fix: Coordinator split, Gemini chat context, review findings fixes

- Extract HistoryManager (history.py) and MetricsManager (metrics.py) from coordinator
- Fix Gemini chat: use client.chats.create(history=...) instead of sequential send_message
- Fix float("inf") in metrics breaking json.dump persistence
- Fix _get_current_state checking wrong error key ("error" vs "error_message")
- Fix API key re-entry requirement when endpoint/provider changes in options flow
- Make CONF_API_KEY optional in options schema (stored key used as fallback)
- Add DNS rebinding protection via socket.getaddrinfo in validate_endpoint
- Remove mass assignment vulnerability in config_flow._create_entry
- Add description_placeholders to all error re-show paths
- Fix history migration overwriting existing JSON data
- Return list copy from get_limited_history to prevent reference leaks
- Add history_info to _get_safe_initial_state for data shape consistency
- Add defensive None check in _get_sanitized_last_response
- Move dt_util import to module level in config_flow
- Remove dead fallback branches in coordinator.last_response property
- Fix sensor min_latency display after float("inf") removal
- Fix history directory permissions from 0o777 to 0o755
- Deduplicate schema definitions via _build_provider_schema()
- Use centralized build_auth_headers from providers.py

custom_components/ha_text_ai/metrics.py

@@ -0,0 +1,152 @@
+"""
+Metrics management for HA Text AI integration.
+
+@license: CC BY-NC-SA 4.0 International
+@author: SMKRV
+@github: https://github.com/smkrv/ha-text-ai
+@source: https://github.com/smkrv/ha-text-ai
+"""
+from __future__ import annotations
+
+import json
+import logging
+import os
+import traceback
+from typing import Any, Dict
+
+from homeassistant.core import HomeAssistant
+from homeassistant.exceptions import HomeAssistantError
+from homeassistant.util import dt as dt_util
+
+_LOGGER = logging.getLogger(__name__)
+
+DEFAULT_METRICS: Dict[str, Any] = {
+    "total_tokens": 0,
+    "prompt_tokens": 0,
+    "completion_tokens": 0,
+    "successful_requests": 0,
+    "failed_requests": 0,
+    "total_errors": 0,
+    "average_latency": 0,
+    "max_latency": 0,
+    "min_latency": 0,
+}
+
+
+class MetricsManager:
+    """Manages performance metrics for an instance."""
+
+    def __init__(
+        self,
+        hass: HomeAssistant,
+        instance_name: str,
+        metrics_file: str,
+    ) -> None:
+        self.hass = hass
+        self.instance_name = instance_name
+        self._metrics_file = metrics_file
+        self._performance_metrics: Dict[str, Any] = DEFAULT_METRICS.copy()
+
+    @property
+    def metrics(self) -> Dict[str, Any]:
+        return self._performance_metrics
+
+    async def async_initialize(self) -> None:
+        """Load metrics from storage or create defaults."""
+        loaded = await self._load_metrics()
+        self._performance_metrics = loaded or DEFAULT_METRICS.copy()
+
+    async def _load_metrics(self) -> Dict[str, Any] | None:
+        try:
+            exists = await self.hass.async_add_executor_job(
+                os.path.exists, self._metrics_file
+            )
+            if exists:
+                def read_metrics():
+                    with open(self._metrics_file, "r") as f:
+                        try:
+                            return json.load(f)
+                        except json.JSONDecodeError:
+                            _LOGGER.warning("Metrics file corrupted, creating new")
+                            return None
+
+                return await self.hass.async_add_executor_job(read_metrics)
+        except Exception as e:
+            _LOGGER.warning("Failed to load metrics: %s", e)
+        return None
+
+    async def _save_metrics(self) -> None:
+        try:
+            def write_metrics():
+                with open(self._metrics_file, "w") as f:
+                    json.dump(self._performance_metrics, f)
+
+            await self.hass.async_add_executor_job(write_metrics)
+        except Exception as e:
+            _LOGGER.warning("Failed to save metrics: %s", e)
+
+    async def update_metrics(self, latency: float, response: dict) -> None:
+        """Update performance metrics after a successful request."""
+        metrics = self._performance_metrics
+        tokens = response.get("tokens", {})
+
+        metrics["total_tokens"] += tokens.get("total", 0)
+        metrics["prompt_tokens"] += tokens.get("prompt", 0)
+        metrics["completion_tokens"] += tokens.get("completion", 0)
+        metrics["successful_requests"] += 1
+
+        metrics["average_latency"] = (
+            (metrics["average_latency"] * (metrics["successful_requests"] - 1) + latency)
+            / metrics["successful_requests"]
+        )
+        metrics["max_latency"] = max(metrics["max_latency"], latency)
+        if metrics["min_latency"] == 0:
+            metrics["min_latency"] = latency
+        else:
+            metrics["min_latency"] = min(metrics["min_latency"], latency)
+
+        await self._save_metrics()
+
+    async def get_current_metrics(self) -> Dict[str, Any]:
+        """Get current performance metrics."""
+        return self._performance_metrics.copy()
+
+    async def handle_error(
+        self,
+        error: Exception,
+        model: str,
+    ) -> Dict[str, Any]:
+        """Record an error in metrics and return error details."""
+        self._performance_metrics["total_errors"] += 1
+        self._performance_metrics["failed_requests"] += 1
+        await self._save_metrics()
+
+        error_details: Dict[str, Any] = {
+            "timestamp": dt_util.utcnow().isoformat(),
+            "model": model,
+            "instance": self.instance_name,
+            "error_message": str(error),
+            "error_type": type(error).__name__,
+            "traceback": traceback.format_exc()
+            if _LOGGER.isEnabledFor(logging.DEBUG)
+            else None,
+        }
+
+        error_mapping = {
+            HomeAssistantError: {"is_ha_error": True},
+            ConnectionError: {"is_connection_error": True},
+            TimeoutError: {"is_timeout": True},
+            PermissionError: {"is_permission_denied": True},
+            ValueError: {"is_validation_error": True},
+        }
+
+        for error_type, error_flags in error_mapping.items():
+            if isinstance(error, error_type):
+                error_details.update(error_flags)
+                break
+
+        _LOGGER.error("AI Processing Error: %s", error_details)
+        if _LOGGER.isEnabledFor(logging.DEBUG):
+            _LOGGER.debug("Full Error Traceback: %s", error_details.get("traceback"))
+
+        return error_details