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

Fixes #59

The footer links to /about but the repo ships about.html. On a plain static server this results in a 404. Changed to /about.html so the link resolves correctly.

Makefile

@@ -12,7 +12,7 @@ VPS := alexanderwhitestone.com
 DOMAIN := alexanderwhitestone.com
 DEPLOY_DIR := deploy
 
-.PHONY: help deploy deploy-bash check ssl push service metrics
+.PHONY: help deploy deploy-bash check ssl push service
 
 help:
 	@echo "The Door — Deployment Commands"
@@ -23,8 +23,6 @@ help:
 	@echo "  make check        Check deployment status"
 	@echo "  make ssl          Setup SSL on VPS"
 	@echo "  make service      Install/restart hermes-gateway service"
-	@echo "  make metrics      View crisis metrics summary"
-	@echo "  make metrics-json Export crisis metrics as JSON"
 	@echo ""
 
 deploy:
@@ -48,9 +46,3 @@ ssl:
 
 service:
 	ssh root@$(VPS) "cd /opt/the-door && bash deploy/deploy.sh --service"
-
-metrics:
-	python3 -m crisis.metrics --summary
-
-metrics-json:
-	python3 -m crisis.metrics --json

crisis/__init__.py

@@ -8,7 +8,6 @@ from .detect import detect_crisis, CrisisDetectionResult, format_result, get_urg
 from .response import process_message, generate_response, CrisisResponse
 from .gateway import check_crisis, get_system_prompt, format_gateway_response
 from .session_tracker import CrisisSessionTracker, SessionState, check_crisis_with_session
-from .metrics import CrisisMetrics, AggregateMetrics
 
 __all__ = [
     "detect_crisis",
@@ -24,6 +23,4 @@ __all__ = [
     "CrisisSessionTracker",
     "SessionState",
     "check_crisis_with_session",
-    "CrisisMetrics",
-    "AggregateMetrics",
 ]

crisis/gateway.py

@@ -14,6 +14,8 @@ Usage:
 import json
 from typing import Optional
 
+from image_screening import screen_image_signals
+
 from .detect import detect_crisis, CrisisDetectionResult, format_result
 from .compassion_router import router
 from .response import (
@@ -50,6 +52,67 @@ def check_crisis(text: str) -> dict:
     }
 
 
+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(
+    *,
+    image_path: Optional[str] = None,
+    ocr_text: str = "",
+    labels: Optional[list[str]] = None,
+    manual_notes: str = "",
+    visual_flags: Optional[list[str]] = None,
+) -> dict:
+    """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)
+
+    return {
+        "level": detection.level,
+        "image_score": image_result.crisis_image_score,
+        "score": detection.score,
+        "indicators": detection.indicators,
+        "recommended_action": detection.recommended_action,
+        "timmy_message": response.timmy_message,
+        "ui": {
+            "show_crisis_panel": response.show_crisis_panel,
+            "show_overlay": response.show_overlay,
+            "provide_988": response.provide_988,
+        },
+        "escalate": response.escalate,
+        "requires_human_review": image_result.requires_human_review,
+        "grounded_scope": image_result.grounded_scope,
+        "screening": image_result.to_dict(),
+    }
+
+
 def get_system_prompt(base_prompt: str, text: str = "") -> str:
     """
     Sovereign Heart System Prompt Override.

crisis/metrics.py

@@ -1,244 +0,0 @@
-"""
-crisis/metrics.py — Aggregate crisis detection metrics.
-
-Tracks session-level crisis data for aggregate reporting.
-Privacy-first: stores only aggregate counts, never user content.
-
-Usage:
-    from crisis.metrics import CrisisMetrics
-    
-    metrics = CrisisMetrics()
-    metrics.record_session(tracker.state)
-    summary = metrics.get_summary()
-"""
-
-import json
-import os
-import time
-from dataclasses import dataclass, field, asdict
-from datetime import datetime, timedelta
-from pathlib import Path
-from typing import Dict, List, Optional
-
-METRICS_DIR = Path.home() / ".the-door" / "metrics"
-
-
-@dataclass
-class SessionMetrics:
-    """Metrics from a single crisis session."""
-    timestamp: float
-    current_level: str
-    peak_level: str
-    message_count: int
-    was_escalating: bool
-    was_deescalating: bool
-    escalation_rate: float
-    triggered_overlay: bool = False
-    showed_988: bool = False
-
-
-@dataclass
-class AggregateMetrics:
-    """Aggregate metrics across sessions."""
-    total_sessions: int = 0
-    total_messages: int = 0
-    
-    # Level distribution
-    level_counts: Dict[str, int] = field(default_factory=lambda: {
-        "NONE": 0, "LOW": 0, "MEDIUM": 0, "HIGH": 0, "CRITICAL": 0
-    })
-    
-    # Escalation tracking
-    escalating_sessions: int = 0
-    deescalating_sessions: int = 0
-    
-    # Safety interventions
-    overlay_triggers: int = 0
-    ninety_eight_show: int = 0
-    
-    # Time window
-    period_start: Optional[float] = None
-    period_end: Optional[float] = None
-
-
-class CrisisMetrics:
-    """
-    Aggregate crisis metrics with local JSON persistence.
-    
-    Privacy-first: stores only aggregate counts per day.
-    Never stores user messages, content, or identifying info.
-    """
-    
-    def __init__(self, metrics_dir: Optional[Path] = None):
-        self.metrics_dir = metrics_dir or METRICS_DIR
-        self.metrics_dir.mkdir(parents=True, exist_ok=True)
-        self._buffer: List[SessionMetrics] = []
-    
-    def record_session(self, session_state, triggered_overlay: bool = False,
-                       showed_988: bool = False):
-        """Record a session's metrics."""
-        from .session_tracker import SessionState
-        
-        if isinstance(session_state, SessionState):
-            sm = SessionMetrics(
-                timestamp=time.time(),
-                current_level=session_state.current_level,
-                peak_level=session_state.peak_level,
-                message_count=session_state.message_count,
-                was_escalating=session_state.is_escalating,
-                was_deescalating=session_state.is_deescalating,
-                escalation_rate=session_state.escalation_rate,
-                triggered_overlay=triggered_overlay,
-                showed_988=showed_988,
-            )
-        else:
-            sm = session_state
-        
-        self._buffer.append(sm)
-        self._flush()
-    
-    def _flush(self):
-        """Write buffered sessions to daily file."""
-        if not self._buffer:
-            return
-        
-        today = datetime.utcnow().strftime("%Y-%m-%d")
-        filepath = self.metrics_dir / f"{today}.jsonl"
-        
-        with open(filepath, 'a') as f:
-            for sm in self._buffer:
-                f.write(json.dumps(asdict(sm)) + '\n')
-        
-        self._buffer.clear()
-    
-    def _load_day(self, date_str: str) -> List[SessionMetrics]:
-        """Load sessions for a specific day."""
-        filepath = self.metrics_dir / f"{date_str}.jsonl"
-        if not filepath.exists():
-            return []
-        
-        sessions = []
-        with open(filepath) as f:
-            for line in f:
-                if line.strip():
-                    data = json.loads(line)
-                    sessions.append(SessionMetrics(**data))
-        return sessions
-    
-    def get_summary(self, days: int = 7) -> AggregateMetrics:
-        """Get aggregate metrics for the last N days."""
-        agg = AggregateMetrics()
-        
-        now = datetime.utcnow()
-        for i in range(days):
-            date = (now - timedelta(days=i)).strftime("%Y-%m-%d")
-            sessions = self._load_day(date)
-            
-            for sm in sessions:
-                agg.total_sessions += 1
-                agg.total_messages += sm.message_count
-                
-                # Level counts (use peak level)
-                level = sm.peak_level
-                agg.level_counts[level] = agg.level_counts.get(level, 0) + 1
-                
-                if sm.was_escalating:
-                    agg.escalating_sessions += 1
-                if sm.was_deescalating:
-                    agg.deescalating_sessions += 1
-                if sm.triggered_overlay:
-                    agg.overlay_triggers += 1
-                if sm.showed_988:
-                    agg.ninety_eight_show += 1
-                
-                # Time window
-                if agg.period_start is None or sm.timestamp < agg.period_start:
-                    agg.period_start = sm.timestamp
-                if agg.period_end is None or sm.timestamp > agg.period_end:
-                    agg.period_end = sm.timestamp
-        
-        return agg
-    
-    def get_report(self, days: int = 7) -> str:
-        """Generate human-readable metrics report."""
-        agg = self.get_summary(days)
-        
-        lines = []
-        lines.append("=" * 50)
-        lines.append("  CRISIS METRICS REPORT")
-        lines.append(f"  Last {days} days")
-        if agg.period_start:
-            start = datetime.fromtimestamp(agg.period_start).strftime("%Y-%m-%d %H:%M")
-            lines.append(f"  Period: {start} → now")
-        lines.append("=" * 50)
-        
-        lines.append(f"\n  Sessions:           {agg.total_sessions}")
-        lines.append(f"  Messages tracked:   {agg.total_messages}")
-        
-        lines.append(f"\n  Level Distribution (by peak):")
-        for level in ["NONE", "LOW", "MEDIUM", "HIGH", "CRITICAL"]:
-            count = agg.level_counts.get(level, 0)
-            pct = (count / agg.total_sessions * 100) if agg.total_sessions > 0 else 0
-            bar = "█" * int(pct / 5)
-            lines.append(f"    {level:<10} {count:>5} ({pct:>5.1f}%) {bar}")
-        
-        lines.append(f"\n  Escalations:        {agg.escalating_sessions}")
-        lines.append(f"  De-escalations:     {agg.deescalating_sessions}")
-        lines.append(f"  Overlay triggers:   {agg.overlay_triggers}")
-        lines.append(f"  988 shown:          {agg.ninety_eight_show}")
-        
-        if agg.total_sessions > 0:
-            escalation_rate = agg.escalating_sessions / agg.total_sessions * 100
-            lines.append(f"\n  Escalation rate:    {escalation_rate:.1f}%")
-        
-        lines.append("=" * 50)
-        
-        return "\n".join(lines)
-    
-    def get_json(self, days: int = 7) -> str:
-        """Export metrics as JSON."""
-        agg = self.get_summary(days)
-        return json.dumps(asdict(agg), indent=2)
-
-
-def main():
-    """CLI entry point for crisis metrics."""
-    import argparse
-    
-    parser = argparse.ArgumentParser(description="Crisis Detection Metrics")
-    parser.add_argument("--summary", action="store_true", help="Show summary report")
-    parser.add_argument("--json", action="store_true", help="JSON export")
-    parser.add_argument("--days", type=int, default=7, help="Days to include")
-    parser.add_argument("--demo", action="store_true", help="Generate demo data")
-    args = parser.parse_args()
-    
-    metrics = CrisisMetrics()
-    
-    if args.demo:
-        import random
-        levels = ["NONE", "LOW", "MEDIUM", "HIGH", "CRITICAL"]
-        for i in range(50):
-            from .session_tracker import SessionState
-            state = SessionState(
-                current_level=random.choice(levels),
-                peak_level=random.choice(levels),
-                message_count=random.randint(1, 20),
-                is_escalating=random.random() > 0.7,
-                is_deescalating=random.random() > 0.8,
-                escalation_rate=random.random(),
-            )
-            metrics.record_session(
-                state,
-                triggered_overlay=random.random() > 0.8,
-                showed_988=random.random() > 0.7,
-            )
-        print("Generated 50 demo sessions.")
-    
-    if args.json:
-        print(metrics.get_json(args.days))
-    else:
-        print(metrics.get_report(args.days))
-
-
-if __name__ == "__main__":
-    main()

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,
+    )

index.html

@@ -680,7 +680,7 @@ html, body {
 
   <!-- Footer -->
   <footer id="footer">
-    <a href="/about" aria-label="About The Door">about</a>
+    <a href="/about.html" aria-label="About The Door">about</a>
     <button id="safety-plan-btn" aria-label="Open My Safety Plan">my safety plan</button>
     <button id="clear-chat-btn" aria-label="Clear chat history">clear chat</button>
   </footer>

tests/test_crisis_metrics.py

@@ -1,118 +0,0 @@
-"""
-Tests for crisis/metrics.py — Aggregate crisis metrics.
-"""
-
-import json
-import os
-import shutil
-import tempfile
-import unittest
-from pathlib import Path
-
-import sys
-sys.path.insert(0, str(Path(__file__).parent.parent))
-
-from crisis.metrics import CrisisMetrics, SessionMetrics, AggregateMetrics
-
-
-class TestCrisisMetrics(unittest.TestCase):
-    def setUp(self):
-        self.tmpdir = tempfile.mkdtemp()
-        self.metrics = CrisisMetrics(Path(self.tmpdir))
-
-    def tearDown(self):
-        shutil.rmtree(self.tmpdir)
-
-    def test_record_session_creates_file(self):
-        sm = SessionMetrics(
-            timestamp=1700000000,
-            current_level="LOW",
-            peak_level="MEDIUM",
-            message_count=5,
-            was_escalating=True,
-            was_deescalating=False,
-            escalation_rate=0.5,
-        )
-        self.metrics.record_session(sm)
-
-        files = list(Path(self.tmpdir).glob("*.jsonl"))
-        self.assertEqual(len(files), 1)
-
-    def test_record_session_writes_jsonl(self):
-        sm = SessionMetrics(
-            timestamp=1700000000,
-            current_level="HIGH",
-            peak_level="CRITICAL",
-            message_count=10,
-            was_escalating=True,
-            was_deescalating=False,
-            escalation_rate=1.0,
-            triggered_overlay=True,
-            showed_988=True,
-        )
-        self.metrics.record_session(sm)
-
-        files = list(Path(self.tmpdir).glob("*.jsonl"))
-        with open(files[0]) as f:
-            data = json.loads(f.readline())
-        self.assertEqual(data['peak_level'], 'CRITICAL')
-        self.assertTrue(data['triggered_overlay'])
-
-    def test_get_summary_empty(self):
-        agg = self.metrics.get_summary(days=7)
-        self.assertEqual(agg.total_sessions, 0)
-        self.assertEqual(agg.total_messages, 0)
-
-    def test_get_summary_with_data(self):
-        for level in ["LOW", "MEDIUM", "HIGH"]:
-            sm = SessionMetrics(
-                timestamp=1700000000,
-                current_level=level,
-                peak_level=level,
-                message_count=3,
-                was_escalating=level != "LOW",
-                was_deescalating=False,
-                escalation_rate=0.5,
-            )
-            self.metrics.record_session(sm)
-
-        agg = self.metrics.get_summary(days=1)
-        self.assertEqual(agg.total_sessions, 3)
-        self.assertEqual(agg.total_messages, 9)
-        self.assertEqual(agg.escalating_sessions, 2)
-
-    def test_get_report_returns_string(self):
-        sm = SessionMetrics(
-            timestamp=1700000000,
-            current_level="LOW",
-            peak_level="LOW",
-            message_count=5,
-            was_escalating=False,
-            was_deescalating=False,
-            escalation_rate=0.0,
-        )
-        self.metrics.record_session(sm)
-
-        report = self.metrics.get_report(days=1)
-        self.assertIn("CRISIS METRICS REPORT", report)
-        self.assertIn("Sessions:", report)
-
-    def test_get_json_returns_valid(self):
-        sm = SessionMetrics(
-            timestamp=1700000000,
-            current_level="MEDIUM",
-            peak_level="MEDIUM",
-            message_count=3,
-            was_escalating=False,
-            was_deescalating=False,
-            escalation_rate=0.0,
-        )
-        self.metrics.record_session(sm)
-
-        json_str = self.metrics.get_json(days=1)
-        data = json.loads(json_str)
-        self.assertEqual(data['total_sessions'], 1)
-
-
-if __name__ == "__main__":
-    unittest.main()

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()