import os, re import zipfile import shutil import time from PIL import Image, ImageDraw import io from rembg import remove import gradio as gr from concurrent.futures import ThreadPoolExecutor from diffusers import StableDiffusionPipeline from transformers import pipeline import numpy as np import json import torch # Load Stable Diffusion Model def load_stable_diffusion_model(): device = "cuda" if torch.cuda.is_available() else "cpu" pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float32).to(device) return pipe # Initialize the model globally sd_model = load_stable_diffusion_model() def remove_background_rembg(input_path): print(f"Removing background using rembg for image: {input_path}") with open(input_path, 'rb') as i: input_image = i.read() output_image = remove(input_image) img = Image.open(io.BytesIO(output_image)).convert("RGBA") return img def remove_background_bria(input_path): print(f"Removing background using bria for image: {input_path}") device = 0 if torch.cuda.is_available() else -1 # Load the segmentation model pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True, device=device) # Process the image result = pipe(input_path) return result # Function to process images using prompts def text_to_image(prompt): os.makedirs("generated_images", exist_ok=True) # Ensure the directory exists image = sd_model(prompt).images[0] # Generate image using the model # Create a sanitized filename by replacing spaces with underscores image_path = f"generated_images/{prompt.replace(' ', '_')}.png" image.save(image_path) # Save the generated image return image, image_path # Return the image and its path # Function to modify an image based on a text prompt def text_image_to_image(input_image, prompt): os.makedirs("generated_images", exist_ok=True) # Ensure the directory exists # Convert input image to PIL Image if necessary if not isinstance(input_image, Image.Image): input_image = Image.open(input_image) # Load image from path if given as string # Generate modified image using the model with the input image and prompt modified_image = sd_model(prompt, init_image=input_image, strength=0.75).images[0] # Create a sanitized filename for the modified image image_path = f"generated_images/{prompt.replace(' ', '_')}_modified.png" modified_image.save(image_path) # Save the modified image return modified_image, image_path # Return the modified image and its path def get_bounding_box_with_threshold(image, threshold): # Convert image to numpy array img_array = np.array(image) # Get alpha channel alpha = img_array[:,:,3] # Find rows and columns where alpha > threshold rows = np.any(alpha > threshold, axis=1) cols = np.any(alpha > threshold, axis=0) # Find the bounding box top, bottom = np.where(rows)[0][[0, -1]] left, right = np.where(cols)[0][[0, -1]] if left < right and top < bottom: return (left, top, right, bottom) else: return None def position_logic(image_path, canvas_size, padding_top, padding_right, padding_bottom, padding_left, use_threshold=True): image = Image.open(image_path) image = image.convert("RGBA") # Get the bounding box of the non-blank area with threshold if use_threshold: bbox = get_bounding_box_with_threshold(image, threshold=10) else: bbox = image.getbbox() log = [] if bbox: # Check 1 pixel around the image for non-transparent pixels width, height = image.size cropped_sides = [] # Define tolerance for transparency tolerance = 30 # Adjust this value as needed # Check top edge if any(image.getpixel((x, 0))[3] > tolerance for x in range(width)): cropped_sides.append("top") # Check bottom edge if any(image.getpixel((x, height-1))[3] > tolerance for x in range(width)): cropped_sides.append("bottom") # Check left edge if any(image.getpixel((0, y))[3] > tolerance for y in range(height)): cropped_sides.append("left") # Check right edge if any(image.getpixel((width-1, y))[3] > tolerance for y in range(height)): cropped_sides.append("right") if cropped_sides: info_message = f"Info for {os.path.basename(image_path)}: The following sides of the image may contain cropped objects: {', '.join(cropped_sides)}" print(info_message) log.append({"info": info_message}) else: info_message = f"Info for {os.path.basename(image_path)}: The image is not cropped." print(info_message) log.append({"info": info_message}) # Crop the image to the bounding box image = image.crop(bbox) log.append({"action": "crop", "bbox": [str(bbox[0]), str(bbox[1]), str(bbox[2]), str(bbox[3])]}) # Calculate the new size to expand the image target_width, target_height = canvas_size aspect_ratio = image.width / image.height if len(cropped_sides) == 4: # If the image is cropped on all sides, center crop it to fit the canvas if aspect_ratio > 1: # Landscape new_height = target_height new_width = int(new_height * aspect_ratio) left = (new_width - target_width) // 2 image = image.resize((new_width, new_height), Image.LANCZOS) image = image.crop((left, 0, left + target_width, target_height)) else: # Portrait or square new_width = target_width new_height = int(new_width / aspect_ratio) top = (new_height - target_height) // 2 image = image.resize((new_width, new_height), Image.LANCZOS) image = image.crop((0, top, target_width, top + target_height)) log.append({"action": "center_crop_resize", "new_size": f"{target_width}x{target_height}"}) x, y = 0, 0 elif not cropped_sides: # If the image is not cropped, expand it from center until it touches the padding new_height = target_height - padding_top - padding_bottom new_width = int(new_height * aspect_ratio) if new_width > target_width - padding_left - padding_right: # If width exceeds available space, adjust based on width new_width = target_width - padding_left - padding_right new_height = int(new_width / aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) x = (target_width - new_width) // 2 y = target_height - new_height - padding_bottom else: # New logic for handling cropped top and left, or top and right if set(cropped_sides) == {"top", "left"} or set(cropped_sides) == {"top", "right"}: new_height = target_height - padding_bottom new_width = int(new_height * aspect_ratio) # If new width exceeds canvas width, adjust based on width if new_width > target_width: new_width = target_width new_height = int(new_width / aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) # Set position if "left" in cropped_sides: x = 0 else: # right in cropped_sides x = target_width - new_width y = 0 # If the resized image is taller than the canvas minus padding, crop from the bottom if new_height > target_height - padding_bottom: crop_bottom = new_height - (target_height - padding_bottom) image = image.crop((0, 0, new_width, new_height - crop_bottom)) new_height = target_height - padding_bottom log.append({"action": "crop_vertical", "bottom_pixels_removed": str(crop_bottom)}) log.append({"action": "position", "x": str(x), "y": str(y)}) elif set(cropped_sides) == {"bottom", "left"} or set(cropped_sides) == {"bottom", "right"}: # Handle bottom & left or bottom & right cropped images new_height = target_height - padding_top new_width = int(new_height * aspect_ratio) # If new width exceeds canvas width, adjust based on width if new_width > target_width - padding_left - padding_right: new_width = target_width - padding_left - padding_right new_height = int(new_width / aspect_ratio) # Resize the image without cropping or stretching image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) # Set position if "left" in cropped_sides: x = 0 else: # right in cropped_sides x = target_width - new_width y = target_height - new_height log.append({"action": "position", "x": str(x), "y": str(y)}) elif set(cropped_sides) == {"bottom", "left", "right"}: # Expand the image from the center new_width = target_width new_height = int(new_width / aspect_ratio) if new_height < target_height: new_height = target_height new_width = int(new_height * aspect_ratio) image = image.resize((new_width, new_height), Image.LANCZOS) # Crop to fit the canvas left = (new_width - target_width) // 2 top = 0 image = image.crop((left, top, left + target_width, top + target_height)) log.append({"action": "expand_and_crop", "new_size": f"{target_width}x{target_height}"}) x, y = 0, 0 elif cropped_sides == ["top"]: # New logic for handling only top-cropped images if image.width > image.height: new_width = target_width new_height = int(target_width / aspect_ratio) else: new_height = target_height - padding_bottom new_width = int(new_height * aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) x = (target_width - new_width) // 2 y = 0 # Align to top # Apply padding only to non-cropped sides x = max(padding_left, min(x, target_width - new_width - padding_right)) elif cropped_sides in [["right"], ["left"]]: # New logic for handling only right-cropped or left-cropped images if image.width > image.height: new_width = target_width - max(padding_left, padding_right) new_height = int(new_width / aspect_ratio) else: new_height = target_height - padding_top - padding_bottom new_width = int(new_height * aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) if cropped_sides == ["right"]: x = target_width - new_width # Align to right else: # cropped_sides == ["left"] x = 0 # Align to left y = target_height - new_height - padding_bottom # Respect bottom padding # Ensure top padding is respected if y < padding_top: y = padding_top log.append({"action": "position", "x": str(x), "y": str(y)}) elif set(cropped_sides) == {"left", "right"}: # Logic for handling images cropped on both left and right sides new_width = target_width # Expand to full width of canvas # Calculate the aspect ratio of the original image aspect_ratio = image.width / image.height # Calculate the new height while maintaining aspect ratio new_height = int(new_width / aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) # Set horizontal position (always 0 as it spans full width) x = 0 # Calculate vertical position to respect bottom padding y = target_height - new_height - padding_bottom # If the resized image is taller than the canvas, crop from the top only if new_height > target_height - padding_bottom: crop_top = new_height - (target_height - padding_bottom) image = image.crop((0, crop_top, new_width, new_height)) new_height = target_height - padding_bottom y = 0 log.append({"action": "crop_vertical", "top_pixels_removed": str(crop_top)}) else: # Align the image to the bottom with padding y = target_height - new_height - padding_bottom log.append({"action": "position", "x": str(x), "y": str(y)}) elif cropped_sides == ["bottom"]: # Logic for handling images cropped on the bottom side # Calculate the aspect ratio of the original image aspect_ratio = image.width / image.height if aspect_ratio < 1: # Portrait orientation new_height = target_height - padding_top # Full height with top padding new_width = int(new_height * aspect_ratio) # If the new width exceeds the canvas width, adjust it if new_width > target_width: new_width = target_width new_height = int(new_width / aspect_ratio) else: # Landscape orientation new_width = target_width - padding_left - padding_right new_height = int(new_width / aspect_ratio) # If the new height exceeds the canvas height, adjust it if new_height > target_height: new_height = target_height new_width = int(new_height * aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) # Set horizontal position (centered) x = (target_width - new_width) // 2 # Set vertical position (touching bottom edge for all cases) y = target_height - new_height log.append({"action": "position", "x": str(x), "y": str(y)}) else: # Use the original resizing logic for other partially cropped images if image.width > image.height: new_width = target_width new_height = int(target_width / aspect_ratio) else: new_height = target_height new_width = int(target_height * aspect_ratio) # Resize the image image = image.resize((new_width, new_height), Image.LANCZOS) log.append({"action": "resize", "new_width": str(new_width), "new_height": str(new_height)}) # Center horizontally for all images x = (target_width - new_width) // 2 y = target_height - new_height - padding_bottom # Adjust positions for cropped sides if "top" in cropped_sides: y = 0 elif "bottom" in cropped_sides: y = target_height - new_height if "left" in cropped_sides: x = 0 elif "right" in cropped_sides: x = target_width - new_width # Apply padding only to non-cropped sides, but keep horizontal centering if "left" not in cropped_sides and "right" not in cropped_sides: x = (target_width - new_width) // 2 # Always center horizontally if "top" not in cropped_sides and "bottom" not in cropped_sides: y = max(padding_top, min(y, target_height - new_height - padding_bottom)) return log, image, x, y def process_single_image(image_path, output_folder, bg_method, canvas_size_name, output_format, bg_choice, custom_color, watermark_path=None): add_padding_line = False if canvas_size_name == 'Rox': canvas_size = (1080, 1080) padding_top = 112 padding_right = 125 padding_bottom = 116 padding_left = 125 elif canvas_size_name == 'Columbia': canvas_size = (730, 610) padding_top = 30 padding_right = 105 padding_bottom = 35 padding_left = 105 elif canvas_size_name == 'Zalora': canvas_size = (763, 1100) padding_top = 50 padding_right = 50 padding_bottom = 200 padding_left = 50 filename = os.path.basename(image_path) try: print(f"Processing image: {filename}") if bg_method == 'rembg': image_with_no_bg = remove_background_rembg(image_path) elif bg_method == 'bria': image_with_no_bg = remove_background_bria(image_path) elif bg_method == None: image_with_no_bg = Image.open(image_path) temp_image_path = os.path.join(output_folder, f"temp_{filename}") image_with_no_bg.save(temp_image_path, format='PNG') log, new_image, x, y = position_logic(temp_image_path, canvas_size, padding_top, padding_right, padding_bottom, padding_left) # Create a new canvas with the appropriate background if bg_choice == 'white': canvas = Image.new("RGBA", canvas_size, "WHITE") elif bg_choice == 'custom': canvas = Image.new("RGBA", canvas_size, custom_color) else: # transparent canvas = Image.new("RGBA", canvas_size, (0, 0, 0, 0)) # Paste the resized image onto the canvas canvas.paste(new_image, (x, y), new_image) log.append({"action": "paste", "position": [str(x), str(y)]}) # Add visible black line for padding when background is not transparent if add_padding_line: draw = ImageDraw.Draw(canvas) draw.rectangle([padding_left, padding_top, canvas_size[0] - padding_right, canvas_size[1] - padding_bottom], outline="black", width=5) log.append({"action": "add_padding_line"}) output_ext = 'jpg' if output_format == 'JPG' else 'png' output_filename = f"{os.path.splitext(filename)[0]}.{output_ext}" output_path = os.path.join(output_folder, output_filename) # Apply watermark only if the filename ends with "_01" and watermark_path is provided if os.path.splitext(filename)[0].endswith("_01") and watermark_path: watermark = Image.open(watermark_path).convert("RGBA") canvas = canvas.convert("RGBA") canvas.paste(watermark, (0, 0), watermark) log.append({"action": "add_watermark"}) if output_format == 'JPG': canvas = canvas.convert('RGB') canvas.save(output_path, format='JPEG') else: canvas.save(output_path, format='PNG') os.remove(temp_image_path) print(f"Processed image path: {output_path}") return [(output_path, image_path)], log except Exception as e: print(f"Error processing {filename}: {e}") return None, None def remove_extension(filename): # Regular expression to match any extension at the end of the string return re.sub(r'\.[^.]+$', '', filename) def process_images(input_files, bg_method='rembg', watermark_path=None, canvas_size='Rox', output_format='PNG', bg_choice='transparent', custom_color="#ffffff", num_workers=4, progress=gr.Progress()): start_time = time.time() output_folder = "processed_images" if os.path.exists(output_folder): shutil.rmtree(output_folder) os.makedirs(output_folder) processed_images = [] original_images = [] all_logs = [] if isinstance(input_files, str) and input_files.lower().endswith(('.zip', '.rar')): # Handle zip file input_folder = "temp_input" if os.path.exists(input_folder): shutil.rmtree(input_folder) os.makedirs(input_folder) try: with zipfile.ZipFile(input_files, 'r') as zip_ref: zip_ref.extractall(input_folder) except zipfile.BadZipFile as e: print(f"Error extracting zip file: {e}") return [], None, 0 image_files = [os.path.join(input_folder, f) for f in os.listdir(input_folder) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif', '.webp'))] elif isinstance(input_files, list): # Handle multiple files image_files = input_files else: # Handle single file image_files = [input_files] total_images = len(image_files) print(f"Total images to process: {total_images}") avg_processing_time = 0 with ThreadPoolExecutor(max_workers=num_workers) as executor: future_to_image = {executor.submit(process_single_image, image_path, output_folder, bg_method, canvas_size, output_format, bg_choice, custom_color, watermark_path): image_path for image_path in image_files} for idx, future in enumerate(future_to_image): try: start_time_image = time.time() result, log = future.result() end_time_image = time.time() image_processing_time = end_time_image - start_time_image # Update average processing time avg_processing_time = (avg_processing_time * idx + image_processing_time) / (idx + 1) if result: if watermark_path: get_name = future_to_image[future].split('/') get_name = remove_extension(get_name[len(get_name)-1]) twibbon_input = f'{get_name}.png' if output_format == 'PNG' else f'{get_name}.jpg' twibbon_output_path = os.path.join(output_folder, f'result_{start_time_image}.png') add_twibbon(f'processed_images/{twibbon_input}', watermark_path, twibbon_output_path) processed_images.append((twibbon_output_path, twibbon_output_path)) else: processed_images.extend(result) original_images.append(future_to_image[future]) all_logs.append({os.path.basename(future_to_image[future]): log}) # Estimate remaining time remaining_images = total_images - (idx + 1) estimated_remaining_time = remaining_images * avg_processing_time progress((idx + 1) / total_images, f"{idx + 1}/{total_images} images processed. Estimated time remaining: {estimated_remaining_time:.2f} seconds") except Exception as e: print(f"Error processing image {future_to_image[future]}: {e}") output_zip_path = "processed_images.zip" with zipfile.ZipFile(output_zip_path, 'w') as zipf: for file, _ in processed_images: zipf.write(file, os.path.basename(file)) # Write the comprehensive log for all images with open(os.path.join(output_folder, 'process_log.json'), 'w') as log_file: json.dump(all_logs, log_file, indent=4) print("Comprehensive log saved to", os.path.join(output_folder, 'process_log.json')) end_time = time.time() processing_time = end_time - start_time print(f"Processing time: {processing_time} seconds") return original_images, processed_images, output_zip_path, processing_time def gradio_interface(input_files, bg_method, watermark, canvas_size, output_format, bg_choice, custom_color, num_workers): progress = gr.Progress() watermark_path = watermark.name if watermark else None # Check input_files, is it single image, list image, or zip/rar if isinstance(input_files, str) and input_files.lower().endswith(('.zip', '.rar')): return process_images(input_files, bg_method, watermark_path, canvas_size, output_format, bg_choice, custom_color, num_workers, progress) elif isinstance(input_files, list): return process_images(input_files, bg_method, watermark_path, canvas_size, output_format, bg_choice, custom_color, num_workers, progress) else: return process_images(input_files.name, bg_method, watermark_path, canvas_size, output_format, bg_choice, custom_color, num_workers, progress) def show_color_picker(bg_choice): if bg_choice == 'custom': return gr.update(visible=True) return gr.update(visible=False) def update_compare(evt: gr.SelectData): if isinstance(evt.value, dict) and 'caption' in evt.value: input_path = evt.value['caption'] output_path = evt.value['image']['path'] input_path = input_path.split("Input: ")[-1] # Open the original and processed images original_img = Image.open(input_path) processed_img = Image.open(output_path) # Calculate the aspect ratios original_ratio = f"{original_img.width}x{original_img.height}" processed_ratio = f"{processed_img.width}x{processed_img.height}" return gr.update(value=input_path), gr.update(value=output_path), gr.update(value=original_ratio), gr.update(value=processed_ratio) else: print("No caption found in selection") return gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None) def process(input_files, bg_method, watermark, canvas_size, output_format, bg_choice, custom_color, num_workers): _, processed_images, zip_path, time_taken = gradio_interface(input_files, bg_method, watermark, canvas_size, output_format, bg_choice, custom_color, num_workers) processed_images_with_captions = [(img, f"Input: {caption}") for img, caption in processed_images] return processed_images_with_captions, zip_path, f"{time_taken:.2f} seconds" def add_twibbon(image_path, twibbon_path, output_path): # Open the original image and the twibbon image = Image.open(image_path) twibbon = Image.open(twibbon_path) # Get the sizes of both images image_width, image_height = image.size twibbon_width, twibbon_height = twibbon.size # Resize the original image to fit inside the twibbon (optional: resize by aspect ratio) aspect_ratio = image_width / image_height if twibbon_width / twibbon_height > aspect_ratio: new_width = twibbon_width new_height = int(new_width / aspect_ratio) else: new_height = twibbon_height new_width = int(new_height * aspect_ratio) image = image.resize((new_width, new_height), Image.Resampling.LANCZOS) # Center the image within the twibbon x_offset = (twibbon_width - new_width) // 2 y_offset = (twibbon_height - new_height) // 2 combined_image = Image.new('RGBA', (twibbon_width, twibbon_height)) combined_image.paste(image, (x_offset, y_offset)) combined_image.paste(twibbon, (0, 0), mask=twibbon) # Twibbon is pasted over the image # Save the result combined_image.save(output_path) return combined_image def process_twibbon(image, twibbon): output_path = "output_image.png" # Output sementara combined_image = add_twibbon(image.name, twibbon.name, output_path) return combined_image def remove_background(image_path, method="none"): image = Image.open(image_path) if method == "none": return image # Return the original image without any background removal elif method == "rembg": image = remove_background_rembg(image_path) elif method == "bria": image = remove_background_bria(image_path) return image # Default return in case no valid method is chosen with gr.Blocks(theme="NoCrypt/miku@1.2.2") as iface: gr.Markdown("# 🎨 Creative Image Suite: Generate, Modify, and Enhance Your Visuals") gr.Markdown(""" **Unlock your creativity with our comprehensive image processing tool! This suite offers three powerful features:** 1. **✏️ Text to Image**: Transform your ideas into stunning visuals by simply entering a descriptive text prompt. Watch your imagination come to life! 2. **🖼️ Image to Image**: Enhance existing images by providing a text description of the modifications you want. Upload any image and specify the changes as you wish to create a unique masterpiece. 3. **🖌️ Image Background Removal and Resizing**: Effortlessly remove backgrounds from images, resize them, and even add watermarks (opitonal). Upload single images or zip files, choose your desired settings, and let our tool process everything seamlessly. """) # Fitur Text to Image gr.Markdown("## Text to Image Feature") gr.Markdown(""" **Example Prompts:** - *A serene mountain landscape at sunset.* - *A futuristic city skyline with flying cars.* - *A whimsical forest filled with colorful mushrooms and fairies.* - *A close-up of a vibrant butterfly resting on a flower.* This feature allows you to create a new image based on a text description. Simply enter your idea in a sentence, and the system will generate an image that matches it. """) gr.Markdown("### ⚠️ Note:") gr.Markdown("Processing may take a while due to the free CPU resources on Hugging Face Spaces. Please be patient!") with gr.Row(): prompt_input = gr.Textbox(label="Enter your prompt for image generation:") generate_button = gr.Button("Generate Image") output_image = gr.Image(label="Generated Image") download_button = gr.File(label="Download Generated Image", type="filepath") generate_button.click(text_to_image, inputs=prompt_input, outputs=[output_image, download_button]) # Fitur Text Image to Image gr.Markdown("## Image to Image Feature") gr.Markdown(""" **Example Prompts:** - *Change the sky to a starry night with a full moon.* - *Add a rainbow across the horizon in this beach scene.* - *Make the flowers in the garden bloom in shades of blue.* - *Transform the cat's fur to a bright orange color.* This feature lets you modify an existing image by adding a text description. Upload an image, specify what you want to change, and the system will alter the image accordingly. """) gr.Markdown("### ⚠️ Note:") gr.Markdown("Processing may take a while due to the free CPU resources on Hugging Face Spaces. Please be patient!") with gr.Row(): input_image = gr.Image(label="Upload Image for Modification", type="pil") prompt_modification = gr.Textbox(label="Enter your prompt for modification:") modify_button = gr.Button("Modify Image") modified_output_image = gr.Image(label="Modified Image") download_modified_button = gr.File(label="Download Modified Image", type="filepath") modify_button.click(text_image_to_image, inputs=[input_image, prompt_modification], outputs=[modified_output_image, download_modified_button]) gr.Markdown("## Image Background Removal and Resizing with Optional Watermark") gr.Markdown("Choose to upload multiple images or a ZIP/RAR file, select the crop mode, optionally upload a watermark image, and choose the output format.") with gr.Row(): input_files = gr.File(label="Upload Image or ZIP/RAR file", file_types=[".zip", ".rar", "image"], interactive=True) watermark = gr.File(label="Upload Watermark Image (Optional)", file_types=[".png"]) with gr.Row(): canvas_size = gr.Radio(choices=["Rox", "Columbia", "Zalora"], label="Canvas Size", value="Rox") output_format = gr.Radio(choices=["PNG", "JPG"], label="Output Format", value="JPG") num_workers = gr.Slider(minimum=1, maximum=16, step=1, label="Number of Workers", value=5) with gr.Row(): bg_method = gr.Radio(choices=["bria", "rembg", None], label="Background Removal Method", value="bria") bg_choice = gr.Radio(choices=["transparent", "white", "custom"], label="Background Choice", value="white") custom_color = gr.ColorPicker(label="Custom Background Color", value="#ffffff", visible=False) process_button = gr.Button("Process Images") with gr.Row(): gallery_processed = gr.Gallery(label="Processed Images") with gr.Row(): image_original = gr.Image(label="Original Images", interactive=False) image_processed = gr.Image(label="Processed Images", interactive=False) with gr.Row(): original_ratio = gr.Textbox(label="Original Ratio") processed_ratio = gr.Textbox(label="Processed Ratio") with gr.Row(): output_zip = gr.File(label="Download Processed Images as ZIP") processing_time = gr.Textbox(label="Processing Time (seconds)") bg_choice.change(show_color_picker, inputs=bg_choice, outputs=custom_color) process_button.click(process, inputs=[input_files, bg_method, watermark, canvas_size, output_format, bg_choice, custom_color, num_workers], outputs=[gallery_processed, output_zip, processing_time]) gallery_processed.select(update_compare, outputs=[image_original, image_processed, original_ratio, processed_ratio]) iface.launch(share=True)