from typing import Any
import numpy as np
from veridex.core.signal import BaseSignal, DetectionResult

class MLEPSignal(BaseSignal):
    """
    Detects AI images using Multi-granularity Local Entropy Patterns (MLEP).

    Based on the research "MLEP: Multi-granularity Local Entropy Patterns for Universal AI-generated Image Detection" (Paper 3).

    Hypothesis:
        Synthetic images often exhibit different local entropy characteristics compared to natural images,
        particularly in high-frequency regions or at specific scales, due to the generative upsampling process.

    Methodology:
        1. Convert image to grayscale.
        2. Calculate local entropy map (using a neighborhood kernel, e.g., disk).
        3. Extract statistical features from the entropy map (e.g., mean, variance, skewness).
        4. (Ideally) Pass features to a classifier.
        5. (Heuristic fallback) Return a score based on entropy anomalies.
           Paper suggests AI images might have lower local entropy complexity in textures.

           *Note*: Without a trained SVM/RF classifier on a large dataset, this signal acts as a
           statistical feature extractor. We provide a heuristic score for demonstration,
           where Extreme Entropy (very low or very high) is penalized/flagged.

    Attributes:
        name (str): 'mlep_entropy'
        dtype (str): 'image'
    """

    @property
    def name(self) -> str:
        return "mlep_entropy"

    @property
    def dtype(self) -> str:
        return "image"

    def check_dependencies(self) -> None:
        try:
            import skimage
            import scipy
        except ImportError as e:
            raise ImportError(
                "MLEPSignal requires 'scikit-image' and 'scipy'. "
                "Install with `pip install veridex[image]` (if configured) or `pip install scikit-image scipy`."
            ) from e

    def run(self, input_data: Any) -> DetectionResult:
        try:
            from PIL import Image
            from skimage.filters.rank import entropy
            from skimage.morphology import disk
            from skimage.color import rgb2gray
            from skimage.util import img_as_ubyte
            import scipy.stats
        except ImportError:
            self.check_dependencies()
            raise

        # 1. Prepare Input
        image = None
        if isinstance(input_data, str):
            try:
                image = Image.open(input_data).convert("RGB")
            except Exception as e:
                return DetectionResult(
                    score=0.0, confidence=0.0, metadata={},
                    error=f"Could not open image: {e}"
                )
        elif isinstance(input_data, Image.Image):
            image = input_data.convert("RGB")
        elif isinstance(input_data, np.ndarray):
             image = Image.fromarray(input_data).convert("RGB")
        else:
             return DetectionResult(
                score=0.0, confidence=0.0, metadata={},
                error="Input must be file path, PIL Image, or numpy array."
            )

        try:
            # Resize for consistency/performance (entropy is slow on large images)
            image_small = image.resize((512, 512), Image.BICUBIC)

            # Convert to grayscale ubyte
            gray_image = img_as_ubyte(rgb2gray(np.array(image_small)))

            # 2. Calculate Local Entropy Map
            # Using a disk of radius 3 (can be multi-scale in full implementation)
            entropy_map = entropy(gray_image, disk(3))

            # 3. Extract Statistics
            mean_ent = np.mean(entropy_map)
            var_ent = np.var(entropy_map)
            skew_ent = scipy.stats.skew(entropy_map.flatten())

            # 4. Heuristic Scoring
            # This is a placeholder for a trained classifier.
            # Research suggests generated images often have distinct entropy distributions.
            # For this 'unsupervised' signal, we measure deviation from "expected natural" entropy.
            # Natural images usually have high entropy in textures.
            # Very low variance in entropy might indicate artificial smoothness.

            # Let's map 'Variance' to a score.
            # Real images often have high variance in local entropy (smooth sky vs complex trees).
            # Some GANs/Diffusion models might have more uniform entropy noise patterns.

            # Heuristic: Lower entropy variance -> Higher probability of being AI (Score -> 1.0)
            # This is a weak heuristic and should be replaced by a classifier in v2.
            # Using a sigmoid-like mapping for demonstration.
            # Suppose typical natural var is ~1.0-2.0?? (Entropy values are usually 0-8 bits)

            # Let's normalize score based on an assumed distribution.
            # This part is highly experimental without the trained SVM.
            # We return the raw stats in metadata for the fusion layer to use.

            score = 0.5 # Default neutral

            metadata = {
                "mean_entropy": float(mean_ent),
                "variance_entropy": float(var_ent),
                "skewness_entropy": float(skew_ent),
                "note": "Score is neutral placeholder. Use metadata features for classification."
            }

            return DetectionResult(
                score=score,
                confidence=0.0, # Zero confidence because this is just feature extraction currently
                metadata=metadata
            )

        except Exception as e:
            return DetectionResult(
                score=0.0,
                confidence=0.0,
                metadata={},
                error=f"MLEP execution failed: {e}"
            )
