[Good First Issue] Extend /api/health with model load diagnostics

[Oshgig]

Overview

The API currently returns a generic {"status": "ok"} from /api/health. We want a lightweight self-check endpoint that verifies the inference pipeline can actually load a model, so ops knows if the system is truly ready or just running.

Scope

• Extend /api/health in src/climatevision/api/main.py to run a diagnostics check
• Try loading each enabled analysis type's model via _load_model(analysis_type)
• Report per-analysis-type status:
  • "model_loaded": true/false
  • "model_path": "models/unet_deforestation.pth" (or null if missing)
  • "config_valid": true/false
• If any model fails to load, return HTTP 200 with "status": "degraded" (do not crash)
• Add pytest test in tests/test_api.py verifying health returns expected shape

Acceptance Criteria

• GET /api/health returns model load status for all enabled analysis types
• Missing weights result in "model_loaded": false, not a 500 error
• Response shape is consistent and documented

Resources

• src/climatevision/api/main.py — existing /api/health
• src/climatevision/inference/pipeline.py — _load_model()
• config.yaml — enabled analysis types

Difficulty: Beginner-friendly
Labels: good first issue, backend, api, observability

[Animesh452]

Hi, I'd like to work on this. Planning to extend /api/health following the scope outlined -- per-analysis-type model status, degraded state on failure, and a pytest covering the response shape. Let me know if I can go ahead.

[Goldokpa]

Hey @Animesh452, thanks for picking this up! 🙌

Go ahead and start working on it. The scope you outlined is exactly right — extending /api/health with per-analysis-type model status, degraded state handling, and a pytest for the response shape.

A couple of quick tips:

• Check src/climatevision/inference/pipeline.py for how _load_model() works
• The enabled analysis types are in config.yaml

Feel free to open a draft PR early if you want feedback along the way. Let me know if you hit any blockers!