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Set-Game-Solver

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import os
import cv2
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.models import load_model
import torch
from ultralytics import YOLO
from itertools import combinations
import gradio as gr
import traceback
import time
from typing import Tuple, List, Dict
import logging

# Force CPU mode for TensorFlow
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
tf.config.set_visible_devices([], 'GPU')

# Import spaces for ZeroGPU wrapper
try:
    import spaces
except ImportError:
    # Create a dummy spaces class for local development
    class spaces:
        @staticmethod
        def GPU(func):
            return func

# =============================================================================
# LOGGING CONFIGURATION
# =============================================================================
logging.basicConfig(level=logging.INFO, 
                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger("set_detector")

# =============================================================================
# MODEL LOADING
# =============================================================================
# Global variables for model caching
_CARD_DETECTOR = None
_SHAPE_DETECTOR = None
_SHAPE_CLASSIFIER = None
_FILL_CLASSIFIER = None

def load_models():
    """
    Load all models needed for SET detection in CPU-only mode.
    Returns tuple of (card_detector, shape_detector, shape_classifier, fill_classifier)
    """
    global _CARD_DETECTOR, _SHAPE_DETECTOR, _SHAPE_CLASSIFIER, _FILL_CLASSIFIER
    
    # Return cached models if already loaded
    if all([_CARD_DETECTOR, _SHAPE_DETECTOR, _SHAPE_CLASSIFIER, _FILL_CLASSIFIER]):
        logger.info("Using cached models")
        return _CARD_DETECTOR, _SHAPE_DETECTOR, _SHAPE_CLASSIFIER, _FILL_CLASSIFIER
    
    try:
        from huggingface_hub import hf_hub_download
        
        logger.info("Loading models from Hugging Face Hub...")
        
        # Load Shape Classification Model (TensorFlow)
        logger.info("Loading shape classification model...")
        shape_classifier = load_model(
            hf_hub_download("Oamitai/shape-classification", "shape_model.keras")
        )
        
        # Load Fill Classification Model (TensorFlow)
        logger.info("Loading fill classification model...")
        fill_classifier = load_model(
            hf_hub_download("Oamitai/fill-classification", "fill_model.keras")
        )
        
        # Load YOLO Card Detection Model (PyTorch)
        logger.info("Loading card detection model...")
        card_model_path = hf_hub_download("Oamitai/card-detection", "best.pt")
        card_detector = YOLO(card_model_path)
        card_detector.conf = 0.5
        
        # Load YOLO Shape Detection Model (PyTorch)
        logger.info("Loading shape detection model...")
        shape_model_path = hf_hub_download("Oamitai/shape-detection", "best.pt")
        shape_detector = YOLO(shape_model_path)
        shape_detector.conf = 0.5
        
        # Explicitly set to CPU mode
        logger.info("Setting models to CPU mode...")
        card_detector.to("cpu")
        shape_detector.to("cpu")
        
        # Cache the models
        _CARD_DETECTOR = card_detector
        _SHAPE_DETECTOR = shape_detector
        _SHAPE_CLASSIFIER = shape_classifier
        _FILL_CLASSIFIER = fill_classifier
        
        logger.info("All models loaded successfully in CPU mode!")
        return card_detector, shape_detector, shape_classifier, fill_classifier
        
    except Exception as e:
        error_msg = f"Error loading models: {str(e)}"
        logger.error(error_msg)
        logger.error(traceback.format_exc())
        raise ValueError(error_msg)

# =============================================================================
# UTILITY & DETECTION FUNCTIONS
# =============================================================================
def verify_and_rotate_image(board_image: np.ndarray, card_detector: YOLO) -> Tuple[np.ndarray, bool]:
    """
    Checks if the detected cards are oriented primarily vertically or horizontally.
    If they're vertical, rotates the board_image 90 degrees clockwise for consistent processing.
    Returns (possibly_rotated_image, was_rotated_flag).
    """
    detection = card_detector(board_image)
    boxes = detection[0].boxes.xyxy.cpu().numpy().astype(int)
    if boxes.size == 0:
        return board_image, False

    widths = boxes[:, 2] - boxes[:, 0]
    heights = boxes[:, 3] - boxes[:, 1]

    # Rotate if average height > average width
    if np.mean(heights) > np.mean(widths):
        return cv2.rotate(board_image, cv2.ROTATE_90_CLOCKWISE), True
    else:
        return board_image, False

def restore_orientation(img: np.ndarray, was_rotated: bool) -> np.ndarray:
    """
    Restores original orientation if the image was previously rotated.
    """
    if was_rotated:
        return cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE)
    return img

def predict_color(img_bgr: np.ndarray) -> str:
    """
    Rough color classification using HSV thresholds to differentiate 'red', 'green', 'purple'.
    """
    hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)
    mask_green = cv2.inRange(hsv, np.array([40, 50, 50]), np.array([80, 255, 255]))
    mask_purple = cv2.inRange(hsv, np.array([120, 50, 50]), np.array([160, 255, 255]))

    # Red can wrap around hue=0, so we combine both ends
    mask_red1 = cv2.inRange(hsv, np.array([0, 50, 50]), np.array([10, 255, 255]))
    mask_red2 = cv2.inRange(hsv, np.array([170, 50, 50]), np.array([180, 255, 255]))
    mask_red = cv2.bitwise_or(mask_red1, mask_red2)

    counts = {
        "green": cv2.countNonZero(mask_green),
        "purple": cv2.countNonZero(mask_purple),
        "red": cv2.countNonZero(mask_red),
    }
    return max(counts, key=counts.get)

def detect_cards(board_img: np.ndarray, card_detector: YOLO) -> List[Tuple[np.ndarray, List[int]]]:
    """
    Runs YOLO on the board_img to detect card bounding boxes.
    Returns a list of (card_image, [x1, y1, x2, y2]) for each detected card.
    """
    result = card_detector(board_img)
    boxes = result[0].boxes.xyxy.cpu().numpy().astype(int)
    detected_cards = []

    for x1, y1, x2, y2 in boxes:
        detected_cards.append((board_img[y1:y2, x1:x2], [x1, y1, x2, y2]))
    return detected_cards

def predict_card_features(
    card_img: np.ndarray,
    shape_detector: YOLO,
    fill_model: tf.keras.Model,
    shape_model: tf.keras.Model,
    card_box: List[int]
) -> Dict:
    """
    Predicts the 'count', 'color', 'fill', 'shape' features for a single card.
    It uses a shape_detector YOLO model to locate shapes, then passes them to fill_model and shape_model.
    """
    # Detect shapes on the card
    shape_detections = shape_detector(card_img)
    c_h, c_w = card_img.shape[:2]
    card_area = c_w * c_h

    # Filter out spurious shape detections
    shape_boxes = []
    for coords in shape_detections[0].boxes.xyxy.cpu().numpy():
        x1, y1, x2, y2 = coords.astype(int)
        if (x2 - x1) * (y2 - y1) > 0.03 * card_area:
            shape_boxes.append([x1, y1, x2, y2])

    if not shape_boxes:
        return {
            'count': 0,
            'color': 'unknown',
            'fill': 'unknown',
            'shape': 'unknown',
            'box': card_box
        }

    fill_input_size = fill_model.input_shape[1:3]
    shape_input_size = shape_model.input_shape[1:3]
    fill_imgs = []
    shape_imgs = []
    color_candidates = []

    # Prepare each detected shape region for classification
    for sb in shape_boxes:
        sx1, sy1, sx2, sy2 = sb
        shape_crop = card_img[sy1:sy2, sx1:sx2]

        fill_crop = cv2.resize(shape_crop, fill_input_size) / 255.0
        shape_crop_resized = cv2.resize(shape_crop, shape_input_size) / 255.0

        fill_imgs.append(fill_crop)
        shape_imgs.append(shape_crop_resized)
        color_candidates.append(predict_color(shape_crop))

    # Handle TensorFlow prediction - process one image at a time to avoid memory issues
    fill_preds = []
    shape_preds = []
    
    for img in fill_imgs:
        try:
            pred = fill_model.predict(np.array([img]), verbose=0)
            fill_preds.append(pred[0])
        except Exception as e:
            logger.error(f"Fill prediction error: {e}")
            fill_preds.append(np.array([0.33, 0.33, 0.34]))  # Fallback
    
    for img in shape_imgs:
        try:
            pred = shape_model.predict(np.array([img]), verbose=0)
            shape_preds.append(pred[0])
        except Exception as e:
            logger.error(f"Shape prediction error: {e}")
            shape_preds.append(np.array([0.33, 0.33, 0.34]))  # Fallback

    fill_labels = ['empty', 'full', 'striped']
    shape_labels = ['diamond', 'oval', 'squiggle']

    fill_result = [fill_labels[np.argmax(fp)] for fp in fill_preds]
    shape_result = [shape_labels[np.argmax(sp)] for sp in shape_preds]

    # Take the most common color/fill/shape across all shape detections for the card
    if color_candidates:
        final_color = max(set(color_candidates), key=color_candidates.count)
    else:
        final_color = "unknown"
        
    if fill_result:
        final_fill = max(set(fill_result), key=fill_result.count)
    else:
        final_fill = "unknown"
        
    if shape_result:
        final_shape = max(set(shape_result), key=shape_result.count)
    else:
        final_shape = "unknown"

    return {
        'count': len(shape_boxes),
        'color': final_color,
        'fill': final_fill,
        'shape': final_shape,
        'box': card_box
    }

def classify_cards_on_board(
    board_img: np.ndarray,
    card_detector: YOLO,
    shape_detector: YOLO,
    fill_model: tf.keras.Model,
    shape_model: tf.keras.Model
) -> pd.DataFrame:
    """
    Detects cards on the board, then classifies each card's features.
    Returns a DataFrame with columns: 'Count', 'Color', 'Fill', 'Shape', 'Coordinates'.
    """
    detected_cards = detect_cards(board_img, card_detector)
    card_rows = []

    for (card_img, box) in detected_cards:
        card_feats = predict_card_features(card_img, shape_detector, fill_model, shape_model, box)
        card_rows.append({
            "Count": card_feats['count'],
            "Color": card_feats['color'],
            "Fill": card_feats['fill'],
            "Shape": card_feats['shape'],
            "Coordinates": card_feats['box']
        })

    return pd.DataFrame(card_rows)

def valid_set(cards: List[dict]) -> bool:
    """
    Checks if the given 3 cards collectively form a valid SET.
    """
    for feature in ["Count", "Color", "Fill", "Shape"]:
        if len({card[feature] for card in cards}) not in (1, 3):
            return False
    return True

def locate_all_sets(cards_df: pd.DataFrame) -> List[dict]:
    """
    Finds all possible SETs from the card DataFrame.
    Each SET is a dictionary with 'set_indices' and 'cards' fields.
    """
    found_sets = []
    for combo in combinations(cards_df.iterrows(), 3):
        cards = [c[1] for c in combo]  # c is (index, row)
        if valid_set(cards):
            found_sets.append({
                'set_indices': [c[0] for c in combo],
                'cards': [
                    {f: card[f] for f in ['Count', 'Color', 'Fill', 'Shape', 'Coordinates']}
                    for card in cards
                ]
            })
    return found_sets

def draw_detected_sets(board_img: np.ndarray, sets_detected: List[dict]) -> np.ndarray:
    """
    Annotates the board image with bounding boxes for each detected SET.
    Each SET is drawn in a different color and offset (thickness & expansion) 
    so that overlapping sets are visible.
    """
    # Some distinct BGR colors
    colors = [
        (255, 0, 0), (0, 255, 0), (0, 0, 255),
        (255, 255, 0), (255, 0, 255), (0, 255, 255)
    ]
    base_thickness = 8
    base_expansion = 5

    for idx, single_set in enumerate(sets_detected):
        color = colors[idx % len(colors)]
        thickness = base_thickness + 2 * idx
        expansion = base_expansion + 15 * idx

        for i, card_info in enumerate(single_set["cards"]):
            x1, y1, x2, y2 = card_info["Coordinates"]
            # Expand the bounding box slightly
            x1e = max(0, x1 - expansion)
            y1e = max(0, y1 - expansion)
            x2e = min(board_img.shape[1], x2 + expansion)
            y2e = min(board_img.shape[0], y2 + expansion)

            cv2.rectangle(board_img, (x1e, y1e), (x2e, y2e), color, thickness)

            # Label only the first card's box with "Set <number>"
            if i == 0:
                cv2.putText(
                    board_img,
                    f"Set {idx + 1}",
                    (x1e, y1e - 10),
                    cv2.FONT_HERSHEY_SIMPLEX,
                    0.9,
                    color,
                    thickness
                )
    return board_img

def optimize_image_size(image_array: np.ndarray, max_dim=1200) -> np.ndarray:
    """
    Resizes an image if its largest dimension exceeds max_dim, to reduce processing time.
    """
    if image_array is None:
        return None
        
    height, width = image_array.shape[:2]
    if max(width, height) > max_dim:
        if width > height:
            new_width = max_dim
            new_height = int(height * (max_dim / width))
        else:
            new_height = max_dim
            new_width = int(width * (max_dim / height))

        return cv2.resize(image_array, (new_width, new_height), interpolation=cv2.INTER_AREA)
    return image_array

def process_image(input_image):
    """
    CPU-only processing function for SET detection.
    """
    if input_image is None:
        return None, "Please upload an image."
    
    try:
        start_time = time.time()
        
        # Load models (CPU-only)
        card_detector, shape_detector, shape_model, fill_model = load_models()
        
        # Optimize image size
        optimized_img = optimize_image_size(input_image)
        
        # Convert to BGR (OpenCV format)
        if len(optimized_img.shape) == 3 and optimized_img.shape[2] == 4:  # RGBA
            optimized_img = cv2.cvtColor(optimized_img, cv2.COLOR_RGBA2BGR)
        elif len(optimized_img.shape) == 3 and optimized_img.shape[2] == 3:
            # RGB to BGR
            optimized_img = cv2.cvtColor(optimized_img, cv2.COLOR_RGB2BGR)
        
        # Check and fix orientation
        processed_img, was_rotated = verify_and_rotate_image(optimized_img, card_detector)
        
        # Detect cards
        cards = detect_cards(processed_img, card_detector)
        if not cards:
            return cv2.cvtColor(optimized_img, cv2.COLOR_BGR2RGB), "No cards detected. Please check that it's a SET game board."
        
        # Classify cards and find sets
        df_cards = classify_cards_on_board(processed_img, card_detector, shape_detector, fill_model, shape_model)
        found_sets = locate_all_sets(df_cards)
        
        if not found_sets:
            return cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB), "Cards detected, but no valid SETs found!"
        
        # Draw sets on the image
        annotated = draw_detected_sets(processed_img.copy(), found_sets)
        
        # Restore original orientation if needed
        final_output = restore_orientation(annotated, was_rotated)
        
        # Convert back to RGB for display
        final_output_rgb = cv2.cvtColor(final_output, cv2.COLOR_BGR2RGB)
        
        process_time = time.time() - start_time
        return final_output_rgb, f"Found {len(found_sets)} SET(s) in {process_time:.2f} seconds."
        
    except Exception as e:
        error_message = f"Error processing image: {str(e)}"
        logger.error(error_message)
        logger.error(traceback.format_exc())
        return input_image, error_message

# Keep the spaces.GPU decorator for ZeroGPU API but use CPU internally
@spaces.GPU
def process_image_wrapper(input_image):
    """
    Wrapper for process_image that uses the spaces.GPU decorator
    but internally works in CPU-only mode.
    """
    return process_image(input_image)

# =============================================================================
# SIMPLIFIED GRADIO INTERFACE
# =============================================================================
with gr.Blocks(title="SET Game Detector") as demo:
    gr.HTML("""
    <div style="text-align: center; margin-bottom: 1rem;">
        <h1 style="margin-bottom: 0.5rem;">🎴 SET Game Detector</h1>
        <p>Upload an image of a SET game board to find all valid sets</p>
    </div>
    """)
    
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(
                label="Upload SET Board Image",
                type="numpy"
            )
            
            find_sets_btn = gr.Button(
                "🔎 Find Sets", 
                variant="primary"
            )
            
        with gr.Column():
            output_image = gr.Image(
                label="Detected Sets"
            )
            status = gr.Textbox(
                label="Status",
                value="Upload an image and click 'Find Sets'",
                interactive=False
            )
    
    # Function bindings
    find_sets_btn.click(
        fn=process_image_wrapper,
        inputs=[input_image],
        outputs=[output_image, status]
    )
    
    gr.HTML("""
    <div style="text-align: center; margin-top: 1rem; padding: 0.5rem; font-size: 0.8rem;">
        <p>SET Game Detector by <a href="https://github.com/omamitai" target="_blank">omamitai</a> | 
        Gradio version adapted for Hugging Face Spaces</p>
    </div>
    """)

# =============================================================================
# MAIN EXECUTION
# =============================================================================
if __name__ == "__main__":
    # Launch the app
    demo.queue().launch()

	import os
	import cv2
	import numpy as np
	import pandas as pd
	import tensorflow as tf
	from tensorflow.keras.models import load_model
	import torch
	from ultralytics import YOLO
	from itertools import combinations
	import gradio as gr
	import traceback
	import time
	from typing import Tuple, List, Dict
	import logging

	# Force CPU mode for TensorFlow
	os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
	tf.config.set_visible_devices([], 'GPU')

	# Import spaces for ZeroGPU wrapper
	try:
	import spaces
	except ImportError:
	# Create a dummy spaces class for local development
	class spaces:
	@staticmethod
	def GPU(func):
	return func

	# =============================================================================
	# LOGGING CONFIGURATION
	# =============================================================================
	logging.basicConfig(level=logging.INFO,
	format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
	logger = logging.getLogger("set_detector")

	# =============================================================================
	# MODEL LOADING
	# =============================================================================
	# Global variables for model caching
	_CARD_DETECTOR = None
	_SHAPE_DETECTOR = None
	_SHAPE_CLASSIFIER = None
	_FILL_CLASSIFIER = None

	def load_models():
	"""
	Load all models needed for SET detection in CPU-only mode.
	Returns tuple of (card_detector, shape_detector, shape_classifier, fill_classifier)
	"""
	global _CARD_DETECTOR, _SHAPE_DETECTOR, _SHAPE_CLASSIFIER, _FILL_CLASSIFIER

	# Return cached models if already loaded
	if all([_CARD_DETECTOR, _SHAPE_DETECTOR, _SHAPE_CLASSIFIER, _FILL_CLASSIFIER]):
	logger.info("Using cached models")
	return _CARD_DETECTOR, _SHAPE_DETECTOR, _SHAPE_CLASSIFIER, _FILL_CLASSIFIER

	try:
	from huggingface_hub import hf_hub_download

	logger.info("Loading models from Hugging Face Hub...")

	# Load Shape Classification Model (TensorFlow)
	logger.info("Loading shape classification model...")
	shape_classifier = load_model(
	hf_hub_download("Oamitai/shape-classification", "shape_model.keras")
	)

	# Load Fill Classification Model (TensorFlow)
	logger.info("Loading fill classification model...")
	fill_classifier = load_model(
	hf_hub_download("Oamitai/fill-classification", "fill_model.keras")
	)

	# Load YOLO Card Detection Model (PyTorch)
	logger.info("Loading card detection model...")
	card_model_path = hf_hub_download("Oamitai/card-detection", "best.pt")
	card_detector = YOLO(card_model_path)
	card_detector.conf = 0.5

	# Load YOLO Shape Detection Model (PyTorch)
	logger.info("Loading shape detection model...")
	shape_model_path = hf_hub_download("Oamitai/shape-detection", "best.pt")
	shape_detector = YOLO(shape_model_path)
	shape_detector.conf = 0.5

	# Explicitly set to CPU mode
	logger.info("Setting models to CPU mode...")
	card_detector.to("cpu")
	shape_detector.to("cpu")

	# Cache the models
	_CARD_DETECTOR = card_detector
	_SHAPE_DETECTOR = shape_detector
	_SHAPE_CLASSIFIER = shape_classifier
	_FILL_CLASSIFIER = fill_classifier

	logger.info("All models loaded successfully in CPU mode!")
	return card_detector, shape_detector, shape_classifier, fill_classifier

	except Exception as e:
	error_msg = f"Error loading models: {str(e)}"
	logger.error(error_msg)
	logger.error(traceback.format_exc())
	raise ValueError(error_msg)

	# =============================================================================
	# UTILITY & DETECTION FUNCTIONS
	# =============================================================================
	def verify_and_rotate_image(board_image: np.ndarray, card_detector: YOLO) -> Tuple[np.ndarray, bool]:
	"""
	Checks if the detected cards are oriented primarily vertically or horizontally.
	If they're vertical, rotates the board_image 90 degrees clockwise for consistent processing.
	Returns (possibly_rotated_image, was_rotated_flag).
	"""
	detection = card_detector(board_image)
	boxes = detection[0].boxes.xyxy.cpu().numpy().astype(int)
	if boxes.size == 0:
	return board_image, False

	widths = boxes[:, 2] - boxes[:, 0]
	heights = boxes[:, 3] - boxes[:, 1]

	# Rotate if average height > average width
	if np.mean(heights) > np.mean(widths):
	return cv2.rotate(board_image, cv2.ROTATE_90_CLOCKWISE), True
	else:
	return board_image, False

	def restore_orientation(img: np.ndarray, was_rotated: bool) -> np.ndarray:
	"""
	Restores original orientation if the image was previously rotated.
	"""
	if was_rotated:
	return cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE)
	return img

	def predict_color(img_bgr: np.ndarray) -> str:
	"""
	Rough color classification using HSV thresholds to differentiate 'red', 'green', 'purple'.
	"""
	hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)
	mask_green = cv2.inRange(hsv, np.array([40, 50, 50]), np.array([80, 255, 255]))
	mask_purple = cv2.inRange(hsv, np.array([120, 50, 50]), np.array([160, 255, 255]))

	# Red can wrap around hue=0, so we combine both ends
	mask_red1 = cv2.inRange(hsv, np.array([0, 50, 50]), np.array([10, 255, 255]))
	mask_red2 = cv2.inRange(hsv, np.array([170, 50, 50]), np.array([180, 255, 255]))
	mask_red = cv2.bitwise_or(mask_red1, mask_red2)

	counts = {
	"green": cv2.countNonZero(mask_green),
	"purple": cv2.countNonZero(mask_purple),
	"red": cv2.countNonZero(mask_red),
	}
	return max(counts, key=counts.get)

	def detect_cards(board_img: np.ndarray, card_detector: YOLO) -> List[Tuple[np.ndarray, List[int]]]:
	"""
	Runs YOLO on the board_img to detect card bounding boxes.
	Returns a list of (card_image, [x1, y1, x2, y2]) for each detected card.
	"""
	result = card_detector(board_img)
	boxes = result[0].boxes.xyxy.cpu().numpy().astype(int)
	detected_cards = []

	for x1, y1, x2, y2 in boxes:
	detected_cards.append((board_img[y1:y2, x1:x2], [x1, y1, x2, y2]))
	return detected_cards

	def predict_card_features(
	card_img: np.ndarray,
	shape_detector: YOLO,
	fill_model: tf.keras.Model,
	shape_model: tf.keras.Model,
	card_box: List[int]
	) -> Dict:
	"""
	Predicts the 'count', 'color', 'fill', 'shape' features for a single card.
	It uses a shape_detector YOLO model to locate shapes, then passes them to fill_model and shape_model.
	"""
	# Detect shapes on the card
	shape_detections = shape_detector(card_img)
	c_h, c_w = card_img.shape[:2]
	card_area = c_w * c_h

	# Filter out spurious shape detections
	shape_boxes = []
	for coords in shape_detections[0].boxes.xyxy.cpu().numpy():
	x1, y1, x2, y2 = coords.astype(int)
	if (x2 - x1) * (y2 - y1) > 0.03 * card_area:
	shape_boxes.append([x1, y1, x2, y2])

	if not shape_boxes:
	return {
	'count': 0,
	'color': 'unknown',
	'fill': 'unknown',
	'shape': 'unknown',
	'box': card_box
	}

	fill_input_size = fill_model.input_shape[1:3]
	shape_input_size = shape_model.input_shape[1:3]
	fill_imgs = []
	shape_imgs = []
	color_candidates = []

	# Prepare each detected shape region for classification
	for sb in shape_boxes:
	sx1, sy1, sx2, sy2 = sb
	shape_crop = card_img[sy1:sy2, sx1:sx2]

	fill_crop = cv2.resize(shape_crop, fill_input_size) / 255.0
	shape_crop_resized = cv2.resize(shape_crop, shape_input_size) / 255.0

	fill_imgs.append(fill_crop)
	shape_imgs.append(shape_crop_resized)
	color_candidates.append(predict_color(shape_crop))

	# Handle TensorFlow prediction - process one image at a time to avoid memory issues
	fill_preds = []
	shape_preds = []

	for img in fill_imgs:
	try:
	pred = fill_model.predict(np.array([img]), verbose=0)
	fill_preds.append(pred[0])
	except Exception as e:
	logger.error(f"Fill prediction error: {e}")
	fill_preds.append(np.array([0.33, 0.33, 0.34])) # Fallback

	for img in shape_imgs:
	try:
	pred = shape_model.predict(np.array([img]), verbose=0)
	shape_preds.append(pred[0])
	except Exception as e:
	logger.error(f"Shape prediction error: {e}")
	shape_preds.append(np.array([0.33, 0.33, 0.34])) # Fallback

	fill_labels = ['empty', 'full', 'striped']
	shape_labels = ['diamond', 'oval', 'squiggle']

	fill_result = [fill_labels[np.argmax(fp)] for fp in fill_preds]
	shape_result = [shape_labels[np.argmax(sp)] for sp in shape_preds]

	# Take the most common color/fill/shape across all shape detections for the card
	if color_candidates:
	final_color = max(set(color_candidates), key=color_candidates.count)
	else:
	final_color = "unknown"

	if fill_result:
	final_fill = max(set(fill_result), key=fill_result.count)
	else:
	final_fill = "unknown"

	if shape_result:
	final_shape = max(set(shape_result), key=shape_result.count)
	else:
	final_shape = "unknown"

	return {
	'count': len(shape_boxes),
	'color': final_color,
	'fill': final_fill,
	'shape': final_shape,
	'box': card_box
	}

	def classify_cards_on_board(
	board_img: np.ndarray,
	card_detector: YOLO,
	shape_detector: YOLO,
	fill_model: tf.keras.Model,
	shape_model: tf.keras.Model
	) -> pd.DataFrame:
	"""
	Detects cards on the board, then classifies each card's features.
	Returns a DataFrame with columns: 'Count', 'Color', 'Fill', 'Shape', 'Coordinates'.
	"""
	detected_cards = detect_cards(board_img, card_detector)
	card_rows = []

	for (card_img, box) in detected_cards:
	card_feats = predict_card_features(card_img, shape_detector, fill_model, shape_model, box)
	card_rows.append({
	"Count": card_feats['count'],
	"Color": card_feats['color'],
	"Fill": card_feats['fill'],
	"Shape": card_feats['shape'],
	"Coordinates": card_feats['box']
	})

	return pd.DataFrame(card_rows)

	def valid_set(cards: List[dict]) -> bool:
	"""
	Checks if the given 3 cards collectively form a valid SET.
	"""
	for feature in ["Count", "Color", "Fill", "Shape"]:
	if len({card[feature] for card in cards}) not in (1, 3):
	return False
	return True

	def locate_all_sets(cards_df: pd.DataFrame) -> List[dict]:
	"""
	Finds all possible SETs from the card DataFrame.
	Each SET is a dictionary with 'set_indices' and 'cards' fields.
	"""
	found_sets = []
	for combo in combinations(cards_df.iterrows(), 3):
	cards = [c[1] for c in combo] # c is (index, row)
	if valid_set(cards):
	found_sets.append({
	'set_indices': [c[0] for c in combo],
	'cards': [
	{f: card[f] for f in ['Count', 'Color', 'Fill', 'Shape', 'Coordinates']}
	for card in cards
	]
	})
	return found_sets

	def draw_detected_sets(board_img: np.ndarray, sets_detected: List[dict]) -> np.ndarray:
	"""
	Annotates the board image with bounding boxes for each detected SET.
	Each SET is drawn in a different color and offset (thickness & expansion)
	so that overlapping sets are visible.
	"""
	# Some distinct BGR colors
	colors = [
	(255, 0, 0), (0, 255, 0), (0, 0, 255),
	(255, 255, 0), (255, 0, 255), (0, 255, 255)
	]
	base_thickness = 8
	base_expansion = 5

	for idx, single_set in enumerate(sets_detected):
	color = colors[idx % len(colors)]
	thickness = base_thickness + 2 * idx
	expansion = base_expansion + 15 * idx

	for i, card_info in enumerate(single_set["cards"]):
	x1, y1, x2, y2 = card_info["Coordinates"]
	# Expand the bounding box slightly
	x1e = max(0, x1 - expansion)
	y1e = max(0, y1 - expansion)
	x2e = min(board_img.shape[1], x2 + expansion)
	y2e = min(board_img.shape[0], y2 + expansion)

	cv2.rectangle(board_img, (x1e, y1e), (x2e, y2e), color, thickness)

	# Label only the first card's box with "Set <number>"
	if i == 0:
	cv2.putText(
	board_img,
	f"Set {idx + 1}",
	(x1e, y1e - 10),
	cv2.FONT_HERSHEY_SIMPLEX,
	0.9,
	color,
	thickness
	)
	return board_img

	def optimize_image_size(image_array: np.ndarray, max_dim=1200) -> np.ndarray:
	"""
	Resizes an image if its largest dimension exceeds max_dim, to reduce processing time.
	"""
	if image_array is None:
	return None

	height, width = image_array.shape[:2]
	if max(width, height) > max_dim:
	if width > height:
	new_width = max_dim
	new_height = int(height * (max_dim / width))
	else:
	new_height = max_dim
	new_width = int(width * (max_dim / height))

	return cv2.resize(image_array, (new_width, new_height), interpolation=cv2.INTER_AREA)
	return image_array

	def process_image(input_image):
	"""
	CPU-only processing function for SET detection.
	"""
	if input_image is None:
	return None, "Please upload an image."

	try:
	start_time = time.time()

	# Load models (CPU-only)
	card_detector, shape_detector, shape_model, fill_model = load_models()

	# Optimize image size
	optimized_img = optimize_image_size(input_image)

	# Convert to BGR (OpenCV format)
	if len(optimized_img.shape) == 3 and optimized_img.shape[2] == 4: # RGBA
	optimized_img = cv2.cvtColor(optimized_img, cv2.COLOR_RGBA2BGR)
	elif len(optimized_img.shape) == 3 and optimized_img.shape[2] == 3:
	# RGB to BGR
	optimized_img = cv2.cvtColor(optimized_img, cv2.COLOR_RGB2BGR)

	# Check and fix orientation
	processed_img, was_rotated = verify_and_rotate_image(optimized_img, card_detector)

	# Detect cards
	cards = detect_cards(processed_img, card_detector)
	if not cards:
	return cv2.cvtColor(optimized_img, cv2.COLOR_BGR2RGB), "No cards detected. Please check that it's a SET game board."

	# Classify cards and find sets
	df_cards = classify_cards_on_board(processed_img, card_detector, shape_detector, fill_model, shape_model)
	found_sets = locate_all_sets(df_cards)

	if not found_sets:
	return cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB), "Cards detected, but no valid SETs found!"

	# Draw sets on the image
	annotated = draw_detected_sets(processed_img.copy(), found_sets)

	# Restore original orientation if needed
	final_output = restore_orientation(annotated, was_rotated)

	# Convert back to RGB for display
	final_output_rgb = cv2.cvtColor(final_output, cv2.COLOR_BGR2RGB)

	process_time = time.time() - start_time
	return final_output_rgb, f"Found {len(found_sets)} SET(s) in {process_time:.2f} seconds."

	except Exception as e:
	error_message = f"Error processing image: {str(e)}"
	logger.error(error_message)
	logger.error(traceback.format_exc())
	return input_image, error_message

	# Keep the spaces.GPU decorator for ZeroGPU API but use CPU internally
	@spaces.GPU
	def process_image_wrapper(input_image):
	"""
	Wrapper for process_image that uses the spaces.GPU decorator
	but internally works in CPU-only mode.
	"""
	return process_image(input_image)

	# =============================================================================
	# SIMPLIFIED GRADIO INTERFACE
	# =============================================================================
	with gr.Blocks(title="SET Game Detector") as demo:
	gr.HTML("""
	<div style="text-align: center; margin-bottom: 1rem;">
	<h1 style="margin-bottom: 0.5rem;">🎴 SET Game Detector</h1>
	<p>Upload an image of a SET game board to find all valid sets</p>
	</div>
	""")

	with gr.Row():
	with gr.Column():
	input_image = gr.Image(
	label="Upload SET Board Image",
	type="numpy"
	)

	find_sets_btn = gr.Button(
	"🔎 Find Sets",
	variant="primary"
	)

	with gr.Column():
	output_image = gr.Image(
	label="Detected Sets"
	)
	status = gr.Textbox(
	label="Status",
	value="Upload an image and click 'Find Sets'",
	interactive=False
	)

	# Function bindings
	find_sets_btn.click(
	fn=process_image_wrapper,
	inputs=[input_image],
	outputs=[output_image, status]
	)

	gr.HTML("""
	<div style="text-align: center; margin-top: 1rem; padding: 0.5rem; font-size: 0.8rem;">
	<p>SET Game Detector by <a href="https://github.com/omamitai" target="_blank">omamitai</a> \|
	Gradio version adapted for Hugging Face Spaces</p>
	</div>
	""")

	# =============================================================================
	# MAIN EXECUTION
	# =============================================================================
	if __name__ == "__main__":
	# Launch the app
	demo.queue().launch()