# contentClassifier.py - Lightweight Content Detection
# FROZEN - DO NOT MODIFY

import numpy as np
from PIL import Image
from collections import Counter

class ContentClassifier:
    """Lightweight content detection - FROZEN."""
    
    def __init__(self):
        self.categories = [
            'simple_graphic', 'human_hair', 'human', 'anime',
            'logo_icon', 'product_white_bg', 'general_photo', 'complex'
        ]
    
    def classify(self, image: Image.Image) -> dict:
        """Classify image content."""
        if image.mode != 'RGB':
            image = image.convert('RGB')
        
        small = image.copy()
        small.thumbnail((150, 150), Image.Resampling.LANCZOS)
        np_img = np.array(small)
        h, w = np_img.shape[:2]
        
        signals = {
            'dimensions': (w, h),
            'aspect_ratio': h / w if w > 0 else 0,
            'border_uniformity': self._border_uniformity(np_img),
            'edge_density': self._edge_density(np_img),
            'color_complexity': self._color_complexity(small),
            'skin_score': self._skin_score(np_img),
            'border_connected': self._border_connected(np_img),
            'texture': self._texture(np_img)
        }
        
        category = self._classify(signals)
        confidence = self._calculate_confidence(category, signals)
        
        return {
            'category': category,
            'confidence': confidence,
            'signals': signals
        }
    
    def _border_uniformity(self, np_img: np.ndarray) -> float:
        h, w = np_img.shape[:2]
        border_pixels = []
        step = max(1, min(h, w) // 10)
        
        for x in range(0, w, step):
            border_pixels.append(tuple(np_img[0, x][:3]))
            border_pixels.append(tuple(np_img[h-1, x][:3]))
        for y in range(0, h, step):
            border_pixels.append(tuple(np_img[y, 0][:3]))
            border_pixels.append(tuple(np_img[y, w-1][:3]))
        
        if not border_pixels:
            return 0.0
        
        unique = len(set(border_pixels))
        return 1 - (unique / len(border_pixels))
    
    def _edge_density(self, np_img: np.ndarray) -> float:
        gray = np.mean(np_img, axis=2).astype(np.float32)
        grad_x = np.abs(gray[:, 1:] - gray[:, :-1])
        grad_y = np.abs(gray[1:, :] - gray[:-1, :])
        
        edges = (grad_x > 25).sum() + (grad_y > 25).sum()
        total = (gray.shape[0] - 1) * gray.shape[1] + gray.shape[0] * (gray.shape[1] - 1)
        
        return edges / total if total > 0 else 0
    
    def _color_complexity(self, image: Image.Image) -> float:
        quantized = image.quantize(colors=32)
        unique = len(quantized.getcolors())
        return min(1.0, unique / 32)
    
    def _skin_score(self, np_img: np.ndarray) -> float:
        h, w = np_img.shape[:2]
        pixels = []
        step = max(1, min(h, w) // 5)
        for y in range(0, h, step):
            for x in range(0, w, step):
                pixels.append(np_img[y, x][:3])
        
        skin = 0
        for r, g, b in pixels:
            if (r > 60 and g > 40 and b > 20 and
                r > g and r > b and
                abs(r - g) < 60 and abs(r - b) < 60):
                skin += 1
        
        return skin / len(pixels) if pixels else 0
    
    def _border_connected(self, np_img: np.ndarray) -> float:
        h, w = np_img.shape[:2]
        border_colors = set()
        border_colors.add(tuple(np_img[0, 0][:3]))
        border_colors.add(tuple(np_img[0, w-1][:3]))
        border_colors.add(tuple(np_img[h-1, 0][:3]))
        border_colors.add(tuple(np_img[h-1, w-1][:3]))
        
        interior_colors = set()
        step = max(1, min(h, w) // 4)
        for y in range(h//4, 3*h//4, step):
            for x in range(w//4, 3*w//4, step):
                interior_colors.add(tuple(np_img[y, x][:3]))
        
        overlap = border_colors & interior_colors
        return len(overlap) / max(len(border_colors), 1)
    
    def _texture(self, np_img: np.ndarray) -> float:
        gray = np.mean(np_img, axis=2).astype(np.float32)
        variance = np.var(gray)
        return min(1.0, variance / 5000)
    
    def _classify(self, signals: dict) -> str:
        s = signals
        
        # Simple graphic
        if (s['color_complexity'] < 0.3 and 
            s['edge_density'] > 0.3 and 
            s['border_uniformity'] > 0.7):
            return 'simple_graphic'
        
        # Human with hair
        if (s['skin_score'] > 0.15 and 
            s['texture'] > 0.3 and 
            s['edge_density'] > 0.2):
            return 'human_hair'
        
        # Human
        if s['skin_score'] > 0.1:
            return 'human'
        
        # Anime
        if (s['skin_score'] < 0.08 and 
            s['color_complexity'] > 0.3 and 
            s['texture'] < 0.3 and 
            s['edge_density'] > 0.3):
            return 'anime'
        
        # Logo/icon
        if (s['edge_density'] > 0.4 and 
            s['color_complexity'] < 0.3 and 
            s['border_uniformity'] > 0.6):
            return 'logo_icon'
        
        # Product on white bg
        if (s['border_uniformity'] > 0.6 and 
            s['color_complexity'] < 0.5 and 
            s['edge_density'] < 0.4):
            return 'product_white_bg'
        
        # General photo
        if s['color_complexity'] > 0.3:
            return 'general_photo'
        
        return 'complex'
    
    def _calculate_confidence(self, category: str, signals: dict) -> float:
        base = 0.7
        
        if category == 'simple_graphic':
            if signals['border_uniformity'] > 0.8:
                base += 0.2
        elif category == 'human_hair':
            if signals['skin_score'] > 0.2 and signals['texture'] > 0.4:
                base += 0.2
        elif category == 'human':
            if signals['skin_score'] > 0.15:
                base += 0.2
        
        return min(1.0, base)