phase 4.1 and 4.2 changes

.gitignore

@@ -1 +1,2 @@
-.env
+.env
+tests/

main.py

@@ -594,6 +594,219 @@ Original wake confidence: {signals.original_wake_confidence:.2f}"""
             return PostWakeResponse(status="ok", message="Activity normal.", severity="none")
 
 
+## ─── Pre-Alarm Intelligence (Phase 4.1) ──────────────────────
+
+class PreAlarmContext(BaseModel):
+    user_id: Optional[str] = None
+    context: dict = {}
+
+class PreAlarmResponse(BaseModel):
+    difficulty_adjustment: str  # "harder", "easier", "normal"
+    reasoning: str
+    briefing_hint: str  # short note for morning companion
+    early_alarm: bool  # suggest triggering alarm early (light sleep detected)
+
+PRE_ALARM_PROMPT = """You are an AI pre-alarm analyst for the Ankr alarm app. You receive contextual data gathered 30 minutes before a user's alarm. Your job is to analyze all signals and recommend difficulty adjustments.
+
+You receive:
+- **weather**: Temperature, conditions (rain/snow may affect commute)
+- **calendar**: Today's events, time until first event (tight schedule = be more aggressive)
+- **sleep**: Sleep duration and quality from Health Connect (poor sleep = harder to wake)
+- **heart_rate**: Recent heart rate (low/resting = deep sleep, may need stronger alarm)
+- **wake_profile**: User's historical success rate, streak, trend, relapse rate
+
+Decision rules:
+- If first event is within 90 min: recommend "harder" (user needs to be on time)
+- If sleep quality is "poor" or total < 5 hours: recommend "harder" (sleep-deprived users struggle)
+- If heart rate is very low (< 55 bpm) and user has high relapse rate: recommend "harder"
+- If it's a weekend with no events and user has high success rate: recommend "easier"
+- If user is on a long streak (7+ days) and trend is improving: recommend "normal" or "easier"
+- If weather is severe (heavy rain, snow, extreme cold): mention in briefing_hint
+- Set early_alarm=true ONLY if sleep data shows user is in light sleep AND alarm is within 10 min
+
+You MUST respond with ONLY a JSON object:
+{
+    "difficulty_adjustment": "harder" | "easier" | "normal",
+    "reasoning": "Brief explanation",
+    "briefing_hint": "One sentence context note for morning briefing",
+    "early_alarm": false
+}
+"""
+
+@app.post("/wake/pre-alarm-context", response_model=PreAlarmResponse)
+async def pre_alarm_context(request: PreAlarmContext):
+    """Analyze pre-alarm context and recommend difficulty adjustments."""
+    ctx = request.context
+
+    signal_summary = f"""Analyze pre-alarm context for this user:
+
+Weather: {json.dumps(ctx.get('weather'), default=str) if ctx.get('weather') else 'unavailable'}
+Calendar: {json.dumps(ctx.get('calendar'), default=str) if ctx.get('calendar') else 'no events'}
+Sleep: {json.dumps(ctx.get('sleep'), default=str) if ctx.get('sleep') else 'no sleep data'}
+Heart Rate: {json.dumps(ctx.get('heart_rate'), default=str) if ctx.get('heart_rate') else 'unavailable'}
+Wake Profile: {json.dumps(ctx.get('wake_profile'), default=str) if ctx.get('wake_profile') else 'new user'}
+Day/Time: {ctx.get('day_of_week', 'unknown')}, {ctx.get('time', 'unknown')}"""
+
+    # Inject user profile into prompt if available
+    profile_context = _get_profile_context(request.user_id)
+    prompt = PRE_ALARM_PROMPT
+    if profile_context:
+        prompt += f"\n\n--- USER PROFILE ---\n{profile_context}\n--- END ---"
+
+    try:
+        response = client.models.generate_content(
+            model=MODEL_ID,
+            config={
+                'system_instruction': prompt,
+                'response_mime_type': 'application/json',
+            },
+            contents=signal_summary,
+        )
+
+        result = json.loads(response.text)
+        adjustment = result.get("difficulty_adjustment", "normal")
+        if adjustment not in ("harder", "easier", "normal"):
+            adjustment = "normal"
+
+        return PreAlarmResponse(
+            difficulty_adjustment=adjustment,
+            reasoning=result.get("reasoning", ""),
+            briefing_hint=result.get("briefing_hint", ""),
+            early_alarm=result.get("early_alarm", False),
+        )
+    except Exception as e:
+        print(f"[PreAlarm] AI error: {e}")
+        return PreAlarmResponse(
+            difficulty_adjustment="normal",
+            reasoning="AI unavailable, using defaults",
+            briefing_hint="",
+            early_alarm=False,
+        )
+
+
+## ─── Morning Companion Briefing (Phase 4.2) ──────────────────
+
+class BriefingRequest(BaseModel):
+    user_id: Optional[str] = None
+    pre_alarm_context: dict = {}
+    wake_session: dict = {}  # how this morning's alarm went
+
+class BriefingResponse(BaseModel):
+    greeting: str
+    weather_note: str
+    calendar_note: str
+    motivation: str
+    tip: str
+    full_spoken: str  # concatenated TTS-optimized version
+
+MORNING_BRIEFING_PROMPT = """You are Ankr's Morning Companion — a friendly, concise morning briefing AI. After the user has woken up and passed post-wake monitoring, you deliver a personalized morning message.
+
+You receive:
+- **weather**: Current conditions and temperature
+- **calendar**: Today's events and schedule
+- **wake_session**: How the user woke up this morning (time taken, escalation reached, steps)
+- **wake_profile**: Historical patterns (streak, success rate, trend)
+
+Your briefing has these sections:
+1. **greeting**: A warm, personalized morning greeting (reference their wake performance today)
+2. **weather_note**: Brief weather summary with practical advice (e.g., "Bring an umbrella" or "Great day for a walk")
+3. **calendar_note**: What's on their schedule today (mention first event, total count)
+4. **motivation**: Streak celebration, trend acknowledgment, or encouragement (reference their data)
+5. **tip**: One practical tip based on their patterns (e.g., "You struggle on Mondays — try laying out clothes the night before")
+
+Rules:
+- Keep EACH section to 1-2 sentences max. This is read aloud via TTS.
+- Be warm and positive but not cheesy. You're a helpful friend, not a motivational poster.
+- Reference specific data naturally (don't just list numbers).
+- If data is missing (no weather, no calendar), skip that section gracefully.
+- The full_spoken field should be all sections concatenated with natural pauses (periods between sections).
+
+You MUST respond with ONLY a JSON object:
+{
+    "greeting": "...",
+    "weather_note": "...",
+    "calendar_note": "...",
+    "motivation": "...",
+    "tip": "...",
+    "full_spoken": "..."
+}
+"""
+
+@app.post("/morning/briefing", response_model=BriefingResponse)
+async def morning_briefing(request: BriefingRequest):
+    """Generate a personalized morning briefing after successful wake-up."""
+    ctx = request.pre_alarm_context
+    wake = request.wake_session
+
+    signal_summary = f"""Generate a morning briefing for this user:
+
+Weather: {json.dumps(ctx.get('weather'), default=str) if ctx.get('weather') else 'unavailable'}
+Calendar: {json.dumps(ctx.get('calendar'), default=str) if ctx.get('calendar') else 'no events'}
+
+This morning's wake-up:
+- Time to wake: {wake.get('time_to_wake_seconds', 'unknown')}s
+- Steps taken: {wake.get('steps', 'unknown')}
+- Escalation level reached: {wake.get('escalation_level', 'unknown')}/5
+- Post-wake monitoring: {'passed' if wake.get('post_wake_passed') else 'unknown'}
+
+Day: {ctx.get('day_of_week', datetime.now().strftime('%A'))}
+Time: {ctx.get('time', datetime.now().strftime('%I:%M %p'))}"""
+
+    # Add profile context
+    profile_context = _get_profile_context(request.user_id)
+    prompt = MORNING_BRIEFING_PROMPT
+    if profile_context:
+        prompt += f"\n\n--- USER PROFILE ---\n{profile_context}\n--- END ---"
+
+    try:
+        response = client.models.generate_content(
+            model=MODEL_ID,
+            config={
+                'system_instruction': prompt,
+                'response_mime_type': 'application/json',
+            },
+            contents=signal_summary,
+        )
+
+        result = json.loads(response.text)
+
+        greeting = result.get("greeting", "Good morning!")
+        weather_note = result.get("weather_note", "")
+        calendar_note = result.get("calendar_note", "")
+        motivation = result.get("motivation", "")
+        tip = result.get("tip", "")
+
+        # Build full_spoken if not provided
+        full_spoken = result.get("full_spoken", "")
+        if not full_spoken:
+            parts = [greeting]
+            if weather_note: parts.append(weather_note)
+            if calendar_note: parts.append(calendar_note)
+            if motivation: parts.append(motivation)
+            if tip: parts.append(tip)
+            full_spoken = " ".join(parts)
+
+        return BriefingResponse(
+            greeting=greeting,
+            weather_note=weather_note,
+            calendar_note=calendar_note,
+            motivation=motivation,
+            tip=tip,
+            full_spoken=full_spoken,
+        )
+    except Exception as e:
+        print(f"[MorningBriefing] AI error: {e}")
+        # Fallback briefing
+        return BriefingResponse(
+            greeting="Good morning! You're up and moving.",
+            weather_note="",
+            calendar_note="",
+            motivation="Keep up the great work!",
+            tip="",
+            full_spoken="Good morning! You're up and moving. Keep up the great work!",
+        )
+
+
 ## ─── User Profile (Phase 3A) ──────────────────────────────────
 
 class UserWakeProfile(BaseModel):