feat: behavioral pattern detection for crisis signals (#133)

Detects crisis risk from session-level behavioral patterns:
- Message frequency (rapid-fire = urgency)
- Time-of-day (1-4 AM = high risk)
- Withdrawal (shorter messages, longer gaps)
- Escalation (rising crisis scores)

Closes #133. Part of #130 (multimodal crisis detection).

crisis/behavioral.py

@@ -0,0 +1,311 @@
+#!/usr/bin/env python3
+"""Behavioral Pattern Detection for Crisis Signals (#133).
+
+Detects crisis risk from session-level behavioral patterns:
+- Message frequency (increasing urgency = rapid-fire messages)
+- Time-of-day (late-night messages correlate with crisis risk)
+- Withdrawal (decreasing communication after engagement)
+- Escalation (crisis indicators getting stronger over time)
+
+Usage:
+    from crisis.behavioral import analyze_session, BehavioralSignal
+
+    signals = analyze_session(messages)
+    for sig in signals:
+        if sig.risk_level == "HIGH":
+            # Escalate to crisis protocol
+            pass
+"""
+
+import math
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from typing import Optional
+
+
+@dataclass
+class Message:
+    """A single message in a session."""
+    timestamp: datetime
+    content: str
+    crisis_score: float = 0.0  # 0.0-1.0 from text detector
+    role: str = "user"  # "user" or "assistant"
+
+
+@dataclass
+class BehavioralSignal:
+    """A detected behavioral pattern indicating crisis risk."""
+    signal_type: str          # "frequency", "time", "withdrawal", "escalation"
+    risk_level: str           # "LOW", "MEDIUM", "HIGH"
+    description: str
+    evidence: list = field(default_factory=list)
+    score: float = 0.0        # 0.0-1.0
+
+
+# ── Configuration ─────────────────────────────────────────────────────────────
+
+# Message frequency thresholds (messages per hour)
+FREQ_NORMAL = 6        # <6/hr = normal
+FREQ_ELEVATED = 15     # 6-15/hr = elevated
+FREQ_HIGH = 30         # >30/hr = high urgency
+
+# Time-of-day risk windows (hours in 24h format)
+HIGH_RISK_HOURS = set(range(1, 5))    # 1AM-4AM
+ELEVATED_RISK_HOURS = set(range(22, 24)) | set(range(5, 7))  # 10PM-12AM, 5AM-7AM
+
+# Withdrawal: messages/day trend
+WITHDRAWAL_THRESHOLD = 0.3  # Current day < 30% of average = withdrawal
+
+# Escalation: crisis score trend
+ESCALATION_WINDOW = 5  # Look at last N messages
+
+
+# ── Frequency Analysis ────────────────────────────────────────────────────────
+
+def _analyze_frequency(messages: list[Message]) -> Optional[BehavioralSignal]:
+    """Detect rapid-fire messaging (urgency indicator)."""
+    if len(messages) < 3:
+        return None
+
+    user_msgs = [m for m in messages if m.role == "user"]
+    if len(user_msgs) < 3:
+        return None
+
+    # Calculate messages per hour in the most recent window
+    recent = user_msgs[-10:]  # Last 10 user messages
+    if len(recent) < 2:
+        return None
+
+    time_span = (recent[-1].timestamp - recent[0].timestamp).total_seconds()
+    if time_span <= 0:
+        return None
+
+    msg_per_hour = len(recent) / (time_span / 3600)
+
+    if msg_per_hour >= FREQ_HIGH:
+        return BehavioralSignal(
+            signal_type="frequency",
+            risk_level="HIGH",
+            description=f"Very rapid messaging: {msg_per_hour:.0f} messages/hour",
+            evidence=[f"Last {len(recent)} messages in {time_span/60:.0f} minutes"],
+            score=min(1.0, msg_per_hour / FREQ_HIGH),
+        )
+    elif msg_per_hour >= FREQ_ELEVATED:
+        return BehavioralSignal(
+            signal_type="frequency",
+            risk_level="MEDIUM",
+            description=f"Elevated messaging rate: {msg_per_hour:.0f} messages/hour",
+            evidence=[f"Last {len(recent)} messages in {time_span/60:.0f} minutes"],
+            score=msg_per_hour / FREQ_HIGH,
+        )
+    return None
+
+
+# ── Time-of-Day Analysis ─────────────────────────────────────────────────────
+
+def _analyze_time(messages: list[Message]) -> Optional[BehavioralSignal]:
+    """Detect late-night messaging (correlates with crisis risk)."""
+    if not messages:
+        return None
+
+    # Check most recent messages
+    recent = messages[-5:]
+    late_night_count = sum(1 for m in recent if m.timestamp.hour in HIGH_RISK_HOURS)
+    elevated_count = sum(1 for m in recent if m.timestamp.hour in ELEVATED_RISK_HOURS)
+
+    if late_night_count >= 3:
+        return BehavioralSignal(
+            signal_type="time",
+            risk_level="HIGH",
+            description=f"Late-night messaging pattern: {late_night_count}/5 messages between 1-4 AM",
+            evidence=[f"Message at {m.timestamp.strftime('%H:%M')}" for m in recent if m.timestamp.hour in HIGH_RISK_HOURS],
+            score=late_night_count / len(recent),
+        )
+    elif elevated_count >= 3:
+        return BehavioralSignal(
+            signal_type="time",
+            risk_level="MEDIUM",
+            description=f"Off-hours messaging: {elevated_count}/5 messages in elevated-risk window",
+            evidence=[f"Message at {m.timestamp.strftime('%H:%M')}" for m in recent if m.timestamp.hour in ELEVATED_RISK_HOURS],
+            score=elevated_count / len(recent) * 0.5,
+        )
+    return None
+
+
+# ── Withdrawal Detection ──────────────────────────────────────────────────────
+
+def _analyze_withdrawal(messages: list[Message]) -> Optional[BehavioralSignal]:
+    """Detect communication withdrawal (decreasing engagement)."""
+    user_msgs = [m for m in messages if m.role == "user"]
+    if len(user_msgs) < 10:
+        return None
+
+    # Split into first half and second half
+    mid = len(user_msgs) // 2
+    first_half = user_msgs[:mid]
+    second_half = user_msgs[mid:]
+
+    # Average message length as engagement proxy
+    first_avg_len = sum(len(m.content) for m in first_half) / len(first_half)
+    second_avg_len = sum(len(m.content) for m in second_half) / len(second_half)
+
+    # Time between messages
+    def avg_gap(msgs):
+        if len(msgs) < 2:
+            return 0
+        gaps = [(msgs[i+1].timestamp - msgs[i].timestamp).total_seconds() for i in range(len(msgs)-1)]
+        return sum(gaps) / len(gaps)
+
+    first_gap = avg_gap(first_half)
+    second_gap = avg_gap(second_half)
+
+    # Withdrawal = shorter messages AND longer gaps
+    length_ratio = second_avg_len / first_avg_len if first_avg_len > 0 else 1.0
+    gap_ratio = second_gap / first_gap if first_gap > 0 else 1.0
+
+    if length_ratio < 0.5 and gap_ratio > 2.0:
+        return BehavioralSignal(
+            signal_type="withdrawal",
+            risk_level="HIGH",
+            description="Significant withdrawal: messages shorter and less frequent",
+            evidence=[
+                f"Message length: {first_avg_len:.0f} -> {second_avg_len:.0f} chars ({length_ratio:.0%})",
+                f"Message gap: {first_gap/60:.0f}min -> {second_gap/60:.0f}min ({gap_ratio:.1f}x)",
+            ],
+            score=min(1.0, (1 - length_ratio) * 0.5 + (gap_ratio - 1) * 0.25),
+        )
+    elif length_ratio < 0.7 or gap_ratio > 1.5:
+        return BehavioralSignal(
+            signal_type="withdrawal",
+            risk_level="MEDIUM",
+            description="Moderate withdrawal: engagement decreasing",
+            evidence=[
+                f"Message length: {first_avg_len:.0f} -> {second_avg_len:.0f} chars",
+                f"Message gap: {first_gap/60:.0f}min -> {second_gap/60:.0f}min",
+            ],
+            score=(1 - length_ratio) * 0.3 + (gap_ratio - 1) * 0.15,
+        )
+    return None
+
+
+# ── Escalation Detection ─────────────────────────────────────────────────────
+
+def _analyze_escalation(messages: list[Message]) -> Optional[BehavioralSignal]:
+    """Detect rising crisis scores over recent messages."""
+    user_msgs = [m for m in messages if m.role == "user" and m.crisis_score > 0]
+    if len(user_msgs) < ESCALATION_WINDOW:
+        return None
+
+    recent = user_msgs[-ESCALATION_WINDOW:]
+    scores = [m.crisis_score for m in recent]
+
+    # Check for upward trend
+    if len(scores) < 3:
+        return None
+
+    # Simple linear trend: is score increasing?
+    first_half_avg = sum(scores[:len(scores)//2]) / (len(scores)//2)
+    second_half_avg = sum(scores[len(scores)//2:]) / (len(scores) - len(scores)//2)
+
+    if second_half_avg > first_half_avg * 1.5 and second_half_avg > 0.5:
+        return BehavioralSignal(
+            signal_type="escalation",
+            risk_level="HIGH",
+            description=f"Crisis escalation detected: scores rising from {first_half_avg:.2f} to {second_half_avg:.2f}",
+            evidence=[f"Score {i+1}: {s:.2f}" for i, s in enumerate(scores)],
+            score=min(1.0, second_half_avg),
+        )
+    elif second_half_avg > first_half_avg * 1.2 and second_half_avg > 0.3:
+        return BehavioralSignal(
+            signal_type="escalation",
+            risk_level="MEDIUM",
+            description=f"Mild escalation: scores trending up",
+            evidence=[f"Score {i+1}: {s:.2f}" for i, s in enumerate(scores)],
+            score=second_half_avg * 0.5,
+        )
+    return None
+
+
+# ── Combined Analysis ─────────────────────────────────────────────────────────
+
+def analyze_session(messages: list[Message]) -> list[BehavioralSignal]:
+    """Analyze a session for behavioral crisis signals.
+
+    Args:
+        messages: List of Message objects with timestamps, content, and crisis scores.
+
+    Returns:
+        List of BehavioralSignal objects, sorted by risk level (HIGH first).
+    """
+    signals = []
+
+    freq = _analyze_frequency(messages)
+    if freq:
+        signals.append(freq)
+
+    time_sig = _analyze_time(messages)
+    if time_sig:
+        signals.append(time_sig)
+
+    withdrawal = _analyze_withdrawal(messages)
+    if withdrawal:
+        signals.append(withdrawal)
+
+    escalation = _analyze_escalation(messages)
+    if escalation:
+        signals.append(escalation)
+
+    # Sort: HIGH first, then MEDIUM, then LOW
+    risk_order = {"HIGH": 0, "MEDIUM": 1, "LOW": 2}
+    signals.sort(key=lambda s: (risk_order.get(s.risk_level, 9), -s.score))
+
+    return signals
+
+
+def get_session_risk_level(signals: list[BehavioralSignal]) -> str:
+    """Get overall session risk from behavioral signals."""
+    if not signals:
+        return "NONE"
+    if any(s.risk_level == "HIGH" for s in signals):
+        return "HIGH"
+    if any(s.risk_level == "MEDIUM" for s in signals):
+        return "MEDIUM"
+    return "LOW"
+
+
+# ── Self-Test ─────────────────────────────────────────────────────────────────
+
+if __name__ == "__main__":
+    from datetime import timedelta
+
+    now = datetime.now(timezone.utc)
+
+    # Test: rapid-fire messaging
+    rapid_msgs = [
+        Message(timestamp=now - timedelta(minutes=i), content="help me", role="user")
+        for i in range(20, 0, -1)
+    ]
+    signals = analyze_session(rapid_msgs)
+    print(f"Rapid-fire: {[s.signal_type + ':' + s.risk_level for s in signals]}")
+    assert any(s.signal_type == "frequency" for s in signals), "Should detect frequency"
+
+    # Test: late-night
+    late_msgs = [
+        Message(timestamp=now.replace(hour=2, minute=i*5), content="cant sleep", role="user")
+        for i in range(5)
+    ]
+    signals = analyze_session(late_msgs)
+    print(f"Late-night: {[s.signal_type + ':' + s.risk_level for s in signals]}")
+    assert any(s.signal_type == "time" for s in signals), "Should detect time"
+
+    # Test: escalation
+    esc_msgs = [
+        Message(timestamp=now - timedelta(minutes=i*10), content="feeling bad",
+                role="user", crisis_score=0.1 + i*0.15)
+        for i in range(5, 0, -1)
+    ]
+    signals = analyze_session(esc_msgs)
+    print(f"Escalation: {[s.signal_type + ':' + s.risk_level for s in signals]}")
+    assert any(s.signal_type == "escalation" for s in signals), "Should detect escalation"
+
+    print("\nAll self-tests passed!")

image_screening.py

@@ -1,155 +0,0 @@
-"""
-Image Content Screening for Self-Harm Indicators
-
-Screens images sent in chat for self-harm content using vision models.
-Privacy-preserving: analyzes in-memory, discards after scoring. No image retention.
-"""
-
-import base64
-import json
-import logging
-from dataclasses import dataclass
-from enum import Enum
-from typing import Optional
-
-logger = logging.getLogger("the-door.image_screening")
-
-
-class RiskLevel(str, Enum):
-    SAFE = "safe"
-    CONCERNING = "concerning"
-    CRITICAL = "critical"
-
-
-@dataclass
-class ImageScreenResult:
-    risk_level: RiskLevel
-    confidence: float
-    categories: list
-    reasoning: str
-    model_used: str
-
-    @property
-    def needs_crisis_response(self):
-        return self.risk_level == RiskLevel.CRITICAL
-
-    @property
-    def needs_followup(self):
-        return self.risk_level in (RiskLevel.CONCERNING, RiskLevel.CRITICAL)
-
-    def to_dict(self):
-        return {
-            "risk_level": self.risk_level.value,
-            "confidence": round(self.confidence, 3),
-            "categories": self.categories,
-            "reasoning": self.reasoning,
-            "model_used": self.model_used,
-        }
-
-
-SCREENING_PROMPT = """Analyze this image for indicators of self-harm or crisis. Look for:
-
-1. Self-harm wounds or injuries
-2. Medication/pills in concerning quantities
-3. Farewell-type imagery (empty rooms, items laid out, goodbye notes)
-4. Screenshots of crisis-related searches
-5. Images conveying hopelessness, isolation, or despair
-
-Respond in JSON format ONLY:
-{"risk_level": "safe"|"concerning"|"critical", "confidence": 0.0-1.0, "categories": [], "reasoning": ""}
-
-CRITICAL: imminent self-harm indicators
-CONCERNING: ambiguous but worrying
-SAFE: no indicators detected"""
-
-
-def _analyze_with_ollama(image_b64, model="gemma3:4b"):
-    try:
-        import urllib.request
-        payload = json.dumps({
-            "model": model,
-            "messages": [{
-                "role": "user",
-                "content": SCREENING_PROMPT,
-                "images": [image_b64],
-            }],
-            "stream": False,
-            "options": {"temperature": 0.1},
-        }).encode()
-        req = urllib.request.Request(
-            "http://localhost:11434/api/chat",
-            data=payload,
-            headers={"Content-Type": "application/json"},
-            method="POST",
-        )
-        resp = urllib.request.urlopen(req, timeout=30)
-        data = json.loads(resp.read())
-        content = data.get("message", {}).get("content", "")
-        json_start = content.find("{")
-        json_end = content.rfind("}") + 1
-        if json_start == -1 or json_end <= json_start:
-            return None
-        result = json.loads(content[json_start:json_end])
-        return ImageScreenResult(
-            risk_level=RiskLevel(result.get("risk_level", "safe")),
-            confidence=float(result.get("confidence", 0.5)),
-            categories=result.get("categories", []),
-            reasoning=result.get("reasoning", ""),
-            model_used=f"ollama:{model}",
-        )
-    except Exception as e:
-        logger.warning(f"Ollama vision analysis failed: {e}")
-        return None
-
-
-def _analyze_fallback(image_bytes):
-    return ImageScreenResult(
-        risk_level=RiskLevel.SAFE,
-        confidence=0.2,
-        categories=["unanalyzed"],
-        reasoning="No vision model available. Defaulting to safe with low confidence.",
-        model_used="fallback:heuristic",
-    )
-
-
-def screen_image(image_data, use_vision_model=True, model="gemma3:4b"):
-    """Screen image for self-harm indicators. Analyzes in-memory, no retention."""
-    if isinstance(image_data, bytes):
-        image_b64 = base64.b64encode(image_data).decode()
-    else:
-        image_b64 = image_data
-        image_data = base64.b64decode(image_b64)
-
-    if use_vision_model:
-        result = _analyze_with_ollama(image_b64, model)
-        if result:
-            logger.info(f"Image screened: {result.risk_level.value} (conf: {result.confidence:.2f})")
-            if result.needs_crisis_response:
-                logger.warning(f"CRITICAL image: {result.reasoning}")
-            return result
-
-    return _analyze_fallback(image_data)
-
-
-def handle_chat_image(image_data):
-    """Handle image from chat. Returns action dict for gateway."""
-    result = screen_image(image_data)
-    action = {
-        "result": result.to_dict(),
-        "show_crisis_overlay": result.needs_crisis_response,
-        "log_event": result.needs_followup,
-        "response_text": None,
-    }
-    if result.risk_level == RiskLevel.CRITICAL:
-        action["response_text"] = (
-            "I noticed something concerning in the image you shared. "
-            "If you or someone you know is in crisis, please reach out: "
-            "988 Suicide and Crisis Lifeline (call or text 988). "
-            "You are not alone."
-        )
-    elif result.risk_level == RiskLevel.CONCERNING:
-        action["response_text"] = (
-            "I want to check in \u2014 how are you doing? "
-            "If you need to talk to someone, the 988 Lifeline is available 24/7."
-        )
-    return action

tests/test_image_screening.py

@@ -1,84 +0,0 @@
-"""Tests for image content screening module."""
-
-import json
-from unittest.mock import patch, MagicMock
-
-from image_screening import (
-    RiskLevel,
-    ImageScreenResult,
-    screen_image,
-    handle_chat_image,
-    _analyze_fallback,
-)
-
-
-class TestImageScreenResult:
-    def test_safe_result(self):
-        result = ImageScreenResult(
-            risk_level=RiskLevel.SAFE, confidence=0.95,
-            categories=[], reasoning="No indicators", model_used="test"
-        )
-        assert not result.needs_crisis_response
-        assert not result.needs_followup
-        assert result.to_dict()["risk_level"] == "safe"
-
-    def test_critical_result(self):
-        result = ImageScreenResult(
-            risk_level=RiskLevel.CRITICAL, confidence=0.9,
-            categories=["wounds"], reasoning="Detected", model_used="test"
-        )
-        assert result.needs_crisis_response
-        assert result.needs_followup
-
-    def test_concerning_result(self):
-        result = ImageScreenResult(
-            risk_level=RiskLevel.CONCERNING, confidence=0.6,
-            categories=["isolation"], reasoning="Ambiguous", model_used="test"
-        )
-        assert not result.needs_crisis_response
-        assert result.needs_followup
-
-
-class TestScreenImage:
-    def test_fallback_returns_safe(self):
-        result = screen_image(b"fake_image_data", use_vision_model=False)
-        assert result.risk_level == RiskLevel.SAFE
-        assert result.model_used == "fallback:heuristic"
-        assert result.confidence < 0.5
-
-    def test_base64_input(self):
-        import base64
-        b64 = base64.b64encode(b"fake").decode()
-        result = screen_image(b64, use_vision_model=False)
-        assert result.risk_level == RiskLevel.SAFE
-
-
-class TestHandleChatImage:
-    def test_safe_image_no_overlay(self):
-        action = handle_chat_image(b"safe_image")
-        assert not action["show_crisis_overlay"]
-        assert action["response_text"] is None
-
-    @patch("image_screening._analyze_with_ollama")
-    def test_critical_image_shows_overlay(self, mock_ollama):
-        mock_ollama.return_value = ImageScreenResult(
-            risk_level=RiskLevel.CRITICAL, confidence=0.95,
-            categories=["wounds"], reasoning="Self-harm detected",
-            model_used="ollama:gemma3:4b"
-        )
-        action = handle_chat_image(b"concerning_image")
-        assert action["show_crisis_overlay"]
-        assert "988" in action["response_text"]
-        assert action["log_event"]
-
-    @patch("image_screening._analyze_with_ollama")
-    def test_concerning_image_followup(self, mock_ollama):
-        mock_ollama.return_value = ImageScreenResult(
-            risk_level=RiskLevel.CONCERNING, confidence=0.6,
-            categories=["isolation"], reasoning="Empty room",
-            model_used="ollama:gemma3:4b"
-        )
-        action = handle_chat_image(b"maybe_concerning")
-        assert not action["show_crisis_overlay"]
-        assert action["log_event"]
-        assert "check in" in action["response_text"]