t2a/tests/test_das_llm.py

"""Tests pour le pass LLM d'extraction de DAS supplémentaires."""

from __future__ import annotations

from pathlib import Path
from unittest.mock import patch, MagicMock

import pytest

from src.config import Diagnostic, DossierMedical, Sejour
from src.medical.ollama_cache import OllamaCache


class TestExtractDasLlm:
    """Tests pour extract_das_llm() dans rag_search.py."""

    def test_returns_das_from_llm(self):
        """Le pass LLM retourne des DAS supplémentaires."""
        from src.medical.rag_search import extract_das_llm

        mock_result = {
            "diagnostics_supplementaires": [
                {
                    "texte": "Hypertension artérielle",
                    "code_cim10": "I10",
                    "justification": "HTA mentionnée dans le texte",
                },
            ]
        }

        with patch("src.medical.rag_search.call_ollama", return_value=mock_result):
            result = extract_das_llm(
                text="Patient hypertendu sous traitement",
                contexte={"sexe": "M", "age": 65},
                existing_das=["Diabète de type 2 (E11.9)"],
                dp_texte="Pancréatite aiguë biliaire",
            )

        assert len(result) == 1
        assert result[0]["code_cim10"] == "I10"
        assert result[0]["texte"] == "Hypertension artérielle"

    def test_returns_empty_when_ollama_unavailable(self):
        """Retourne une liste vide si Ollama est indisponible."""
        from src.medical.rag_search import extract_das_llm

        with patch("src.medical.rag_search.call_ollama", return_value=None):
            result = extract_das_llm(
                text="Texte médical",
                contexte={},
                existing_das=[],
                dp_texte="",
            )

        assert result == []

    def test_returns_empty_on_bad_format(self):
        """Retourne une liste vide si le format de réponse est inattendu."""
        from src.medical.rag_search import extract_das_llm

        with patch("src.medical.rag_search.call_ollama", return_value={"other_key": "value"}):
            result = extract_das_llm(
                text="Texte médical",
                contexte={},
                existing_das=[],
                dp_texte="",
            )

        assert result == []

    def test_cache_hit(self, tmp_path):
        """Le cache est utilisé quand disponible."""
        from src.medical.rag_search import extract_das_llm

        cache = OllamaCache(tmp_path / "cache.json", "test-model")

        mock_result = {
            "diagnostics_supplementaires": [
                {"texte": "Anémie", "code_cim10": "D64.9", "justification": "test"},
            ]
        }

        # Premier appel : cache miss, appelle Ollama
        with patch("src.medical.rag_search.call_ollama", return_value=mock_result) as mock_call:
            result1 = extract_das_llm(
                text="Patient anémique Hb basse",
                contexte={},
                existing_das=[],
                dp_texte="",
                cache=cache,
            )
            assert mock_call.call_count == 1
            assert len(result1) == 1

        # Vérifier que le cache contient bien l'entrée
        assert len(cache) > 0

        # Deuxième appel : cache hit, pas d'appel Ollama
        with patch("src.medical.rag_search.call_ollama") as mock_call:
            result2 = extract_das_llm(
                text="Patient anémique Hb basse",
                contexte={},
                existing_das=[],
                dp_texte="",
                cache=cache,
            )
            mock_call.assert_not_called()

        assert len(result2) == 1
        assert result2[0]["code_cim10"] == "D64.9"

    def test_prompt_includes_context(self):
        """Le prompt contient le contexte patient et les DAS existants."""
        from src.medical.rag_search import _build_prompt_das_extraction

        prompt = _build_prompt_das_extraction(
            text="Patient hypertendu diabétique",
            contexte={"sexe": "F", "age": 72, "duree_sejour": 5},
            existing_das=["Diabète de type 2 (E11.9)", "Obésité (E66.0)"],
            dp_texte="Pancréatite aiguë biliaire",
        )

        assert "Pancréatite aiguë biliaire" in prompt
        assert "Diabète de type 2 (E11.9)" in prompt
        assert "Obésité (E66.0)" in prompt
        assert "Patient hypertendu diabétique" in prompt


class TestExtractDasLlmIntegration:
    """Tests d'intégration pour le pass LLM DAS dans cim10_extractor.py."""

    def test_das_llm_called_when_use_rag_true(self):
        """Le pass LLM DAS est appelé quand use_rag=True."""
        from src.medical.cim10_extractor import extract_medical_info

        parsed = {
            "type": "CRH",
            "patient": {"sexe": "M"},
            "sejour": {},
            "diagnostics": [
                {"libelle": "Pancréatite aiguë biliaire", "code_cim10": "K85.1", "type": "principal"},
            ],
        }

        with patch("src.medical.cim10_extractor._extract_das_llm") as mock_llm, \
             patch("src.medical.cim10_extractor._enrich_with_rag"):
            extract_medical_info(parsed, "texte médical", use_rag=True)
            mock_llm.assert_called_once()

    def test_das_llm_not_called_when_use_rag_false(self):
        """Le pass LLM DAS n'est PAS appelé quand use_rag=False."""
        from src.medical.cim10_extractor import extract_medical_info

        parsed = {
            "type": "CRH",
            "patient": {"sexe": "M"},
            "sejour": {},
            "diagnostics": [
                {"libelle": "Pancréatite aiguë biliaire", "code_cim10": "K85.1", "type": "principal"},
            ],
        }

        with patch("src.medical.cim10_extractor._extract_das_llm") as mock_llm:
            extract_medical_info(parsed, "texte médical", use_rag=False)
            mock_llm.assert_not_called()

    def test_das_llm_filters_invalid_codes(self):
        """Les codes CIM-10 invalides sont filtrés lors de l'intégration."""
        from src.medical.cim10_extractor import _extract_das_llm

        dossier = DossierMedical()
        dossier.sejour = Sejour(sexe="M", age=50)
        dossier.diagnostic_principal = Diagnostic(
            texte="Pancréatite aiguë", cim10_suggestion="K85.9",
        )

        mock_result = [
            {"texte": "Hypertension artérielle", "code_cim10": "I10", "justification": "ok"},
            {"texte": "Diagnostic bidon", "code_cim10": "ZZZ.99", "justification": "invalide"},
        ]

        with patch("src.medical.rag_search.extract_das_llm", return_value=mock_result):
            _extract_das_llm("texte médical", dossier)

        # I10 est valide → ajouté ; ZZZ.99 est invalide → filtré
        codes = [d.cim10_suggestion for d in dossier.diagnostics_associes]
        assert "I10" in codes
        assert "ZZZ.99" not in codes

    def test_das_llm_deduplicates(self):
        """Les codes déjà présents dans les DAS ne sont pas dupliqués."""
        from src.medical.cim10_extractor import _extract_das_llm

        dossier = DossierMedical()
        dossier.sejour = Sejour(sexe="M", age=50)
        dossier.diagnostic_principal = Diagnostic(
            texte="Pancréatite aiguë", cim10_suggestion="K85.9",
        )
        dossier.diagnostics_associes = [
            Diagnostic(texte="Hypertension artérielle", cim10_suggestion="I10"),
        ]

        mock_result = [
            {"texte": "HTA essentielle", "code_cim10": "I10", "justification": "doublon"},
            {"texte": "Obésité", "code_cim10": "E66.0", "justification": "nouveau"},
        ]

        with patch("src.medical.rag_search.extract_das_llm", return_value=mock_result):
            _extract_das_llm("texte médical", dossier)

        codes = [d.cim10_suggestion for d in dossier.diagnostics_associes]
        assert codes.count("I10") == 1  # Pas de doublon
        assert "E66.0" in codes  # Nouveau ajouté