#novel_generator/finalization.py
# -*- coding: utf-8 -*-
"""
定稿章节和扩写章节（finalize_chapter、enrich_chapter_text）
"""
import os
import logging
from llm_adapters import create_llm_adapter
from embedding_adapters import create_embedding_adapter
from prompt_definitions import summary_prompt, update_character_state_prompt
from novel_generator.common import invoke_with_cleaning
from utils import read_file, clear_file_content, save_string_to_txt
from novel_generator.vectorstore_utils import update_vector_store

def finalize_chapter(
    novel_number: int,
    word_number: int,
    api_key: str,
    base_url: str,
    model_name: str,
    temperature: float,
    filepath: str,
    embedding_api_key: str,
    embedding_url: str,
    embedding_interface_format: str,
    embedding_model_name: str,
    interface_format: str,
    max_tokens: int,
    timeout: int = 600
):
    """
    对指定章节做最终处理：更新全局摘要、更新角色状态、插入向量库等。
    默认无需再做扩写操作，若有需要可在外部调用 enrich_chapter_text 处理后再定稿。
    """
    chapters_dir = os.path.join(filepath, "chapters")
    chapter_file = os.path.join(chapters_dir, f"chapter_{novel_number}.txt")
    chapter_text = read_file(chapter_file).strip()
    if not chapter_text:
        logging.warning(f"Chapter {novel_number} is empty, cannot finalize.")
        return

    global_summary_file = os.path.join(filepath, "global_summary.txt")
    old_global_summary = read_file(global_summary_file)
    character_state_file = os.path.join(filepath, "character_state.txt")
    old_character_state = read_file(character_state_file)

    llm_adapter = create_llm_adapter(
        interface_format=interface_format,
        base_url=base_url,
        model_name=model_name,
        api_key=api_key,
        temperature=temperature,
        max_tokens=max_tokens,
        timeout=timeout
    )

    prompt_summary = summary_prompt.format(
        chapter_text=chapter_text,
        global_summary=old_global_summary
    )
    new_global_summary = invoke_with_cleaning(llm_adapter, prompt_summary)
    if not new_global_summary.strip():
        new_global_summary = old_global_summary

    prompt_char_state = update_character_state_prompt.format(
        chapter_text=chapter_text,
        old_state=old_character_state
    )
    new_char_state = invoke_with_cleaning(llm_adapter, prompt_char_state)
    if not new_char_state.strip():
        new_char_state = old_character_state

    clear_file_content(global_summary_file)
    save_string_to_txt(new_global_summary, global_summary_file)
    clear_file_content(character_state_file)
    save_string_to_txt(new_char_state, character_state_file)

    update_vector_store(
        embedding_adapter=create_embedding_adapter(
            embedding_interface_format,
            embedding_api_key,
            embedding_url,
            embedding_model_name
        ),
        new_chapter=chapter_text,
        filepath=filepath
    )

    logging.info(f"Chapter {novel_number} has been finalized.")

def enrich_chapter_text(
    chapter_text: str,
    word_number: int,
    api_key: str,
    base_url: str,
    model_name: str,
    temperature: float,
    interface_format: str,
    max_tokens: int,
    timeout: int=600
) -> str:
    """
    对章节文本进行扩写，使其更接近 word_number 字数，保持剧情连贯。
    """
    llm_adapter = create_llm_adapter(
        interface_format=interface_format,
        base_url=base_url,
        model_name=model_name,
        api_key=api_key,
        temperature=temperature,
        max_tokens=max_tokens,
        timeout=timeout
    )
    prompt = f"""以下章节文本较短，请在保持剧情连贯的前提下进行扩写，使其更充实，接近 {word_number} 字左右：
原内容：
{chapter_text}
"""
    enriched_text = invoke_with_cleaning(llm_adapter, prompt)
    return enriched_text if enriched_text else chapter_text