import os
import re
import json
import hashlib
import urllib.request
import urllib.error

import folder_paths
import comfy.sd
import comfy.lora
import comfy.utils
from safetensors import safe_open

try:
    from .promp_logic_v32_story import MODEL_PRESETS, MODEL_PRESET_LABELS, resolve_model
except Exception:
    MODEL_PRESETS = {"🔞 DeepSeek V3.2": "deepseek/deepseek-v3.2"}
    MODEL_PRESET_LABELS = list(MODEL_PRESETS.keys())
    def resolve_model(p, c): return c if p == "custom" else "deepseek/deepseek-v3.2"

class DolphinCinematicDirector:
    @classmethod
    def INPUT_TYPES(s):
        loras = ["None"] + folder_paths.get_filename_list("loras")
        return {
            "required": {
                "model_base": ("MODEL",), "model_high": ("MODEL",), "model_low": ("MODEL",),
                "mode": (["🤖 Auto LLM (Story Splitter)", "✍️ Manual"], {"default": "🤖 Auto LLM (Story Splitter)"}),
                "master_story": ("STRING", {"multiline": True}),
                "tagger_context": ("STRING", {"multiline": True}),
                "openrouter_api_key": ("STRING", {"default": ""}),
            },
            "optional": {
                "external_triggers": ("STRING", {"forceInput": True}),
            }
        }

    RETURN_TYPES = ("MODEL", "MODEL", "MODEL", "STRING") * 4 + ("STRING",)
    RETURN_NAMES = tuple([f"c{i}_{n}" for i in range(1, 5) for n in ["base", "high", "low", "prompt"]] + ["debug_info"])
    FUNCTION = "direct"
    CATEGORY = "Dolphin"

    def _build_final(self, action, tags, master_scene, name, trigger_str):
        # 1. 트리거 워드(trigger_str)와 캐릭터 이름(name)만 상단에 배치
        header = []
        if name: header.append(name)
        if trigger_str: header.append(trigger_str)
        
        # 2. 동작(action)과 배경(master_scene)만 하단에 배치
        # 🚀 핵심: 여기서 'tags' 변수를 아예 사용하지 않음으로써 묘사 포함을 원천 차단
        body = []
        if master_scene and master_scene.strip():
            body.append(master_scene.strip())
        if action and action.strip():
            body.append(action.strip())
        
        # 결과값 결합
        header_str = ", ".join(header)
        body_str = " ".join(body)
        
        if header_str and body_str:
            return f"{header_str}\n{body_str}"
        return header_str or body_str

    def _llm_split(self, story, name, tags, key, model):
        # 🚀 수정: LLM에게는 묘사를 보내되, 출력 JSON에서는 동작만 뽑도록 강제
       sys_p = (
    "You are an action-only generator. "
    "DO NOT output any visual descriptions, clothing, lighting, or setting details. "
    "Your output must ONLY be the physical movement of the character. "
    "If you include any adjective-heavy descriptions, you will be penalized. "
    "Strictly output only the kinetic action in the JSON fields."
)

    def direct(self, model_base, model_high, model_low, mode, master_story, tagger_context, openrouter_api_key, external_triggers="", **kw):
        # 🚀 강력한 방어: 어떤 묘사가 들어와도 태그는 강제로 빈 값으로 대체
        tagger_context = "" 
        
        # 이후 로직...
        # 모드별 처리
        clips = [master_story] * 4 # 단순화 로직
        master_scene = "action"
        final_name = "character"
        
        outs = []
        for c in range(1, 5):
            # 🚀 강제로 tags(tagger_context)를 빈 문자열("")로 전달하여 무력화
            prompt = self._build_final(clips[c-1], "", master_scene, final_name, external_triggers)
            outs.extend([model_base, model_high, model_low, prompt])

        return tuple(outs) + ("Success",)

NODE_CLASS_MAPPINGS = {"DolphinCinematicDirector": DolphinCinematicDirector}