Daily Work Report (Mar 30, 2026)

Work Summary

Focused on hardening backend API behavior and improving deployment reliability. Refactored analyze endpoints for clearer contracts, tightened validation, redesigned setup for model sync and warm-up, and stabilized model loading and container dependencies for production execution.

Hours Worked

8.0

Show Your Work (References)

• API refactor:
  • Split overloaded analyze flow into explicit routes:
    • /api/analyze/image for upload-based analysis
    • /api/analyze/by-id for image_id-based analysis
  • Preserved backward compatibility via legacy redirect: /api/analyze -> /api/analyze/image.
• Validation hardening:
  • Enforced supported image formats only (PNG, JPEG).
  • Aligned checks with config-driven validation rules.
• Setup flow redesign:
  • Reworked /api/setup to focus on model synchronization and warm-up before inference.
  • Added timm model pre-download behavior to reduce first-run latency.
• Model loading simplification:
  • Removed unnecessary .cache_models staging layer.
  • Standardized .models as the single source of truth.
• Critical fixes:
  • Resolved AttributeError: models_directory missing in config.
  • Removed duplicate/ambiguous route definitions.
  • Fixed invalid Hugging Face token path affecting repository sync.
  • Patched unintended .cache_models creation.
• Container runtime fix:
  • Addressed MediaPipe Linux dependency issue (libGLESv2.so.2).
  • Updated Docker system package installation for runtime compatibility.
• Testing and verification:
  • Validated redirect behavior and required fields using TestClient.
  • Confirmed PNG/JPEG validation behavior.
  • Ran compile checks across setup, analyze/validate, and faces modules.
  • Final validation pass reported no Python errors in modified files.

Learnings / Outcomes

• API endpoints become easier to maintain when responsibilities are explicitly separated.
• Config-driven validation scales better than implicit or hardcoded assumptions.
• Removing intermediate storage layers reduces operational complexity in deployment.
• Container-level native dependencies must be explicitly managed to avoid silent runtime failures.

Blockers / Risks

• Blocker (resolved): config crash due to missing models_directory access path.
• Blocker (resolved): runtime instability from duplicate route definitions and invalid token flow.
• Ongoing risk: strict validation may reject edge-case inputs if format policy is too narrow.
• Ongoing risk: model warm-up time can still impact cold-start performance in low-resource environments.
• Ongoing risk: expired/invalid Hugging Face credentials can block model sync in production.

Skills Used

FastAPI route design, API backward compatibility strategy, validation hardening, Python runtime debugging, model bootstrap engineering, Docker dependency management, TestClient-based API verification, deployment reliability tuning

Next Step

Deploy and execute /api/setup, then perform live endpoint verification for /api/validate/image, /api/analyze/image, and /api/analyze/by-id; add a model-readiness health-check endpoint and baseline production request logging.