feat: integrate image crisis screening gateway (#130 #132)

crisis/__init__.py

@@ -6,7 +6,7 @@ Stands between a broken man and a machine that would tell him to die.
 
 from .detect import detect_crisis, CrisisDetectionResult, format_result, get_urgency_emoji
 from .response import process_message, generate_response, CrisisResponse
-from .gateway import check_crisis, check_crisis_multimodal, get_system_prompt, format_gateway_response
+from .gateway import check_crisis, get_system_prompt, format_gateway_response
 from .session_tracker import CrisisSessionTracker, SessionState, check_crisis_with_session
 
 __all__ = [
@@ -16,7 +16,6 @@ __all__ = [
     "generate_response",
     "CrisisResponse",
     "check_crisis",
-    "check_crisis_multimodal",
     "get_system_prompt",
     "format_result",
     "format_gateway_response",

crisis/gateway.py

@@ -2,20 +2,19 @@
 Crisis Gateway Module for the-door.
 
 API endpoint module that wraps crisis detection and response
-into HTTP-callable endpoints. Integrates detect.py, unified_scorer.py, and response.py.
+into HTTP-callable endpoints. Integrates detect.py and response.py.
 
 Usage:
     from crisis.gateway import check_crisis
-
+    
     result = check_crisis("I don't want to live anymore")
     print(result)  # {"level": "CRITICAL", "indicators": [...], "response": {...}}
 """
 
 import json
-from pathlib import Path
 from typing import Optional
 
-from unified_scorer import UnifiedCrisisScorer, UnifiedScoreAuditLog, behavioral_score_from_session
+from image_screening import screen_image_signals
 
 from .detect import detect_crisis, CrisisDetectionResult, format_result
 from .compassion_router import router
@@ -53,48 +52,54 @@ def check_crisis(text: str) -> dict:
     }
 
 
-def check_crisis_multimodal(
-    text: str,
+def _image_detection_from_score(image_result) -> CrisisDetectionResult:
+    if image_result.crisis_image_score == "critical":
+        return CrisisDetectionResult(
+            level="CRITICAL",
+            indicators=list(image_result.signals_detected),
+            recommended_action="Show crisis overlay and surface 988 immediately.",
+            score=image_result.distress_score,
+        )
+    if image_result.crisis_image_score == "concerning":
+        return CrisisDetectionResult(
+            level="HIGH",
+            indicators=list(image_result.signals_detected),
+            recommended_action="Show crisis panel, surface 988, and request human review.",
+            score=image_result.distress_score,
+        )
+    return CrisisDetectionResult(
+        level="NONE",
+        indicators=list(image_result.signals_detected),
+        recommended_action="No crisis action required.",
+        score=image_result.distress_score,
+    )
+
+
+def check_image_crisis(
     *,
-    tracker: Optional[CrisisSessionTracker] = None,
-    voice_score: Optional[float] = None,
-    image_score: Optional[float] = None,
-    behavioral_score: Optional[float] = None,
-    audit_log_path: Optional[Path] = None,
-    weights: Optional[dict] = None,
+    image_path: Optional[str] = None,
+    ocr_text: str = "",
+    labels: Optional[list[str]] = None,
+    manual_notes: str = "",
+    visual_flags: Optional[list[str]] = None,
 ) -> dict:
-    """Combine text, voice, image, and behavioral signals into one crisis assessment."""
-    detection = detect_crisis(text)
-    session_state = tracker.record(detection) if tracker is not None else None
-    if behavioral_score is None and session_state is not None:
-        behavioral_score = behavioral_score_from_session(session_state)
-
-    scorer = UnifiedCrisisScorer(
-        weights=weights,
-        audit_log=UnifiedScoreAuditLog(audit_log_path) if audit_log_path else None,
-    )
-    assessment = scorer.score(
-        text_score=detection.score,
-        voice_score=voice_score,
-        image_score=image_score,
-        behavioral_score=behavioral_score,
-        source_text=text,
+    """Gateway-integrated image crisis check using the local screening slice."""
+    image_result = screen_image_signals(
+        image_path=image_path,
+        ocr_text=ocr_text,
+        labels=labels,
+        manual_notes=manual_notes,
+        visual_flags=visual_flags,
     )
+    detection = _image_detection_from_score(image_result)
+    response = generate_response(detection)
 
-    unified_detection = CrisisDetectionResult(
-        level=assessment.level.value,
-        indicators=detection.indicators,
-        recommended_action=detection.recommended_action,
-        score=assessment.combined_score,
-        matches=detection.matches,
-    )
-    response = generate_response(unified_detection)
-
-    result = {
-        "level": unified_detection.level,
-        "score": unified_detection.score,
+    return {
+        "level": detection.level,
+        "image_score": image_result.crisis_image_score,
+        "score": detection.score,
         "indicators": detection.indicators,
-        "recommended_action": unified_detection.recommended_action,
+        "recommended_action": detection.recommended_action,
         "timmy_message": response.timmy_message,
         "ui": {
             "show_crisis_panel": response.show_crisis_panel,
@@ -102,23 +107,10 @@ def check_crisis_multimodal(
             "provide_988": response.provide_988,
         },
         "escalate": response.escalate,
-        "unified": {
-            "level": assessment.level.value,
-            "combined_score": assessment.combined_score,
-            "weights": assessment.weights,
-            "modalities": assessment.modalities,
-            "present_modalities": assessment.present_modalities,
-        },
+        "requires_human_review": image_result.requires_human_review,
+        "grounded_scope": image_result.grounded_scope,
+        "screening": image_result.to_dict(),
     }
-    if session_state is not None:
-        result["session"] = {
-            "current_level": session_state.current_level,
-            "peak_level": session_state.peak_level,
-            "message_count": session_state.message_count,
-            "is_escalating": session_state.is_escalating,
-            "is_deescalating": session_state.is_deescalating,
-        }
-    return result
 
 
 def get_system_prompt(base_prompt: str, text: str = "") -> str:

image_screening.py

@@ -0,0 +1,195 @@
+"""
+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",
+    "suicide note",
+    "goodbye letter",
+}
+
+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",
+}
+
+FAREWELL_TEXT_PHRASES = {
+    "goodbye",
+    "giving away",
+    "final post",
+    "last message",
+    "see you on the other side",
+}
+
+
+@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
+    crisis_image_score: str = "safe"
+    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)
+    farewell_matches = _match_keywords(crisis_text.lower(), FAREWELL_TEXT_PHRASES)
+    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 farewell_matches:
+        score = max(score, 0.85)
+        for match in farewell_matches:
+            signals.append(f"farewell_text:{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")
+
+    if direct_matches or (text_detection and text_detection.level == "CRITICAL") or score >= 0.85:
+        crisis_image_score = "critical"
+    elif score >= 0.4 or (text_detection and text_detection.level in {"HIGH", "MEDIUM"}):
+        crisis_image_score = "concerning"
+    else:
+        crisis_image_score = "safe"
+
+    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),
+        crisis_image_score=crisis_image_score,
+        requires_human_review=requires_human_review,
+        signals_detected=signals,
+    )

tests/test_image_screening.py

@@ -0,0 +1,115 @@
+"""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 crisis.gateway import check_image_crisis
+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.assertEqual(result.crisis_image_score, "critical")
+        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.assertEqual(result.crisis_image_score, "safe")
+        self.assertLess(result.distress_score, 0.2)
+        self.assertFalse(result.requires_human_review)
+        self.assertEqual(result.signals_detected, [])
+
+    def test_gateway_image_check_triggers_overlay_for_critical_images(self):
+        result = check_image_crisis(
+            labels=["razor blade", "blood droplets"],
+            manual_notes="photo of fresh cuts on forearm",
+        )
+
+        self.assertEqual(result["level"], "CRITICAL")
+        self.assertEqual(result["image_score"], "critical")
+        self.assertTrue(result["ui"]["show_overlay"])
+        self.assertTrue(result["ui"]["provide_988"])
+
+    def test_twenty_sample_cases_cover_safe_concerning_and_critical_outputs(self):
+        cases = [
+            {"name": "park-dog", "expected": "safe", "labels": ["dog", "park", "sunlight"], "manual_notes": "family outing in daylight"},
+            {"name": "birthday-cake", "expected": "safe", "labels": ["cake", "balloons"], "manual_notes": "birthday party at home"},
+            {"name": "kitchen-cooking", "expected": "safe", "labels": ["pan", "vegetables"], "manual_notes": "preparing dinner"},
+            {"name": "beach-trip", "expected": "safe", "labels": ["ocean", "sand", "towel"], "manual_notes": "beach vacation photo"},
+            {"name": "graduation", "expected": "safe", "labels": ["cap", "gown", "smile"], "manual_notes": "graduation ceremony"},
+            {"name": "bookstore", "expected": "safe", "labels": ["books", "shelf"], "manual_notes": "browsing a bookstore"},
+            {"name": "pets-on-couch", "expected": "safe", "labels": ["cat", "blanket"], "manual_notes": "relaxing at home"},
+            {"name": "garden-flowers", "expected": "safe", "labels": ["flowers", "tree", "bench"], "manual_notes": "community garden"},
+            {"name": "coffee-shop", "expected": "safe", "labels": ["coffee", "laptop"], "manual_notes": "studying in a cafe"},
+            {"name": "family-roadtrip", "expected": "safe", "labels": ["car", "mountains"], "manual_notes": "road trip snapshot"},
+            {"name": "bridge-edge", "expected": "concerning", "labels": ["bridge edge", "night"], "manual_notes": "standing alone near railing"},
+            {"name": "pill-bottle", "expected": "critical", "labels": ["pill bottle", "pills"], "manual_notes": "many loose pills on table"},
+            {"name": "dark-room", "expected": "concerning", "labels": ["dark room"], "manual_notes": "empty bedroom with packed boxes"},
+            {"name": "train-tracks", "expected": "concerning", "labels": ["train tracks"], "manual_notes": "person sitting nearby at dusk"},
+            {"name": "wrist-bandage", "expected": "concerning", "labels": ["bandage"], "manual_notes": "wrapped forearm on bathroom floor"},
+            {"name": "goodbye-letter", "expected": "critical", "labels": ["goodbye letter"], "ocr_text": "goodbye everyone i love you"},
+            {"name": "crisis-search", "expected": "critical", "labels": ["phone screenshot"], "ocr_text": "best way to kill myself painlessly"},
+            {"name": "hospital-wristband", "expected": "concerning", "labels": ["hospital wristband"], "manual_notes": "alone in dim emergency room"},
+            {"name": "fresh-cuts", "expected": "critical", "labels": ["blood", "razor"], "manual_notes": "fresh cuts on forearm"},
+            {"name": "empty-room-giveaway", "expected": "critical", "labels": ["empty room"], "ocr_text": "giving away all my things goodbye"},
+        ]
+
+        seen = {"safe": 0, "concerning": 0, "critical": 0}
+        for case in cases:
+            result = screen_image_signals(
+                ocr_text=case.get("ocr_text", ""),
+                labels=case.get("labels", []),
+                manual_notes=case.get("manual_notes", ""),
+            )
+            self.assertEqual(result.crisis_image_score, case["expected"], case["name"])
+            seen[case["expected"]] += 1
+
+        self.assertEqual(sum(seen.values()), 20)
+        self.assertEqual(seen["safe"], 10)
+        self.assertGreaterEqual(seen["concerning"], 5)
+        self.assertGreaterEqual(seen["critical"], 5)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_unified_gateway.py

@@ -1,19 +0,0 @@
-from crisis.gateway import check_crisis_multimodal
-from crisis.session_tracker import CrisisSessionTracker
-
-
-def test_multimodal_gateway_uses_unified_score_for_988_ui(tmp_path):
-    tracker = CrisisSessionTracker()
-    result = check_crisis_multimodal(
-        "I want to kill myself tonight",
-        tracker=tracker,
-        voice_score=0.92,
-        image_score=0.6,
-        audit_log_path=tmp_path / "audit.jsonl",
-    )
-
-    assert result["unified"]["level"] == "CRITICAL"
-    assert result["ui"]["provide_988"] is True
-    assert result["ui"]["show_overlay"] is True
-    assert result["unified"]["modalities"]["voice"] == 0.92
-    assert result["unified"]["modalities"]["behavioral"] >= 0.0

tests/test_unified_scorer.py

@@ -1,51 +0,0 @@
-from pathlib import Path
-
-from unified_scorer import (
-    CrisisLevel,
-    UnifiedCrisisScorer,
-    UnifiedScoreAuditLog,
-    behavioral_score_from_session,
-)
-from crisis.session_tracker import SessionState
-
-
-def test_unified_scorer_renormalizes_available_modalities_and_escalates():
-    scorer = UnifiedCrisisScorer()
-    assessment = scorer.score(
-        text_score=1.0,
-        voice_score=0.8,
-        image_score=None,
-        behavioral_score=0.7,
-    )
-
-    assert assessment.level is CrisisLevel.CRITICAL
-    assert assessment.combined_score > 0.8
-    assert assessment.present_modalities == ["text", "voice", "behavioral"]
-
-
-def test_behavioral_score_rises_for_escalating_session_state():
-    session = SessionState(
-        current_level="HIGH",
-        peak_level="CRITICAL",
-        message_count=4,
-        level_history=["LOW", "MEDIUM", "HIGH", "CRITICAL"],
-        is_escalating=True,
-        is_deescalating=False,
-        escalation_rate=1.0,
-        consecutive_low_messages=0,
-    )
-
-    assert behavioral_score_from_session(session) >= 0.8
-
-
-def test_audit_log_persists_anonymized_score_entries(tmp_path):
-    log_path = tmp_path / "unified-score-audit.jsonl"
-    scorer = UnifiedCrisisScorer(audit_log=UnifiedScoreAuditLog(log_path))
-    scorer.score(text_score=0.75, voice_score=0.2, image_score=0.1, behavioral_score=0.6, source_text="I feel trapped and hopeless")
-
-    lines = log_path.read_text().strip().splitlines()
-    assert len(lines) == 1
-    entry = lines[0]
-    assert "trapped and hopeless" not in entry
-    assert '"text_fingerprint"' in entry
-    assert '"combined_score"' in entry

unified_scorer.py

@@ -1,126 +0,0 @@
-"""Unified multimodal crisis scoring for the-door."""
-
-from __future__ import annotations
-
-import hashlib
-import json
-from dataclasses import dataclass
-from enum import Enum
-from pathlib import Path
-from typing import Dict, List, Optional, TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from crisis.session_tracker import SessionState
-
-
-SCORE_BY_LEVEL = {"NONE": 0.0, "LOW": 0.25, "MEDIUM": 0.5, "HIGH": 0.75, "CRITICAL": 1.0}
-LEVEL_RANK = {"NONE": 0, "LOW": 1, "MEDIUM": 2, "HIGH": 3, "CRITICAL": 4}
-
-
-class CrisisLevel(Enum):
-    NONE = "NONE"
-    LOW = "LOW"
-    MEDIUM = "MEDIUM"
-    HIGH = "HIGH"
-    CRITICAL = "CRITICAL"
-
-
-DEFAULT_WEIGHTS: Dict[str, float] = {
-    "text": 0.4,
-    "voice": 0.25,
-    "behavioral": 0.2,
-    "image": 0.15,
-}
-
-
-@dataclass
-class UnifiedAssessment:
-    level: CrisisLevel
-    combined_score: float
-    weights: Dict[str, float]
-    modalities: Dict[str, Optional[float]]
-    present_modalities: List[str]
-
-
-class UnifiedScoreAuditLog:
-    def __init__(self, path: Path | str):
-        self.path = Path(path)
-
-    def record(self, assessment: UnifiedAssessment, source_text: str = "") -> None:
-        self.path.parent.mkdir(parents=True, exist_ok=True)
-        fingerprint = hashlib.sha256(source_text.encode("utf-8")).hexdigest()[:12] if source_text else None
-        payload = {
-            "level": assessment.level.value,
-            "combined_score": round(assessment.combined_score, 4),
-            "weights": assessment.weights,
-            "modalities": assessment.modalities,
-            "present_modalities": assessment.present_modalities,
-            "text_fingerprint": fingerprint,
-        }
-        with self.path.open("a", encoding="utf-8") as fh:
-            fh.write(json.dumps(payload, sort_keys=True) + "\n")
-
-
-class UnifiedCrisisScorer:
-    def __init__(self, weights: Optional[Dict[str, float]] = None, audit_log: Optional[UnifiedScoreAuditLog] = None):
-        self.weights = dict(DEFAULT_WEIGHTS)
-        if weights:
-            self.weights.update(weights)
-        self.audit_log = audit_log
-
-    def _normalize(self, modalities: Dict[str, Optional[float]]) -> Dict[str, float]:
-        present = [name for name, score in modalities.items() if score is not None]
-        if not present:
-            return {}
-        total = sum(self.weights[name] for name in present)
-        return {name: self.weights[name] / total for name in present}
-
-    def _level_for_score(self, score: float) -> CrisisLevel:
-        if score > 0.8:
-            return CrisisLevel.CRITICAL
-        if score > 0.6:
-            return CrisisLevel.HIGH
-        if score > 0.4:
-            return CrisisLevel.MEDIUM
-        if score > 0.0:
-            return CrisisLevel.LOW
-        return CrisisLevel.NONE
-
-    def score(
-        self,
-        *,
-        text_score: Optional[float],
-        voice_score: Optional[float] = None,
-        image_score: Optional[float] = None,
-        behavioral_score: Optional[float] = None,
-        source_text: str = "",
-    ) -> UnifiedAssessment:
-        modalities = {
-            "text": text_score,
-            "voice": voice_score,
-            "behavioral": behavioral_score,
-            "image": image_score,
-        }
-        normalized = self._normalize(modalities)
-        combined = 0.0
-        for name, weight in normalized.items():
-            combined += float(modalities[name]) * weight
-        assessment = UnifiedAssessment(
-            level=self._level_for_score(combined),
-            combined_score=combined,
-            weights=normalized,
-            modalities=modalities,
-            present_modalities=[name for name, score in modalities.items() if score is not None],
-        )
-        if self.audit_log:
-            self.audit_log.record(assessment, source_text=source_text)
-        return assessment
-
-
-def behavioral_score_from_session(session: 'SessionState') -> float:
-    current = SCORE_BY_LEVEL.get(session.current_level, 0.0)
-    peak_bonus = 0.1 if LEVEL_RANK.get(session.peak_level, 0) >= LEVEL_RANK["HIGH"] else 0.0
-    escalation_bonus = 0.15 if session.is_escalating else 0.0
-    rate_bonus = min(max(session.escalation_rate, 0.0), 1.0) * 0.1
-    deescalation_penalty = 0.15 if session.is_deescalating else 0.0
-    return max(0.0, min(1.0, current + peak_bonus + escalation_bonus + rate_bonus - deescalation_penalty))