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| """ | |
| Explainability layer contracts. | |
| Every prediction served by VARUNA AI must be explainable. This package | |
| turns model outputs into human-understandable evidence: | |
| - SHAP feature attributions for tabular/meteorological inputs (Phase 6) | |
| - Grad-CAM heatmaps for satellite-image inputs (Phase 6) | |
| - A plain-language narrative synthesised from both | |
| Implementations arrive in Phase 6; the interface is fixed now so models | |
| built in Phases 3-5 expose the right hooks (see | |
| ai_models.base.ModelOutput.explanation_context). | |
| """ | |
| from abc import ABC, abstractmethod | |
| from typing import Any | |
| from ai_models.base import ModelOutput | |
| class Explainer(ABC): | |
| """Contract for all explanation backends (SHAP, Grad-CAM, ...).""" | |
| def explain(self, output: ModelOutput) -> dict[str, Any]: | |
| """Produce an explanation payload conforming to the API schema | |
| (backend/app/schemas/prediction.py::Explanation).""" | |