"""Abstract LLM client interface."""

from __future__ import annotations

import os
import re
from abc import ABC, abstractmethod
from pathlib import Path

import yaml


def load_config(config_path: str = "config.yaml") -> dict:
    with open(config_path) as f:
        return yaml.safe_load(f)


def load_prompt_template(template_path: str) -> str:
    return Path(template_path).read_text()


# Strip HTML comments (maintainer-only notes) from knowledge files before
# passing to LLM. Authors put checklists / cross-file-update warnings in
# <!-- ... --> blocks at the top of each knowledge/*.md; those are meta
# about how to edit the file, not content the LLM should read.
_HTML_COMMENT_RE = re.compile(r"<!--.*?-->", re.DOTALL)


def load_expert_knowledge(knowledge_path: str) -> str:
    path = Path(knowledge_path)
    if not path.exists():
        return ""
    return _HTML_COMMENT_RE.sub("", path.read_text()).lstrip()


class LLMClient(ABC):
    """Abstract base class for LLM backends."""

    @abstractmethod
    def call(
        self,
        prompt_template: str,
        variables: dict,
        expert_knowledge: str = "",
        seed: int | None = None,
    ) -> str:
        """Call the LLM with a prompt template, variables, and optional expert knowledge.

        Args:
            prompt_template: The prompt template string with {placeholders}.
            variables: Dictionary of variables to fill into the template.
            expert_knowledge: Expert knowledge text to inject via {expert_knowledge} placeholder.
            seed: Optional integer seed for sampling reproducibility. Local backends
                must honor this when temperature > 0; cloud backends (claude /
                claude-cli) currently raise NotImplementedError when seed is not None.

        Returns:
            The LLM's response text.
        """

    def call_with_config(
        self,
        prompt_key: str,
        knowledge_key: str,
        variables: dict,
        config: dict | None = None,
        seed: int | None = None,
    ) -> str:
        """Convenience method: load prompt template and knowledge from config paths, then call.

        Args:
            prompt_key: Key in config['prompts'] for the prompt template file path.
            knowledge_key: Key in config['knowledge'] for the expert knowledge file path.
            variables: Dictionary of variables to fill into the template.
            config: Config dict. If None, loads from config.yaml.
            seed: Optional seed forwarded to the underlying call() (see LLMClient.call).
        """
        if config is None:
            config = load_config()

        template = load_prompt_template(config["prompts"][prompt_key])
        knowledge = load_expert_knowledge(config["knowledge"][knowledge_key])
        return self.call(template, variables, knowledge, seed=seed)