Week 30 - Daily Log

Continuing the detailed daily logging format. Each day’s work and learning outcomes are tracked in separate files for clarity and granularity.

Overview

Week 30 focused on stabilizing the production inference pipeline and preparing the backend for handoff: completing a major functional refactor, fixing serialization and model-loading inefficiencies, simplifying API contracts, and adding developer tooling for rapid testing.

Weekly Summary

Execution Snapshot

Active implementation days: 6 (Day 52 → Day 57)
Primary themes: functional refactor, inference optimization, API contract simplification, validation hardening, developer testing tooling, and deployment/runtime hardening

What Was Delivered

Architecture & refactor:
- Removed legacy faces.py and transitioned to a function-driven, modular backend (extracted preprocessing, validation, analyzer functions, and centralized model utilities).
- Created face_models.py to centralize model loading and device management.
Inference & performance:
- Introduced a shared Analyzer singleton and updated /api/setup to preload models (CLIP/embedding pipeline), reducing per-request model load and inference latency.
- Fixed critical YAML serialization issues to ensure clean production-safe outputs.
API contract & validation:
- Standardized validation responses to a minimal, frontend-friendly schema centered on image_id.
- Unified Validate → Analyze flow (Upload → Validate → return image_id → Analyze using image_id).
- Added wipe_cache semantics and multiplexed input support (file uploads or image_url).
Developer tooling & verification:
- Built an internal developer SPA at /app for rapid Validate → Analyze testing with visual debugging (image preview, JSON viewers, image_id tracing).
- Performed server verification and static/compile checks across modified modules.

Key Learnings

Functional, decoupled architecture reduces hidden coupling and improves maintainability after large refactors.
Preloading heavy ML components and reusing singletons measurably improves runtime latency and stability.
Minimal API responses and consistent naming (image_id) simplify frontend integration and multi-step pipelines.
Proper serialization and environment dependency management are critical for production reliability.

Risks and Follow-Up

Preserve and apply repository-wide migration notes when removing legacy fields to avoid consumer breakage.
Track .models versioning and ensure model warm-up health checks are available to avoid cold-start regressions.
Harden remote-image handling (size limits, timeouts, allowed domains) to mitigate security and reliability risks.
Continue adding unit and integration tests for validation, preprocessing, and analyze flows.

Daily Work Logs

See the sidebar or the links above for each day’s detailed log.