t2a/tests/test_justification.py

"""Tests pour la validation batch des justifications (QC post-codage)."""

from unittest.mock import patch, MagicMock

import pytest

from src.config import (
    Diagnostic,
    DossierMedical,
    PreuveClinique,
    Sejour,
    BiologieCle,
)


class TestPreuveClinique:
    def test_create(self):
        p = PreuveClinique(type="biologie", element="CRP 180 mg/L", interpretation="syndrome inflammatoire majeur")
        assert p.type == "biologie"
        assert p.element == "CRP 180 mg/L"
        assert p.interpretation == "syndrome inflammatoire majeur"

    def test_diagnostic_with_preuves(self):
        d = Diagnostic(
            texte="Pancréatite aiguë",
            cim10_suggestion="K85.9",
            preuves_cliniques=[
                PreuveClinique(type="biologie", element="Lipasémie 450 UI/L", interpretation="pancréatite biologique"),
                PreuveClinique(type="imagerie", element="TDM: pancréatite stade D", interpretation="confirmation"),
            ],
        )
        assert len(d.preuves_cliniques) == 2
        assert d.preuves_cliniques[0].type == "biologie"

    def test_diagnostic_default_empty_preuves(self):
        d = Diagnostic(texte="Test")
        assert d.preuves_cliniques == []

    def test_serialization_round_trip(self):
        d = Diagnostic(
            texte="Test",
            preuves_cliniques=[
                PreuveClinique(type="clinique", element="fièvre 39°C", interpretation="syndrome infectieux"),
            ],
        )
        data = d.model_dump()
        assert data["preuves_cliniques"][0]["type"] == "clinique"
        d2 = Diagnostic(**data)
        assert d2.preuves_cliniques[0].element == "fièvre 39°C"


class TestApplyLlmResultPreuves:
    """Teste le stockage des preuves cliniques dans _apply_llm_result_diagnostic."""

    def test_preuves_stored(self):
        from src.medical.rag_search import _apply_llm_result_diagnostic

        diag = Diagnostic(texte="Pneumopathie")
        llm_result = {
            "code": "J18.9",
            "confidence": "high",
            "justification": "Pneumopathie confirmée",
            "preuves_cliniques": [
                {"type": "biologie", "element": "CRP 120 mg/L", "interpretation": "syndrome inflammatoire"},
                {"type": "imagerie", "element": "Radio thorax: opacité", "interpretation": "foyer pulmonaire"},
            ],
        }
        _apply_llm_result_diagnostic(diag, llm_result)
        assert len(diag.preuves_cliniques) == 2
        assert diag.preuves_cliniques[0].type == "biologie"
        assert diag.preuves_cliniques[1].element == "Radio thorax: opacité"

    def test_preuves_empty_list(self):
        from src.medical.rag_search import _apply_llm_result_diagnostic

        diag = Diagnostic(texte="Test")
        llm_result = {"code": "K85.9", "confidence": "medium", "preuves_cliniques": []}
        _apply_llm_result_diagnostic(diag, llm_result)
        assert diag.preuves_cliniques == []

    def test_preuves_missing(self):
        from src.medical.rag_search import _apply_llm_result_diagnostic

        diag = Diagnostic(texte="Test")
        llm_result = {"code": "K85.9", "confidence": "medium"}
        _apply_llm_result_diagnostic(diag, llm_result)
        assert diag.preuves_cliniques == []

    def test_preuves_malformed_skipped(self):
        from src.medical.rag_search import _apply_llm_result_diagnostic

        diag = Diagnostic(texte="Test")
        llm_result = {
            "code": "K85.9",
            "confidence": "high",
            "preuves_cliniques": [
                {"type": "bio"},  # manque 'element' → ignoré
                {"type": "imagerie", "element": "TDM ok", "interpretation": "normal"},
                "not a dict",  # ignoré
            ],
        }
        _apply_llm_result_diagnostic(diag, llm_result)
        assert len(diag.preuves_cliniques) == 1
        assert diag.preuves_cliniques[0].element == "TDM ok"


class TestValidateJustifications:
    """Teste la fonction _validate_justifications."""

    @patch("src.medical.ollama_client.call_ollama")
    def test_confidence_adjusted(self, mock_ollama):
        from src.medical.cim10_extractor import _validate_justifications

        mock_ollama.return_value = {
            "validations": [
                {
                    "numero": 1,
                    "code": "K85.9",
                    "verdict": "maintenir",
                    "confidence_recommandee": "high",
                    "commentaire": "bien justifié",
                },
                {
                    "numero": 2,
                    "code": "I10",
                    "verdict": "maintenir",
                    "confidence_recommandee": "low",
                    "commentaire": "pas de preuve tensionnelle",
                },
            ],
            "alertes_globales": [],
        }

        dossier = DossierMedical(
            sejour=Sejour(sexe="M", age=60),
            diagnostic_principal=Diagnostic(
                texte="Pancréatite aiguë",
                cim10_suggestion="K85.9",
                cim10_confidence="medium",
            ),
            diagnostics_associes=[
                Diagnostic(
                    texte="HTA",
                    cim10_suggestion="I10",
                    cim10_confidence="high",
                ),
            ],
        )

        _validate_justifications(dossier)

        # DP: medium → high
        assert dossier.diagnostic_principal.cim10_confidence == "high"
        # DAS: high → low
        assert dossier.diagnostics_associes[0].cim10_confidence == "low"
        # Alertes de confiance
        assert any("QC:" in a and "I10" in a for a in dossier.alertes_codage)

    @patch("src.medical.ollama_client.call_ollama")
    def test_das_supprimer_alerte(self, mock_ollama):
        from src.medical.cim10_extractor import _validate_justifications

        mock_ollama.return_value = {
            "validations": [
                {
                    "numero": 1,
                    "code": "K85.9",
                    "verdict": "maintenir",
                    "confidence_recommandee": "high",
                    "commentaire": "ok",
                },
                {
                    "numero": 2,
                    "code": "R10.4",
                    "verdict": "supprimer",
                    "confidence_recommandee": "low",
                    "commentaire": "symptôme couvert par le DP",
                },
            ],
            "alertes_globales": ["Vérifier la spécificité du DP"],
        }

        dossier = DossierMedical(
            diagnostic_principal=Diagnostic(
                texte="Pancréatite aiguë",
                cim10_suggestion="K85.9",
                cim10_confidence="high",
            ),
            diagnostics_associes=[
                Diagnostic(
                    texte="Douleur abdominale",
                    cim10_suggestion="R10.4",
                    cim10_confidence="medium",
                ),
            ],
        )

        _validate_justifications(dossier)

        # Le DAS n'est pas supprimé automatiquement, mais une alerte est ajoutée
        assert any("à reconsidérer" in a for a in dossier.alertes_codage)
        assert any("Vérifier la spécificité" in a for a in dossier.alertes_codage)

    @patch("src.medical.ollama_client.call_ollama")
    def test_ollama_returns_none(self, mock_ollama):
        from src.medical.cim10_extractor import _validate_justifications

        mock_ollama.return_value = None
        dossier = DossierMedical(
            diagnostic_principal=Diagnostic(
                texte="Test",
                cim10_suggestion="K85.9",
                cim10_confidence="high",
            ),
        )
        _validate_justifications(dossier)
        assert dossier.alertes_codage == []

    def test_no_diags(self):
        from src.medical.cim10_extractor import _validate_justifications

        dossier = DossierMedical()
        _validate_justifications(dossier)
        assert dossier.alertes_codage == []

    @patch("src.medical.ollama_client.call_ollama")
    def test_invalid_validation_nums_skipped(self, mock_ollama):
        from src.medical.cim10_extractor import _validate_justifications

        mock_ollama.return_value = {
            "validations": [
                {"numero": 0, "code": "X", "verdict": "supprimer", "confidence_recommandee": "low", "commentaire": "oob"},
                {"numero": 99, "code": "Y", "verdict": "supprimer", "confidence_recommandee": "low", "commentaire": "oob"},
                {"numero": "abc", "code": "Z", "verdict": "supprimer", "confidence_recommandee": "low", "commentaire": "type"},
            ],
            "alertes_globales": [],
        }

        dossier = DossierMedical(
            diagnostic_principal=Diagnostic(texte="T", cim10_suggestion="A00", cim10_confidence="high"),
        )
        _validate_justifications(dossier)
        # Aucune modification, tous les numéros sont invalides
        assert dossier.diagnostic_principal.cim10_confidence == "high"
        assert dossier.alertes_codage == []