"""
EWAAST: Monk Skin Tone (MST) Classifier

Uses MedGemma's VQA capabilities to classify patient skin tone
on the 10-point Monk Skin Tone Scale.
"""

from enum import Enum
from dataclasses import dataclass
from typing import Optional
from PIL import Image


class MSTCategory(Enum):
    """Monk Skin Tone categories for clinical guidance."""
    LIGHT = "light"      # MST 1-3
    MEDIUM = "medium"    # MST 4-7
    DEEP = "deep"        # MST 8-10


@dataclass
class MSTResult:
    """Result of Monk Skin Tone classification."""
    value: int  # 1-10
    category: MSTCategory
    confidence: float
    visual_guidance: str
    
    @property
    def description(self) -> str:
        """Human-readable description of the MST value."""
        category_labels = {
            MSTCategory.LIGHT: "Light",
            MSTCategory.MEDIUM: "Medium", 
            MSTCategory.DEEP: "Deep"
        }
        return f"{category_labels[self.category]} (MST {self.value})"


class MSTClassifier:
    """
    Classifier for Monk Skin Tone (MST) Scale.
    
    The MST Scale is a 10-point skin tone representation developed
    by Dr. Ellis Monk to improve AI fairness across diverse skin tones.
    
    Usage:
        classifier = MSTClassifier()
        result = classifier.classify(image)
        print(f"Detected: {result.description}")
    """
    
    # Clinical visual guidance based on MST category
    VISUAL_GUIDANCE = {
        MSTCategory.LIGHT: (
            "Look for: Non-blanchable erythema (redness), warmth, "
            "pallor for ischemia. Inflammatory signs typically present as red."
        ),
        MSTCategory.MEDIUM: (
            "Look for: Subtle color changes (slightly darker/redder than surrounding skin), "
            "warmth, shiny or taut skin. Erythema may not be bright red."
        ),
        MSTCategory.DEEP: (
            "Look for: Purple, blue, or ashen discoloration (NOT redness), "
            "induration (hardness), localized heat, edema. "
            "Stage 1 pressure ulcers may appear as persistent violet/maroon areas."
        ),
    }
    
    def __init__(self, model_name: str = "google/medgemma-1.5-4b-it"):
        """
        Initialize the MST Classifier.
        
        Args:
            model_name: HuggingFace model ID for MedGemma
        """
        self.model_name = model_name
        self.model = None  # Lazy loading
        self.processor = None
    
    def _load_model(self) -> None:
        """Load MedGemma model for VQA tasks."""
        # TODO: Implement actual model loading
        # from transformers import AutoProcessor, AutoModelForVision2Seq
        # self.processor = AutoProcessor.from_pretrained(self.model_name)
        # self.model = AutoModelForVision2Seq.from_pretrained(self.model_name)
        pass
    
    def _get_category(self, mst_value: int) -> MSTCategory:
        """Determine MST category from numeric value."""
        if mst_value <= 3:
            return MSTCategory.LIGHT
        elif mst_value <= 7:
            return MSTCategory.MEDIUM
        else:
            return MSTCategory.DEEP
    
    def classify(self, image: Image.Image) -> MSTResult:
        """
        Classify the skin tone of the patient in the image.
        
        Uses the healthy skin visible around the wound area
        to determine the patient's Monk Skin Tone value.
        
        Args:
            image: PIL Image containing the wound and surrounding skin
            
        Returns:
            MSTResult with value (1-10), category, and visual guidance
        """
        # TODO: Implement actual classification using MedGemma VQA
        # Prompt: "What is the Monk Skin Tone (1-10) of the patient's skin 
        #          visible in this image? Only return a number 1-10."
        
        # Placeholder: Return middle value with medium confidence
        mst_value = 5
        confidence = 0.0  # Indicates placeholder
        
        category = self._get_category(mst_value)
        visual_guidance = self.VISUAL_GUIDANCE[category]
        
        return MSTResult(
            value=mst_value,
            category=category,
            confidence=confidence,
            visual_guidance=visual_guidance
        )
    
    def get_guidance_for_mst(self, mst_value: int) -> str:
        """
        Get clinical visual guidance for a given MST value.
        
        Args:
            mst_value: Monk Skin Tone value (1-10)
            
        Returns:
            String with visual examination guidance
        """
        category = self._get_category(mst_value)
        return self.VISUAL_GUIDANCE[category]