Update app.py

app.py

@@ -1,142 +1,721 @@
+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 random
-#import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+import json
 import torch
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
+# Load Stable Diffusion Model
+def load_stable_diffusion_model():
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    return StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16).to(device)
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+sd_model = load_stable_diffusion_model()
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+def remove_background_stable_diffusion(image_path):
+    image = Image.open(image_path).convert("RGB")
+    prompt = "Remove background from the image"
+    output = sd_model(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
+    return output
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+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
 
-#@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+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=0)
+    pillow_image = pipe(input_path)
+    return pillow_image
 
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+# Fungsi untuk memproses gambar menggunakan prompt
+def text_to_image(prompt):
+    image = pipe(prompt).images[0]
+    image_path = f"generated_images/{prompt.replace(' ', '_')}.png"
+    image.save(image_path)
+    return image, image_path
+
+def text_image_to_image(image, prompt):
+    # Convert input image to PIL Image for processing
+    modified_image = pipe(prompt, init_image=image, strength=0.75).images[0]
+    image_path = f"generated_images/{prompt.replace(' ', '_')}_modified.png"
+    modified_image.save(image_path)
+    return modified_image, image_path
+
+def process_images(image_paths):
+    with ThreadPoolExecutor() as executor:
+        results = list(executor.map(remove_background_rembg, image_paths))
+    return results
+
+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 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)
         
-    generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
-    return image, seed
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        """)
+            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)
         
-        with gr.Row():
+            # 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)})
             
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
+                # Set position
+                if "left" in cropped_sides:
+                    x = 0
+                else:  # right in cropped_sides
+                    x = target_width - new_width
+                y = 0
             
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
+                # 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)})
             
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
+                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)
             
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
+                # 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
             
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                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)
             
-            with gr.Row():
+                image = image.resize((new_width, new_height), Image.LANCZOS)
                 
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024, #Replace with defaults that work for your model
-                )
+                # 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)
                 
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024, #Replace with defaults that work for your model
-                )
-            
-            with gr.Row():
+                # 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)
                 
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0, #Replace with defaults that work for your model
-                )
+                    # 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)
                 
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2, #Replace with defaults that work for your model
-                )
+                    # 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 == 'stable_diffusion':
+            image_with_no_bg = remove_background_stable_diffusion(image_path)
+        elif 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
         
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
-    )
-
-demo.queue().launch()
+        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.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))
+
+def apply_watermark(canvas, watermark_path):
+    watermark = Image.open(watermark_path).convert("RGBA")
+    canvas.paste(watermark, (0, 0), watermark)
+
+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", "stable_diffusion"], label="Background Removal Method", value="stable_diffusion")
+        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():
+        input_image = gr.File(label="Upload Image", file_types=["image"])
+        prompt = gr.Textbox(label="Prompt for Image Modification")
+        process_button = gr.Button("Generate Image")
+        output_image = gr.Image(label="Generated Image")
+    
+    process_button.click(gradio_interface, inputs=[input_image, prompt], outputs=[output_image])
+
+
+    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)