Improve analysis prioritization and article structure
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hbrain
parent
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commit
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3 changed files with 260 additions and 31 deletions
258
ha_observer.py
258
ha_observer.py
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@ -18,10 +18,12 @@ import os
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import re
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import subprocess
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import sys
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import tempfile
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from datetime import datetime, timedelta, timezone
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from email.utils import format_datetime
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from pathlib import Path
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from typing import Any
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from zoneinfo import ZoneInfo
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import requests
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@ -38,6 +40,8 @@ HISTORY_HOURS = int(os.environ.get("HISTORY_HOURS", "24"))
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MAX_HISTORY_PER_ENTITY = int(os.environ.get("MAX_HISTORY_PER_ENTITY", "20"))
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ANALYZE_SNAPSHOT_HOURS = int(os.environ.get("ANALYZE_SNAPSHOT_HOURS", "24"))
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ARTICLE_CONTEXT_DAYS = int(os.environ.get("ARTICLE_CONTEXT_DAYS", "7"))
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MAX_ANALYZE_CHARS = int(os.environ.get("MAX_ANALYZE_CHARS", "80000"))
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DISPLAY_TIMEZONE = os.environ.get("DISPLAY_TIMEZONE", "Europe/Copenhagen")
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KEEP_SNAPSHOT_DAYS = int(os.environ.get("KEEP_SNAPSHOT_DAYS", "14"))
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# LLM_MODE: none | pi | ollama | openai
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@ -75,6 +79,44 @@ ALLOWED_ATTRIBUTES = {
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"assumed_state",
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}
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IMPORTANT_ENTITY_KEYWORDS = {
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"alarm": 100,
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"smoke": 100,
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"co_": 100,
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"carbon_monoxide": 100,
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"leak": 95,
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"water": 80,
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"door": 85,
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"window": 80,
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"lock": 85,
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"motion": 70,
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"presence": 70,
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"occupancy": 70,
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"person": 75,
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"device_tracker": 75,
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"phone": 70,
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"laptop": 60,
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"battery": 65,
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"humidity": 60,
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"temperature": 55,
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"climate": 55,
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"heating": 55,
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"dehumidifier": 70,
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"backup": 70,
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"internet": 65,
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"speedtest": 65,
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"router": 60,
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"light": 45,
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"switch": 35,
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"sonos": 45,
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"media": 40,
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"tv": 40,
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"megane": 50,
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"fjr": 50,
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"plant": 45,
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"smb_": 60,
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}
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class ConfigError(RuntimeError):
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pass
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@ -198,28 +240,106 @@ def load_recent_snapshots(hours: int) -> list[dict[str, Any]]:
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return snapshots
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def display_time(value: str | None) -> str:
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if not value:
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return ""
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try:
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dt = datetime.fromisoformat(value.replace("Z", "+00:00"))
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if dt.tzinfo is None:
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dt = dt.replace(tzinfo=timezone.utc)
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local = dt.astimezone(ZoneInfo(DISPLAY_TIMEZONE))
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return local.strftime("%Y-%m-%d %H:%M:%S %Z")
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except Exception:
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return value
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def entity_importance(entity_id: str, attrs: dict[str, Any] | None = None) -> int:
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attrs = attrs or {}
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domain = entity_id.split(".", 1)[0]
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text = f"{entity_id} {attrs.get('friendly_name', '')} {attrs.get('device_class', '')}".lower()
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score = 0
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domain_scores = {
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"alarm_control_panel": 100,
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"lock": 90,
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"person": 80,
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"device_tracker": 75,
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"binary_sensor": 60,
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"climate": 55,
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"cover": 50,
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"sensor": 45,
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"light": 35,
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"switch": 30,
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"media_player": 25,
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}
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score += domain_scores.get(domain, 10)
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for keyword, points in IMPORTANT_ENTITY_KEYWORDS.items():
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if keyword in text:
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score += points
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# Sønderborg/Denmark home is the primary residence and absolute priority.
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# Samobor/Croatia entities use the smb_ prefix and are still included, but
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# they should lose ties when the LLM input has to be size-limited.
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if "smb_" in entity_id.lower():
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score -= 40
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else:
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score += 120
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state = str(attrs.get("state", "")).lower()
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if state in {"on", "open", "unlocked", "detected", "home"}:
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score += 15
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return score
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def summarize_snapshot(snapshot: dict[str, Any]) -> str:
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lines = [f"Snapshot: {snapshot.get('generated_at')}", "Current states:"]
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for state in snapshot.get("states", []):
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lines = [
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f"Snapshot: {display_time(snapshot.get('generated_at'))}",
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"Priority current states first; lower-priority entities follow only if the LLM size limit allows.",
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"Current states:",
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]
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states = sorted(
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snapshot.get("states", []),
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key=lambda state: (-entity_importance(state.get("entity_id", ""), state.get("attributes", {})), state.get("entity_id", "")),
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)
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for state in states:
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attrs = state.get("attributes", {})
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name = attrs.get("friendly_name", state.get("entity_id"))
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unit = attrs.get("unit_of_measurement", "")
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value = f"{state.get('state')} {unit}".strip()
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lines.append(f"- {name} ({state.get('entity_id')}): {value}; last_changed={state.get('last_changed')}")
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score = entity_importance(state.get("entity_id", ""), attrs)
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lines.append(f"- importance={score} {name} ({state.get('entity_id')}): {value}; last_changed={display_time(state.get('last_changed'))}")
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lines.append("Recently changed entities:")
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for item in snapshot.get("history", []):
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transitions = ", ".join(f"{x.get('state')} @ {x.get('last_changed')}" for x in item.get("recent_states", [])[-8:])
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lines.append(f"- {item.get('entity_id')}: {transitions}")
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history = sorted(
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snapshot.get("history", []),
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key=lambda item: (-entity_importance(item.get("entity_id", "")), item.get("entity_id", "")),
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)
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for item in history:
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transitions = ", ".join(f"{x.get('state')} @ {display_time(x.get('last_changed'))}" for x in item.get("recent_states", [])[-8:])
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score = entity_importance(item.get("entity_id", ""))
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lines.append(f"- importance={score} {item.get('entity_id')}: {transitions}")
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return "\n".join(lines)
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def build_daily_summary(snapshots: list[dict[str, Any]]) -> str:
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parts = [
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f"Daily Home Assistant bundle generated {datetime.now().isoformat(timespec='seconds')}",
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f"Daily Home Assistant bundle generated {datetime.now(ZoneInfo(DISPLAY_TIMEZONE)).isoformat(timespec='seconds')}",
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f"Contains {len(snapshots)} snapshots from roughly the last {ANALYZE_SNAPSHOT_HOURS} hours.",
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f"Input capped at roughly {MAX_ANALYZE_CHARS} characters for the LLM.",
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f"All times in this bundle are converted to {DISPLAY_TIMEZONE} local time.",
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]
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for snapshot in snapshots:
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parts.append("\n---\n" + summarize_snapshot(snapshot))
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total = len("\n".join(parts))
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included = 0
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for snapshot in reversed(snapshots):
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block = "\n---\n" + summarize_snapshot(snapshot)
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if total + len(block) > MAX_ANALYZE_CHARS and included > 0:
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break
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if len(block) > MAX_ANALYZE_CHARS:
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block = block[:MAX_ANALYZE_CHARS] + "\n[Snapshot truncated for LLM size limit]"
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parts.append(block)
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total += len(block)
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included += 1
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parts.insert(2, f"Included {included} most recent snapshots after size limiting.")
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return "\n".join(parts)
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@ -310,22 +430,33 @@ def call_openai(prompt: str) -> str:
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def call_pi(prompt: str) -> str:
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cmd = [PI_BIN, "--no-tools"]
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if PI_MODEL:
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cmd.extend(["--model", PI_MODEL])
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cmd.extend(["-p", "Analyze the Home Assistant data from stdin and write the requested briefing."])
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result = subprocess.run(
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cmd,
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input=prompt,
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text=True,
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capture_output=True,
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timeout=PI_TIMEOUT,
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check=False,
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)
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# Avoid piping the prompt on stdin here. In pi print mode, piped stdin can be
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# treated as the primary output/input stream in surprising ways. Passing the
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# prompt as an @file gives reliable non-interactive cron behavior.
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with tempfile.NamedTemporaryFile("w", encoding="utf-8", suffix=".md", delete=False) as tmp:
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tmp.write(prompt)
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prompt_path = tmp.name
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try:
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cmd = [PI_BIN, "--no-tools"]
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if PI_MODEL:
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cmd.extend(["--model", PI_MODEL])
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cmd.extend(["-p", f"@{prompt_path}"])
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result = subprocess.run(
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cmd,
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text=True,
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capture_output=True,
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timeout=PI_TIMEOUT,
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check=False,
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)
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finally:
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Path(prompt_path).unlink(missing_ok=True)
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if result.returncode != 0:
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stderr = result.stderr.strip()
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raise RuntimeError(f"pi exited with status {result.returncode}: {stderr[-1000:]}")
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return result.stdout.strip()
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output = result.stdout.strip()
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if not output:
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raise RuntimeError("pi returned an empty analysis")
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return output
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def get_llm_conclusions(input_summary: str, previous_articles: str = "") -> str:
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@ -353,6 +484,84 @@ def inline_markdown(text: str) -> str:
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return safe
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def move_bottom_line_before_serious(blocks: list[str]) -> list[str]:
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serious_start = None
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bottom_start = None
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bottom_end = None
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for i, block in enumerate(blocks):
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heading = re.match(r"<h([23])>(.*?)</h\1>$", block, flags=re.DOTALL)
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if not heading:
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continue
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title = re.sub(r"<[^>]+>", "", html.unescape(heading.group(2))).lower()
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if serious_start is None and ("part ii" in title or "serious briefing" in title):
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serious_start = i
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elif serious_start is not None and ("bottom line" in title or "conclusion" in title):
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bottom_start = i
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break
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if serious_start is None or bottom_start is None:
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return blocks
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bottom_end = len(blocks)
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for i in range(bottom_start + 1, len(blocks)):
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if re.match(r"<h[23]>.*?</h[23]>$", blocks[i], flags=re.DOTALL):
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bottom_end = i
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break
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bottom_section = blocks[bottom_start:bottom_end]
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remaining = blocks[:bottom_start] + blocks[bottom_end:]
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return remaining[:serious_start] + bottom_section + remaining[serious_start:]
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def collapse_serious_sections(blocks: list[str]) -> list[str]:
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output: list[str] = []
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in_serious = False
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after_bottom_line = False
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current_summary = ""
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current_content: list[str] = []
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def close_detail() -> None:
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nonlocal current_summary, current_content
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if current_summary:
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content = "\n".join(current_content).strip()
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output.append(f"<details class=\"briefing-section\"><summary>{current_summary}</summary>\n{content}\n</details>")
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current_summary = ""
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current_content = []
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for block in blocks:
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heading = re.match(r"<h([23])>(.*?)</h\1>$", block, flags=re.DOTALL)
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if heading:
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title = heading.group(2)
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plain_title = re.sub(r"<[^>]+>", "", html.unescape(title)).lower()
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is_bottom_line = "bottom line" in plain_title or "conclusion" in plain_title
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if is_bottom_line:
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close_detail()
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in_serious = False
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after_bottom_line = True
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output.append(block)
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continue
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if not in_serious and ("part ii" in plain_title or "serious briefing" in plain_title):
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in_serious = True
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output.append(block)
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continue
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if in_serious or after_bottom_line:
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in_serious = True
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close_detail()
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current_summary = title
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continue
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if in_serious:
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if current_summary:
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current_content.append(block)
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else:
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output.append(block)
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else:
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output.append(block)
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close_detail()
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return output
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def markdownish_to_html(text: str) -> str:
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blocks: list[str] = []
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paragraph: list[str] = []
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@ -393,7 +602,8 @@ def markdownish_to_html(text: str) -> str:
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flush_paragraph()
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flush_list()
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return "\n".join(blocks)
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blocks = move_bottom_line_before_serious(blocks)
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return "\n".join(collapse_serious_sections(blocks))
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BLOG_CSS = """
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@ -454,6 +664,8 @@ BLOG_CSS = """
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a:hover { color:white; text-decoration:none; filter:drop-shadow(0 0 8px var(--cyan)); }
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.meta { color:#9eeaff; font:.95rem ui-monospace,SFMono-Regular,Menlo,monospace; letter-spacing:.04em; }
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details { margin-top:1.5rem; border-top:1px solid #22d3ee33; padding-top:1rem; }
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details.briefing-section { background:#02061788; border:1px solid #22d3ee33; padding:.75rem 1rem; margin:.8rem 0; }
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details.briefing-section summary { font-size:1.05rem; }
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summary { cursor:pointer; color:var(--amber); text-transform:uppercase; letter-spacing:.08em; }
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pre { white-space:pre-wrap; background:#01040acc; color:#bff8ff; padding:1rem; border:1px solid #22d3ee44; border-radius:0; overflow:auto; font-size:.82rem; box-shadow:0 0 22px #00d9ff11 inset; }
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footer { color:#7dd3fc; text-align:center; padding:2rem; font:.82rem ui-monospace,SFMono-Regular,Menlo,monospace; text-transform:uppercase; letter-spacing:.12em; }
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