **Code Structure** * The project organization clearly separates frontend source (`frontend/src`), database access (`~/server/db`), and dedicated ML infrastructure components (`video-sentiment-model`), adhering to a standard monorepo-like structure. * The API logic uses clean file-based routing (`app/api/sentiment-inference/route.ts`), centralizing inference orchestration responsibilities. * Naming conventions, particularly around AWS services (S3Client, SageMakerRuntimeClient), are clear and standard. 4. **Specific Improvements** * The inference API directly uses environment AWS credentials, risking severe security vulnerability. * SageMaker integration is robustly implemented, using PyTorch estimators for large-scale multimodal training. * Modern Next.js patterns, like Server Components and NextAuth caching, ensure fast application performance. * Refactoring the API key validation and quota checks should minimize database lookup latency. * The model utilizes complex multimodal fusion layers for comprehensive video and sentiment analytics.