Initial Home Assistant observer
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hbrain
commit
325917c09b
8 changed files with 624 additions and 0 deletions
373
ha_observer.py
Executable file
373
ha_observer.py
Executable file
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#!/usr/bin/env python3
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"""
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Home Assistant observer
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Modes:
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collect - run every 30 minutes; stores a compact JSON snapshot locally
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analyze - run at 05:00; sends the last snapshots to AI and publishes a funny local web page
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Configuration is via environment variables. See .env.example.
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"""
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from __future__ import annotations
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import argparse
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import html
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import json
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import os
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import re
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import sys
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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from typing import Any
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import requests
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HA_URL = os.environ.get("HA_URL", "").rstrip("/")
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HA_TOKEN = os.environ.get("HA_TOKEN", "")
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DATA_DIR = Path(os.environ.get("DATA_DIR", "./data"))
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REPORT_DIR = Path(os.environ.get("REPORT_DIR", "./reports"))
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WEB_DIR = Path(os.environ.get("WEB_DIR", "./web"))
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PROMPT_FILE = Path(os.environ.get("PROMPT_FILE", "./llm_instructions.md"))
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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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KEEP_SNAPSHOT_DAYS = int(os.environ.get("KEEP_SNAPSHOT_DAYS", "14"))
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# LLM_MODE: none | ollama | openai
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LLM_MODE = os.environ.get("LLM_MODE", "none").lower()
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OLLAMA_URL = os.environ.get("OLLAMA_URL", "http://localhost:11434").rstrip("/")
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OLLAMA_MODEL = os.environ.get("OLLAMA_MODEL", "llama3.1")
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OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "")
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OPENAI_MODEL = os.environ.get("OPENAI_MODEL", "gpt-4o-mini")
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RELEVANT_DOMAINS = set(
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x.strip()
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for x in os.environ.get(
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"RELEVANT_DOMAINS",
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"sensor,binary_sensor,person,device_tracker,climate,light,switch,lock,cover,alarm_control_panel,media_player,calendar,weather",
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).split(",")
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if x.strip()
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)
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EXCLUDED_ENTITIES = set(x.strip() for x in os.environ.get("EXCLUDED_ENTITIES", "").split(",") if x.strip())
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ALLOWED_ATTRIBUTES = {
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"friendly_name",
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"unit_of_measurement",
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"device_class",
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"state_class",
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"current_temperature",
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"temperature",
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"humidity",
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"battery_level",
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"brightness",
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"gps_accuracy",
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"source_type",
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"assumed_state",
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}
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class ConfigError(RuntimeError):
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pass
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def require_config(for_ai: bool = False) -> None:
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if not HA_URL:
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raise ConfigError("HA_URL is not set")
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if not HA_TOKEN:
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raise ConfigError("HA_TOKEN is not set")
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if for_ai and LLM_MODE == "openai" and not OPENAI_API_KEY:
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raise ConfigError("LLM_MODE=openai but OPENAI_API_KEY is not set")
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def ha_get(path: str) -> Any:
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headers = {"Authorization": f"Bearer {HA_TOKEN}", "Content-Type": "application/json"}
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response = requests.get(f"{HA_URL}{path}", headers=headers, timeout=60)
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response.raise_for_status()
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return response.json()
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def is_relevant_entity(entity_id: str) -> bool:
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return entity_id not in EXCLUDED_ENTITIES and entity_id.split(".", 1)[0] in RELEVANT_DOMAINS
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def compact_attributes(attrs: dict[str, Any]) -> dict[str, Any]:
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return {k: v for k, v in attrs.items() if k in ALLOWED_ATTRIBUTES}
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def get_states() -> list[dict[str, Any]]:
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useful: list[dict[str, Any]] = []
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for item in ha_get("/api/states"):
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entity_id = item.get("entity_id", "")
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state = item.get("state")
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if not is_relevant_entity(entity_id) or state in {"unknown", "unavailable", None}:
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continue
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useful.append(
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{
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"entity_id": entity_id,
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"state": state,
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"attributes": compact_attributes(item.get("attributes", {})),
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"last_changed": item.get("last_changed"),
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"last_updated": item.get("last_updated"),
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}
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)
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return sorted(useful, key=lambda x: x["entity_id"])
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def get_history(hours: int) -> list[dict[str, Any]]:
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start = datetime.now(timezone.utc) - timedelta(hours=hours)
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data = ha_get(f"/api/history/period/{start.isoformat()}?minimal_response")
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changes: list[dict[str, Any]] = []
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for entity_history in data:
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if not entity_history:
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continue
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entity_id = entity_history[0].get("entity_id", "")
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if not is_relevant_entity(entity_id):
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continue
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compact = []
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for item in entity_history[-MAX_HISTORY_PER_ENTITY:]:
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state = item.get("state")
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if state in {"unknown", "unavailable", None}:
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continue
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compact.append({"state": state, "last_changed": item.get("last_changed")})
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if len(set(x["state"] for x in compact)) > 1:
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changes.append({"entity_id": entity_id, "recent_states": compact})
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return sorted(changes, key=lambda x: x["entity_id"])
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def make_snapshot() -> dict[str, Any]:
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return {
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"generated_at": datetime.now().isoformat(timespec="seconds"),
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"history_hours": HISTORY_HOURS,
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"states": get_states(),
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"history": get_history(HISTORY_HOURS),
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}
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def save_snapshot(snapshot: dict[str, Any]) -> Path:
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DATA_DIR.mkdir(parents=True, exist_ok=True)
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stamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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path = DATA_DIR / f"snapshot-{stamp}.json"
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path.write_text(json.dumps(snapshot, indent=2, ensure_ascii=False), encoding="utf-8")
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return path
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def cleanup_old_snapshots() -> None:
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cutoff = datetime.now() - timedelta(days=KEEP_SNAPSHOT_DAYS)
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for path in DATA_DIR.glob("snapshot-*.json"):
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if datetime.fromtimestamp(path.stat().st_mtime) < cutoff:
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path.unlink(missing_ok=True)
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def load_recent_snapshots(hours: int) -> list[dict[str, Any]]:
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cutoff = datetime.now() - timedelta(hours=hours)
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snapshots = []
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for path in sorted(DATA_DIR.glob("snapshot-*.json")):
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if datetime.fromtimestamp(path.stat().st_mtime) < cutoff:
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continue
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try:
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snapshots.append(json.loads(path.read_text(encoding="utf-8")))
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except Exception as exc:
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print(f"Skipping unreadable snapshot {path}: {exc}", file=sys.stderr)
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return snapshots
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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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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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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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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"Contains {len(snapshots)} snapshots from roughly the last {ANALYZE_SNAPSHOT_HOURS} hours.",
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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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return "\n".join(parts)
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def read_extra_llm_instructions() -> str:
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if not PROMPT_FILE.exists():
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return ""
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return PROMPT_FILE.read_text(encoding="utf-8").strip()
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def analysis_prompt(input_summary: str) -> str:
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extra_instructions = read_extra_llm_instructions()
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extra_block = ""
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if extra_instructions:
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extra_block = f"""
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ADDITIONAL OWNER INSTRUCTIONS FROM {PROMPT_FILE}:
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{extra_instructions}
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"""
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return f"""You are analyzing a day of Home Assistant smart-home data for the owner.
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Write a funny but useful morning briefing. Use light humor, emojis, and playful headings,
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but remain factual and privacy-aware. Include:
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- A short comedy headline for the day
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- What seemed to happen at home
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- Behavioral patterns that can reasonably be inferred
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- What a nosy raccoon/hacker could figure out about the resident
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- Anomalies, risks, or privacy/security concerns
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- Suggested Home Assistant automations or fixes
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Distinguish strong evidence from guesses. Do not invent facts not supported by the data.
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{extra_block}
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DATA:
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{input_summary}
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"""
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def call_ollama(prompt: str) -> str:
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response = requests.post(f"{OLLAMA_URL}/api/generate", json={"model": OLLAMA_MODEL, "prompt": prompt, "stream": False}, timeout=300)
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response.raise_for_status()
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return response.json().get("response", "").strip()
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def call_openai(prompt: str) -> str:
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response = requests.post(
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"https://api.openai.com/v1/chat/completions",
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headers={"Authorization": f"Bearer {OPENAI_API_KEY}", "Content-Type": "application/json"},
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json={
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"model": OPENAI_MODEL,
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"messages": [
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{"role": "system", "content": "You are a careful but funny smart-home analyst."},
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{"role": "user", "content": prompt},
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],
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"temperature": 0.35,
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},
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timeout=300,
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)
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response.raise_for_status()
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return response.json()["choices"][0]["message"]["content"].strip()
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def get_llm_conclusions(input_summary: str) -> str:
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if LLM_MODE == "none":
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return "AI analysis disabled. Set LLM_MODE=ollama or LLM_MODE=openai in .env. The raccoon analyst is asleep. 🦝💤"
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prompt = analysis_prompt(input_summary)
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if LLM_MODE == "ollama":
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return call_ollama(prompt)
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if LLM_MODE == "openai":
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return call_openai(prompt)
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return f"Unknown LLM_MODE={LLM_MODE!r}. Use none, ollama, or openai."
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def markdownish_to_html(text: str) -> str:
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safe = html.escape(text)
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safe = re.sub(r"^### (.*)$", r"<h3>\1</h3>", safe, flags=re.MULTILINE)
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safe = re.sub(r"^## (.*)$", r"<h2>\1</h2>", safe, flags=re.MULTILINE)
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safe = re.sub(r"^# (.*)$", r"<h1>\1</h1>", safe, flags=re.MULTILINE)
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safe = re.sub(r"^- (.*)$", r"<li>\1</li>", safe, flags=re.MULTILINE)
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safe = safe.replace("\n", "<br>\n")
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return safe
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def publish_webpage(conclusions: str, raw_summary: str) -> Path:
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WEB_DIR.mkdir(parents=True, exist_ok=True)
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now = datetime.now().strftime("%Y-%m-%d %H:%M")
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body = markdownish_to_html(conclusions)
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raw = html.escape(raw_summary[:60000])
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page = f"""<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<meta http-equiv="refresh" content="1800">
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<title>Smart Home Gossip Gazette</title>
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<style>
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body {{ margin:0; font-family: system-ui, sans-serif; background: #151522; color:#f7f7fb; }}
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header {{ padding: 2rem; background: linear-gradient(135deg,#7c3aed,#db2777,#f59e0b); color:white; }}
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main {{ max-width: 950px; margin: 0 auto; padding: 2rem; }}
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.card {{ background:#222238; border:1px solid #39395b; border-radius:20px; padding:1.4rem; box-shadow:0 12px 30px #0006; }}
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h1 {{ margin:0; font-size: clamp(2rem, 5vw, 4rem); }}
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h2,h3 {{ color:#fde68a; }}
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li {{ margin:.35rem 0; }}
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.mascot {{ font-size:3rem; float:right; animation: wiggle 2s infinite; }}
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details {{ margin-top: 2rem; }}
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pre {{ white-space: pre-wrap; background:#0f0f18; color:#d1d5db; padding:1rem; border-radius:12px; overflow:auto; }}
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a {{ color:#93c5fd; }}
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@keyframes wiggle {{ 0%,100% {{ transform: rotate(-3deg); }} 50% {{ transform: rotate(3deg); }} }}
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</style>
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</head>
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<body>
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<header>
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<div class="mascot">🦝🏠</div>
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<h1>Smart Home Gossip Gazette</h1>
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<p>Fresh 5AM nonsense-powered intelligence briefing · Generated {html.escape(now)}</p>
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</header>
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<main>
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<section class="card">{body}</section>
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<details>
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<summary>Raw data bundle shown to the AI goblin</summary>
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<pre>{raw}</pre>
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</details>
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</main>
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</body>
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</html>
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"""
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path = WEB_DIR / "index.html"
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path.write_text(page, encoding="utf-8")
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return path
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def write_markdown_report(summary: str, conclusions: str) -> Path:
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REPORT_DIR.mkdir(parents=True, exist_ok=True)
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stamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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path = REPORT_DIR / f"daily-ai-analysis-{stamp}.md"
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path.write_text(f"# Daily Home Assistant AI Analysis\n\n{conclusions}\n\n## Data bundle\n\n```text\n{summary}\n```\n", encoding="utf-8")
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return path
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def cmd_collect() -> int:
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require_config(for_ai=False)
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snapshot = make_snapshot()
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path = save_snapshot(snapshot)
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cleanup_old_snapshots()
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print(f"Collected snapshot: {path}")
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return 0
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def cmd_analyze() -> int:
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require_config(for_ai=True)
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snapshots = load_recent_snapshots(ANALYZE_SNAPSHOT_HOURS)
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if not snapshots:
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raise RuntimeError(f"No snapshots found in {DATA_DIR}; run collect first")
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summary = build_daily_summary(snapshots)
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conclusions = get_llm_conclusions(summary)
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md_path = write_markdown_report(summary, conclusions)
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html_path = publish_webpage(conclusions, summary)
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print(f"Wrote report: {md_path}")
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print(f"Published webpage: {html_path}")
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return 0
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def main() -> int:
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parser = argparse.ArgumentParser(description="Home Assistant observer")
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parser.add_argument("mode", nargs="?", default="collect", choices=["collect", "analyze"], help="collect snapshots or analyze/publish them")
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args = parser.parse_args()
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try:
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return cmd_collect() if args.mode == "collect" else cmd_analyze()
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except Exception as exc:
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print(f"ERROR: {exc}", file=sys.stderr)
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return 1
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if __name__ == "__main__":
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raise SystemExit(main())
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