"""Smoke tests for the BioPatch AI engine.

Run with `python -m pytest backend/tests` once pytest is installed, or
just `python backend/tests/test_ai_engine.py` for a manual sanity check.
"""

from __future__ import annotations

import sys
from datetime import datetime, timedelta, timezone
from pathlib import Path

sys.path.insert(0, str(Path(__file__).resolve().parent.parent))

from ai.features import PatientFactors  # noqa: E402
from core.ai_engine import Reading, SepsisRiskEngine, assess_reading  # noqa: E402


def test_stable_healing_reading_is_low_risk() -> None:
    result = assess_reading(temperature_c=33.5, ph=6.0, humidity=75.0)
    assert result["score"] < 25, result
    assert result["severity"] == "stable"
    assert result["bacterial_signature"] is None


def test_classic_ssi_pattern_is_critical() -> None:
    result = assess_reading(temperature_c=38.6, ph=8.1, humidity=82.0)
    assert result["score"] >= 75, result
    assert result["severity"] == "critical"
    assert result["bacterial_signature"] is not None


def test_pseudomonas_signature_detection() -> None:
    result = assess_reading(temperature_c=37.8, ph=8.3, humidity=88.0)
    sig = result["bacterial_signature"] or ""
    assert "Pseudomonas" in sig, result


def test_velocity_term_flags_rapid_co_deterioration() -> None:
    engine = SepsisRiskEngine()
    base = datetime.now(timezone.utc)
    # Baseline reading (6h earlier, in the velocity window)
    engine.assess(Reading(36.8, 7.0, 78.0, timestamp=base - timedelta(hours=5)))
    # Now: temp + pH have risen significantly
    later = engine.assess(Reading(38.4, 7.9, 80.0, timestamp=base))
    drivers_text = " | ".join(later.drivers)
    assert "rising" in drivers_text.lower() or "shift" in drivers_text.lower(), later


def test_scores_are_bounded() -> None:
    extreme = assess_reading(temperature_c=42.0, ph=10.0, humidity=99.0)
    assert 0.0 <= extreme["score"] <= 100.0


def test_granulation_classification() -> None:
    result = assess_reading(temperature_c=34.5, ph=7.2, humidity=80.0)
    assert result["tissue_state"] == "granulation"
    assert result["tissue_confidence"] >= 0.65
    assert "Granulação" in result["tissue_state_label_pt"]


def test_devitalized_classification_ischemic_acidic() -> None:
    """Spec: low temp + acidic pH (<6.5) -> devitalized."""
    result = assess_reading(temperature_c=30.5, ph=6.0, humidity=60.0)
    assert result["tissue_state"] == "devitalized", result
    assert "Desvitalização" in result["tissue_state_label_pt"]


def test_inflammatory_tissue_state() -> None:
    result = assess_reading(temperature_c=37.8, ph=7.6, humidity=78.0)
    assert result["tissue_state"] == "inflammatory"


def test_fusion_quality_penalises_isolated_axis_spike() -> None:
    isolated = assess_reading(temperature_c=33.5, ph=8.4, humidity=78.0)
    full     = assess_reading(temperature_c=38.4, ph=8.4, humidity=88.0)
    # Sensor contributions should be more concentrated in the isolated case
    assert isolated["fusion_quality"] < full["fusion_quality"], (isolated, full)
    assert full["score"] > isolated["score"]


def test_xai_sensor_contributions_sum_to_one() -> None:
    result = assess_reading(temperature_c=38.2, ph=7.6, humidity=84.0)
    contribs = result["sensor_contributions"]
    assert set(contribs) == {"temperature", "ph", "humidity"}
    total = sum(contribs.values())
    assert 0.97 <= total <= 1.03, total


def test_context_factor_raises_with_risk_factors() -> None:
    base_result = assess_reading(
        temperature_c=37.2, ph=7.3, humidity=80.0,
        factors=PatientFactors(age=45, diabetes=False, smoker=False),
    )
    frail_result = assess_reading(
        temperature_c=37.2, ph=7.3, humidity=80.0,
        factors=PatientFactors(age=78, diabetes=True, smoker=True),
    )
    assert frail_result["context_factor"] > base_result["context_factor"]
    # Same sensor numbers, more fragile patient -> at least same severity bucket
    sev_rank = {"stable": 0, "watch": 1, "warning": 2, "critical": 3}
    assert sev_rank[frail_result["severity"]] >= sev_rank[base_result["severity"]]


def test_model_loaded_and_returns_class_probabilities() -> None:
    result = assess_reading(temperature_c=36.0, ph=7.0, humidity=78.0)
    probs = result["class_probabilities"]
    assert set(probs) == {"stable", "watch", "warning", "critical"}
    assert 0.97 <= sum(probs.values()) <= 1.03
    assert result["model_version"].startswith("rf-")


def test_feature_importance_reasonable_after_training() -> None:
    result = assess_reading(temperature_c=36.0, ph=7.0, humidity=78.0)
    fi = result["feature_importance"]
    expected = {
        "temperature_c", "ph", "humidity",
        "temp_delta_6h", "ph_delta_6h", "humidity_volatility_6h",
        "age", "diabetes", "smoker",
    }
    assert set(fi) == expected
    # The three sensors should carry most of the model's signal
    sensor_sum = fi["temperature_c"] + fi["ph"] + fi["humidity"]
    velocity_sum = fi["temp_delta_6h"] + fi["ph_delta_6h"] + fi["humidity_volatility_6h"]
    assert sensor_sum + velocity_sum > 0.7


if __name__ == "__main__":
    test_stable_healing_reading_is_low_risk()
    test_classic_ssi_pattern_is_critical()
    test_pseudomonas_signature_detection()
    test_velocity_term_flags_rapid_co_deterioration()
    test_scores_are_bounded()
    test_granulation_classification()
    test_devitalized_classification_ischemic_acidic()
    test_inflammatory_tissue_state()
    test_fusion_quality_penalises_isolated_axis_spike()
    test_xai_sensor_contributions_sum_to_one()
    test_context_factor_raises_with_risk_factors()
    test_model_loaded_and_returns_class_probabilities()
    test_feature_importance_reasonable_after_training()
    print("OK - all ai_engine smoke tests passed")