image_screening.py

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+"""
+image_screening.py — local image crisis screening slice for epic #130.
+
+Grounded scope:
+- screens OCR text, upstream object labels, and operator notes for crisis signals
+- intentionally does NOT claim raw computer-vision understanding of pixels
+- designed to plug into future multimodal scoring once a dedicated image model lands
+"""
+
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass, field
+from typing import Iterable, List, Optional
+
+from crisis.detect import detect_crisis
+
+
+DIRECT_SELF_HARM_LABELS = {
+    "blood",
+    "blade",
+    "razor",
+    "knife",
+    "scissors",
+    "noose",
+    "ligature",
+    "hanging",
+    "pills",
+    "pill bottle",
+    "overdose",
+    "gun",
+    "firearm",
+    "rope",
+    "cuts",
+    "self-harm",
+}
+
+INJURY_LABELS = {
+    "wound",
+    "wounds",
+    "bruise",
+    "bruises",
+    "bandage",
+    "bandages",
+    "injury",
+    "injuries",
+    "scar",
+    "scars",
+    "burn",
+    "burns",
+    "bleeding",
+}
+
+HIGH_RISK_SCENE_LABELS = {
+    "bridge edge",
+    "rooftop edge",
+    "train tracks",
+    "ledge",
+    "cliff edge",
+    "dark room",
+    "bathroom floor",
+    "hospital wristband",
+    "suicide note",
+    "goodbye letter",
+}
+
+
+@dataclass
+class ImageScreeningResult:
+    ocr_text: str = ""
+    labels: List[str] = field(default_factory=list)
+    visual_flags: List[str] = field(default_factory=list)
+    distress_score: float = 0.0
+    requires_human_review: bool = False
+    signals_detected: List[str] = field(default_factory=list)
+    grounded_scope: str = (
+        "heuristic screening over OCR text, upstream labels, and operator notes; "
+        "raw vision-model inference is not implemented in this slice"
+    )
+
+    def to_dict(self) -> dict:
+        return asdict(self)
+
+
+def _normalize_items(values: Optional[Iterable[str]]) -> List[str]:
+    if not values:
+        return []
+    normalized = []
+    for value in values:
+        text = str(value).strip().lower()
+        if text:
+            normalized.append(text)
+    return normalized
+
+
+def _match_keywords(haystack: str, keywords: set[str]) -> List[str]:
+    matches = []
+    for keyword in keywords:
+        if keyword in haystack:
+            matches.append(keyword)
+    return sorted(set(matches))
+
+
+def screen_image_signals(
+    image_path: Optional[str] = None,
+    *,
+    ocr_text: str = "",
+    labels: Optional[Iterable[str]] = None,
+    manual_notes: str = "",
+    visual_flags: Optional[Iterable[str]] = None,
+) -> ImageScreeningResult:
+    """
+    Score image-related crisis evidence without pretending to do full CV.
+
+    Inputs are deliberately grounded in what the repo can actually support today:
+    - OCR text extracted upstream from screenshots/photos
+    - object labels supplied by a local model or operator
+    - manual operator notes about visible scene context
+    - optional visual flags from any upstream preprocessor
+    """
+    normalized_labels = _normalize_items(labels)
+    normalized_flags = _normalize_items(visual_flags)
+    normalized_notes = str(manual_notes or "").strip().lower()
+    normalized_ocr = str(ocr_text or "").strip()
+
+    combined_label_text = " ".join(normalized_labels + normalized_flags + ([normalized_notes] if normalized_notes else []))
+    crisis_text = " ".join(part for part in [normalized_ocr, normalized_notes] if part).strip()
+
+    direct_matches = _match_keywords(combined_label_text, DIRECT_SELF_HARM_LABELS)
+    injury_matches = _match_keywords(combined_label_text, INJURY_LABELS)
+    scene_matches = _match_keywords(combined_label_text, HIGH_RISK_SCENE_LABELS)
+    text_detection = detect_crisis(crisis_text) if crisis_text else None
+
+    signals: List[str] = []
+    score = 0.0
+
+    if direct_matches:
+        score = max(score, 0.85)
+        for match in direct_matches:
+            signals.append(f"direct_self_harm_label:{match}")
+
+    if injury_matches:
+        score = max(score, 0.55)
+        for match in injury_matches:
+            signals.append(f"injury_indicator:{match}")
+
+    if scene_matches:
+        score = max(score, 0.4)
+        for match in scene_matches:
+            signals.append(f"high_risk_scene:{match}")
+
+    if text_detection and text_detection.level != "NONE":
+        score = max(score, min(1.0, text_detection.score))
+        signals.append(f"ocr_crisis_level:{text_detection.level}")
+        for indicator in text_detection.indicators[:3]:
+            signals.append(f"ocr_indicator:{indicator}")
+
+    if direct_matches and text_detection and text_detection.level in {"HIGH", "CRITICAL"}:
+        score = min(1.0, max(score, 0.95))
+        signals.append("cross_modal_confirmation:text_plus_visual")
+
+    requires_human_review = score >= 0.4 or bool(direct_matches)
+
+    return ImageScreeningResult(
+        ocr_text=normalized_ocr,
+        labels=list(normalized_labels),
+        visual_flags=list(normalized_flags),
+        distress_score=round(score, 4),
+        requires_human_review=requires_human_review,
+        signals_detected=signals,
+    )

tests/test_image_screening.py

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+"""Tests for local image crisis screening slice under epic #130."""
+
+import os
+import sys
+import unittest
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from image_screening import ImageScreeningResult, screen_image_signals
+
+
+class TestImageScreeningResult(unittest.TestCase):
+    def test_to_dict_preserves_core_fields(self):
+        result = ImageScreeningResult(
+            ocr_text="help me",
+            labels=["blood"],
+            visual_flags=["dark_scene"],
+            distress_score=0.8,
+            requires_human_review=True,
+            signals_detected=["direct_self_harm_label:blood"],
+        )
+
+        data = result.to_dict()
+        self.assertEqual(data["ocr_text"], "help me")
+        self.assertEqual(data["labels"], ["blood"])
+        self.assertTrue(data["requires_human_review"])
+
+
+class TestScreenImageSignals(unittest.TestCase):
+    def test_direct_self_harm_labels_trigger_high_risk(self):
+        result = screen_image_signals(
+            labels=["razor blade", "blood droplets"],
+            manual_notes="photo of fresh cuts on forearm",
+        )
+
+        self.assertGreaterEqual(result.distress_score, 0.8)
+        self.assertTrue(result.requires_human_review)
+        self.assertTrue(any("self_harm" in signal for signal in result.signals_detected))
+
+    def test_ocr_text_uses_existing_crisis_detector(self):
+        result = screen_image_signals(
+            ocr_text="I want to kill myself tonight",
+            labels=["handwritten note"],
+        )
+
+        self.assertGreaterEqual(result.distress_score, 0.7)
+        self.assertTrue(result.requires_human_review)
+        self.assertTrue(any(signal.startswith("ocr_crisis_level:") for signal in result.signals_detected))
+
+    def test_neutral_image_stays_low_risk(self):
+        result = screen_image_signals(
+            labels=["dog", "park", "sunlight"],
+            manual_notes="family outing in daylight",
+        )
+
+        self.assertLess(result.distress_score, 0.2)
+        self.assertFalse(result.requires_human_review)
+        self.assertEqual(result.signals_detected, [])
+
+
+if __name__ == "__main__":
+    unittest.main()