"""TDD tests for Gemini planner service."""

import json
from unittest.mock import MagicMock, patch

import pytest

from trialpath.models.eligibility_ledger import EligibilityLedger, OverallAssessment
from trialpath.models.search_anchors import SearchAnchors
from trialpath.services.gemini_planner import GeminiPlanner


@pytest.fixture
def sample_profile():
    return {
        "patient_id": "P001",
        "demographics": {"age": 52, "sex": "female"},
        "diagnosis": {
            "primary_condition": "Non-Small Cell Lung Cancer",
            "histology": "adenocarcinoma",
            "stage": "IVa",
        },
        "biomarkers": [
            {"name": "EGFR", "result": "Exon 19 deletion"},
        ],
        "performance_status": {"scale": "ECOG", "value": 1},
    }


@pytest.fixture
def mock_gemini():
    """Fixture that patches Gemini client for all tests."""
    with patch("google.genai.Client") as MockClient:
        mock_generate = MagicMock()
        MockClient.return_value.models.generate_content = mock_generate
        yield mock_generate


def _set_mock_response(mock_generate, text: str):
    mock_response = MagicMock()
    mock_response.text = text
    mock_generate.return_value = mock_response


class TestGeminiSearchAnchorsGeneration:
    """Test Gemini structured output for SearchAnchors generation."""

    @pytest.mark.asyncio
    async def test_search_anchors_has_correct_condition(self, sample_profile, mock_gemini):
        """Generated SearchAnchors should reference NSCLC."""
        _set_mock_response(
            mock_gemini,
            SearchAnchors(
                condition="Non-Small Cell Lung Cancer",
                subtype="adenocarcinoma",
                biomarkers=["EGFR exon 19 deletion"],
                stage="IV",
                age=52,
                performance_status_max=1,
            ).model_dump_json(),
        )

        planner = GeminiPlanner()
        anchors = await planner.generate_search_anchors(sample_profile)

        assert "lung" in anchors.condition.lower() or "nsclc" in anchors.condition.lower()
        assert anchors.age == 52

    @pytest.mark.asyncio
    async def test_search_anchors_includes_biomarkers(self, sample_profile, mock_gemini):
        """SearchAnchors should include patient biomarkers."""
        _set_mock_response(
            mock_gemini,
            SearchAnchors(
                condition="NSCLC",
                biomarkers=["EGFR exon 19 deletion"],
            ).model_dump_json(),
        )

        planner = GeminiPlanner()
        anchors = await planner.generate_search_anchors(sample_profile)

        assert len(anchors.biomarkers) > 0
        assert any("EGFR" in b for b in anchors.biomarkers)

    @pytest.mark.asyncio
    async def test_search_anchors_json_schema_passed(self, sample_profile, mock_gemini):
        """Verify that Gemini is called with response_json_schema."""
        _set_mock_response(mock_gemini, SearchAnchors(condition="NSCLC").model_dump_json())

        planner = GeminiPlanner()
        await planner.generate_search_anchors(sample_profile)

        call_args = mock_gemini.call_args
        config = call_args.kwargs.get("config", call_args[1].get("config", {}))
        assert config.get("response_mime_type") == "application/json"
        assert "response_json_schema" in config


class TestGeminiEligibilityEvaluation:
    """Test Gemini eligibility evaluation output."""

    @pytest.mark.asyncio
    async def test_ledger_has_all_required_fields(self):
        """EligibilityLedger from Gemini should have patient_id, nct_id, assessment."""
        mock_ledger = EligibilityLedger(
            patient_id="P001",
            nct_id="NCT01234567",
            overall_assessment=OverallAssessment.UNCERTAIN,
            criteria=[],
            gaps=[],
        )

        assert mock_ledger.patient_id == "P001"
        assert mock_ledger.nct_id == "NCT01234567"
        assert mock_ledger.overall_assessment in OverallAssessment

    @pytest.mark.asyncio
    async def test_error_handling_invalid_json(self):
        """Should raise error on invalid Gemini JSON response."""
        with patch("google.genai.Client") as MockClient:
            mock_response = MagicMock()
            mock_response.text = "not valid json"

            MockClient.return_value.models.generate_content = MagicMock(return_value=mock_response)

            planner = GeminiPlanner()
            with pytest.raises(Exception):
                await planner.evaluate_eligibility({}, {}, None)


class TestGeminiRefineSearch:
    """Test search refinement methods."""

    @pytest.mark.asyncio
    async def test_refine_search_returns_search_anchors(self, mock_gemini):
        """refine_search should return valid SearchAnchors."""
        refined = SearchAnchors(
            condition="NSCLC",
            biomarkers=["EGFR exon 19 deletion"],
            trial_filters={"recruitment_status": ["Recruiting"], "phase": ["Phase 3"]},
        )
        _set_mock_response(mock_gemini, refined.model_dump_json())

        planner = GeminiPlanner()
        anchors = SearchAnchors(condition="NSCLC", biomarkers=["EGFR"])
        result = await planner.refine_search(anchors, result_count=100)

        assert isinstance(result, SearchAnchors)
        assert result.condition == "NSCLC"

    @pytest.mark.asyncio
    async def test_relax_search_returns_search_anchors(self, mock_gemini):
        """relax_search should return valid SearchAnchors."""
        relaxed = SearchAnchors(
            condition="NSCLC",
            trial_filters={"recruitment_status": ["Recruiting", "Not yet recruiting"]},
        )
        _set_mock_response(mock_gemini, relaxed.model_dump_json())

        planner = GeminiPlanner()
        anchors = SearchAnchors(condition="NSCLC", biomarkers=["EGFR"])
        result = await planner.relax_search(anchors, result_count=0)

        assert isinstance(result, SearchAnchors)

    @pytest.mark.asyncio
    async def test_refine_passes_result_count_in_prompt(self, mock_gemini):
        """refine_search should include result_count in prompt."""
        _set_mock_response(mock_gemini, SearchAnchors(condition="NSCLC").model_dump_json())

        planner = GeminiPlanner()
        anchors = SearchAnchors(condition="NSCLC")
        await planner.refine_search(anchors, result_count=150)

        call_args = mock_gemini.call_args
        prompt = call_args.kwargs.get("contents", call_args[1].get("contents", ""))
        assert "150" in prompt


class TestGeminiSliceCriteria:
    """Test criteria slicing."""

    @pytest.mark.asyncio
    async def test_slice_criteria_returns_list(self, mock_gemini):
        """slice_criteria should return list of criterion dicts."""
        _set_mock_response(
            mock_gemini,
            json.dumps(
                {
                    "criteria": [
                        {
                            "criterion_id": "inc_1",
                            "type": "inclusion",
                            "text": "Age >= 18",
                            "category": "structural",
                        },
                        {
                            "criterion_id": "inc_2",
                            "type": "inclusion",
                            "text": "Confirmed NSCLC",
                            "category": "medical",
                        },
                    ]
                }
            ),
        )

        planner = GeminiPlanner()
        result = await planner.slice_criteria({"nct_id": "NCT001"})

        assert len(result) == 2
        assert result[0]["criterion_id"] == "inc_1"
        assert result[1]["category"] == "medical"


class TestGeminiStructuralCriterion:
    """Test structural criterion evaluation."""

    @pytest.mark.asyncio
    async def test_evaluate_structural_returns_decision(self, mock_gemini):
        """evaluate_structural_criterion should return decision dict."""
        _set_mock_response(
            mock_gemini,
            json.dumps(
                {
                    "decision": "met",
                    "reasoning": "Patient age 52 >= 18",
                    "confidence": 0.99,
                }
            ),
        )

        planner = GeminiPlanner()
        result = await planner.evaluate_structural_criterion(
            "Age >= 18 years",
            {"demographics": {"age": 52}},
        )

        assert result["decision"] == "met"
        assert result["confidence"] == 0.99


class TestGeminiAggregateAssessments:
    """Test assessment aggregation."""

    @pytest.mark.asyncio
    async def test_aggregate_returns_ledger(self, mock_gemini):
        """aggregate_assessments should return EligibilityLedger."""
        ledger = EligibilityLedger(
            patient_id="P001",
            nct_id="NCT001",
            overall_assessment=OverallAssessment.LIKELY_ELIGIBLE,
            criteria=[],
            gaps=[],
        )
        _set_mock_response(mock_gemini, ledger.model_dump_json())

        planner = GeminiPlanner()
        result = await planner.aggregate_assessments({}, {}, [])

        assert isinstance(result, EligibilityLedger)
        assert result.overall_assessment == OverallAssessment.LIKELY_ELIGIBLE


class TestGeminiGapAnalysis:
    """Test gap analysis."""

    @pytest.mark.asyncio
    async def test_analyze_gaps_returns_list(self, mock_gemini):
        """analyze_gaps should return list of gap dicts."""
        _set_mock_response(
            mock_gemini,
            json.dumps(
                {
                    "gaps": [
                        {
                            "description": "Brain MRI results needed",
                            "recommended_action": "Upload brain MRI report",
                            "clinical_importance": "high",
                            "affected_trial_count": 2,
                        },
                    ]
                }
            ),
        )

        planner = GeminiPlanner()
        result = await planner.analyze_gaps({}, [])

        assert len(result) == 1
        assert result[0]["clinical_importance"] == "high"
        assert result[0]["affected_trial_count"] == 2

    @pytest.mark.asyncio
    async def test_analyze_gaps_uses_json_schema(self, mock_gemini):
        """analyze_gaps should pass JSON schema to Gemini."""
        _set_mock_response(mock_gemini, json.dumps({"gaps": []}))

        planner = GeminiPlanner()
        await planner.analyze_gaps({}, [])

        call_args = mock_gemini.call_args
        config = call_args.kwargs.get("config", call_args[1].get("config", {}))
        assert config.get("response_mime_type") == "application/json"
        assert "response_json_schema" in config