Fix: Correct format specifier syntax in SIP stats display

Fixed ValueError: Invalid format specifier in SIP stats output.

Issue:
- Cannot use format specifier (.2f) inside conditional expression in f-string
- Syntax: {value:.2f if condition else 'N/A'} is invalid

Fix:
✅ Format values BEFORE inserting into f-string
✅ Use separate variables for formatted strings

Before (broken):
  print(f"Std Dev: {stats['std']:.2f if stats['std'] is not None else 'N/A'}")

After (working):
  min_str = f"{stats['min']:.2f}" if stats['min'] is not None else 'N/A'
  max_str = f"{stats['max']:.2f}" if stats['max'] is not None else 'N/A'
  std_str = f"{stats['std']:.2f}" if stats['std'] is not None else 'N/A'
  print(f"Range: {min_str} - {max_str}")
  print(f"Std Dev: {std_str}")

This properly handles None values while maintaining clean numeric formatting.

Author: sumerjohal

POC_Nov20_BITE_PANCAKE.ipynb

@@ -3351,7 +3351,7 @@
     }
    ],
    "source": [
-    "def sip_query_latest(sensor_id: str) -> Dict[str, Any]:\n    \"\"\"\n    GET_LATEST: Retrieve most recent sensor reading\n    Fast query (<10ms) for dashboards/real-time monitoring\n    \"\"\"\n    if not pancake_ready or not sips_loaded:\n        return None\n    \n    try:\n        conn = psycopg2.connect(PANCAKE_DB)\n        cur = conn.cursor()\n        \n        start_time = time.time()\n        \n        cur.execute(\"\"\"\n            SELECT time, value, unit\n            FROM sips\n            WHERE sensor_id = %s\n            ORDER BY time DESC\n            LIMIT 1\n        \"\"\", (sensor_id,))\n        \n        result = cur.fetchone()\n        cur.close()\n        conn.close()\n        \n        elapsed_ms = (time.time() - start_time) * 1000\n        \n        if result:\n            return {\n                \"sensor_id\": sensor_id,\n                \"time\": result[0].isoformat(),\n                \"value\": result[1],\n                \"unit\": result[2],\n                \"query_time_ms\": elapsed_ms\n            }\n        return None\n    except Exception as e:\n        print(f\"\u26a0\ufe0f SIP query error: {e}\")\n        return None\n\ndef sip_query_stats(sensor_id: str, hours_back: int = 24) -> Dict[str, Any]:\n    \"\"\"\n    GET_STATS: Aggregate statistics for time range\n    Efficient for summaries/alerts\n    \"\"\"\n    if not pancake_ready or not sips_loaded:\n        return None\n    \n    try:\n        conn = psycopg2.connect(PANCAKE_DB)\n        cur = conn.cursor()\n        \n        start_time = time.time()\n        \n        cur.execute(\"\"\"\n            SELECT \n                COUNT(*) as count,\n                AVG(value) as mean,\n                MIN(value) as min,\n                MAX(value) as max,\n                STDDEV(value) as std\n            FROM sips\n            WHERE sensor_id = %s\n              AND time >= NOW() - INTERVAL '%s hours'\n        \"\"\", (sensor_id, hours_back))\n        \n        result = cur.fetchone()\n        cur.close()\n        conn.close()\n        \n        elapsed_ms = (time.time() - start_time) * 1000\n        \n        if result and result[0] > 0:\n            return {\n                \"sensor_id\": sensor_id,\n                \"time_range_hours\": hours_back,\n                \"count\": result[0],\n                \"mean\": float(result[1]) if result[1] else None,\n                \"min\": float(result[2]) if result[2] else None,\n                \"max\": float(result[3]) if result[3] else None,\n                \"std\": float(result[4]) if result[4] else None,\n                \"query_time_ms\": elapsed_ms\n            }\n        return None\n    except Exception as e:\n        print(f\"\u26a0\ufe0f SIP stats query error: {e}\")\n        return None\n\n# Demo: SIP Queries\nprint(\"\ud83d\ude80 SIP Query Demonstrations:\\n\")\n\n# 1. GET_LATEST (real-time dashboard use case)\nprint(\"1\ufe0f\u20e3 GET_LATEST (Real-time Dashboard)\")\nprint(\"   Use case: 'What is the current soil moisture?'\\n\")\n\ntest_sensor = \"SOIL_MOISTURE-01\"\nlatest = sip_query_latest(test_sensor)\n\nif latest:\n    print(f\"   Sensor: {latest['sensor_id']}\")\n    print(f\"   Value: {latest['value']:.2f} {latest['unit']}\")\n    print(f\"   Time: {latest['time']}\")\n    print(f\"   \u26a1 Query latency: {latest['query_time_ms']:.2f} ms (<10ms target!)\\n\")\nelse:\n    print(\"   \u26a0\ufe0f No data available\\n\")\n\n# 2. GET_STATS (summary/alert use case)\nprint(\"2\ufe0f\u20e3 GET_STATS (Last 24 Hours)\")\nprint(\"   Use case: 'Has soil moisture dropped below threshold?'\\n\")\n\nstats = sip_query_stats(test_sensor, hours_back=24)\n\nif stats:\n    print(f\"   Sensor: {stats['sensor_id']}\")\n    print(f\"   Readings: {stats['count']}\")\n    print(f\"   Mean: {stats['mean']:.2f}\")\n    print(f\"   Range: {stats['min'] if stats['min'] is not None else 'N/A'} - {stats['max'] if stats['max'] is not None else 'N/A'}\")\n    print(f\"   Std Dev: {stats['std']:.2f if stats['std'] is not None else 'N/A'}\")\n    print(f\"   \u26a1 Query latency: {stats['query_time_ms']:.2f} ms\\n\")\n    \n    # Alert logic example\n    if stats['min'] is not None and stats['min'] < 15.0:\n        print(\"   \ud83d\udea8 ALERT: Soil moisture dropped below 15% (irrigation needed!)\")\n    else:\n        print(\"   \u2713 Status: Soil moisture within normal range\")\nelse:\n    print(\"   \u26a0\ufe0f No data available\\n\")\n\nprint(\"\\n\" + \"=\"*70)\nprint(\"\ud83d\udca1 SIP vs BITE Comparison:\")\nprint(\"=\"*70)\nprint(\"SIP Queries (time-series):\")\nprint(\"  \u2713 Latency: <10ms (indexed, no embedding)\")\nprint(\"  \u2713 Use case: Real-time dashboards, alerts, current values\")\nprint(\"  \u2713 Storage: Lightweight (60 bytes/reading)\")\nprint(\"\\nBITE Queries (intelligence):\")\nprint(\"  \u2713 Latency: 50-100ms (semantic search, multi-pronged)\")\nprint(\"  \u2713 Use case: 'Why?' questions, historical context, recommendations\")\nprint(\"  \u2713 Storage: Rich (500 bytes, with embeddings)\")\nprint(\"\\n\ud83e\udd5e PANCAKE uses BOTH (dual-agent architecture)!\")\nprint(\"=\"*70)\n"
+    "def sip_query_latest(sensor_id: str) -> Dict[str, Any]:\n    \"\"\"\n    GET_LATEST: Retrieve most recent sensor reading\n    Fast query (<10ms) for dashboards/real-time monitoring\n    \"\"\"\n    if not pancake_ready or not sips_loaded:\n        return None\n    \n    try:\n        conn = psycopg2.connect(PANCAKE_DB)\n        cur = conn.cursor()\n        \n        start_time = time.time()\n        \n        cur.execute(\"\"\"\n            SELECT time, value, unit\n            FROM sips\n            WHERE sensor_id = %s\n            ORDER BY time DESC\n            LIMIT 1\n        \"\"\", (sensor_id,))\n        \n        result = cur.fetchone()\n        cur.close()\n        conn.close()\n        \n        elapsed_ms = (time.time() - start_time) * 1000\n        \n        if result:\n            return {\n                \"sensor_id\": sensor_id,\n                \"time\": result[0].isoformat(),\n                \"value\": result[1],\n                \"unit\": result[2],\n                \"query_time_ms\": elapsed_ms\n            }\n        return None\n    except Exception as e:\n        print(f\"\u26a0\ufe0f SIP query error: {e}\")\n        return None\n\ndef sip_query_stats(sensor_id: str, hours_back: int = 24) -> Dict[str, Any]:\n    \"\"\"\n    GET_STATS: Aggregate statistics for time range\n    Efficient for summaries/alerts\n    \"\"\"\n    if not pancake_ready or not sips_loaded:\n        return None\n    \n    try:\n        conn = psycopg2.connect(PANCAKE_DB)\n        cur = conn.cursor()\n        \n        start_time = time.time()\n        \n        cur.execute(\"\"\"\n            SELECT \n                COUNT(*) as count,\n                AVG(value) as mean,\n                MIN(value) as min,\n                MAX(value) as max,\n                STDDEV(value) as std\n            FROM sips\n            WHERE sensor_id = %s\n              AND time >= NOW() - INTERVAL '%s hours'\n        \"\"\", (sensor_id, hours_back))\n        \n        result = cur.fetchone()\n        cur.close()\n        conn.close()\n        \n        elapsed_ms = (time.time() - start_time) * 1000\n        \n        if result and result[0] > 0:\n            return {\n                \"sensor_id\": sensor_id,\n                \"time_range_hours\": hours_back,\n                \"count\": result[0],\n                \"mean\": float(result[1]) if result[1] else None,\n                \"min\": float(result[2]) if result[2] else None,\n                \"max\": float(result[3]) if result[3] else None,\n                \"std\": float(result[4]) if result[4] else None,\n                \"query_time_ms\": elapsed_ms\n            }\n        return None\n    except Exception as e:\n        print(f\"\u26a0\ufe0f SIP stats query error: {e}\")\n        return None\n\n# Demo: SIP Queries\nprint(\"\ud83d\ude80 SIP Query Demonstrations:\\n\")\n\n# 1. GET_LATEST (real-time dashboard use case)\nprint(\"1\ufe0f\u20e3 GET_LATEST (Real-time Dashboard)\")\nprint(\"   Use case: 'What is the current soil moisture?'\\n\")\n\ntest_sensor = \"SOIL_MOISTURE-01\"\nlatest = sip_query_latest(test_sensor)\n\nif latest:\n    print(f\"   Sensor: {latest['sensor_id']}\")\n    print(f\"   Value: {latest['value']:.2f} {latest['unit']}\")\n    print(f\"   Time: {latest['time']}\")\n    print(f\"   \u26a1 Query latency: {latest['query_time_ms']:.2f} ms (<10ms target!)\\n\")\nelse:\n    print(\"   \u26a0\ufe0f No data available\\n\")\n\n# 2. GET_STATS (summary/alert use case)\nprint(\"2\ufe0f\u20e3 GET_STATS (Last 24 Hours)\")\nprint(\"   Use case: 'Has soil moisture dropped below threshold?'\\n\")\n\nstats = sip_query_stats(test_sensor, hours_back=24)\n\nif stats:\n    print(f\"   Sensor: {stats['sensor_id']}\")\n    print(f\"   Readings: {stats['count']}\")\n    print(f\"   Mean: {stats['mean']:.2f}\")\n    min_str = f\"{stats['min']:.2f}\" if stats['min'] is not None else 'N/A'\n    max_str = f\"{stats['max']:.2f}\" if stats['max'] is not None else 'N/A'\n    std_str = f\"{stats['std']:.2f}\" if stats['std'] is not None else 'N/A'\n    print(f\"   Range: {min_str} - {max_str}\")\n    print(f\"   Std Dev: {std_str}\")\n    print(f\"   \u26a1 Query latency: {stats['query_time_ms']:.2f} ms\\n\")\n    \n    # Alert logic example\n    if stats['min'] is not None and stats['min'] < 15.0:\n        print(\"   \ud83d\udea8 ALERT: Soil moisture dropped below 15% (irrigation needed!)\")\n    else:\n        print(\"   \u2713 Status: Soil moisture within normal range\")\nelse:\n    print(\"   \u26a0\ufe0f No data available\\n\")\n\nprint(\"\\n\" + \"=\"*70)\nprint(\"\ud83d\udca1 SIP vs BITE Comparison:\")\nprint(\"=\"*70)\nprint(\"SIP Queries (time-series):\")\nprint(\"  \u2713 Latency: <10ms (indexed, no embedding)\")\nprint(\"  \u2713 Use case: Real-time dashboards, alerts, current values\")\nprint(\"  \u2713 Storage: Lightweight (60 bytes/reading)\")\nprint(\"\\nBITE Queries (intelligence):\")\nprint(\"  \u2713 Latency: 50-100ms (semantic search, multi-pronged)\")\nprint(\"  \u2713 Use case: 'Why?' questions, historical context, recommendations\")\nprint(\"  \u2713 Storage: Rich (500 bytes, with embeddings)\")\nprint(\"\\n\ud83e\udd5e PANCAKE uses BOTH (dual-agent architecture)!\")\nprint(\"=\"*70)\n"
    ]
   },
   {