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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))
import pytest
from ai.features import PatientFactors # noqa: E402
from ai.jarvis import ( # noqa: E402
AutonomousMonitor,
ClinicalBrain,
JarvisPredictor,
PatientSummary,
ReasonerInputs,
active_risk_factors,
cite,
get_brain,
get_monitor,
get_rag,
get_reasoner,
get_store,
ontology_snapshot,
)
from ai.jarvis.knowledge_store import KnowledgeStore # noqa: E402
from ai.jarvis.medical_rag import MedicalRAG, expand_query # noqa: E402
from ai.jarvis.recommendations import build_recommendations # noqa: E402
# ---------------------------------------------------------------------------
# Ontology
# ---------------------------------------------------------------------------
def test_ontology_snapshot_has_three_sensors() -> None:
snap = ontology_snapshot()
assert set(snap["sensors"].keys()) == {"ph", "temperature_c", "humidity"}
for sensor in snap["sensors"].values():
assert sensor["bands"], "every sensor must declare bands"
for band in sensor["bands"]:
assert band["severity"] in {"stable", "watch", "warning", "critical"}
assert band["rationale"]
assert band["citations"], "every band must point at a citation"
def test_active_risk_factors_diabetic_elderly() -> None:
rfs = active_risk_factors(age=82, diabetes=True, smoker=False)
keys = {rf.key for rf in rfs}
assert keys == {"diabetes", "elderly_80"}
def test_cite_returns_known_metadata() -> None:
out = cite("WHO_SSI_2018")
assert out and out[0]["publisher"] == "World Health Organization"
def test_cite_skips_unknown_keys() -> None:
assert cite("does-not-exist") == []
# ---------------------------------------------------------------------------
# Knowledge store + RAG
# ---------------------------------------------------------------------------
def test_store_bootstrapped_from_corpus() -> None:
store = get_store()
assert store.count() >= 25
assert "ph" in store.topics()
def test_search_returns_relevant_passages() -> None:
rag = get_rag()
hits = rag.search("Why is alkaline pH a sign of infection?", top_k=3)
assert hits, "expected at least one hit"
# Top result should be ph-related
assert hits[0].passage.topic == "ph"
def test_search_supports_language_filter() -> None:
rag = get_rag()
hits_fr = rag.search("humidite cicatrisation", top_k=3, lang="fr")
assert any(h.passage.lang == "fr" for h in hits_fr)
def test_expand_query_adds_synonyms() -> None:
expanded = expand_query("pH infection")
assert "alkaline" in expanded
assert "SSI" in expanded
def test_writable_upsert(tmp_path: Path) -> None:
store = KnowledgeStore(store_path=tmp_path / "jarvis.sqlite")
passage = store.upsert(
id="custom-test-1",
topic="custom",
tags=["unit-test"],
lang="en",
text="Unit-test snippet describing arbitrary clinical content for retrieval.",
citations=["WHO_SSI_2018"],
)
assert store.get_by_id("custom-test-1") is not None
rag = MedicalRAG(store)
hits = rag.search("arbitrary clinical content", top_k=1, expand=False)
assert hits and hits[0].passage.id == passage.id
def test_explain_threshold_returns_context() -> None:
rag = get_rag()
ctx = rag.explain_threshold("ph", lang="en")
assert ctx.hits, "expected at least one passage"
assert ctx.citation_keys
# ---------------------------------------------------------------------------
# Explainable predictor
# ---------------------------------------------------------------------------
def test_predictor_marginal_attribution_signs() -> None:
from core.ai_engine import get_clinical_model
predictor = JarvisPredictor(get_clinical_model())
pred = predictor.predict(
temperature_c=38.4,
ph=8.0,
humidity=88.0,
velocity={"temp_delta_6h": 1.4, "ph_delta_6h": 0.7, "humidity_volatility_6h": 4.0},
factors=PatientFactors(age=70, diabetes=True, smoker=False),
)
assert pred.base.severity in ("warning", "critical")
top = pred.top_features(3)
assert len(top) == 3
# At least one of the temperature / pH features should push the score up
assert any(a.delta_score > 0 for a in top if a.feature in {"ph", "temperature_c"})
def test_predictor_returns_band_hits_in_order() -> None:
from core.ai_engine import get_clinical_model
predictor = JarvisPredictor(get_clinical_model())
pred = predictor.predict(
temperature_c=34.0,
ph=6.0,
humidity=78.0,
velocity={"temp_delta_6h": 0.0, "ph_delta_6h": 0.0, "humidity_volatility_6h": 1.0},
factors=PatientFactors(),
)
params = [bh.parameter for bh in pred.band_hits]
assert params == ["temperature_c", "ph", "humidity"]
assert all(bh.band.severity == "stable" for bh in pred.band_hits)
# ---------------------------------------------------------------------------
# Recommendations
# ---------------------------------------------------------------------------
def test_recommendations_for_critical_include_culture() -> None:
from core.ai_engine import get_clinical_model
predictor = JarvisPredictor(get_clinical_model())
pred = predictor.predict(
temperature_c=38.8,
ph=8.2,
humidity=90.0,
velocity={"temp_delta_6h": 1.6, "ph_delta_6h": 0.8, "humidity_volatility_6h": 5.0},
factors=PatientFactors(age=72, diabetes=True, smoker=False),
)
recs = build_recommendations(prediction=pred, bacterial_signature="Pseudomonas aeruginosa (suspected)")
assert any("culture" in r.text_en.lower() for r in recs)
assert any(r.tier == "immediate" for r in recs)
assert any("piperacillin" in r.text_en.lower() or "pseudomonas" in r.text_en.lower() for r in recs)
def test_recommendations_for_stable_are_empty_or_routine() -> None:
from core.ai_engine import get_clinical_model
predictor = JarvisPredictor(get_clinical_model())
pred = predictor.predict(
temperature_c=33.8,
ph=6.0,
humidity=78.0,
velocity={"temp_delta_6h": 0.0, "ph_delta_6h": 0.0, "humidity_volatility_6h": 0.5},
factors=PatientFactors(),
)
recs = build_recommendations(prediction=pred)
assert all(r.tier in {"monitoring", "prevention"} for r in recs)
assert not any(r.tier == "immediate" for r in recs)
# ---------------------------------------------------------------------------
# Clinical brain
# ---------------------------------------------------------------------------
@pytest.mark.parametrize("lang", ["en", "fr", "pt"])
def test_clinical_note_languages(lang: str) -> None:
from core.ai_engine import get_clinical_model
predictor = JarvisPredictor(get_clinical_model())
pred = predictor.predict(
temperature_c=38.0,
ph=7.7,
humidity=89.0,
velocity={"temp_delta_6h": 1.0, "ph_delta_6h": 0.5, "humidity_volatility_6h": 4.0},
factors=PatientFactors(age=68, diabetes=True, smoker=False),
)
recs = build_recommendations(prediction=pred, bacterial_signature="Staphylococcus aureus (suspected)")
brain = get_brain()
note = brain.generate_note(
patient=PatientSummary(
name="Test Patient",
age=68,
sex="F",
surgery_type="colectomy",
days_post_op=3,
diabetes=True,
smoker=False,
),
prediction=pred,
recommendations=recs,
rag_context=None,
bacterial_signature="Staphylococcus aureus (suspected)",
signature_confidence=0.72,
lang=lang,
)
assert note.lang == lang
assert note.headline
assert len(note.body) > 200
assert note.citations
assert note.summary_bullets
# ---------------------------------------------------------------------------
# Reasoner orchestration
# ---------------------------------------------------------------------------
def test_reasoner_full_pipeline_serialisable() -> None:
reasoner = get_reasoner()
inputs = ReasonerInputs(
patient=PatientSummary(
name="Maria Silva",
age=66,
sex="F",
surgery_type="appendectomy",
days_post_op=2,
diabetes=True,
smoker=False,
),
temperature_c=37.9,
ph=7.7,
humidity=88.0,
velocity={"temp_delta_6h": 1.2, "ph_delta_6h": 0.6, "humidity_volatility_6h": 4.5},
factors=PatientFactors(age=66, diabetes=True, smoker=False),
bacterial_signature="Staphylococcus aureus (suspected)",
signature_confidence=0.72,
tissue_state="inflammatory",
tissue_confidence=0.81,
lang="en",
)
analysis = reasoner.analyze(inputs)
payload = analysis.to_dict()
# All nested dataclasses serialise cleanly
assert payload["prediction"]["severity"] in {"warning", "critical"}
assert payload["clinical_note"]["body"]
assert payload["recommendations"]
assert payload["rag_context"]["passages"]
# The serialised dict should be JSON-roundtrippable
import json
json.loads(json.dumps(payload))
# ---------------------------------------------------------------------------
# Autonomous monitor
# ---------------------------------------------------------------------------
def _synthetic_history(values: list[tuple[float, float, float]], start: datetime | None = None):
start = start or datetime.now(timezone.utc) - timedelta(hours=5)
rows = []
for i, (t, p, h) in enumerate(values):
rows.append(
{
"timestamp": start + timedelta(minutes=i * 5),
"temperature_c": t,
"ph": p,
"humidity": h,
}
)
return rows
def test_autonomous_monitor_flags_coupled_climb() -> None:
monitor = get_monitor()
values = []
for i in range(20):
values.append((33.0 + i * 0.1, 6.5 + i * 0.05, 78.0))
report = monitor.analyze(_synthetic_history(values), factors=PatientFactors())
assert report.findings, "expected at least one finding"
kinds = {f.kind for f in report.findings}
assert "coupled_climb" in kinds
def test_autonomous_monitor_stable_baseline_quiet() -> None:
monitor = AutonomousMonitor()
values = [(34.0, 6.2, 80.0)] * 30
report = monitor.analyze(_synthetic_history(values), factors=PatientFactors())
assert report.composite_score < 0.2
assert report.severity_hint == "stable"
def test_autonomous_monitor_detects_humidity_drift() -> None:
monitor = AutonomousMonitor()
values = []
for i in range(24):
values.append((34.0, 6.2, 75.0 + i * 0.7))
report = monitor.analyze(_synthetic_history(values), factors=PatientFactors())
kinds = {f.kind for f in report.findings}
assert "humidity_drift" in kinds
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