feat: crisis overlay full Tab cycle + Escape dismiss (#95)

Three fixes:
1. Focus lands on Call 988 link on open (not disabled dismiss button)
2. Focus trap catches escaped focus outside overlay
3. Escape key closes overlay, returns focus to chat input

Closes #95

crisis/behavioral.py

@@ -1,311 +0,0 @@
-#!/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!")

index.html

@@ -1001,6 +1001,13 @@ Sovereignty and service always.`;
     var first = focusable[0];
     var last = focusable[focusable.length - 1];
 
+    // If focus escaped outside the overlay (e.g. to body), redirect to first
+    if (!crisisOverlay.contains(document.activeElement)) {
+      e.preventDefault();
+      first.focus();
+      return;
+    }
+
     if (e.shiftKey) {
       // Shift+Tab: if on first, wrap to last
       if (document.activeElement === first) {
@@ -1050,38 +1057,55 @@ Sovereignty and service always.`;
       }
     }, 1000);
 
-    overlayDismissBtn.focus();
+    // Focus the first focusable element (call link) — dismiss button is still disabled
+    var firstFocusable = crisisOverlay.querySelector('a[href], button:not([disabled]), [tabindex]:not([tabindex="-1"])');
+    if (firstFocusable) {
+      firstFocusable.focus();
+    }
   }
 
   // Register focus trap on document (always listening, gated by class check)
   document.addEventListener('keydown', trapFocusInOverlay);
 
+  function dismissOverlay() {
+    crisisOverlay.classList.remove('active');
+    if (overlayTimer) {
+      clearInterval(overlayTimer);
+      overlayTimer = null;
+    }
+
+    // Re-enable background interaction
+    var mainApp = document.querySelector('.app');
+    if (mainApp) mainApp.removeAttribute('inert');
+    var chatSection = document.getElementById('chat');
+    if (chatSection) chatSection.removeAttribute('aria-hidden');
+    var footerEl = document.querySelector('footer');
+    if (footerEl) footerEl.removeAttribute('aria-hidden');
+
+    // Restore focus to the element that had it before the overlay opened
+    if (_preOverlayFocusElement && typeof _preOverlayFocusElement.focus === 'function') {
+      _preOverlayFocusElement.focus();
+    } else {
+      msgInput.focus();
+    }
+    _preOverlayFocusElement = null;
+  }
+
   overlayDismissBtn.addEventListener('click', function() {
     if (!overlayDismissBtn.disabled) {
-      crisisOverlay.classList.remove('active');
-      if (overlayTimer) {
-        clearInterval(overlayTimer);
-        overlayTimer = null;
-      }
-
-      // Re-enable background interaction
-      var mainApp = document.querySelector('.app');
-      if (mainApp) mainApp.removeAttribute('inert');
-      var chatSection = document.getElementById('chat');
-      if (chatSection) chatSection.removeAttribute('aria-hidden');
-      var footerEl = document.querySelector('footer');
-      if (footerEl) footerEl.removeAttribute('aria-hidden');
-
-      // Restore focus to the element that had it before the overlay opened
-      if (_preOverlayFocusElement && typeof _preOverlayFocusElement.focus === 'function') {
-        _preOverlayFocusElement.focus();
-      } else {
-        msgInput.focus();
-      }
-      _preOverlayFocusElement = null;
+      dismissOverlay();
     }
   });
 
+  // Escape key closes crisis overlay (only after dismiss button is enabled)
+  document.addEventListener('keydown', function(e) {
+    if (e.key !== 'Escape') return;
+    if (!crisisOverlay.classList.contains('active')) return;
+    if (overlayDismissBtn.disabled) return;  // Don't bypass countdown
+    e.preventDefault();
+    dismissOverlay();
+  });
+
   // ===== MESSAGE RENDERING =====
   function addMessage(role, text, skipSave) {
     var div = document.createElement('div');