"""
Thread-safe metrics database access.

This module provides a way for background threads to write metrics
to the user's encrypted database by creating thread-local connections
with the provided password.
"""

import threading
from contextlib import contextmanager
from typing import Optional

from loguru import logger
from sqlalchemy.orm import Session

from .encrypted_db import db_manager


class ThreadSafeMetricsWriter:
    """
    Thread-safe writer for metrics to encrypted user databases.
    Creates encrypted connections per thread using provided passwords.
    """

    def __init__(self):
        self._thread_local = threading.local()

    def set_user_password(self, username: str, password: str):
        """
        Store user password for the current thread.
        This allows the thread to create its own encrypted connection.

        IMPORTANT: This is safe because:
        1. Password is already in memory (user is logged in)
        2. It's only stored thread-locally
        3. It's cleared when the thread ends
        """

        if not hasattr(self._thread_local, "passwords"):
            self._thread_local.passwords = {}
        self._thread_local.passwords[username] = password

    @contextmanager
    def get_session(self, username: str = None) -> Session:
        """
        Get a database session for metrics in the current thread.
        Creates a new encrypted connection if needed.

        Args:
            username: The username for database access. If not provided,
                     will attempt to get it from Flask session.
        """
        # If username not provided, try to get it from Flask session
        if username is None:
            try:
                from flask import session as flask_session
                from werkzeug.exceptions import Unauthorized

                username = flask_session.get("username")
                if not username:
                    raise Unauthorized("No username in Flask session")
            except (ImportError, RuntimeError) as e:
                # Flask context not available or no session
                raise ValueError(f"Cannot determine username: {e}")

        # Get password for this user in this thread
        if not hasattr(self._thread_local, "passwords"):
            raise ValueError("No password set for thread metrics access")

        password = self._thread_local.passwords.get(username)

        if not password:
            raise ValueError(
                f"No password available for user {username} in this thread"
            )

        # Create a thread-safe session for this user
        session = None
        try:
            session = db_manager.create_thread_safe_session_for_metrics(
                username, password
            )
            if not session:
                raise ValueError(  # noqa: TRY301 — except does session rollback before re-raise
                    f"Failed to create session for user {username}"
                )
            yield session
            session.commit()
        except Exception:
            logger.exception(f"Session error for {username}")
            if session:
                session.rollback()
            raise
        finally:
            if session:
                from ..utilities.resource_utils import safe_close

                safe_close(session, "thread metrics session")

    def write_token_metrics(
        self, username: str, research_id: Optional[int], token_data: dict
    ):
        """
        Write token metrics from any thread.

        Args:
            username: The username (for database access)
            research_id: The research ID
            token_data: Dictionary with token metrics data
        """
        with self.get_session(username) as session:
            # Import here to avoid circular imports
            from .models import TokenUsage

            # Create TokenUsage record
            token_usage = TokenUsage(
                research_id=research_id,
                model_name=token_data.get("model_name"),
                model_provider=token_data.get("provider"),
                prompt_tokens=token_data.get("prompt_tokens", 0),
                completion_tokens=token_data.get("completion_tokens", 0),
                total_tokens=token_data.get("prompt_tokens", 0)
                + token_data.get("completion_tokens", 0),
                # Research context
                research_query=token_data.get("research_query"),
                research_mode=token_data.get("research_mode"),
                research_phase=token_data.get("research_phase"),
                search_iteration=token_data.get("search_iteration"),
                # Performance metrics
                response_time_ms=token_data.get("response_time_ms"),
                success_status=token_data.get("success_status", "success"),
                error_type=token_data.get("error_type"),
                # Search engine context
                search_engines_planned=token_data.get("search_engines_planned"),
                search_engine_selected=token_data.get("search_engine_selected"),
                # Call stack tracking
                calling_file=token_data.get("calling_file"),
                calling_function=token_data.get("calling_function"),
                call_stack=token_data.get("call_stack"),
                # Context overflow detection
                context_limit=token_data.get("context_limit"),
                context_truncated=token_data.get("context_truncated", False),
                tokens_truncated=token_data.get("tokens_truncated"),
                truncation_ratio=token_data.get("truncation_ratio"),
                # Raw Ollama metrics
                ollama_prompt_eval_count=token_data.get(
                    "ollama_prompt_eval_count"
                ),
                ollama_eval_count=token_data.get("ollama_eval_count"),
                ollama_total_duration=token_data.get("ollama_total_duration"),
                ollama_load_duration=token_data.get("ollama_load_duration"),
                ollama_prompt_eval_duration=token_data.get(
                    "ollama_prompt_eval_duration"
                ),
                ollama_eval_duration=token_data.get("ollama_eval_duration"),
            )
            session.add(token_usage)


# Global instance for thread-safe metrics
metrics_writer = ThreadSafeMetricsWriter()
