feat: Ipad app production readiness, Colony orchestration, Social posting

backend/routers/sentinel.py

@@ -7,6 +7,8 @@ from __future__ import annotations
 import asyncio
 import json
 import logging
+import math
+import os
 import re
 import uuid
 from datetime import datetime, timezone
@@ -16,7 +18,7 @@ import asyncpg
 from fastapi import APIRouter, Depends, HTTPException, WebSocket, WebSocketDisconnect
 from pydantic import BaseModel
 
-from backend.auth.dependencies import UserPrincipal, require_role
+from backend.auth.dependencies import UserPrincipal, get_current_user, require_role
 from backend.db.pool import get_pool
 from backend.services.auto_mode_matcher import auto_mode_match_session
 from backend.services.nemoclaw_client import score_qd, tag_lead
@@ -57,8 +59,12 @@ class SentinelConnectionManager:
         for channel in self._channels:
             await self.broadcast(payload, channel)
 
+    def connection_count(self, channel: str) -> int:
+        return len(self._channels.get(channel, set()))
+
 
 manager = SentinelConnectionManager()
+_perception_producer_task: asyncio.Task | None = None
 
 
 def _is_uuid(value: str | None) -> bool:
@@ -371,6 +377,115 @@ async def _persist_canonical_qd(
     )
 
 
+async def _build_showroom_perception_payload(pool: asyncpg.Pool) -> dict[str, Any]:
+    now = datetime.now(timezone.utc)
+    seconds = now.timestamp()
+    simulated_count = max(1, int(round(9 + 5 * math.sin(seconds / 300.0) + 2 * math.sin(seconds / 53.0))))
+    simulated_sentiment = max(0.0, min(1.0, 0.62 + 0.18 * math.sin(seconds / 210.0)))
+
+    try:
+        async with pool.acquire() as conn:
+            row = await conn.fetchrow(
+                """
+                SELECT
+                    COUNT(*) FILTER (WHERE happened_at >= NOW() - INTERVAL '15 minutes')::int AS recent_events,
+                    COUNT(DISTINCT session_ref) FILTER (WHERE happened_at >= NOW() - INTERVAL '15 minutes')::int AS active_sessions,
+                    COALESCE(AVG(engagement_score) FILTER (WHERE happened_at >= NOW() - INTERVAL '15 minutes'), 0.0)::float AS avg_engagement,
+                    COUNT(*) FILTER (
+                        WHERE event_type IN ('engagement_spike', 'positive_shift')
+                          AND happened_at >= NOW() - INTERVAL '15 minutes'
+                    )::int AS positive_events,
+                    COUNT(*) FILTER (
+                        WHERE event_type = 'negative_shift'
+                          AND happened_at >= NOW() - INTERVAL '15 minutes'
+                    )::int AS negative_events
+                FROM intel_perception_events
+                """
+            )
+            active_sessions = int(row["active_sessions"] or 0)
+            recent_events = int(row["recent_events"] or 0)
+            avg_engagement = float(row["avg_engagement"] or 0.0)
+            if recent_events > 0:
+                visitor_count = max(active_sessions, recent_events)
+                sentiment_score = max(0.0, min(1.0, avg_engagement))
+                source = "canonical_perception_events"
+            else:
+                visitor_count = simulated_count
+                sentiment_score = simulated_sentiment
+                source = "mathematical_simulation"
+            positive_events = int(row["positive_events"] or 0)
+            negative_events = int(row["negative_events"] or 0)
+    except Exception:
+        active_sessions = 0
+        visitor_count = simulated_count
+        sentiment_score = simulated_sentiment
+        positive_events = 0
+        negative_events = 0
+        source = "mathematical_simulation"
+
+    showroom_heat = max(0.0, min(1.0, (visitor_count / 18.0) * 0.45 + sentiment_score * 0.55))
+    conversion_intent = max(0.0, min(1.0, sentiment_score * 0.7 + min(visitor_count, 20) / 20.0 * 0.3))
+    sentiment_label = "positive" if sentiment_score >= 0.66 else ("neutral" if sentiment_score >= 0.42 else "negative")
+
+    return {
+        "type": "PERCEPTION_ANALYTICS",
+        "data": {
+            "generated_at": now.isoformat(),
+            "source": source,
+            "visitor_count": visitor_count,
+            "active_sessions": active_sessions,
+            "sentiment_score": round(sentiment_score, 3),
+            "sentiment_label": sentiment_label,
+            "positive_events": positive_events,
+            "negative_events": negative_events,
+            "showroom_intelligence": {
+                "showroom_heat": round(showroom_heat, 3),
+                "conversion_intent": round(conversion_intent, 3),
+                "recommended_staffing": "high_touch" if visitor_count >= 12 or conversion_intent >= 0.72 else "standard",
+                "zones": [
+                    {
+                        "zone": "model_apartment",
+                        "visitors": max(1, int(visitor_count * 0.42)),
+                        "dwell_seconds": int(220 + 140 * showroom_heat),
+                    },
+                    {
+                        "zone": "pricing_desk",
+                        "visitors": max(0, int(visitor_count * conversion_intent * 0.35)),
+                        "dwell_seconds": int(120 + 180 * conversion_intent),
+                    },
+                    {
+                        "zone": "amenities_gallery",
+                        "visitors": max(0, visitor_count - int(visitor_count * 0.42)),
+                        "dwell_seconds": int(90 + 90 * sentiment_score),
+                    },
+                ],
+            },
+        },
+    }
+
+
+async def _perception_producer(pool: asyncpg.Pool) -> None:
+    while True:
+        try:
+            if manager.connection_count("perception") == 0:
+                await asyncio.sleep(1.0)
+                continue
+            payload = await _build_showroom_perception_payload(pool)
+            await manager.broadcast(payload, "perception")
+            await asyncio.sleep(float(os.getenv("SENTINEL_PERCEPTION_INTERVAL_SECONDS", "3")))
+        except asyncio.CancelledError:
+            raise
+        except Exception as exc:
+            logger.exception("Sentinel perception producer failed: %s", exc)
+            await asyncio.sleep(5.0)
+
+
+def _ensure_perception_producer(pool: asyncpg.Pool) -> None:
+    global _perception_producer_task
+    if _perception_producer_task is None or _perception_producer_task.done():
+        _perception_producer_task = asyncio.create_task(_perception_producer(pool))
+
+
 @router.websocket("/ws/notifications")
 async def notifications_ws(ws: WebSocket) -> None:
     await manager.connect(ws, "notifications")
@@ -390,6 +505,7 @@ async def perception_ws(ws: WebSocket) -> None:
         await ws.send_text(json.dumps({"type": "system", "data": {"error": "Database unavailable"}}))
         await ws.close(code=1011)
         return
+    _ensure_perception_producer(pool)
 
     try:
         while True:
@@ -801,5 +917,78 @@ async def get_qd_score(
     }
 
 
+@router.get("/analytics/live", summary="Live Sentinel perception analytics for native clients")
+async def live_perception_analytics(
+    pool: asyncpg.Pool = Depends(get_pool),
+    user: UserPrincipal = Depends(get_current_user),
+) -> dict[str, Any]:
+    del user
+    async with pool.acquire() as conn:
+        session = await conn.fetchrow(
+            """
+            SELECT
+                COUNT(*) FILTER (WHERE ended_at IS NULL)::int AS active_sessions,
+                COUNT(*) FILTER (WHERE started_at >= NOW() - INTERVAL '24 hours')::int AS visitor_count_24h,
+                COALESCE(ROUND(AVG(final_qd_score) FILTER (WHERE final_qd_score IS NOT NULL)::numeric, 1), 0)::float AS avg_qd_score
+            FROM perception_sessions
+            """
+        )
+        sentiment_rows = await conn.fetch(
+            """
+            SELECT
+                CASE
+                    WHEN event_type IN ('engagement_spike', 'positive_shift') OR COALESCE(engagement_score, 0) >= 0.70 THEN 'positive'
+                    WHEN event_type IN ('negative_shift', 'exit_risk') OR COALESCE(engagement_score, 0) <= 0.35 THEN 'negative'
+                    ELSE 'neutral'
+                END AS bucket,
+                COUNT(*)::int AS count
+            FROM intel_perception_events
+            WHERE happened_at >= NOW() - INTERVAL '24 hours'
+            GROUP BY bucket
+            """
+        )
+        journey_rows = await conn.fetch(
+            """
+            SELECT
+                perception_id::text,
+                COALESCE(event_type, 'perception') AS event_type,
+                COALESCE(session_ref, '') AS session_ref,
+                COALESCE(media_ref, '') AS scene_label,
+                COALESCE(engagement_score, 0)::float AS engagement_score,
+                happened_at,
+                COALESCE(metadata_json, '{}'::jsonb) AS metadata_json
+            FROM intel_perception_events
+            ORDER BY happened_at DESC
+            LIMIT 12
+            """
+        )
+    sentiment = {"positive": 0, "neutral": 0, "negative": 0}
+    for row in sentiment_rows:
+        sentiment[row["bucket"]] = row["count"]
+    journey = [
+        {
+            "eventId": row["perception_id"],
+            "eventType": row["event_type"],
+            "sessionRef": row["session_ref"],
+            "sceneLabel": row["scene_label"],
+            "engagementScore": row["engagement_score"],
+            "happenedAt": row["happened_at"].isoformat() if row["happened_at"] else None,
+            "metadata": dict(row["metadata_json"] or {}),
+        }
+        for row in journey_rows
+    ]
+    return {
+        "status": "ok",
+        "data": {
+            "liveStreamPath": "/api/sentinel/ws/perception",
+            "activeSessions": session["active_sessions"] if session else 0,
+            "visitorCount24h": session["visitor_count_24h"] if session else 0,
+            "averageQdScore": session["avg_qd_score"] if session else 0,
+            "sentimentDistribution": sentiment,
+            "journey": journey,
+        },
+    }
+
+
 async def broadcast_sentinel_event(payload: dict[str, Any]) -> None:
     await manager.broadcast(payload, "notifications")