# metadata/parser.py
import json
import logging
from pathlib import Path
from typing import Optional, Dict, Any, Tuple
from PIL import Image
import piexif
import piexif.helper

logger = logging.getLogger("ComfyGallery.MetadataParser")

class MetadataParser:
    @staticmethod
    def extract_raw_metadata(filepath: str) -> Tuple[Optional[str], Optional[str]]:
        path = Path(filepath)
        if not path.exists():
            return None, None
        suffix = path.suffix.lower()
        if suffix in ('.png', '.webp'):
            return MetadataParser._extract_from_png_webp(path)
        elif suffix in ('.jpg', '.jpeg'):
            return MetadataParser._extract_from_jpeg(path)
        return None, None

    @staticmethod
    def _extract_from_png_webp(path: Path) -> Tuple[Optional[str], Optional[str]]:
        try:
            with Image.open(path) as img:
                info = img.info
                prompt = info.get("prompt")
                workflow = info.get("workflow")
                if not prompt and "comment" in info:
                    prompt = info.get("comment")
                return prompt, workflow
        except Exception as e:
            logger.error(f"Ошибка чтения PNG/WebP метаданных {path.name}: {e}")
            return None, None

    @staticmethod
    def _extract_from_jpeg(path: Path) -> Tuple[Optional[str], Optional[str]]:
        try:
            with Image.open(path) as img:
                if "exif" not in img.info:
                    return None, None
                exif_dict = piexif.load(img.info["exif"])
                user_comment_bytes = exif_dict.get("Exif", {}).get(piexif.ExifIFD.UserComment, b"")
                if not user_comment_bytes:
                    return None, None
                try:
                    comment_str = piexif.helper.UserComment.load(user_comment_bytes)
                except ValueError:
                    comment_str = user_comment_bytes.decode('utf-8', errors='ignore')
                if comment_str.startswith("{"):
                    try:
                        data = json.loads(comment_str)
                        prompt = data.get("prompt")
                        workflow = data.get("workflow")
                        if isinstance(prompt, dict):
                            prompt = json.dumps(prompt)
                        if isinstance(workflow, dict):
                            workflow = json.dumps(workflow)
                        return prompt, workflow
                    except json.JSONDecodeError:
                        return comment_str, None
                return None, None
        except Exception as e:
            logger.error(f"Ошибка чтения JPEG EXIF {path.name}: {e}")
            return None, None

    @classmethod
    def parse_comfy_parameters(cls, prompt_json_str: Optional[str]) -> Dict[str, Any]:
        result = {
            "positive_prompt": None, "negative_prompt": None, "seed": None,
            "model_name": None, "sampler": None, "steps": None, "cfg": None
        }
        if not prompt_json_str:
            return result
        try:
            prompt_graph = json.loads(prompt_json_str)
            if not isinstance(prompt_graph, dict):
                return result
        except json.JSONDecodeError:
            return result

        sampler_node = None
        for node_id, node in prompt_graph.items():
            class_type = node.get("class_type", "")
            if "KSampler" in class_type:
                sampler_node = node
                break

        if sampler_node:
            inputs = sampler_node.get("inputs", {})
            result["seed"] = inputs.get("seed") or inputs.get("noise_seed")
            result["steps"] = inputs.get("steps")
            result["cfg"] = inputs.get("cfg")
            result["sampler"] = inputs.get("sampler_name")
            result["positive_prompt"] = cls._trace_conditioning(inputs.get("positive"), prompt_graph)
            result["negative_prompt"] = cls._trace_conditioning(inputs.get("negative"), prompt_graph)
            result["model_name"] = cls._trace_model(inputs.get("model"), prompt_graph)
        else:
            positives = []
            for node in prompt_graph.values():
                if node.get("class_type") == "CLIPTextEncode":
                    text = node.get("inputs", {}).get("text", "")
                    if text and len(text.strip()) > 0:
                        positives.append(text.strip())
            if positives:
                result["positive_prompt"] = "\n---\n".join(positives)

        def clean_string(val) -> Optional[str]:
            if val is None: return None
            if isinstance(val, list):
                if all(isinstance(x, str) for x in val):
                    return "\n".join(val)
                return json.dumps(val)
            if isinstance(val, dict): return json.dumps(val)
            return str(val)

        def clean_int(val) -> Optional[int]:
            if val is None or isinstance(val, (list, dict)): return None
            try: return int(val)
            except (ValueError, TypeError): return None

        def clean_float(val) -> Optional[float]:
            if val is None or isinstance(val, (list, dict)): return None
            try: return float(val)
            except (ValueError, TypeError): return None

        result["positive_prompt"] = clean_string(result["positive_prompt"])
        result["negative_prompt"] = clean_string(result["negative_prompt"])
        result["model_name"] = clean_string(result["model_name"])
        result["sampler"] = clean_string(result["sampler"])
        result["seed"] = clean_int(result["seed"])
        result["steps"] = clean_int(result["steps"])
        result["cfg"] = clean_float(result["cfg"])
        return result

    @classmethod
    def _trace_conditioning(cls, link: Optional[list], graph: dict) -> Optional[str]:
        if not link or not isinstance(link, list) or len(link) < 1:
            return None
        node_id = str(link[0])
        node = graph.get(node_id)
        if not node:
            return None
        class_type = node.get("class_type", "")
        inputs = node.get("inputs", {})
        if class_type in ("CLIPTextEncode", "CLIPTextEncodeSDXL", "CLIPTextEncodeSequence"):
            return inputs.get("text") or inputs.get("text_g")
        if "Conditioning" in class_type:
            for key, val in inputs.items():
                if isinstance(val, list) and len(val) >= 1:
                    text = cls._trace_conditioning(val, graph)
                    if text: return text
        return None

    @classmethod
    def _trace_model(cls, link: Optional[list], graph: dict) -> Optional[str]:
        if not link or not isinstance(link, list) or len(link) < 1:
            return None
        node_id = str(link[0])
        node = graph.get(node_id)
        if not node:
            return None
        class_type = node.get("class_type", "")
        inputs = node.get("inputs", {})
        if "CheckpointLoader" in class_type:
            return inputs.get("ckpt_name")
        elif "LoraLoader" in class_type or "ModelMerge" in class_type:
            return cls._trace_model(inputs.get("model"), graph)
        return None