import os 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}") pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True, device=1) pillow_image = pipe(input_path) return pillow_image # Fungsi untuk memproses gambar menggunakan prompt def text_to_image(prompt): os.makedirs("generated_images", exist_ok=True) image = sd_model(prompt).images[0] # Use sd_model instead of pipe image_path = f"generated_images/{prompt.replace(' ', '_')}.png" image.save(image_path) return image, image_path def text_image_to_image(image, prompt): os.makedirs("generated_images", exist_ok=True) # Convert input image to PIL Image for processing modified_image = sd_model(prompt, init_image=image, strength=0.75).images[0] # Use sd_model here too image_path = f"generated_images/{prompt.replace(' ', '_')}_modified.png" modified_image.save(image_path) return modified_image, image_path def process_images(image_paths, remove_bg_method): with ThreadPoolExecutor() as executor: if remove_bg_method == 'rembg': results = list(executor.map(remove_background_rembg, image_paths)) elif remove_bg_method == 'bria': results = list(executor.map(remove_background_bria, image_paths)) else: # No background removal results = image_paths # Just return the original paths return results def get_bounding_box_with_threshold(image, threshold): img_array = np.array(image) alpha = img_array[:, :, 3] rows = np.any(alpha > threshold, axis=1) cols = np.any(alpha > threshold, axis=0) try: top, bottom = np.where(rows)[0][[0, -1]] left, right = np.where(cols)[0][[0, -1]] except IndexError: return None # No non-transparent pixels found return (left, top, right, bottom) if left < right and top < bottom else None def check_cropped_sides(image, tolerance): cropped_sides = [] width, height = image.size edges = { "top": [(x, 0) for x in range(width)], "bottom": [(x, height - 1) for x in range(width)], "left": [(0, y) for y in range(height)], "right": [(width - 1, y) for y in range(height)] } for side, pixels in edges.items(): if any(image.getpixel(pixel)[3] > tolerance for pixel in pixels): cropped_sides.append(side) return cropped_sides def resize_image(image, target_size, aspect_ratio): target_width, target_height = target_size if aspect_ratio > 1: # Landscape new_height = target_height new_width = int(new_height * aspect_ratio) else: # Portrait or square new_width = target_width new_height = int(new_width / aspect_ratio) return image.resize((new_width, new_height), Image.LANCZOS), new_width, new_height 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 get_canvas_size(canvas_size_name): sizes = { 'Rox': ((1080, 1080), (112, 125, 116, 125)), 'Columbia': ((730, 610), (30, 105, 35, 105)), 'Zalora': ((763, 1100), (50, 50, 200, 50)), } return sizes.get(canvas_size_name, ((1080, 1080), (0, 0, 0, 0))) 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) # Placeholder for existing function elif bg_method == 'bria': image_with_no_bg = remove_background_bria(image_path) # Placeholder for existing function 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 if watermark_path is provided if watermark_path: watermark = Image.open(watermark_path).convert("RGBA") canvas.paste(watermark, (0, 0), watermark) log.append({"action": "add_watermark"}) if output_format == 'JPG': canvas.convert('RGB').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 create_canvas(canvas_size, bg_choice, custom_color): if bg_choice == 'white': return Image.new("RGBA", canvas_size, "WHITE") elif bg_choice == 'custom': return Image.new("RGBA", canvas_size, custom_color) else: # transparent return Image.new("RGBA", canvas_size, (0, 0, 0, 0)) # Function to add watermark def add_watermark(image, watermark_path): if watermark_path: watermark = Image.open(watermark_path).convert("RGBA") width, height = image.size watermark = watermark.resize((width // 4, height // 4), Image.ANTIALIAS) # Resize watermark w_width, w_height = watermark.size # Position the watermark in the center position = ((width - w_width) // 2, (height - w_height) // 2) image.paste(watermark, position, watermark) return image def process_image(image_path, watermark_path): try: # Menghapus latar belakang image_without_bg = remove_background_bria(image_path) if image_without_bg is None: raise Exception("Background removal failed.") # Menambahkan watermark final_image = add_watermark(image_without_bg, watermark_path) return final_image except Exception as e: print(f"Error processing image: {e}") return None def save_image(canvas, output_folder, filename, output_format): output_ext = 'jpg' if output_format == 'JPG' else 'png' output_path = os.path.join(output_folder, f"{os.path.splitext(filename)[0]}.{output_ext}") if output_format == 'JPG': canvas.convert('RGB').save(output_path, format='JPEG') else: canvas.save(output_path, format='PNG') return output_path 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')): 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): image_files = input_files else: 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: 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 extract_image_files(input_files): if isinstance(input_files, str) and input_files.lower().endswith(('.zip', '.rar')): input_folder = "temp_input" if os.path.exists(input_folder): shutil.rmtree(input_folder) os.makedirs(input_folder) with zipfile.ZipFile(input_files, 'r') as zip_ref: zip_ref.extractall(input_folder) return [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): return input_files else: return [input_files] def create_output_zip(processed_images): 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)) return output_zip_path def save_log(all_logs, output_folder): 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')) 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 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] original_img = Image.open(input_path) processed_img = Image.open(output_path) 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" with gr.Blocks(theme="NoCrypt/miku@1.2.2") as iface: 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.") # Fitur Text to Image gr.Markdown("# Text to Image Feature") 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("# Text Image to Image Feature") 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]) 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"], 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)