feat: 修复 AI 风险语义并隔离 Mock 数据

ai-service/tests/test_detector.py

@@ -2,7 +2,7 @@ import base64
 
 import pytest
 
-from app.detector import MockDetector
+from app.detector import MockDetector, abnormal_probability, detection_status
 
 # 1x1 透明 PNG
 TINY_PNG = base64.b64decode(
@@ -23,3 +23,22 @@ def test_mock_detector_rejects_invalid_image():
     det = MockDetector()
     with pytest.raises(ValueError):
         det.detect(b"not an image")
+
+
+def test_abnormal_probability_ignores_healthy_and_unknown():
+    detections = [
+        {"class_name": "healthy", "confidence": 0.95},
+        {"class_name": "unknown", "confidence": 0.8},
+        {"class_name": "sick", "confidence": 0.72},
+    ]
+    assert abnormal_probability(detections, ("healthy", "sick")) == 0.72
+
+
+def test_detection_status_returns_unknown_for_empty_or_unknown():
+    assert detection_status([], ("healthy", "sick")) == "unknown"
+    assert detection_status([{"class_name": "unknown", "confidence": 0.8}], ("healthy", "sick")) == "unknown"
+    assert detection_status([{"class_name": "healthy", "confidence": 0.95}], ("healthy", "sick")) == "healthy"
+    assert detection_status(
+        [{"class_name": "white_muscardine", "confidence": 0.7}],
+        ("healthy", "white_muscardine", "nuclear_polyhedrosis"),
+    ) == "abnormal"