import json

import folder_paths

# Import parent LoraLoader from ComfyUI built-in nodes
from nodes import LoraLoader

from .cache_manager import cache_manager


class KaiCivitaiLoRALoader(LoraLoader):
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "model": ("MODEL", {}),
                "clip": ("CLIP", {}),
                "lora_name": (folder_paths.get_filename_list("loras"), {}),
                "strength_model": (
                    "FLOAT",
                    {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01},
                ),
                "strength_clip": (
                    "FLOAT",
                    {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01},
                ),
            }
        }

    RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING")
    RETURN_NAMES = ("model", "clip", "trigger_words", "civitai_json")
    FUNCTION = "load_lora_with_info"
    CATEGORY = "KaiUtilities/loaders"

    def load_lora_with_info(self, model, clip, lora_name, strength_model, strength_clip):
        # Load LoRA using parent logic
        model_out, clip_out = self.load_lora(
            model, clip, lora_name, strength_model, strength_clip
        )

        # Get cached info if available (node does NOT auto-fetch; user must click button)
        try:
            lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
        except (FileNotFoundError, KeyError):
            # If path resolution fails, still return LoRA outputs with empty extras
            return (model_out, clip_out, "", "")

        # Use sync cache lookup because ComfyUI node FUNCTIONs run synchronously
        # in worker threads (not in the async event loop). The sync hash function
        # is safe here because worker threads can block on file I/O.
        try:
            cache = cache_manager.get_cache_for_file_sync(lora_path)
        except Exception:
            # Defensive: any unexpected failure in cache lookup should not crash the node
            return (model_out, clip_out, "", "")

        trigger_words = ""
        civitai_json = ""
        if cache:
            words = cache.get("triggerWords")
            if isinstance(words, list):
                trigger_words = ", ".join(str(w) for w in words)
            civitai_json = json.dumps(cache)

        return (model_out, clip_out, trigger_words, civitai_json)