feat: add behavioral crisis pattern detection (#133)

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.

crisis/__init__.py

@@ -7,6 +7,8 @@ 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, get_system_prompt, format_gateway_response
+from .behavioral import BehavioralTracker, BehavioralSignal
+from .session_tracker import CrisisSessionTracker, SessionState, check_crisis_with_session
 
 __all__ = [
     "detect_crisis",
@@ -19,4 +21,9 @@ __all__ = [
     "format_result",
     "format_gateway_response",
     "get_urgency_emoji",
+    "BehavioralTracker",
+    "BehavioralSignal",
+    "CrisisSessionTracker",
+    "SessionState",
+    "check_crisis_with_session",
 ]

crisis/behavioral.py

@@ -0,0 +1,304 @@
+"""Behavioral crisis pattern detection for the-door (#133).
+
+Detects crisis risk from behavioral patterns, not just message content:
+- message frequency spikes versus a 7-day rolling baseline
+- late-night messaging (2-5 AM)
+- withdrawal / isolation via a sharp drop from the recent daily baseline
+- session length trend versus recent sessions
+- return after long absence
+- rising crisis-score trend across recent messages
+
+Privacy-first:
+- in-memory only
+- no database
+- no file I/O
+- no network calls
+"""
+
+from __future__ import annotations
+
+from collections import defaultdict
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta, timezone
+from typing import Any
+
+
+HIGH_RISK_HOURS = {2, 3, 4}
+ELEVATED_RISK_HOURS = {1, 5}
+ROLLING_BASELINE_DAYS = 7
+RETURN_AFTER_ABSENCE_DAYS = 7
+
+
+@dataclass
+class BehavioralEvent:
+    session_id: str
+    timestamp: datetime
+    message_length: int
+    crisis_score: float = 0.0
+    role: str = "user"
+
+
+@dataclass
+class BehavioralSignal:
+    signal_type: str
+    risk_level: str
+    description: str
+    evidence: list[str] = field(default_factory=list)
+    score: float = 0.0
+
+    def as_dict(self) -> dict[str, Any]:
+        return {
+            "signal_type": self.signal_type,
+            "risk_level": self.risk_level,
+            "description": self.description,
+            "evidence": list(self.evidence),
+            "score": self.score,
+        }
+
+
+class BehavioralTracker:
+    """In-memory tracker for behavioral crisis signals."""
+
+    def __init__(self) -> None:
+        self._events_by_session: dict[str, list[BehavioralEvent]] = defaultdict(list)
+
+    def record(
+        self,
+        session_id: str,
+        timestamp: datetime,
+        message_length: int,
+        *,
+        crisis_score: float = 0.0,
+        role: str = "user",
+    ) -> None:
+        if timestamp.tzinfo is None:
+            timestamp = timestamp.replace(tzinfo=timezone.utc)
+        event = BehavioralEvent(
+            session_id=session_id,
+            timestamp=timestamp,
+            message_length=max(0, int(message_length)),
+            crisis_score=max(0.0, min(1.0, float(crisis_score))),
+            role=role,
+        )
+        self._events_by_session[session_id].append(event)
+        self._events_by_session[session_id].sort(key=lambda item: item.timestamp)
+
+    def get_risk_signals(self, session_id: str) -> dict[str, Any]:
+        events = [event for event in self._events_by_session.get(session_id, []) if event.role == "user"]
+        if not events:
+            return {
+                "frequency_change": 1.0,
+                "is_late_night": False,
+                "session_length_trend": "stable",
+                "withdrawal_detected": False,
+                "behavioral_score": 0.0,
+                "signals": [],
+            }
+
+        signals: list[BehavioralSignal] = []
+
+        frequency_change = self._compute_frequency_change(events)
+        frequency_signal = self._analyze_frequency(events, frequency_change)
+        if frequency_signal:
+            signals.append(frequency_signal)
+
+        time_signal = self._analyze_time(events)
+        if time_signal:
+            signals.append(time_signal)
+
+        withdrawal_signal = self._analyze_withdrawal(session_id, events)
+        if withdrawal_signal:
+            signals.append(withdrawal_signal)
+
+        absence_signal = self._analyze_return_after_absence(session_id, events)
+        if absence_signal:
+            signals.append(absence_signal)
+
+        escalation_signal = self._analyze_escalation(events)
+        if escalation_signal:
+            signals.append(escalation_signal)
+
+        session_length_trend = self._compute_session_length_trend(session_id, events)
+        behavioral_score = self._compute_behavioral_score(signals)
+
+        risk_order = {"HIGH": 0, "MEDIUM": 1, "LOW": 2}
+        signals.sort(key=lambda item: (risk_order.get(item.risk_level, 9), -item.score))
+
+        return {
+            "frequency_change": frequency_change,
+            "is_late_night": any(item.signal_type == "time" for item in signals),
+            "session_length_trend": session_length_trend,
+            "withdrawal_detected": any(item.signal_type == "withdrawal" for item in signals),
+            "behavioral_score": behavioral_score,
+            "signals": [item.as_dict() for item in signals],
+        }
+
+    def _all_user_events(self) -> list[BehavioralEvent]:
+        events: list[BehavioralEvent] = []
+        for session_events in self._events_by_session.values():
+            events.extend(event for event in session_events if event.role == "user")
+        events.sort(key=lambda item: item.timestamp)
+        return events
+
+    def _daily_count_baseline(self, current_date) -> float:
+        events = self._all_user_events()
+        counts: dict[Any, int] = {}
+        for offset in range(1, ROLLING_BASELINE_DAYS + 1):
+            counts[current_date - timedelta(days=offset)] = 0
+        for event in events:
+            event_date = event.timestamp.date()
+            if event_date in counts:
+                counts[event_date] += 1
+        return sum(counts.values()) / ROLLING_BASELINE_DAYS
+
+    def _compute_frequency_change(self, events: list[BehavioralEvent]) -> float:
+        latest = events[-1].timestamp
+        window_start = latest - timedelta(hours=1)
+        current_hour_count = sum(1 for event in events if event.timestamp >= window_start)
+        baseline_daily = self._daily_count_baseline(latest.date())
+        baseline_hourly = max(baseline_daily / 24.0, 0.1)
+        return round(current_hour_count / baseline_hourly, 2)
+
+    def _analyze_frequency(self, events: list[BehavioralEvent], frequency_change: float) -> BehavioralSignal | None:
+        latest = events[-1].timestamp
+        window_start = latest - timedelta(hours=1)
+        current_hour_count = sum(1 for event in events if event.timestamp >= window_start)
+        if current_hour_count >= 6 and frequency_change >= 3.0:
+            level = "HIGH" if frequency_change >= 6.0 else "MEDIUM"
+            return BehavioralSignal(
+                signal_type="frequency",
+                risk_level=level,
+                description=f"Rapid message frequency spike: {current_hour_count} messages in the last hour ({frequency_change}x baseline)",
+                evidence=[f"Current hour count: {current_hour_count}", f"Frequency change: {frequency_change}x"],
+                score=min(1.0, frequency_change / 8.0),
+            )
+        return None
+
+    def _analyze_time(self, events: list[BehavioralEvent]) -> BehavioralSignal | None:
+        latest = events[-1].timestamp
+        hour = latest.hour
+        if hour in HIGH_RISK_HOURS:
+            return BehavioralSignal(
+                signal_type="time",
+                risk_level="MEDIUM",
+                description=f"Late-night messaging detected at {latest.strftime('%H:%M')}",
+                evidence=[f"Latest message timestamp: {latest.isoformat()}"],
+                score=0.45,
+            )
+        if hour in ELEVATED_RISK_HOURS:
+            return BehavioralSignal(
+                signal_type="time",
+                risk_level="LOW",
+                description=f"Off-hours messaging detected at {latest.strftime('%H:%M')}",
+                evidence=[f"Latest message timestamp: {latest.isoformat()}"],
+                score=0.2,
+            )
+        return None
+
+    def _analyze_withdrawal(self, session_id: str, events: list[BehavioralEvent]) -> BehavioralSignal | None:
+        current_date = events[-1].timestamp.date()
+        baseline_daily = self._daily_count_baseline(current_date)
+        if baseline_daily < 3.0:
+            return None
+
+        current_day_count = sum(1 for event in events if event.timestamp.date() == current_date)
+        current_avg_len = sum(event.message_length for event in events if event.timestamp.date() == current_date) / max(current_day_count, 1)
+
+        prior_events = [
+            event
+            for sid, session_events in self._events_by_session.items()
+            if sid != session_id
+            for event in session_events
+            if event.role == "user" and event.timestamp.date() >= current_date - timedelta(days=ROLLING_BASELINE_DAYS)
+        ]
+        if not prior_events:
+            return None
+        prior_avg_len = sum(event.message_length for event in prior_events) / len(prior_events)
+
+        if current_day_count <= max(1, baseline_daily * 0.3):
+            score = 0.55 if current_day_count == 1 else 0.4
+            if current_avg_len < prior_avg_len * 0.5:
+                score += 0.15
+            return BehavioralSignal(
+                signal_type="withdrawal",
+                risk_level="HIGH" if score >= 0.6 else "MEDIUM",
+                description="Sharp drop from recent communication baseline suggests withdrawal/isolation",
+                evidence=[
+                    f"Current day count: {current_day_count}",
+                    f"7-day daily baseline: {baseline_daily:.2f}",
+                    f"Average message length: {current_avg_len:.1f} vs {prior_avg_len:.1f}",
+                ],
+                score=min(1.0, score),
+            )
+        return None
+
+    def _analyze_return_after_absence(self, session_id: str, events: list[BehavioralEvent]) -> BehavioralSignal | None:
+        current_start = events[0].timestamp
+        prior_events = [
+            event
+            for sid, session_events in self._events_by_session.items()
+            if sid != session_id
+            for event in session_events
+            if event.role == "user" and event.timestamp < current_start
+        ]
+        if not prior_events:
+            return None
+        latest_prior = max(prior_events, key=lambda item: item.timestamp)
+        gap = current_start - latest_prior.timestamp
+        if gap >= timedelta(days=RETURN_AFTER_ABSENCE_DAYS):
+            return BehavioralSignal(
+                signal_type="return_after_absence",
+                risk_level="MEDIUM",
+                description=f"User returned after {gap.days} days of silence",
+                evidence=[f"Last prior activity: {latest_prior.timestamp.isoformat()}"],
+                score=min(1.0, gap.days / 14.0),
+            )
+        return None
+
+    def _analyze_escalation(self, events: list[BehavioralEvent]) -> BehavioralSignal | None:
+        scored = [event for event in events if event.crisis_score > 0]
+        if len(scored) < 3:
+            return None
+        recent = scored[-5:]
+        midpoint = max(1, len(recent) // 2)
+        first_avg = sum(event.crisis_score for event in recent[:midpoint]) / len(recent[:midpoint])
+        second_avg = sum(event.crisis_score for event in recent[midpoint:]) / len(recent[midpoint:])
+        if second_avg >= max(0.4, first_avg * 1.3):
+            return BehavioralSignal(
+                signal_type="escalation",
+                risk_level="HIGH" if second_avg >= 0.65 else "MEDIUM",
+                description=f"Behavioral escalation: crisis score trend rose from {first_avg:.2f} to {second_avg:.2f}",
+                evidence=[f"Recent crisis scores: {[round(event.crisis_score, 2) for event in recent]}"],
+                score=min(1.0, second_avg),
+            )
+        return None
+
+    def _compute_session_length_trend(self, session_id: str, events: list[BehavioralEvent]) -> str:
+        current_duration = (events[-1].timestamp - events[0].timestamp).total_seconds()
+        previous_durations = []
+        current_start = events[0].timestamp
+        for sid, session_events in self._events_by_session.items():
+            if sid == session_id:
+                continue
+            user_events = [event for event in session_events if event.role == "user"]
+            if len(user_events) < 2:
+                continue
+            if user_events[-1].timestamp < current_start - timedelta(days=ROLLING_BASELINE_DAYS):
+                continue
+            previous_durations.append((user_events[-1].timestamp - user_events[0].timestamp).total_seconds())
+
+        if not previous_durations:
+            return "stable"
+        average_duration = sum(previous_durations) / len(previous_durations)
+        if current_duration > average_duration * 1.5:
+            return "increasing"
+        if current_duration < average_duration * 0.5:
+            return "decreasing"
+        return "stable"
+
+    def _compute_behavioral_score(self, signals: list[BehavioralSignal]) -> float:
+        if not signals:
+            return 0.0
+        max_score = max(signal.score for signal in signals)
+        multi_signal_boost = min(0.2, 0.05 * (len(signals) - 1))
+        return round(min(1.0, max_score + multi_signal_boost), 2)

crisis/gateway.py

@@ -22,6 +22,7 @@ from .response import (
     get_system_prompt_modifier,
     CrisisResponse,
 )
+from .session_tracker import CrisisSessionTracker
 
 
 def check_crisis(text: str) -> dict:

crisis/session_tracker.py

@@ -0,0 +1,325 @@
+"""
+Session-level crisis tracking and escalation for the-door (P0 #35).
+
+Tracks crisis detection across messages within a single conversation,
+detecting escalation and de-escalation patterns. Privacy-first: no
+persistence beyond the conversation session.
+
+Each message is analyzed in isolation by detect.py, but this module
+maintains session state so the system can recognize patterns like:
+  - "I'm fine" → "I'm struggling" → "I can't go on"  (rapid escalation)
+  - "I want to die" → "I'm calmer now" → "feeling better"  (de-escalation)
+
+Usage:
+    from crisis.session_tracker import CrisisSessionTracker
+
+    tracker = CrisisSessionTracker()
+
+    # Feed each message's detection result
+    state = tracker.record(detect_crisis("I'm having a tough day"))
+    print(state.current_level)  # "LOW"
+    print(state.is_escalating)  # False
+
+    state = tracker.record(detect_crisis("I feel hopeless"))
+    print(state.is_escalating)  # True (LOW → MEDIUM/HIGH in 2 messages)
+
+    # Get system prompt modifier
+    modifier = tracker.get_session_modifier()
+    # "User has escalated from LOW to HIGH over 2 messages."
+
+    # Reset for new session
+    tracker.reset()
+"""
+
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+from .behavioral import BehavioralTracker
+from .detect import CrisisDetectionResult, SCORES
+
+# Level ordering for comparison (higher = more severe)
+LEVEL_ORDER = {"NONE": 0, "LOW": 1, "MEDIUM": 2, "HIGH": 3, "CRITICAL": 4}
+
+
+@dataclass
+class SessionState:
+    """Immutable snapshot of session crisis tracking state."""
+
+    current_level: str = "NONE"
+    peak_level: str = "NONE"
+    message_count: int = 0
+    level_history: List[str] = field(default_factory=list)
+    is_escalating: bool = False
+    is_deescalating: bool = False
+    escalation_rate: float = 0.0  # levels gained per message
+    consecutive_low_messages: int = 0  # for de-escalation tracking
+    behavioral_score: float = 0.0
+    behavioral_signals: List[dict] = field(default_factory=list)
+    frequency_change: float = 1.0
+    is_late_night: bool = False
+    session_length_trend: str = "stable"
+    withdrawal_detected: bool = False
+
+
+class CrisisSessionTracker:
+    """
+    Session-level crisis state tracker.
+
+    Privacy-first: no database, no network calls, no cross-session
+    persistence. State lives only in memory for the duration of
+    a conversation, then is discarded on reset().
+    """
+
+    # Thresholds (from issue #35)
+    ESCALATION_WINDOW = 3   # messages: LOW → HIGH in ≤3 messages = rapid escalation
+    DEESCALATION_WINDOW = 5  # messages: need 5+ consecutive LOW messages after CRITICAL
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        """Reset all session state. Call on new conversation."""
+        self._current_level = "NONE"
+        self._peak_level = "NONE"
+        self._message_count = 0
+        self._level_history: List[str] = []
+        self._consecutive_low = 0
+        self._behavioral_tracker = BehavioralTracker()
+        self._behavioral_session_id = "current-session"
+
+    @property
+    def state(self) -> SessionState:
+        """Return immutable snapshot of current session state."""
+        is_escalating = self._detect_escalation()
+        is_deescalating = self._detect_deescalation()
+        rate = self._compute_escalation_rate()
+        behavioral = self._behavioral_tracker.get_risk_signals(self._behavioral_session_id)
+
+        return SessionState(
+            current_level=self._current_level,
+            peak_level=self._peak_level,
+            message_count=self._message_count,
+            level_history=list(self._level_history),
+            is_escalating=is_escalating,
+            is_deescalating=is_deescalating,
+            escalation_rate=rate,
+            consecutive_low_messages=self._consecutive_low,
+            behavioral_score=behavioral["behavioral_score"],
+            behavioral_signals=behavioral["signals"],
+            frequency_change=behavioral["frequency_change"],
+            is_late_night=behavioral["is_late_night"],
+            session_length_trend=behavioral["session_length_trend"],
+            withdrawal_detected=behavioral["withdrawal_detected"],
+        )
+
+    def record(
+        self,
+        detection: CrisisDetectionResult,
+        *,
+        timestamp=None,
+        message_length: int = 0,
+        role: str = "user",
+    ) -> SessionState:
+        """
+        Record a crisis detection result for the current message.
+
+        Returns updated SessionState.
+        """
+        from datetime import datetime, timezone
+
+        level = detection.level
+        self._message_count += 1
+        self._level_history.append(level)
+
+        # Update peak
+        if LEVEL_ORDER.get(level, 0) > LEVEL_ORDER.get(self._peak_level, 0):
+            self._peak_level = level
+
+        # Track consecutive LOW/NONE messages for de-escalation
+        if LEVEL_ORDER.get(level, 0) <= LEVEL_ORDER["LOW"]:
+            self._consecutive_low += 1
+        else:
+            self._consecutive_low = 0
+
+        if role == "user":
+            if timestamp is None:
+                timestamp = datetime.now(timezone.utc)
+            self._behavioral_tracker.record(
+                self._behavioral_session_id,
+                timestamp,
+                message_length=message_length,
+                crisis_score=detection.score,
+                role=role,
+            )
+
+        self._current_level = level
+        return self.state
+
+    def _detect_escalation(self) -> bool:
+        """
+        Detect rapid escalation: LOW → HIGH within ESCALATION_WINDOW messages.
+
+        Looks at the last N messages and checks if the level has climbed
+        significantly (at least 2 tiers).
+        """
+        if len(self._level_history) < 2:
+            return False
+
+        window = self._level_history[-self.ESCALATION_WINDOW:]
+        if len(window) < 2:
+            return False
+
+        first_level = window[0]
+        last_level = window[-1]
+
+        first_score = LEVEL_ORDER.get(first_level, 0)
+        last_score = LEVEL_ORDER.get(last_level, 0)
+
+        # Escalation = climbed at least 2 tiers in the window
+        return (last_score - first_score) >= 2
+
+    def _detect_deescalation(self) -> bool:
+        """
+        Detect de-escalation: was at CRITICAL/HIGH, now sustained LOW/NONE
+        for DEESCALATION_WINDOW consecutive messages.
+        """
+        if LEVEL_ORDER.get(self._peak_level, 0) < LEVEL_ORDER["HIGH"]:
+            return False
+
+        return self._consecutive_low >= self.DEESCALATION_WINDOW
+
+    def _compute_escalation_rate(self) -> float:
+        """
+        Compute levels gained per message over the conversation.
+
+        Positive = escalating, negative = de-escalating, 0 = stable.
+        """
+        if self._message_count < 2:
+            return 0.0
+
+        first = LEVEL_ORDER.get(self._level_history[0], 0)
+        current = LEVEL_ORDER.get(self._current_level, 0)
+
+        return (current - first) / (self._message_count - 1)
+
+    def get_session_modifier(self) -> str:
+        """
+        Generate a system prompt modifier reflecting session-level crisis state.
+
+        Returns empty string if no session context is relevant.
+        """
+        if self._message_count < 2:
+            return ""
+
+        s = self.state
+
+        if s.is_escalating:
+            return (
+                f"User has escalated from {self._level_history[0]} to "
+                f"{s.current_level} over {s.message_count} messages. "
+                f"Peak crisis level this session: {s.peak_level}. "
+                "Respond with heightened awareness. The trajectory is "
+                "worsening — prioritize safety and connection."
+            )
+
+        if s.is_deescalating:
+            return (
+                f"User previously reached {s.peak_level} crisis level "
+                f"but has been at {s.current_level} or below for "
+                f"{s.consecutive_low_messages} consecutive messages. "
+                "The situation appears to be stabilizing. Continue "
+                "supportive engagement while remaining vigilant."
+            )
+
+        notes = []
+
+        if s.peak_level in ("CRITICAL", "HIGH") and s.current_level not in ("CRITICAL", "HIGH"):
+            notes.append(
+                f"User previously reached {s.peak_level} crisis level this session (currently {s.current_level}). "
+                "Continue with care and awareness of the earlier crisis."
+            )
+
+        if s.behavioral_score >= 0.35 and s.behavioral_signals:
+            signal_names = ", ".join(item["signal_type"] for item in s.behavioral_signals)
+            notes.append(
+                f"Behavioral risk signals detected this session: {signal_names}. "
+                "Use the behavioral context to increase sensitivity and warmth."
+            )
+
+        return " ".join(notes)
+
+    def get_ui_hints(self) -> dict:
+        """
+        Return UI hints based on session state for the frontend.
+
+        These are advisory — the frontend decides what to show.
+        """
+        s = self.state
+
+        hints = {
+            "session_escalating": s.is_escalating,
+            "session_deescalating": s.is_deescalating,
+            "session_peak_level": s.peak_level,
+            "session_message_count": s.message_count,
+            "behavioral_score": s.behavioral_score,
+            "is_late_night": s.is_late_night,
+            "withdrawal_detected": s.withdrawal_detected,
+            "session_length_trend": s.session_length_trend,
+        }
+
+        if s.is_escalating:
+            hints["escalation_warning"] = True
+            hints["suggested_action"] = (
+                "User crisis level is rising across messages. "
+                "Consider increasing intervention level."
+            )
+
+        if s.behavioral_score >= 0.5:
+            hints["behavioral_warning"] = True
+            hints.setdefault(
+                "suggested_action",
+                "Behavioral risk patterns are active. Keep the response warm, grounded, and alert."
+            )
+
+        return hints
+
+
+def check_crisis_with_session(
+    text: str,
+    tracker: CrisisSessionTracker,
+    timestamp=None,
+) -> dict:
+    """
+    Convenience: detect crisis and update session state in one call.
+
+    Returns combined single-message detection + session-level context.
+    """
+    from .detect import detect_crisis
+    from .gateway import check_crisis
+
+    single_result = check_crisis(text)
+    detection = detect_crisis(text)
+    session_state = tracker.record(detection, timestamp=timestamp, message_length=len(text))
+
+    behavioral = {
+        "frequency_change": session_state.frequency_change,
+        "is_late_night": session_state.is_late_night,
+        "session_length_trend": session_state.session_length_trend,
+        "withdrawal_detected": session_state.withdrawal_detected,
+        "behavioral_score": session_state.behavioral_score,
+        "signals": session_state.behavioral_signals,
+    }
+
+    return {
+        **single_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,
+            "modifier": tracker.get_session_modifier(),
+            "ui_hints": tracker.get_ui_hints(),
+            "behavioral": behavioral,
+        },
+    }

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>
@@ -808,6 +808,7 @@ Sovereignty and service always.`;
   var crisisPanel = document.getElementById('crisis-panel');
   var crisisOverlay = document.getElementById('crisis-overlay');
   var overlayDismissBtn = document.getElementById('overlay-dismiss-btn');
+  var overlayCallLink = document.querySelector('.overlay-call');
   var statusDot = document.querySelector('.status-dot');
   var statusText = document.getElementById('status-text');
   
@@ -1050,7 +1051,8 @@ Sovereignty and service always.`;
       }
     }, 1000);
 
-    overlayDismissBtn.focus();
+    // Focus the Call 988 link (always enabled) — disabled buttons cannot receive focus
+    if (overlayCallLink) overlayCallLink.focus();
   }
 
   // Register focus trap on document (always listening, gated by class check)

tests/test_behavioral_tracker.py

@@ -0,0 +1,101 @@
+"""
+Tests for behavioral crisis pattern detection (#133).
+"""
+
+import os
+import sys
+import unittest
+from datetime import datetime, timedelta, timezone
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from crisis.session_tracker import CrisisSessionTracker, check_crisis_with_session
+from crisis.behavioral import BehavioralTracker
+
+
+class TestBehavioralTracker(unittest.TestCase):
+    def _seed_day(self, tracker, *, session_id, day, count, start_hour=10, message_length=48, crisis_score=0.0):
+        base = datetime(2026, 4, day, start_hour, 0, tzinfo=timezone.utc)
+        for i in range(count):
+            tracker.record(
+                session_id,
+                base + timedelta(minutes=i * 10),
+                message_length=message_length,
+                crisis_score=crisis_score,
+            )
+
+    def test_frequency_change_uses_seven_day_baseline(self):
+        tracker = BehavioralTracker()
+        for day in range(1, 8):
+            self._seed_day(tracker, session_id=f"baseline-{day}", day=day, count=2)
+
+        burst_base = datetime(2026, 4, 8, 14, 0, tzinfo=timezone.utc)
+        for i in range(8):
+            tracker.record(
+                "current-session",
+                burst_base + timedelta(minutes=i),
+                message_length=72,
+                crisis_score=0.1,
+            )
+
+        summary = tracker.get_risk_signals("current-session")
+
+        self.assertGreater(summary["frequency_change"], 2.0)
+        self.assertTrue(any(sig["signal_type"] == "frequency" for sig in summary["signals"]))
+        self.assertGreater(summary["behavioral_score"], 0.0)
+
+    def test_late_night_messages_raise_flag(self):
+        tracker = BehavioralTracker()
+        base = datetime(2026, 4, 10, 2, 15, tzinfo=timezone.utc)
+        for i in range(3):
+            tracker.record(
+                "late-night",
+                base + timedelta(minutes=i * 7),
+                message_length=35,
+                crisis_score=0.0,
+            )
+
+        summary = tracker.get_risk_signals("late-night")
+
+        self.assertTrue(summary["is_late_night"])
+        self.assertTrue(any(sig["signal_type"] == "time" for sig in summary["signals"]))
+
+    def test_withdrawal_detected_after_large_drop_from_baseline(self):
+        tracker = BehavioralTracker()
+        for day in range(1, 8):
+            self._seed_day(tracker, session_id=f"baseline-{day}", day=day, count=10, message_length=80)
+
+        tracker.record(
+            "withdrawal-session",
+            datetime(2026, 4, 9, 11, 0, tzinfo=timezone.utc),
+            message_length=18,
+            crisis_score=0.0,
+        )
+
+        summary = tracker.get_risk_signals("withdrawal-session")
+
+        self.assertTrue(summary["withdrawal_detected"])
+        self.assertTrue(any(sig["signal_type"] == "withdrawal" for sig in summary["signals"]))
+
+
+class TestBehavioralSessionIntegration(unittest.TestCase):
+    def test_check_crisis_with_session_includes_behavioral_summary(self):
+        tracker = CrisisSessionTracker()
+        base = datetime(2026, 4, 20, 2, 0, tzinfo=timezone.utc)
+
+        check_crisis_with_session("can't sleep", tracker, timestamp=base)
+        check_crisis_with_session("still here", tracker, timestamp=base + timedelta(minutes=1))
+        result = check_crisis_with_session("everything feels loud", tracker, timestamp=base + timedelta(minutes=2))
+
+        behavioral = result["session"]["behavioral"]
+        self.assertIn("frequency_change", behavioral)
+        self.assertIn("is_late_night", behavioral)
+        self.assertIn("session_length_trend", behavioral)
+        self.assertIn("withdrawal_detected", behavioral)
+        self.assertIn("behavioral_score", behavioral)
+        self.assertTrue(behavioral["is_late_night"])
+        self.assertGreater(behavioral["behavioral_score"], 0.0)
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_crisis_overlay_focus_trap.py

@@ -52,6 +52,34 @@ class TestCrisisOverlayFocusTrap(unittest.TestCase):
             'Expected overlay dismissal to restore focus to the prior target.',
         )
 
+    def test_overlay_initial_focus_targets_enabled_call_link(self):
+        """Overlay must focus the Call 988 link, not the disabled dismiss button."""
+        # Find the showOverlay function body (up to the closing of the setInterval callback
+        # and the focus call that follows)
+        show_start = self.html.find('function showOverlay()')
+        self.assertGreater(show_start, -1, "showOverlay function not found")
+        # Find the focus call within showOverlay (before the next function registration)
+        focus_section = self.html[show_start:show_start + 2000]
+        self.assertIn(
+            'overlayCallLink',
+            focus_section,
+            "Expected showOverlay to reference overlayCallLink for initial focus.",
+        )
+        # Ensure the old buggy pattern is gone
+        focus_line_region = self.html[show_start + 800:show_start + 1200]
+        self.assertNotIn(
+            'overlayDismissBtn.focus()',
+            focus_line_region,
+            "showOverlay must not focus the disabled dismiss button.",
+        )
+
+    def test_overlay_call_link_variable_is_declared(self):
+        self.assertIn(
+            "querySelector('.overlay-call')",
+            self.html,
+            "Expected a JS reference to the .overlay-call link element.",
+        )
+
 
 if __name__ == '__main__':
     unittest.main()

tests/test_service_worker_offline.py

@@ -50,6 +50,22 @@ class TestCrisisOfflinePage(unittest.TestCase):
         for phrase in required_phrases:
             self.assertIn(phrase, self.lower_html)
 
+    def test_no_external_resources(self):
+        """Offline page must work without any network — no external CSS/JS."""
+        import re
+        html = self.html
+        # No https:// links (except tel: and sms: which are protocol links, not network)
+        external_urls = re.findall(r'href=["\']https://|src=["\']https://', html)
+        self.assertEqual(external_urls, [], 'Offline page must not load external resources')
+        # CSS and JS must be inline
+        self.assertIn('<style>', html, 'CSS must be inline')
+        self.assertIn('<script>', html, 'JS must be inline')
+
+    def test_retry_button_present(self):
+        """User must be able to retry connection from offline page."""
+        self.assertIn('retry-connection', self.html)
+        self.assertIn('Retry connection', self.html)
+
 
 if __name__ == '__main__':
     unittest.main()

tests/test_session_tracker.py

@@ -0,0 +1,277 @@
+"""
+Tests for crisis session tracking and escalation (P0 #35).
+
+Covers: session_tracker.py
+Run with: python -m pytest tests/test_session_tracker.py -v
+"""
+
+import unittest
+import sys
+import os
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from crisis.detect import detect_crisis
+from crisis.session_tracker import (
+    CrisisSessionTracker,
+    SessionState,
+    check_crisis_with_session,
+)
+
+
+class TestSessionState(unittest.TestCase):
+    """Test SessionState defaults."""
+
+    def test_default_state(self):
+        s = SessionState()
+        self.assertEqual(s.current_level, "NONE")
+        self.assertEqual(s.peak_level, "NONE")
+        self.assertEqual(s.message_count, 0)
+        self.assertEqual(s.level_history, [])
+        self.assertFalse(s.is_escalating)
+        self.assertFalse(s.is_deescalating)
+
+
+class TestSessionTracking(unittest.TestCase):
+    """Test basic session state tracking."""
+
+    def setUp(self):
+        self.tracker = CrisisSessionTracker()
+
+    def test_record_none_message(self):
+        state = self.tracker.record(detect_crisis("Hello Timmy"))
+        self.assertEqual(state.current_level, "NONE")
+        self.assertEqual(state.message_count, 1)
+        self.assertEqual(state.peak_level, "NONE")
+
+    def test_record_low_message(self):
+        self.tracker.record(detect_crisis("Hello"))
+        state = self.tracker.record(detect_crisis("Having a rough day"))
+        self.assertIn(state.current_level, ("LOW", "NONE"))
+        self.assertEqual(state.message_count, 2)
+
+    def test_record_critical_updates_peak(self):
+        self.tracker.record(detect_crisis("Having a rough day"))
+        state = self.tracker.record(detect_crisis("I want to kill myself"))
+        self.assertEqual(state.current_level, "CRITICAL")
+        self.assertEqual(state.peak_level, "CRITICAL")
+
+    def test_peak_preserved_after_drop(self):
+        """Peak level should stay at the highest seen, even after de-escalation."""
+        self.tracker.record(detect_crisis("I want to kill myself"))
+        state = self.tracker.record(detect_crisis("I'm feeling a bit better"))
+        self.assertEqual(state.peak_level, "CRITICAL")
+
+    def test_level_history(self):
+        self.tracker.record(detect_crisis("Hello"))
+        self.tracker.record(detect_crisis("Having a rough day"))
+        state = self.tracker.record(detect_crisis("I want to die"))
+        self.assertEqual(len(state.level_history), 3)
+        self.assertEqual(state.level_history[0], "NONE")
+        self.assertEqual(state.level_history[2], "CRITICAL")
+
+    def test_reset_clears_state(self):
+        self.tracker.record(detect_crisis("I want to kill myself"))
+        self.tracker.reset()
+        state = self.tracker.state
+        self.assertEqual(state.current_level, "NONE")
+        self.assertEqual(state.peak_level, "NONE")
+        self.assertEqual(state.message_count, 0)
+        self.assertEqual(state.level_history, [])
+
+
+class TestEscalationDetection(unittest.TestCase):
+    """Test escalation detection: LOW → HIGH in ≤3 messages."""
+
+    def setUp(self):
+        self.tracker = CrisisSessionTracker()
+
+    def test_no_escalation_single_message(self):
+        self.tracker.record(detect_crisis("Hello"))
+        self.assertFalse(self.tracker.state.is_escalating)
+
+    def test_no_escalation_stable(self):
+        """Two normal messages should not trigger escalation."""
+        self.tracker.record(detect_crisis("Hello"))
+        state = self.tracker.record(detect_crisis("How are you?"))
+        self.assertFalse(state.is_escalating)
+
+    def test_rapid_escalation_low_to_high(self):
+        """LOW → HIGH in 2 messages = rapid escalation."""
+        self.tracker.record(detect_crisis("Having a rough day"))
+        state = self.tracker.record(detect_crisis("I can't take this anymore, everything is pointless"))
+        # Depending on detection, this could be HIGH or CRITICAL
+        if state.current_level in ("HIGH", "CRITICAL"):
+            self.assertTrue(state.is_escalating)
+
+    def test_rapid_escalation_three_messages(self):
+        """NONE → LOW → HIGH in 3 messages = escalation."""
+        self.tracker.record(detect_crisis("Hello"))
+        self.tracker.record(detect_crisis("Having a rough day"))
+        state = self.tracker.record(detect_crisis("I feel completely hopeless with no way out"))
+        if state.current_level in ("HIGH", "CRITICAL"):
+            self.assertTrue(state.is_escalating)
+
+    def test_escalation_rate(self):
+        """Rate should be positive when escalating."""
+        self.tracker.record(detect_crisis("Hello"))
+        self.tracker.record(detect_crisis("I want to die"))
+        state = self.tracker.state
+        self.assertGreater(state.escalation_rate, 0)
+
+
+class TestDeescalationDetection(unittest.TestCase):
+    """Test de-escalation: sustained LOW after HIGH/CRITICAL."""
+
+    def setUp(self):
+        self.tracker = CrisisSessionTracker()
+
+    def test_no_deescalation_without_prior_crisis(self):
+        """No de-escalation if never reached HIGH/CRITICAL."""
+        for _ in range(6):
+            self.tracker.record(detect_crisis("Hello"))
+        self.assertFalse(self.tracker.state.is_deescalating)
+
+    def test_deescalation_after_critical(self):
+        """5+ consecutive LOW/NONE messages after CRITICAL = de-escalation."""
+        self.tracker.record(detect_crisis("I want to kill myself"))
+        for _ in range(5):
+            self.tracker.record(detect_crisis("I'm doing better today"))
+        state = self.tracker.state
+        if state.peak_level == "CRITICAL":
+            self.assertTrue(state.is_deescalating)
+
+    def test_deescalation_after_high(self):
+        """5+ consecutive LOW/NONE messages after HIGH = de-escalation."""
+        self.tracker.record(detect_crisis("I feel completely hopeless with no way out"))
+        for _ in range(5):
+            self.tracker.record(detect_crisis("Feeling okay"))
+        state = self.tracker.state
+        if state.peak_level == "HIGH":
+            self.assertTrue(state.is_deescalating)
+
+    def test_interrupted_deescalation(self):
+        """De-escalation resets if a HIGH message interrupts."""
+        self.tracker.record(detect_crisis("I want to kill myself"))
+        for _ in range(3):
+            self.tracker.record(detect_crisis("Doing better"))
+        # Interrupt with another crisis
+        self.tracker.record(detect_crisis("I feel hopeless again"))
+        self.tracker.record(detect_crisis("Feeling okay now"))
+        state = self.tracker.state
+        # Should NOT be de-escalating yet (counter reset)
+        self.assertFalse(state.is_deescalating)
+
+
+class TestSessionModifier(unittest.TestCase):
+    """Test system prompt modifier generation."""
+
+    def setUp(self):
+        self.tracker = CrisisSessionTracker()
+
+    def test_no_modifier_for_single_message(self):
+        self.tracker.record(detect_crisis("Hello"))
+        self.assertEqual(self.tracker.get_session_modifier(), "")
+
+    def test_no_modifier_for_stable_session(self):
+        self.tracker.record(detect_crisis("Hello"))
+        self.tracker.record(detect_crisis("Good morning"))
+        self.assertEqual(self.tracker.get_session_modifier(), "")
+
+    def test_escalation_modifier(self):
+        """Escalating session should produce a modifier."""
+        self.tracker.record(detect_crisis("Hello"))
+        self.tracker.record(detect_crisis("I want to die"))
+        modifier = self.tracker.get_session_modifier()
+        if self.tracker.state.is_escalating:
+            self.assertIn("escalated", modifier.lower())
+            self.assertIn("NONE", modifier)
+            self.assertIn("CRITICAL", modifier)
+
+    def test_deescalation_modifier(self):
+        """De-escalating session should mention stabilizing."""
+        self.tracker.record(detect_crisis("I want to kill myself"))
+        for _ in range(5):
+            self.tracker.record(detect_crisis("I'm feeling okay"))
+        modifier = self.tracker.get_session_modifier()
+        if self.tracker.state.is_deescalating:
+            self.assertIn("stabilizing", modifier.lower())
+
+    def test_prior_crisis_modifier(self):
+        """Past crisis should be noted even without active escalation."""
+        self.tracker.record(detect_crisis("I want to die"))
+        self.tracker.record(detect_crisis("Feeling a bit better"))
+        modifier = self.tracker.get_session_modifier()
+        # Should note the prior CRITICAL
+        if modifier:
+            self.assertIn("CRITICAL", modifier)
+
+
+class TestUIHints(unittest.TestCase):
+    """Test UI hint generation."""
+
+    def setUp(self):
+        self.tracker = CrisisSessionTracker()
+
+    def test_ui_hints_structure(self):
+        self.tracker.record(detect_crisis("Hello"))
+        hints = self.tracker.get_ui_hints()
+        self.assertIn("session_escalating", hints)
+        self.assertIn("session_deescalating", hints)
+        self.assertIn("session_peak_level", hints)
+        self.assertIn("session_message_count", hints)
+
+    def test_ui_hints_escalation_warning(self):
+        """Escalating session should have warning hint."""
+        self.tracker.record(detect_crisis("Hello"))
+        self.tracker.record(detect_crisis("I want to die"))
+        hints = self.tracker.get_ui_hints()
+        if hints["session_escalating"]:
+            self.assertTrue(hints.get("escalation_warning"))
+            self.assertIn("suggested_action", hints)
+
+
+class TestCheckCrisisWithSession(unittest.TestCase):
+    """Test the convenience function combining detection + session tracking."""
+
+    def test_returns_combined_data(self):
+        tracker = CrisisSessionTracker()
+        result = check_crisis_with_session("I want to die", tracker)
+        self.assertIn("level", result)
+        self.assertIn("session", result)
+        self.assertIn("current_level", result["session"])
+        self.assertIn("peak_level", result["session"])
+        self.assertIn("modifier", result["session"])
+
+    def test_session_updates_across_calls(self):
+        tracker = CrisisSessionTracker()
+        check_crisis_with_session("Hello", tracker)
+        result = check_crisis_with_session("I want to die", tracker)
+        self.assertEqual(result["session"]["message_count"], 2)
+        self.assertEqual(result["session"]["peak_level"], "CRITICAL")
+
+
+class TestPrivacy(unittest.TestCase):
+    """Verify privacy-first design principles."""
+
+    def test_no_persistence_mechanism(self):
+        """Session tracker should have no database, file, or network calls."""
+        import inspect
+        source = inspect.getsource(CrisisSessionTracker)
+        # Should not import database, requests, or file I/O
+        forbidden = ["sqlite", "requests", "urllib", "open(", "httpx", "aiohttp"]
+        for word in forbidden:
+            self.assertNotIn(word, source.lower(),
+                f"Session tracker should not use {word} — privacy-first design")
+
+    def test_state_contained_in_memory(self):
+        """All state should be instance attributes, not module-level."""
+        tracker = CrisisSessionTracker()
+        tracker.record(detect_crisis("I want to die"))
+        # New tracker should have clean state (no global contamination)
+        fresh = CrisisSessionTracker()
+        self.assertEqual(fresh.state.current_level, "NONE")
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_voice_analysis.py

@@ -1,134 +0,0 @@
-"""Tests for voice message distress analysis (#131)."""
-
-from __future__ import annotations
-
-import pytest
-
-from voice_analysis import (
-    VoiceAnalysisResult,
-    compute_speech_rate,
-    compute_distress_score,
-    DISTRESS_THRESHOLDS,
-    NORMAL_SPEECH_RATE,
-    NORMAL_PITCH_VAR,
-)
-
-
-class TestDistressScore:
-    """Distress score computation from paralinguistic features."""
-
-    def test_normal_speech_no_distress(self):
-        score, signals = compute_distress_score(
-            speech_rate=140,  # normal
-            pitch_variability=50,  # normal
-            silence_ratio=0.15,  # normal
-            volume_db=-20,  # normal
-        )
-        assert score < 0.1
-        assert not signals
-
-    def test_slow_speech_detected(self):
-        score, signals = compute_distress_score(
-            speech_rate=60,  # very slow
-            pitch_variability=50,
-            silence_ratio=0.15,
-            volume_db=-20,
-        )
-        assert score > 0.1
-        assert any("slow" in s for s in signals)
-
-    def test_monotone_detected(self):
-        score, signals = compute_distress_score(
-            speech_rate=140,
-            pitch_variability=10,  # very monotone
-            silence_ratio=0.15,
-            volume_db=-20,
-        )
-        assert score > 0.1
-        assert any("monotone" in s for s in signals)
-
-    def test_long_pauses_detected(self):
-        score, signals = compute_distress_score(
-            speech_rate=140,
-            pitch_variability=50,
-            silence_ratio=0.50,  # very quiet
-            volume_db=-20,
-        )
-        assert score > 0.1
-        assert any("pause" in s for s in signals)
-
-    def test_quiet_voice_detected(self):
-        score, signals = compute_distress_score(
-            speech_rate=140,
-            pitch_variability=50,
-            silence_ratio=0.15,
-            volume_db=-45,  # very quiet
-        )
-        assert score > 0.1
-        assert any("quiet" in s for s in signals)
-
-    def test_multiple_signals_compound(self):
-        score, signals = compute_distress_score(
-            speech_rate=50,  # very slow
-            pitch_variability=5,  # very monotone
-            silence_ratio=0.55,  # long pauses
-            volume_db=-50,  # very quiet
-        )
-        assert score > 0.5
-        assert len(signals) >= 3
-
-    def test_max_score_is_1(self):
-        score, _ = compute_distress_score(
-            speech_rate=0,
-            pitch_variability=0,
-            silence_ratio=1.0,
-            volume_db=-100,
-        )
-        assert score <= 1.0
-
-
-class TestSpeechRate:
-    """Speech rate computation."""
-
-    def test_normal_rate(self):
-        # 100 words in 60 seconds = 100 wpm
-        segments = [{"start": 0.0, "end": 60.0, "text": "x"}]
-        wpm = compute_speech_rate("word " * 100, segments)
-        assert abs(wpm - 100) < 5
-
-    def test_empty_transcript(self):
-        assert compute_speech_rate("", []) == 0.0
-
-    def test_no_segments(self):
-        assert compute_speech_rate("hello world", []) == 0.0
-
-
-class TestDistressThresholds:
-    """Threshold configuration."""
-
-    def test_thresholds_ordered(self):
-        assert DISTRESS_THRESHOLDS["low"] < DISTRESS_THRESHOLDS["medium"]
-        assert DISTRESS_THRESHOLDS["medium"] < DISTRESS_THRESHOLDS["high"]
-
-    def test_low_is_03(self):
-        assert DISTRESS_THRESHOLDS["low"] == 0.3
-
-    def test_high_is_10(self):
-        assert DISTRESS_THRESHOLDS["high"] == 1.0
-
-
-class TestVoiceAnalysisResult:
-    """Result data structure."""
-
-    def test_creation(self):
-        result = VoiceAnalysisResult(
-            transcript="hello", speech_rate_wpm=120.0,
-            pitch_mean_hz=150.0, pitch_variability=40.0,
-            silence_ratio=0.2, volume_db=-20.0,
-            volume_variability=5.0, duration_seconds=10.0,
-            distress_score=0.1, distress_level="low",
-            distress_signals=[],
-        )
-        assert result.transcript == "hello"
-        assert result.distress_level == "low"
-        assert not result.distress_signals

voice_analysis.py

@@ -1,356 +0,0 @@
-"""Voice message distress analysis — paralinguistic features (#131).
-
-Analyzes audio (OGG/MP3/WAV) for distress signals using audio
-features extracted without a neural model — pure DSP analysis.
-
-Signals detected:
-- Speech rate (words per minute from timestamps)
-- Pitch variability (F0 std deviation — monotone = depression indicator)
-- Silence ratio (long pauses)
-- Volume dynamics (drops, tremor proxy)
-
-Uses whisper for transcription + word timestamps. All other features
-are computed from raw audio via librosa.
-
-Refs: #131 — Epic #102 (Multimodal Crisis Detection)
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-import os
-import subprocess
-import tempfile
-from dataclasses import dataclass, asdict
-from pathlib import Path
-from typing import Optional, List, Dict, Any
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class VoiceAnalysisResult:
-    """Result of voice message paralinguistic analysis."""
-    transcript: str
-    speech_rate_wpm: float          # words per minute
-    pitch_mean_hz: float            # mean F0 in Hz
-    pitch_variability: float        # F0 standard deviation (low = monotone)
-    silence_ratio: float            # fraction of audio that is silence (0-1)
-    volume_db: float                # mean volume in dB
-    volume_variability: float       # volume std deviation
-    duration_seconds: float         # total audio duration
-    distress_score: float           # 0-1 composite score
-    distress_level: str             # "low", "medium", "high"
-    distress_signals: List[str]     # list of detected signals
-
-
-# Distress thresholds
-DISTRESS_THRESHOLDS = {
-    "low": 0.3,
-    "medium": 0.7,
-    "high": 1.0,
-}
-
-# Paralinguistic distress indicators
-# These are heuristic — the model learns what "normal" looks like
-# and flags deviations.
-NORMAL_SPEECH_RATE = (100, 180)     # words per minute
-NORMAL_PITCH_VAR = (20, 80)         # F0 std deviation in Hz
-NORMAL_SILENCE_RATIO = (0.05, 0.35) # fraction of silence
-NORMAL_VOLUME_DB = (-30, -10)       # dB range
-
-
-def _ensure_whisper():
-    """Check if whisper is available."""
-    try:
-        result = subprocess.run(
-            ["whisper", "--help"],
-            capture_output=True, text=True, timeout=5,
-        )
-        return True
-    except (FileNotFoundError, subprocess.TimeoutExpired):
-        return False
-
-
-def _ensure_librosa():
-    """Check if librosa is available."""
-    try:
-        import librosa
-        return True
-    except ImportError:
-        return False
-
-
-def transcribe_with_timestamps(audio_path: str) -> Dict[str, Any]:
-    """Transcribe audio using whisper and extract word-level timestamps.
-
-    Returns dict with 'text' and 'segments' (list of {start, end, text}).
-    Falls back to subprocess whisper if Python whisper not available.
-    """
-    try:
-        import whisper
-        model = whisper.load_model("base")
-        result = model.transcribe(audio_path, word_timestamps=True)
-        return {
-            "text": result["text"],
-            "segments": [
-                {"start": s["start"], "end": s["end"], "text": s["text"]}
-                for s in result.get("segments", [])
-            ],
-        }
-    except ImportError:
-        pass
-
-    # Fallback: subprocess whisper
-    with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as f:
-        json_out = f.name
-
-    try:
-        subprocess.run(
-            ["whisper", audio_path, "--model", "base", "--output_format", "json",
-             "--output_dir", os.path.dirname(json_out)],
-            capture_output=True, text=True, timeout=120,
-        )
-
-        # Whisper outputs to <filename>.json in output_dir
-        base = Path(audio_path).stem
-        whisper_out = Path(os.path.dirname(json_out)) / f"{base}.json"
-
-        if whisper_out.exists():
-            with open(whisper_out) as f:
-                data = json.load(f)
-            os.unlink(whisper_out)
-            return {
-                "text": data.get("text", ""),
-                "segments": [
-                    {"start": s["start"], "end": s["end"], "text": s["text"]}
-                    for s in data.get("segments", [])
-                ],
-            }
-    except Exception as e:
-        logger.warning("Whisper transcription failed: %s", e)
-    finally:
-        if os.path.exists(json_out):
-            os.unlink(json_out)
-
-    return {"text": "", "segments": []}
-
-
-def extract_audio_features(audio_path: str) -> Dict[str, float]:
-    """Extract paralinguistic features from raw audio using librosa.
-
-    Returns dict with pitch, volume, and silence metrics.
-    """
-    try:
-        import librosa
-        import numpy as np
-    except ImportError:
-        logger.warning("librosa not available — returning defaults")
-        return {
-            "pitch_mean_hz": 0.0, "pitch_variability": 0.0,
-            "silence_ratio": 0.0, "volume_db": 0.0, "volume_variability": 0.0,
-            "duration_seconds": 0.0,
-        }
-
-    try:
-        y, sr = librosa.load(audio_path, sr=None)
-    except Exception as e:
-        logger.warning("Failed to load audio %s: %s", audio_path, e)
-        return {
-            "pitch_mean_hz": 0.0, "pitch_variability": 0.0,
-            "silence_ratio": 0.0, "volume_db": 0.0, "volume_variability": 0.0,
-            "duration_seconds": 0.0,
-        }
-
-    duration = len(y) / sr
-
-    # Pitch (F0) estimation using pyin
-    try:
-        f0, voiced_flag, _ = librosa.pyin(y, fmin=50, fmax=500, sr=sr)
-        f0_voiced = f0[~np.isnan(f0)]
-        if len(f0_voiced) > 0:
-            pitch_mean = float(np.mean(f0_voiced))
-            pitch_var = float(np.std(f0_voiced))
-        else:
-            pitch_mean = 0.0
-            pitch_var = 0.0
-    except Exception:
-        pitch_mean = 0.0
-        pitch_var = 0.0
-
-    # Volume (RMS energy)
-    rms = librosa.feature.rms(y=y)[0]
-    volume_db = float(librosa.amplitude_to_db(rms, ref=np.max).mean())
-    volume_var = float(librosa.amplitude_to_db(rms, ref=np.max).std())
-
-    # Silence ratio
-    try:
-        intervals = librosa.effects.split(y, top_db=30)
-        speech_samples = sum(end - start for start, end in intervals)
-        silence_ratio = 1.0 - (speech_samples / len(y)) if len(y) > 0 else 0.0
-    except Exception:
-        silence_ratio = 0.0
-
-    return {
-        "pitch_mean_hz": round(pitch_mean, 1),
-        "pitch_variability": round(pitch_var, 1),
-        "silence_ratio": round(silence_ratio, 3),
-        "volume_db": round(volume_db, 1),
-        "volume_variability": round(volume_var, 1),
-        "duration_seconds": round(duration, 2),
-    }
-
-
-def compute_speech_rate(transcript: str, segments: List[dict]) -> float:
-    """Compute words per minute from transcript and timestamps."""
-    words = len(transcript.split())
-    if words == 0:
-        return 0.0
-
-    if not segments:
-        return 0.0
-
-    total_duration = max(s["end"] for s in segments) - min(s["start"] for s in segments)
-    if total_duration <= 0:
-        return 0.0
-
-    wpm = words / (total_duration / 60.0)
-    return round(wpm, 1)
-
-
-def compute_distress_score(
-    speech_rate: float,
-    pitch_variability: float,
-    silence_ratio: float,
-    volume_db: float,
-) -> tuple[float, List[str]]:
-    """Compute composite distress score from paralinguistic features.
-
-    Returns (score, signals) where score is 0-1 and signals is a list
-    of detected distress indicators.
-    """
-    signals = []
-    scores = []
-
-    # Speech rate: very slow (<80) or very fast (>200) is concerning
-    if speech_rate > 0:
-        if speech_rate < NORMAL_SPEECH_RATE[0]:
-            signals.append(f"very_slow_speech ({speech_rate:.0f} wpm)")
-            scores.append(min(1.0, (NORMAL_SPEECH_RATE[0] - speech_rate) / 50))
-        elif speech_rate > NORMAL_SPEECH_RATE[1]:
-            signals.append(f"very_fast_speech ({speech_rate:.0f} wpm)")
-            scores.append(min(1.0, (speech_rate - NORMAL_SPEECH_RATE[1]) / 80))
-        else:
-            scores.append(0.0)
-
-    # Pitch variability: low = monotone (depression indicator)
-    if pitch_variability > 0:
-        if pitch_variability < NORMAL_PITCH_VAR[0]:
-            signals.append(f"monotone_voice (F0_var={pitch_variability:.0f}Hz)")
-            scores.append(min(1.0, (NORMAL_PITCH_VAR[0] - pitch_variability) / NORMAL_PITCH_VAR[0]))
-        else:
-            scores.append(0.0)
-
-    # Silence ratio: high = long pauses
-    if silence_ratio > NORMAL_SILENCE_RATIO[1]:
-        signals.append(f"long_pauses (silence={silence_ratio:.0%})")
-        scores.append(min(1.0, (silence_ratio - NORMAL_SILENCE_RATIO[1]) / 0.4))
-    else:
-        scores.append(0.0)
-
-    # Volume: very quiet
-    if volume_db < NORMAL_VOLUME_DB[0]:
-        signals.append(f"very_quiet ({volume_db:.0f}dB)")
-        scores.append(min(1.0, abs(volume_db - NORMAL_VOLUME_DB[0]) / 20))
-    else:
-        scores.append(0.0)
-
-    # Composite: max of individual signals (not average — one severe signal is enough)
-    if scores:
-        score = max(scores)
-    else:
-        score = 0.0
-
-    return round(score, 3), signals
-
-
-def analyze_voice_message(audio_path: str) -> VoiceAnalysisResult:
-    """Analyze a voice message for distress signals.
-
-    Args:
-        audio_path: Path to audio file (OGG, MP3, WAV).
-
-    Returns:
-        VoiceAnalysisResult with all paralinguistic features.
-    """
-    # Step 1: Transcribe with timestamps
-    transcription = transcribe_with_timestamps(audio_path)
-    transcript = transcription["text"]
-    segments = transcription["segments"]
-
-    # Step 2: Extract audio features
-    features = extract_audio_features(audio_path)
-
-    # Step 3: Compute speech rate
-    wpm = compute_speech_rate(transcript, segments)
-
-    # Step 4: Compute distress score
-    distress_score, distress_signals = compute_distress_score(
-        speech_rate=wpm,
-        pitch_variability=features["pitch_variability"],
-        silence_ratio=features["silence_ratio"],
-        volume_db=features["volume_db"],
-    )
-
-    # Determine level
-    if distress_score >= DISTRESS_THRESHOLDS["high"]:
-        level = "high"
-    elif distress_score >= DISTRESS_THRESHOLDS["medium"]:
-        level = "medium"
-    else:
-        level = "low"
-
-    return VoiceAnalysisResult(
-        transcript=transcript,
-        speech_rate_wpm=wpm,
-        pitch_mean_hz=features["pitch_mean_hz"],
-        pitch_variability=features["pitch_variability"],
-        silence_ratio=features["silence_ratio"],
-        volume_db=features["volume_db"],
-        volume_variability=features["volume_variability"],
-        duration_seconds=features["duration_seconds"],
-        distress_score=distress_score,
-        distress_level=level,
-        distress_signals=distress_signals,
-    )
-
-
-def main():
-    import argparse
-    p = argparse.ArgumentParser(description="Voice message distress analysis")
-    p.add_argument("audio", help="Path to audio file")
-    p.add_argument("--json", action="store_true")
-    a = p.parse_args()
-
-    if not os.path.exists(a.audio):
-        print(f"File not found: {a.audio}", file=sys.stderr)
-        sys.exit(1)
-
-    result = analyze_voice_message(a.audio)
-
-    if a.json:
-        print(json.dumps(asdict(result), indent=2))
-    else:
-        print(f"Transcript: {result.transcript[:100]}...")
-        print(f"Speech rate: {result.speech_rate_wpm} wpm")
-        print(f"Pitch: {result.pitch_mean_hz} Hz (variability: {result.pitch_variability})")
-        print(f"Silence: {result.silence_ratio:.0%}")
-        print(f"Volume: {result.volume_db} dB")
-        print(f"Distress: {result.distress_score:.2f} ({result.distress_level})")
-        if result.distress_signals:
-            print(f"Signals: {', '.join(result.distress_signals)}")
-
-
-if __name__ == "__main__":
-    main()