app.py

from __future__ import annotations
import os
import gc
import base64
import io
import time
import shutil
import numpy as np
import torch
import cv2
import ezdxf
from ezdxf.addons.text2path import make_paths_from_str
from ezdxf import path
from ezdxf.addons import text2path
from ezdxf.enums import TextEntityAlignment
from ezdxf.fonts.fonts import FontFace, get_font_face
import gradio as gr
from PIL import Image, ImageEnhance
from pathlib import Path
from typing import List, Union
from ultralytics import YOLOWorld, YOLO
from ultralytics.engine.results import Results
from ultralytics.utils.plotting import save_one_box
from transformers import AutoModelForImageSegmentation
from torchvision import transforms
from scalingtestupdated import calculate_scaling_factor
from shapely.geometry import Polygon, Point, MultiPolygon
from scipy.interpolate import splprep, splev
from scipy.ndimage import gaussian_filter1d
from u2net import U2NETP

# ---------------------
# Create a cache folder for models
# ---------------------
CACHE_DIR = os.path.join(os.path.dirname(__file__), ".cache")
os.makedirs(CACHE_DIR, exist_ok=True)

# ---------------------
# Custom Exceptions
# ---------------------
class DrawerNotDetectedError(Exception):
    """Raised when the drawer cannot be detected in the image"""
    pass

class ReferenceBoxNotDetectedError(Exception):
    """Raised when the Reference coin cannot be detected in the image"""
    pass

class BoundaryOverlapError(Exception):
    """The specified boundary dimensions are too small and overlap with the inner contours.Please provide larger value for boundary length and width."""
    pass

class TextOverlapError(Exception):
    """Raised when the text overlaps with the inner contours (with a margin of 0.75).Please provide larger value for boundary length and width."""
    pass

class FingerCutOverlapError(Exception):
    """There was an overlap with fingercuts... Please try again to generate dxf."""
    pass
# ---------------------
# Global Model Initialization with caching and print statements (Original code preserved)
# ---------------------
print("Loading YOLOWorld model...")
start_time = time.time()
yolo_model_path = os.path.join(CACHE_DIR, "yolov8x-worldv2.pt")
if not os.path.exists(yolo_model_path):
    print("Caching YOLOWorld model to", yolo_model_path)
    shutil.copy("yolov8x-worldv2.pt", yolo_model_path)
drawer_detector_global = YOLOWorld(yolo_model_path)
drawer_detector_global.set_classes(["box"])
print("YOLOWorld model loaded in {:.2f} seconds".format(time.time() - start_time))

print("Loading YOLO reference model...")
start_time = time.time()
reference_model_path = os.path.join(CACHE_DIR, "best_coin.pt")
if not os.path.exists(reference_model_path):
    print("Caching YOLO reference model to", reference_model_path)
    shutil.copy("best_coin.pt", reference_model_path)
reference_detector_global = YOLO(reference_model_path)
print("YOLO reference model loaded in {:.2f} seconds".format(time.time() - start_time))

print("Loading U²-Net model for reference background removal (U2NETP)...")
start_time = time.time()
u2net_model_path = os.path.join(CACHE_DIR, "u2netp.pth")
if not os.path.exists(u2net_model_path):
    print("Caching U²-Net model to", u2net_model_path)
    shutil.copy("u2netp.pth", u2net_model_path)
u2net_global = U2NETP(3, 1)
u2net_global.load_state_dict(torch.load(u2net_model_path, map_location="cpu"))
device = "cpu"
u2net_global.to(device)
u2net_global.eval()
print("U²-Net model loaded in {:.2f} seconds".format(time.time() - start_time))

print("Loading BiRefNet model...")
start_time = time.time()
birefnet_global = AutoModelForImageSegmentation.from_pretrained(
    "zhengpeng7/BiRefNet", trust_remote_code=True, cache_dir=CACHE_DIR
)
torch.set_float32_matmul_precision("high")
birefnet_global.to(device)
birefnet_global.eval()
print("BiRefNet model loaded in {:.2f} seconds".format(time.time() - start_time))

# Define transform for BiRefNet
transform_image_global = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])

# ---------------------
# Helper Functions (Preserved as is)
# ---------------------
def unload_and_reload_models():
    global drawer_detector_global, reference_detector_global, birefnet_global, u2net_global
    print("Reloading models...")
    start_time = time.time()
    del drawer_detector_global, reference_detector_global, birefnet_global, u2net_global
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    gc.collect()
    new_drawer_detector = YOLOWorld(os.path.join(CACHE_DIR, "yolov8x-worldv2.pt"))
    new_drawer_detector.set_classes(["box"])
    new_reference_detector = YOLO(os.path.join(CACHE_DIR, "best_coin.pt"))
    new_birefnet = AutoModelForImageSegmentation.from_pretrained(
        "zhengpeng7/BiRefNet", trust_remote_code=True, cache_dir=CACHE_DIR
    )
    new_birefnet.to(device)
    new_birefnet.eval()
    new_u2net = U2NETP(3, 1)
    new_u2net.load_state_dict(torch.load(os.path.join(CACHE_DIR, "u2netp.pth"), map_location="cpu"))
    new_u2net.to(device)
    new_u2net.eval()
    drawer_detector_global = new_drawer_detector
    reference_detector_global = new_reference_detector
    birefnet_global = new_birefnet
    u2net_global = new_u2net
    print("Models reloaded in {:.2f} seconds".format(time.time() - start_time))

# ---------------------
# Helper Function: resize_img (defined once)
# ---------------------
def resize_img(img: np.ndarray, resize_dim):
    return np.array(Image.fromarray(img).resize(resize_dim))

# ---------------------
# Other Helper Functions for Detection & Processing
# ---------------------
def yolo_detect(image: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor]) -> np.ndarray:
    t = time.time()
    results: List[Results] = drawer_detector_global.predict(image)
    if not results or len(results) == 0 or len(results[0].boxes) == 0:
        raise DrawerNotDetectedError("Drawer not detected in the image.")
    print("Drawer detection completed in {:.2f} seconds".format(time.time() - t))
    return save_one_box(results[0].cpu().boxes.xyxy, im=results[0].orig_img, save=False)

def detect_reference_square(img: np.ndarray):
    t = time.time()
    res = reference_detector_global.predict(img, conf=0.35)
    if not res or len(res) == 0 or len(res[0].boxes) == 0:
        raise ReferenceBoxNotDetectedError("Reference Coin not detected in the image.")
    print("Reference coin detection completed in {:.2f} seconds".format(time.time() - t))
    return (
        save_one_box(res[0].cpu().boxes.xyxy, res[0].orig_img, save=False),
        res[0].cpu().boxes.xyxy[0]
    )

# Use U2NETP for reference background removal.
def remove_bg_u2netp(image: np.ndarray) -> np.ndarray:
    t = time.time()
    image_pil = Image.fromarray(image)
    transform_u2netp = transforms.Compose([
        transforms.Resize((320, 320)),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    ])
    input_tensor = transform_u2netp(image_pil).unsqueeze(0).to("cpu")
    with torch.no_grad():
        outputs = u2net_global(input_tensor)
    pred = outputs[0]
    pred = (pred - pred.min()) / (pred.max() - pred.min() + 1e-8)
    pred_np = pred.squeeze().cpu().numpy()
    pred_np = cv2.resize(pred_np, (image_pil.width, image_pil.height))
    pred_np = (pred_np * 255).astype(np.uint8)
    print("U2NETP background removal completed in {:.2f} seconds".format(time.time() - t))
    return pred_np

# Use BiRefNet for main object background removal.
def remove_bg(image: np.ndarray) -> np.ndarray:
    t = time.time()
    image_pil = Image.fromarray(image)
    input_images = transform_image_global(image_pil).unsqueeze(0).to("cpu")
    with torch.no_grad():
        preds = birefnet_global(input_images)[-1].sigmoid().cpu()
    pred = preds[0].squeeze()
    pred_pil = transforms.ToPILImage()(pred)
    scale_ratio = 1024 / max(image_pil.size)
    scaled_size = (int(image_pil.size[0] * scale_ratio), int(image_pil.size[1] * scale_ratio))
    result = np.array(pred_pil.resize(scaled_size))
    print("BiRefNet background removal completed in {:.2f} seconds".format(time.time() - t))
    return result

def make_square(img: np.ndarray):
    height, width = img.shape[:2]
    max_dim = max(height, width)
    pad_height = (max_dim - height) // 2
    pad_width = (max_dim - width) // 2
    pad_height_extra = max_dim - height - 2 * pad_height
    pad_width_extra = max_dim - width - 2 * pad_width
    if len(img.shape) == 3:
        padded = np.pad(img, ((pad_height, pad_height + pad_height_extra),
                              (pad_width, pad_width + pad_width_extra),
                              (0, 0)), mode="edge")
    else:
        padded = np.pad(img, ((pad_height, pad_height + pad_height_extra),
                              (pad_width, pad_width + pad_width_extra)), mode="edge")
    return padded

def shrink_bbox(image: np.ndarray, shrink_factor: float):
    height, width = image.shape[:2]
    center_x, center_y = width // 2, height // 2
    new_width = int(width * shrink_factor)
    new_height = int(height * shrink_factor)
    x1 = max(center_x - new_width // 2, 0)
    y1 = max(center_y - new_height // 2, 0)
    x2 = min(center_x + new_width // 2, width)
    y2 = min(center_y + new_height // 2, height)
    return image[y1:y2, x1:x2]

def exclude_scaling_box(image: np.ndarray, bbox: np.ndarray, orig_size: tuple, processed_size: tuple, expansion_factor: float = 1.2) -> np.ndarray:
    x_min, y_min, x_max, y_max = map(int, bbox)
    scale_x = processed_size[1] / orig_size[1]
    scale_y = processed_size[0] / orig_size[0]
    x_min = int(x_min * scale_x)
    x_max = int(x_max * scale_x)
    y_min = int(y_min * scale_y)
    y_max = int(y_max * scale_y)
    box_width = x_max - x_min
    box_height = y_max - y_min
    expanded_x_min = max(0, int(x_min - (expansion_factor - 1) * box_width / 2))
    expanded_x_max = min(image.shape[1], int(x_max + (expansion_factor - 1) * box_width / 2))
    expanded_y_min = max(0, int(y_min - (expansion_factor - 1) * box_height / 2))
    expanded_y_max = min(image.shape[0], int(y_max + (expansion_factor - 1) * box_height / 2))
    image[expanded_y_min:expanded_y_max, expanded_x_min:expanded_x_max] = 0
    return image

import logging
import time
import signal
import numpy as np
import cv2
from scipy.interpolate import splprep, splev
from scipy.ndimage import gaussian_filter1d
from shapely.geometry import Point, Polygon
import random
import ezdxf
import functools

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Custom TimeoutError class
class TimeoutReachedError(Exception):
    pass

# Timeout context manager
class TimeoutContext:
    def __init__(self, seconds):
        self.seconds = seconds
        self.original_handler = None
        
    def timeout_handler(self, signum, frame):
        raise TimeoutReachedError(f"Function timed out after {self.seconds} seconds")
        
    def __enter__(self):
        if hasattr(signal, 'SIGALRM'):  # Unix-like systems
            self.original_handler = signal.getsignal(signal.SIGALRM)
            signal.signal(signal.SIGALRM, self.timeout_handler)
            signal.alarm(self.seconds)
        self.start_time = time.time()
        return self
        
    def __exit__(self, exc_type, exc_val, exc_tb):
        if hasattr(signal, 'SIGALRM'):  # Unix-like systems
            signal.alarm(0)
            signal.signal(signal.SIGALRM, self.original_handler)
        if exc_type is TimeoutReachedError:
            logger.warning(f"Timeout reached: {exc_val}")
            return True  # Suppress the exception
        return False

def resample_contour(contour):
    logger.info(f"Starting resample_contour with contour of shape {contour.shape}")
    
    num_points = 1000
    smoothing_factor = 5
    spline_degree = 3
    
    if len(contour) < spline_degree + 1:
        error_msg = f"Contour must have at least {spline_degree + 1} points, but has {len(contour)} points."
        logger.error(error_msg)
        raise ValueError(error_msg)
        
    try:
        contour = contour[:, 0, :]
        logger.debug(f"Reshaped contour to shape {contour.shape}")
        
        tck, _ = splprep([contour[:, 0], contour[:, 1]], s=smoothing_factor)
        logger.debug("Generated spline parameters")
        
        u = np.linspace(0, 1, num_points)
        resampled_points = splev(u, tck)
        logger.debug(f"Resampled to {num_points} points")
        
        smoothed_x = gaussian_filter1d(resampled_points[0], sigma=1)
        smoothed_y = gaussian_filter1d(resampled_points[1], sigma=1)
        
        result = np.array([smoothed_x, smoothed_y]).T
        logger.info(f"Completed resample_contour with result shape {result.shape}")
        return result
    except Exception as e:
        logger.error(f"Error in resample_contour: {e}")
        raise

def extract_outlines(binary_image: np.ndarray) -> (np.ndarray, list):
    logger.info(f"Starting extract_outlines with image shape {binary_image.shape}")
    
    try:
        contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        logger.debug(f"Found {len(contours)} contours")
        
        outline_image = np.zeros_like(binary_image)
        cv2.drawContours(outline_image, contours, -1, (255), thickness=2)
        
        result_image = cv2.bitwise_not(outline_image)
        logger.info(f"Completed extract_outlines with {len(contours)} contours")
        return result_image, contours
    except Exception as e:
        logger.error(f"Error in extract_outlines: {e}")
        raise

def union_tool_and_circle(tool_polygon: Polygon, center_inch, circle_diameter=1.0):
    logger.info(f"Starting union_tool_and_circle with center at {center_inch}")
    
    try:
        radius = circle_diameter / 2.0
        circle_poly = Point(center_inch).buffer(radius, resolution=64)
        logger.debug(f"Created circle with radius {radius} at {center_inch}")
        
        union_poly = tool_polygon.union(circle_poly)
        logger.info(f"Completed union_tool_and_circle, result area: {union_poly.area}")
        return union_poly
    except Exception as e:
        logger.error(f"Error in union_tool_and_circle: {e}")
        raise

def build_tool_polygon(points_inch):
    logger.info(f"Starting build_tool_polygon with {len(points_inch)} points")
    
    try:
        polygon = Polygon(points_inch)
        logger.info(f"Completed build_tool_polygon, polygon area: {polygon.area}")
        return polygon
    except Exception as e:
        logger.error(f"Error in build_tool_polygon: {e}")
        raise

def polygon_to_exterior_coords(poly):
    logger.info(f"Starting polygon_to_exterior_coords with polygon type {poly.geom_type}")
    
    try:
        # Handle GeometryCollection case specifically
        if poly.geom_type == "GeometryCollection":
            logger.warning("Converting GeometryCollection to Polygon")
            # Find the largest geometry in the collection that has an exterior
            largest_area = 0
            largest_geom = None
            for geom in poly.geoms:
                if hasattr(geom, 'area') and geom.area > largest_area:
                    if hasattr(geom, 'exterior') or geom.geom_type == "MultiPolygon":
                        largest_area = geom.area
                        largest_geom = geom
            
            if largest_geom is None:
                logger.warning("No valid geometry found in GeometryCollection")
                return []
                
            poly = largest_geom
        
        if poly.geom_type == "MultiPolygon":
            logger.debug("Converting MultiPolygon to single Polygon")
            biggest = max(poly.geoms, key=lambda g: g.area)
            poly = biggest
            
        if not hasattr(poly, 'exterior') or poly.exterior is None:
            logger.warning("Polygon has no exterior")
            return []
            
        coords = list(poly.exterior.coords)
        logger.info(f"Completed polygon_to_exterior_coords with {len(coords)} coordinates")
        return coords
    except Exception as e:
        logger.error(f"Error in polygon_to_exterior_coords: {e}")
        # Return empty list as fallback
        return []
    
def place_finger_cut_adjusted(
    tool_polygon: Polygon,
    points_inch: list,
    existing_centers: list,
    all_polygons: list,
    circle_diameter: float = 1.0,
    min_gap: float = 0.5,
    max_attempts: int = 100
) -> (Polygon, tuple):
    logger.info(f"Starting place_finger_cut_adjusted with {len(points_inch)} points")
    
    # Define fallback function for timeout case
    def fallback_solution():
        logger.warning("Using fallback approach for finger cut placement")
        candidate_center = points_inch[len(points_inch) // 2]
        radius = circle_diameter / 2.0
        candidate_circle = Point(candidate_center).buffer(radius, resolution=64)
        
        try:
            union_poly = tool_polygon.union(candidate_circle)
        except Exception as e:
            logger.warning(f"Fallback union failed, using buffer trick: {e}")
            union_poly = tool_polygon.buffer(0).union(candidate_circle.buffer(0))
            
        existing_centers.append(candidate_center)
        logger.info(f"Used fallback finger cut at center {candidate_center}")
        return union_poly, candidate_center

    needed_center_distance = circle_diameter + min_gap
    radius = circle_diameter / 2.0
    
    # Limit points to prevent timeout - use a subset for efficient processing
    if len(points_inch) > 100:
        logger.info(f"Limiting points from {len(points_inch)} to 100 for efficiency")
        step = len(points_inch) // 100
        points_inch = points_inch[::step]
    
    # Randomize candidate points order
    indices = list(range(len(points_inch)))
    random.shuffle(indices)
    logger.debug(f"Shuffled {len(indices)} point indices")

    # Use a non-blocking timeout approach with explicit time checks
    start_time = time.time()
    timeout_seconds = 5
    attempts = 0
    
    try:
        while attempts < max_attempts:
            # Check if we're approaching the timeout
            current_time = time.time()
            if current_time - start_time > timeout_seconds - 0.1:  # Leave 0.1s margin
                logger.warning(f"Approaching timeout after {attempts} attempts")
                return fallback_solution()
                
            # Process a batch of points to improve efficiency
            for i in indices:
                # Check timeout frequently
                if time.time() - start_time > timeout_seconds - 0.05:
                    logger.warning("Timeout during point processing")
                    return fallback_solution()
                    
                cx, cy = points_inch[i]
                # Reduce the number of adjustments to speed up processing
                for dx, dy in [(0,0), (-0.2,0), (0.2,0), (0,0.2), (0,-0.2)]:
                    candidate_center = (cx + dx, cy + dy)
                    
                    # Quick check for existing centers distance
                    if any(np.hypot(candidate_center[0] - ex, candidate_center[1] - ey) < needed_center_distance
                           for ex, ey in existing_centers):
                        continue

                    # Create candidate circle
                    candidate_circle = Point(candidate_center).buffer(radius, resolution=32)  # Reduced resolution
                    
                    # Quick geometric checks
                    if tool_polygon.contains(candidate_circle) or not candidate_circle.intersects(tool_polygon):
                        continue
                    
                    # Check intersection area - use simplified geometry for speed
                    try:
                        inter_area = candidate_circle.intersection(tool_polygon).area
                        if inter_area <= 0 or inter_area >= candidate_circle.area:
                            continue
                    except Exception:
                        continue

                    # Quick distance check to other polygons
                    too_close = False
                    for other_poly in all_polygons:
                        if other_poly.equals(tool_polygon):
                            continue
                        if other_poly.distance(candidate_circle) < min_gap:
                            too_close = True
                            break
                    if too_close:
                        continue
                    
                    # Attempt the union
                    try:
                        union_poly = tool_polygon.union(candidate_circle)
                        # Check if we got a multi-polygon when we don't want one
                        if union_poly.geom_type == "MultiPolygon" and len(union_poly.geoms) > 1:
                            continue
                        # Check if the union actually changed anything
                        if union_poly.equals(tool_polygon):
                            continue
                    except Exception:
                        continue
                    
                    # We found a valid candidate
                    existing_centers.append(candidate_center)
                    logger.info(f"Completed place_finger_cut_adjusted successfully at center {candidate_center}")
                    return union_poly, candidate_center
            
            attempts += 1
            # If we've made several attempts and are running out of time, use fallback
            if attempts >= max_attempts // 2 and (time.time() - start_time) > timeout_seconds * 0.8:
                logger.warning(f"Approaching timeout after {attempts} attempts")
                return fallback_solution()
                
            logger.debug(f"Completed attempt {attempts}/{max_attempts}")
            
        # If we reached max attempts without finding a solution
        logger.warning(f"No suitable finger cut found after {max_attempts} attempts, using fallback")
        return fallback_solution()
            
    except Exception as e:
        logger.error(f"Error in place_finger_cut_adjusted: {e}")
        return fallback_solution()

def save_dxf_spline(offset_value,inflated_contours, scaling_factor, height, finger_clearance=False):
    logger.info(f"Starting save_dxf_spline with {len(inflated_contours)} contours")
    
    degree = 3
    closed = True
    
    try:
        doc = ezdxf.new(units=0)
        doc.units = ezdxf.units.IN
        doc.header["$INSUNITS"] = ezdxf.units.IN
        msp = doc.modelspace()
        
        finger_cut_centers = []
        final_polygons_inch = []
        
        for idx, contour in enumerate(inflated_contours):
            logger.debug(f"Processing contour {idx+1}/{len(inflated_contours)}")
            
            try:
                resampled_contour = resample_contour(contour)
                points_inch = [(x * scaling_factor, (height - y) * scaling_factor) for x, y in resampled_contour]
                
                if len(points_inch) < 3:
                    logger.warning(f"Skipping contour {idx}: insufficient points ({len(points_inch)})")
                    continue
                    
                if np.linalg.norm(np.array(points_inch[0]) - np.array(points_inch[-1])) > 1e-2:
                    logger.debug("Closing contour by adding first point to end")
                    points_inch.append(points_inch[0])
                    
                tool_polygon = build_tool_polygon(points_inch)
                
                if finger_clearance:
                    logger.debug("Applying finger clearance")
                    try:
                        # Use a hard 5-second timeout for the entire finger cut operation
                        start_time = time.time()
                        union_poly, center = place_finger_cut_adjusted(
                            tool_polygon, 
                            points_inch, 
                            finger_cut_centers, 
                            final_polygons_inch, 
                            circle_diameter=1.0, 
                            min_gap=(0.5+offset_value), 
                            max_attempts=100
                        )
                        
                        # Check if we exceeded the timeout anyway
                        if time.time() - start_time > 5:
                            logger.warning(f"Finger cut took too long for contour {idx} ({time.time() - start_time:.2f}s)")
                        
                        if union_poly is not None:
                            tool_polygon = union_poly
                            logger.debug(f"Applied finger cut at {center}")
                    except Exception as e:
                        logger.warning(f"Finger cut failed for contour {idx}: {e}, using original polygon")
                        
                exterior_coords = polygon_to_exterior_coords(tool_polygon)
                
                if len(exterior_coords) < 3:
                    logger.warning(f"Skipping contour {idx}: insufficient exterior points ({len(exterior_coords)})")
                    continue
                for existing_poly in final_polygons_inch:
                    if tool_polygon.intersects(existing_poly):
                        # Check if the intersection is more than just touching points
                        intersection = tool_polygon.intersection(existing_poly)
                        # If the intersection has ANY area (not just points touching)
                        if intersection.area > 0:  # Zero tolerance for overlap
                            logger.error(f"Polygon {idx} overlaps with an existing polygon")
                            raise FingerCutOverlapError("There was an overlap with fingercuts... Please try again to generate dxf.")
                    
                msp.add_spline(exterior_coords, degree=degree, dxfattribs={"layer": "TOOLS"})
                final_polygons_inch.append(tool_polygon)
                logger.debug(f"Added spline for contour {idx}")
                
            except ValueError as e:
                logger.warning(f"Skipping contour {idx}: {e}")
                
        logger.info(f"Completed save_dxf_spline with {len(final_polygons_inch)} successful polygons")
        return doc, final_polygons_inch
        
    except Exception as e:
        logger.error(f"Error in save_dxf_spline: {e}")
        raise

def add_rectangular_boundary(doc, polygons_inch, boundary_length, boundary_width, offset_unit, annotation_text="", image_height_in=None, image_width_in=None):
    msp = doc.modelspace()
    # Convert from mm if necessary
    if offset_unit.lower() == "mm":
        if boundary_length < 50:
            boundary_length = boundary_length * 25.4
        if boundary_width < 50:
            boundary_width = boundary_width * 25.4
        boundary_length_in = boundary_length / 25.4
        boundary_width_in = boundary_width / 25.4
    else:
        boundary_length_in = boundary_length
        boundary_width_in = boundary_width

    # Compute bounding box of inner contours
    min_x = float("inf")
    min_y = float("inf")
    max_x = -float("inf")
    max_y = -float("inf")
    for poly in polygons_inch:
        b = poly.bounds
        min_x = min(min_x, b[0])
        min_y = min(min_y, b[1])
        max_x = max(max_x, b[2])
        max_y = max(max_y, b[3])
    if min_x == float("inf"):
        print("No tool polygons found, skipping boundary.")
        return None

    # Compute inner bounding box dimensions
    inner_width = max_x - min_x
    inner_length = max_y - min_y

    # Set clearance margins
    clearance_side = 0.25  # left/right clearance
    clearance_tb = 0.25    # top/bottom clearance
    if annotation_text.strip():
        clearance_tb = 0.75

    # Calculate center of inner contours
    center_x = (min_x + max_x) / 2
    center_y = (min_y + max_y) / 2

    # Draw rectangle centered at (center_x, center_y)
    left = center_x - boundary_width_in / 2
    right = center_x + boundary_width_in / 2
    bottom = center_y - boundary_length_in / 2
    top = center_y + boundary_length_in / 2

    rect_coords = [(left, bottom), (right, bottom), (right, top), (left, top), (left, bottom)]
    from shapely.geometry import Polygon 
    boundary_polygon = Polygon(rect_coords)
    msp.add_lwpolyline(rect_coords, close=True, dxfattribs={"layer": "BOUNDARY"})
    
    text_top = boundary_polygon.bounds[1] + 1
    too_small = boundary_width_in < inner_width + 2 * clearance_side or boundary_length_in < inner_length + 2 * clearance_tb
    if too_small:
        raise BoundaryOverlapError("Error: The specified boundary dimensions are too small and overlap with the inner contours. Please provide larger value for boundary length and width.")
    if annotation_text.strip() and text_top > min_y - 1:
        raise TextOverlapError("Error: The text is too close to the inner contours. Please provide larger value for boundary length and width.")
    return boundary_polygon

def draw_polygons_inch(polygons_inch, image_rgb, scaling_factor, image_height, color=(0,0,255), thickness=2):
    for poly in polygons_inch:
        if poly.geom_type == "MultiPolygon":
            for subpoly in poly.geoms:
                draw_single_polygon(subpoly, image_rgb, scaling_factor, image_height, color, thickness)
        else:
            draw_single_polygon(poly, image_rgb, scaling_factor, image_height, color, thickness)

def draw_single_polygon(poly, image_rgb, scaling_factor, image_height, color=(0,0,255), thickness=2):
    ext = list(poly.exterior.coords)
    if len(ext) < 3:
        return
    pts_px = []
    for (x_in, y_in) in ext:
        px = int(x_in / scaling_factor)
        py = int(image_height - (y_in / scaling_factor))
        pts_px.append([px, py])
    pts_px = np.array(pts_px, dtype=np.int32)
    cv2.polylines(image_rgb, [pts_px], isClosed=True, color=color, thickness=thickness, lineType=cv2.LINE_AA)

import numpy as np
import cv2
from shapely.geometry import Polygon

def draw_and_pad(polygons_inch,
                               scaling_factor,      # inches per pixel
                               boundary_polygon=None,
                               max_res=1024,
                               simplify_tol_px=1.0,
                               padding_px=50,
                               color=(0,0,255),
                               thickness=2):
    # 1) Simplify & collect raw coords in inches
    all_x, all_y = [], []
    simple_polys = []
    for poly in polygons_inch:
        tol_in = simplify_tol_px * scaling_factor / max_res
        simp = poly.simplify(tolerance=tol_in, preserve_topology=True)
        coords = np.array(simp.exterior.coords)  # (N,2) in inches
        all_x.extend(coords[:,0])
        all_y.extend(coords[:,1])
        simple_polys.append(coords)

    # 2) Compute full‑res pixel extents
    min_x_in, max_x_in = min(all_x), max(all_x)
    min_y_in, max_y_in = min(all_y), max(all_y)
    w_in = (max_x_in - min_x_in) if boundary_polygon is None else (max_x_in - min_x_in)
    h_in = (max_y_in - min_y_in) if boundary_polygon is None else (max_y_in - min_y_in)
    full_w_px = np.ceil(w_in / scaling_factor)
    full_h_px = np.ceil(h_in / scaling_factor)

    # 3) Compute preview scale ≤1 so dims ≤ max_res
    scale = min(max_res / full_w_px, max_res / full_h_px, 1.0)

    # 4) Compute preview dims & allocate _fully‐padded_ canvas
    W = int(np.ceil(full_w_px * scale))
    H = int(np.ceil(full_h_px * scale))
    PW, PH = W + 2*padding_px, H + 2*padding_px
    canvas = 255 * np.ones((PH, PW, 3), dtype=np.uint8)

    # Precompute offsets (in preview px) of the “world origin”
    off_x = int(np.floor(min_x_in / scaling_factor * scale))
    off_y = int(np.floor(min_y_in / scaling_factor * scale))

    # 5) Draw each polygon, now fully inside the padded canvas
    for coords in simple_polys:
        # inch→preview‐px transform
        pts = ((coords / scaling_factor) * scale).round().astype(int)
        # shift by both the minimum and the padding:
        pts[:,0] = pts[:,0] - off_x + padding_px
        pts[:,1] = pts[:,1] - off_y + padding_px
        # flip Y into image coords
        pts[:,1] = PH - 1 - pts[:,1]
        cv2.polylines(canvas,
                      [pts],
                      isClosed=True,
                      color=color,
                      thickness=thickness,
                      lineType=cv2.LINE_AA)

    return canvas, scale, off_y, padding_px, PH



# ---------------------
# Main Predict Function with Finger Cut Clearance, Boundary Box, Annotation and Sharpness Enhancement
# ---------------------
def predict(
    image: Union[str, bytes, np.ndarray],
    offset_value: float,
    offset_unit: str,         # "mm" or "inches"
    finger_clearance: str,    # "Yes" or "No"
    add_boundary: str,        # "Yes" or "No"
    boundary_length: float,
    boundary_width: float,
    annotation_text: str
):
    overall_start = time.time()
    # Convert image to NumPy array if needed
    if isinstance(image, str):
        if os.path.exists(image):
            image = np.array(Image.open(image).convert("RGB"))
        else:
            try:
                image = np.array(Image.open(io.BytesIO(base64.b64decode(image))).convert("RGB"))
            except Exception:
                raise ValueError("Invalid base64 image data")

    # Apply brightness and sharpness enhancement
    if isinstance(image, np.ndarray):
        pil_image = Image.fromarray(image)
        enhanced_image = ImageEnhance.Sharpness(pil_image).enhance(1.5)
        image = np.array(enhanced_image)

    # ---------------------
    # 1) Detect the drawer with YOLOWorld (or use original image if not detected)
    # ---------------------
    drawer_detected = True
    try:
        t = time.time()
        drawer_img = yolo_detect(image)
        print("Drawer detection completed in {:.2f} seconds".format(time.time() - t))
    except DrawerNotDetectedError as e:
        print(f"Drawer not detected: {e}, using original image.")
        drawer_detected = False
        drawer_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

    # Process the image (either cropped drawer or original)
    t = time.time()
    if drawer_detected:
        # For detected drawers: shrink and square
        shrunked_img = make_square(shrink_bbox(drawer_img, 0.95))
    else:
        # For non-drawer images: keep original dimensions
        shrunked_img = drawer_img  # Already in BGR format from above
    del drawer_img
    gc.collect()
    print("Image processing completed in {:.2f} seconds".format(time.time() - t))

    # ---------------------
    # 2) Detect the reference box with YOLO (now works on either cropped or original image)
    # ---------------------
    try:
        t = time.time()
        reference_obj_img, scaling_box_coords = detect_reference_square(shrunked_img)
        print("Reference coin detection completed in {:.2f} seconds".format(time.time() - t))
    except ReferenceBoxNotDetectedError as e:
        return None, None, None, None, f"Error: {str(e)}"

    # ---------------------
    # 3) Remove background of the reference box to compute scaling factor
    # ---------------------
    t = time.time()
    reference_obj_img = make_square(reference_obj_img)
    reference_square_mask = remove_bg_u2netp(reference_obj_img)
    reference_square_mask= resize_img(reference_square_mask,(reference_obj_img.shape[1],reference_obj_img.shape[0]))
    print("Reference image processing completed in {:.2f} seconds".format(time.time() - t))

    t = time.time()
    try:
        cv2.imwrite("mask.jpg", cv2.cvtColor(reference_obj_img, cv2.COLOR_RGB2GRAY))
        scaling_factor = calculate_scaling_factor(
            target_image=reference_square_mask,
            reference_obj_size_mm=0.955,
            feature_detector="ORB",
        )
    except ZeroDivisionError:
        scaling_factor = None
        print("Error calculating scaling factor: Division by zero")
    except Exception as e:
        scaling_factor = None
        print(f"Error calculating scaling factor: {e}")

    if scaling_factor is None or scaling_factor == 0:
        scaling_factor = 0.7
        print("Using default scaling factor of 0.7 due to calculation error")
    gc.collect()
    print("Scaling factor determined: {}".format(scaling_factor))

    # ---------------------
    # 4) Optional boundary dimension checks (now without size limits)
    # ---------------------
    if add_boundary.lower() == "yes":
        if offset_unit.lower() == "mm":
            if boundary_length < 50:
                boundary_length = boundary_length * 25.4
            if boundary_width < 50:
                boundary_width = boundary_width * 25.4
            boundary_length_in = boundary_length / 25.4
            boundary_width_in = boundary_width / 25.4
        else:
            boundary_length_in = boundary_length
            boundary_width_in = boundary_width
            
    # ---------------------
    # 5) Remove background from the shrunked drawer image (main objects)
    # ---------------------
    if offset_unit.lower() == "mm":
        if offset_value < 1:
            offset_value = offset_value * 25.4
        offset_inches = offset_value / 25.4
        if offset_value==0:
            offset_value = offset_value * 25.4
            offset_inches = offset_value / 25.4
            offset_inches+=0.005
    else:
        offset_inches = offset_value
        if offset_inches==0:
            offset_inches+=0.005

    t = time.time()
    orig_size = shrunked_img.shape[:2]
    objects_mask = remove_bg(shrunked_img)
    processed_size = objects_mask.shape[:2]

    objects_mask = exclude_scaling_box(objects_mask, scaling_box_coords, orig_size, processed_size, expansion_factor=1.2)
    objects_mask = resize_img(objects_mask, (shrunked_img.shape[1], shrunked_img.shape[0]))
    del scaling_box_coords
    gc.collect()
    print("Object masking completed in {:.2f} seconds".format(time.time() - t))

    # Dilate mask by offset_pixels
    t = time.time()
    offset_pixels = (offset_inches / scaling_factor) * 2 + 1 if scaling_factor != 0 else 1
    dilated_mask = cv2.dilate(objects_mask, np.ones((int(offset_pixels), int(offset_pixels)), np.uint8))
    del objects_mask
    gc.collect()
    print("Mask dilation completed in {:.2f} seconds".format(time.time() - t))

    Image.fromarray(dilated_mask).save("./outputs/scaled_mask_new.jpg")

    # ---------------------
    # 6) Extract outlines from the mask and convert them to DXF splines
    # ---------------------
    t = time.time()
    outlines, contours = extract_outlines(dilated_mask)
    print("Outline extraction completed in {:.2f} seconds".format(time.time() - t))

    output_img = shrunked_img.copy()
    del shrunked_img
    gc.collect()

    t = time.time()
    use_finger_clearance = True if finger_clearance.lower() == "yes" else False
    doc, final_polygons_inch = save_dxf_spline(
        offset_inches,contours, scaling_factor, processed_size[0], finger_clearance=use_finger_clearance
    )
    del contours
    gc.collect()
    print("DXF generation completed in {:.2f} seconds".format(time.time() - t))

    # ---------------------
    # Compute bounding box of inner tool contours BEFORE adding optional boundary
    # ---------------------
    inner_min_x = float("inf")
    inner_min_y = float("inf")
    inner_max_x = -float("inf")
    inner_max_y = -float("inf")
    for poly in final_polygons_inch:
        b = poly.bounds
        inner_min_x = min(inner_min_x, b[0])
        inner_min_y = min(inner_min_y, b[1])
        inner_max_x = max(inner_max_x, b[2])
        inner_max_y = max(inner_max_y, b[3])

    # ---------------------
    # 7) Add optional rectangular boundary
    # ---------------------
    boundary_polygon = None
    if add_boundary.lower() == "yes":
        boundary_polygon = add_rectangular_boundary(
            doc,
            final_polygons_inch,
            boundary_length,
            boundary_width,
            offset_unit,
            annotation_text,
            image_height_in=output_img.shape[0] * scaling_factor,
            image_width_in=output_img.shape[1] * scaling_factor
        )
        if boundary_polygon is not None:
            final_polygons_inch.append(boundary_polygon)

    # ---------------------
    # 8) Add annotation text (if provided) in the DXF
    # ---------------------
    msp = doc.modelspace()
    
    if annotation_text.strip():
        if boundary_polygon is not None:
            text_height_dxf = 0.75
            text_y_dxf = boundary_polygon.bounds[1] + 0.25
            font = get_font_face("Arial")
            
            # Create text paths first
            paths = text2path.make_paths_from_str(
                annotation_text.strip().upper(),
                font=font,
                size=text_height_dxf,
                align=TextEntityAlignment.LEFT
            )
            
            # Calculate actual text width from the path's bounds
            text_bbox = path.bbox(paths)
            #text_width = text_bbox[2] - text_bbox[0]  # xmax - xmin
            #text_width = text_bbox.width
            # Calculate center point of inner tool contours
            center_x = (inner_min_x + inner_max_x) / 2.0
            text_width = text_bbox.extmax.x - text_bbox.extmin.x
            # Calculate starting x position for truly centered text
            text_x = center_x - (text_width / 2.0)
            
            # Create a translation matrix
            translation = ezdxf.math.Matrix44.translate(text_x, text_y_dxf, 0)
            # Apply the translation to each path
            translated_paths = [p.transform(translation) for p in paths]
        
            # Render the paths as splines and polylines
            path.render_splines_and_polylines(
                msp, 
                translated_paths, 
                dxfattribs={"layer": "ANNOTATION", "color": 7}
            )

    # Save the DXF
    dxf_filepath = os.path.join("./outputs", "out.dxf")
    doc.saveas(dxf_filepath)

    # ---------------------
    # 9) For the preview images, draw the polygons and place text similarly
    # ---------------------
    #draw_polygons_inch(final_polygons_inch, output_img, scaling_factor, processed_size[0], color=(0, 0, 255), thickness=2)
    for poly in final_polygons_inch:
    # Skip the boundary polygon
        if boundary_polygon is not None and poly == boundary_polygon:
            continue
        draw_single_polygon(poly, output_img, scaling_factor, processed_size[0], color=(0, 0, 255), thickness=2)
    new_outlines,preview_scale, off_y, padding_px, PH= draw_and_pad(final_polygons_inch, scaling_factor,boundary_polygon)

    #draw_polygons_inch(final_polygons_inch, new_outlines, scaling_factor, processed_size[0], color=(0, 0, 255), thickness=2)
    import math
    if annotation_text.strip():
        # Common variables
        font = cv2.FONT_HERSHEY_SIMPLEX
        text = annotation_text.strip().upper()
        canvas_height, canvas_width = new_outlines.shape[:2]
        
        if boundary_polygon is not None:
            # Keep original code for output_img
            text_x_img = int(((inner_min_x + inner_max_x) / 2.0) / scaling_factor)
            text_y_in = boundary_polygon.bounds[1] + 0.25
            text_y_img = int(processed_size[0] - (text_y_in / scaling_factor))
            org = (text_x_img - int(len(annotation_text.strip()) * 6), text_y_img)
            
            # Process for output_img with mask (keeping your original code)
            temp_img = np.zeros_like(output_img)
            cv2.putText(temp_img, text, org, font, 2, (0, 0, 255), 4, cv2.LINE_AA)
            cv2.putText(temp_img, text, org, font, 2, (255, 255, 255), 2, cv2.LINE_AA)
            outline_mask = cv2.cvtColor(temp_img, cv2.COLOR_BGR2GRAY)
            _, outline_mask = cv2.threshold(outline_mask, 1, 255, cv2.THRESH_BINARY)
            output_img[outline_mask > 0] = temp_img[outline_mask > 0]
            
            # For new_outlines - simple, centered text
            def optimal_font_dims(img, font_scale = 1e-3, thickness_scale = 2e-3):
                h, w, _ = img.shape
                font_scale = min(w, h) * font_scale
                thickness = math.ceil(min(w, h) * thickness_scale)
                return font_scale, thickness
            font_scale,thickness = optimal_font_dims(new_outlines)
            (text_width, text_height), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, thickness)
            text_x = (canvas_width - text_width) // 2
            raw_y       = (text_y_in / scaling_factor) * preview_scale
            y1          = raw_y - off_y + padding_px
            text_y_px   = int(round(PH - 1 - y1))
            text_y_px_adjusted = text_y_px - baseline
            # bottom_margin_px = int(0.25 / scaling_factor)
            # font_scale,_ = optimal_font_dims(new_outlines)
            #text_y_outlines = int(canvas_height - (text_y_in + (0.75) / scaling_factor))
            
            # First outline, then inner text
            cv2.putText(new_outlines, text, (text_x, text_y_px_adjusted), font, font_scale, (0, 0, 255), thickness+2, cv2.LINE_AA)
            cv2.putText(new_outlines, text, (text_x, text_y_px_adjusted), font, font_scale, (255, 255, 255), thickness-1, cv2.LINE_AA)
        

    outlines_color = cv2.cvtColor(new_outlines, cv2.COLOR_BGR2RGB)
    print("Total prediction time: {:.2f} seconds".format(time.time() - overall_start))
    return (
        cv2.cvtColor(output_img, cv2.COLOR_BGR2RGB),
        outlines_color,
        dxf_filepath,
        dilated_mask,
        str(scaling_factor)
    )


# ---------------------
# Documentation Strings for Gradio
# ---------------------

QUICK_START = """
## 1. Quick Start Guide: From Photo to DXF Cut File
This application converts a single photograph of tools in a drawer/tray into a ready-to-use DXF file for CNC cutting foam inserts.

1.  **Preparation**: Place your tools in a drawer and include a **reference coin** (assumed to be 0.955 inches / 24.25mm wide, e.g., a US Quarter). Ensure clear, even lighting.
2.  **Upload**: Upload the image.
3.  **Configure**: Adjust the `Offset Value` (clearance around the tool) and select options like `Finger Clearance` and `Boundary`.
4.  **Run**: Click the **"Submit"** button (automatically generated by Gradio).
5.  **Download**: Review the `Outlines` and `Output Image`, then download the final **DXF file** for your cutting machine.
"""

INPUT_EXPLANATION = """
## 2. Expected Inputs

### Image Requirements
*   **Reference Object is Mandatory:** The system *must* detect a specific reference object (default: coin roughly 0.955 inches wide) to correctly calculate the real-world scale (inches/pixel).
*   **Placement:** The image should be taken from directly above the drawer (minimal perspective distortion).
*   **Lighting:** Clear, shadow-free lighting is crucial for object detection and masking.

### Processing Parameters

| Parameter | Purpose | Default Value | Guidance for Non-Tech Users |
| :--- | :--- | :--- | :--- |
| **Offset Value** | The amount of space (clearance) added around the tool profile. This is the buffer to ensure the tool fits easily into the foam cutout. | 0.075 | Increase this value for thicker tools or if you need looser cutouts. |
| **Offset Unit** | Specifies whether the offset value is in millimeters or inches. | inches | Match this to your preferred measurement system. |
| **Add Finger Clearance?** | Adds a small circular cutout to the side of the tool profile to allow easy removal using a finger. | Yes | Recommended for most tools. |
| **Add Rectangular Boundary?**| Defines the exterior rectangular shape of the entire foam insert (the boundary of the drawer). | Yes | Required if you need a rectangular outer boundary for cutting. |
| **Boundary Length/Width** | The actual dimensions of the drawer/tray area for the foam insert. | 50.0 (in the selected unit) | Must be larger than the combined area of all tools plus margins. |
| **Annotation** | Text to engrave/cut into the foam, typically placed at the top of the boundary box. (Max 20 chars) | (Empty) | Use this to label the tray (e.g., "Wrench Set"). |
"""

OUTPUT_EXPLANATION = """
## 3. Expected Outputs

| Output Field | Description | Purpose |
| :--- | :--- | :--- |
| **Output Image** | The original image overlaid with the detected tool outlines (including offset and finger clearance). | Visual confirmation that the detection and sizing are correct relative to the original photo. |
| **Outlines of Objects**| A clean, scaled, white-background image showing only the outlines (blue lines). This visually represents the final geometry exported to the DXF. | Final quality check for shape and spacing before CNC cutting. |
| **DXF file** | The final vector file containing the tool outlines, boundary box, and text annotation (if included). | Ready to upload to CNC machine software (AutoCAD DXF R2000 format). |
| **Mask** | The binary image used internally to generate the tool contours after applying the offset value. | Technical output for debugging segmentation issues. |
| **Scaling Factor**| The calculated real-world scale (inches per pixel) determined by the reference coin. | Confirmation of measurement accuracy. Values close to 0.7 are common for phone photos. |
"""


# ---------------------
# Gradio Interface
# ---------------------
if __name__ == "__main__":
    os.makedirs("./outputs", exist_ok=True)
    
    def gradio_predict(img, offset, offset_unit, finger_clearance, add_boundary, boundary_length, boundary_width, annotation_text):
        try:
            return predict(img, offset, offset_unit, finger_clearance, add_boundary, boundary_length, boundary_width, annotation_text)
        except Exception as e:
            # Handle specific known errors for clearer user feedback
            if isinstance(e, (DrawerNotDetectedError, ReferenceBoxNotDetectedError)):
                gr.Warning(f"Processing Error: {str(e)} Please check image quality and coin placement.")
            elif isinstance(e, (BoundaryOverlapError, TextOverlapError)):
                 gr.Error(f"Boundary/Text Placement Error: {str(e)}")
            else:
                 gr.Error(f"An unexpected error occurred: {str(e)}")
                 
            return None, None, None, None, f"Error: {str(e)}"
        
    with gr.Blocks(title="Tool Cutout DXF Generator") as demo:
        gr.Markdown("<h1 style='text-align: center;'> DXF Generator </h1>")
        gr.Markdown("Convert a photo of your tool drawer into precise CNC-ready vector files.")
        
        # 1. Guidelines & Instructions
        with gr.Accordion(" Tips & User Guide", open=False):
            gr.Markdown(QUICK_START)
            gr.Markdown("---")
            gr.Markdown(INPUT_EXPLANATION)
            gr.Markdown("---")
            gr.Markdown(OUTPUT_EXPLANATION)

        # 2. Main Interface Setup

        with gr.Row():
            with gr.Column():
                gr.Markdown("## Step 1: Upload Image and Configure Parameters")
                input_image = gr.Image(label="Input Image", type="numpy")

            with gr.Column():
                gr.Markdown("## Step 2: Configure Parameters")
                offset_value = gr.Number(label="Offset value for Mask", value=0.075)
                offset_unit = gr.Dropdown(label="Offset Unit", choices=["mm", "inches"], value="inches")
                finger_clearance = gr.Dropdown(label="Add Finger Clearance?", choices=["Yes", "No"], value="Yes")
                add_boundary = gr.Dropdown(label="Add Rectangular Boundary?", choices=["Yes", "No"], value="Yes")
                boundary_length = gr.Number(label="Boundary Length", value=50.0, precision=2)
                boundary_width = gr.Number(label="Boundary Width", value=50.0, precision=2)
                annotation_text = gr.Textbox(label="Annotation (max 20 chars)", max_length=20, placeholder="Type up to 20 characters")
                
        gr.Markdown("## Step 3: Click Generate DXF & Previews")
        submit_button = gr.Button("Generate DXF & Previews", variant="primary")
        
        # 3. Outputs
        gr.Markdown("## Outputs")
        
        with gr.Row():
            output_img = gr.Image(format="png", label="Output Image (Tools overlaid with outlines)")
            outlines_color = gr.Image(format="png", label="Outlines of Objects (Final DXF Geometry Preview)")

        with gr.Row():
            dxf_file = gr.File(label="DXF file")
            with gr.Column():
                scaling_factor_display = gr.Textbox(label="Scaling Factor (inches/pixel)")
                dilated_mask = gr.Image(label="Mask (Technical Preview)")

        # 4. Examples
        gr.Markdown("---")
        gr.Markdown("## Example Data (Click a row to load the image and parameters)")
        gr.Examples(
            examples=[
                ["data/Test20.jpg", 0.075, "inches", "Yes", "No", 300.0, 200.0, "MyTool"],
                ["data/Test21.jpg", 0.075, "inches", "Yes", "Yes", 300.0, 200.0, "Tool2"]
            ],
            inputs=[input_image, offset_value, offset_unit, finger_clearance, add_boundary, boundary_length, boundary_width, annotation_text],
            outputs=[output_img, outlines_color, dxf_file, dilated_mask, scaling_factor_display],
            fn=gradio_predict,
            cache_examples=False,
        )

        # Event Handler
        submit_button.click(
            fn=gradio_predict,
            inputs=[input_image, offset_value, offset_unit, finger_clearance, add_boundary, boundary_length, boundary_width, annotation_text],
            outputs=[output_img, outlines_color, dxf_file, dilated_mask, scaling_factor_display]
        )
        
    demo.launch(share=True)