Update app.py

app.py

@@ -1,462 +1,466 @@
-import os
-import sys
-
-sys.path.append(os.path.abspath(os.path.dirname(os.getcwd())))
-# os.chdir("../")
-import cv2
-import gradio as gr
-import numpy as np
-from pathlib import Path
-from matplotlib import pyplot as plt
-import torch
-import tempfile
-
-from stable_diffusion_inpaint import fill_img_with_sd, replace_img_with_sd
-from lama_inpaint import (
-    inpaint_img_with_lama,
-    build_lama_model,
-    inpaint_img_with_builded_lama,
-)
-from utils import (
-    load_img_to_array,
-    save_array_to_img,
-    dilate_mask,
-    show_mask,
-    show_points,
-)
-from PIL import Image
-from segment_anything import SamPredictor, sam_model_registry
-import argparse
-
-
-def setup_args(parser):
-    parser.add_argument(
-        "--lama_config",
-        type=str,
-        default="./lama/configs/prediction/default.yaml",
-        help="The path to the config file of lama model. "
-        "Default: the config of big-lama",
-    )
-    parser.add_argument(
-        "--lama_ckpt",
-        type=str,
-        default="pretrained_models/big-lama",
-        help="The path to the lama checkpoint.",
-    )
-    parser.add_argument(
-        "--sam_ckpt",
-        type=str,
-        default="./pretrained_models/sam_vit_h_4b8939.pth",
-        help="The path to the SAM checkpoint to use for mask generation.",
-    )
-
-
-def mkstemp(suffix, dir=None):
-    fd, path = tempfile.mkstemp(suffix=f"{suffix}", dir=dir)
-    os.close(fd)
-    return Path(path)
-
-
-def get_sam_feat(img):
-    model["sam"].set_image(img)
-    features = model["sam"].features
-    orig_h = model["sam"].orig_h
-    orig_w = model["sam"].orig_w
-    input_h = model["sam"].input_h
-    input_w = model["sam"].input_w
-    model["sam"].reset_image()
-    return features, orig_h, orig_w, input_h, input_w
-
-
-def get_fill_img_with_sd(image, mask, image_resolution, text_prompt):
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    if len(mask.shape) == 3:
-        mask = mask[:, :, 0]
-    np_image = np.array(image, dtype=np.uint8)
-    H, W, C = np_image.shape
-    np_image = HWC3(np_image)
-    np_image = resize_image(np_image, image_resolution)
-    mask = cv2.resize(
-        mask, (np_image.shape[1], np_image.shape[0]), interpolation=cv2.INTER_NEAREST
-    )
-
-    img_fill = fill_img_with_sd(np_image, mask, text_prompt, device=device)
-    img_fill = img_fill.astype(np.uint8)
-    return img_fill
-
-
-def get_replace_img_with_sd(image, mask, image_resolution, text_prompt):
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    if len(mask.shape) == 3:
-        mask = mask[:, :, 0]
-    np_image = np.array(image, dtype=np.uint8)
-    H, W, C = np_image.shape
-    np_image = HWC3(np_image)
-    np_image = resize_image(np_image, image_resolution)
-    mask = cv2.resize(
-        mask, (np_image.shape[1], np_image.shape[0]), interpolation=cv2.INTER_NEAREST
-    )
-
-    img_replaced = replace_img_with_sd(np_image, mask, text_prompt, device=device)
-    img_replaced = img_replaced.astype(np.uint8)
-    return img_replaced
-
-
-def HWC3(x):
-    assert x.dtype == np.uint8
-    if x.ndim == 2:
-        x = x[:, :, None]
-    assert x.ndim == 3
-    H, W, C = x.shape
-    assert C == 1 or C == 3 or C == 4
-    if C == 3:
-        return x
-    if C == 1:
-        return np.concatenate([x, x, x], axis=2)
-    if C == 4:
-        color = x[:, :, 0:3].astype(np.float32)
-        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
-        y = color * alpha + 255.0 * (1.0 - alpha)
-        y = y.clip(0, 255).astype(np.uint8)
-        return y
-
-
-def resize_image(input_image, resolution):
-    H, W, C = input_image.shape
-    k = float(resolution) / min(H, W)
-    H = int(np.round(H * k / 64.0)) * 64
-    W = int(np.round(W * k / 64.0)) * 64
-    img = cv2.resize(
-        input_image,
-        (W, H),
-        interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA,
-    )
-    return img
-
-
-def resize_points(clicked_points, original_shape, resolution):
-    original_height, original_width, _ = original_shape
-    original_height = float(original_height)
-    original_width = float(original_width)
-
-    scale_factor = float(resolution) / min(original_height, original_width)
-    resized_points = []
-
-    for point in clicked_points:
-        x, y, lab = point
-        resized_x = int(round(x * scale_factor))
-        resized_y = int(round(y * scale_factor))
-        resized_point = (resized_x, resized_y, lab)
-        resized_points.append(resized_point)
-
-    return resized_points
-
-
-def get_click_mask(
-    clicked_points, features, orig_h, orig_w, input_h, input_w, dilate_kernel_size
-):
-    # model['sam'].set_image(image)
-    model["sam"].is_image_set = True
-    model["sam"].features = features
-    model["sam"].orig_h = orig_h
-    model["sam"].orig_w = orig_w
-    model["sam"].input_h = input_h
-    model["sam"].input_w = input_w
-
-    # Separate the points and labels
-    points, labels = zip(*[(point[:2], point[2]) for point in clicked_points])
-
-    # Convert the points and labels to numpy arrays
-    input_point = np.array(points)
-    input_label = np.array(labels)
-
-    masks, _, _ = model["sam"].predict(
-        point_coords=input_point,
-        point_labels=input_label,
-        multimask_output=False,
-    )
-    if dilate_kernel_size is not None:
-        masks = [dilate_mask(mask, dilate_kernel_size) for mask in masks]
-    else:
-        masks = [mask for mask in masks]
-
-    return masks
-
-
-def process_image_click(
-    original_image,
-    point_prompt,
-    clicked_points,
-    image_resolution,
-    features,
-    orig_h,
-    orig_w,
-    input_h,
-    input_w,
-    dilate_kernel_size,
-    evt: gr.SelectData,
-):
-    if clicked_points is None:
-        clicked_points = []
-
-    # print("Received click event:", evt)
-    if original_image is None:
-        # print("No image loaded.")
-        return None, clicked_points, None
-
-    clicked_coords = evt.index
-    if clicked_coords is None:
-        # print("No valid coordinates received.")
-        return None, clicked_points, None
-
-    x, y = clicked_coords
-    label = point_prompt
-    lab = 1 if label == "Foreground Point" else 0
-    clicked_points.append((x, y, lab))
-    # print("Updated points list:", clicked_points)
-
-    input_image = np.array(original_image, dtype=np.uint8)
-    H, W, C = input_image.shape
-    input_image = HWC3(input_image)
-    img = resize_image(input_image, image_resolution)
-    # print("Processed image size:", img.shape)
-
-    resized_points = resize_points(clicked_points, input_image.shape, image_resolution)
-    mask_click_np = get_click_mask(
-        resized_points, features, orig_h, orig_w, input_h, input_w, dilate_kernel_size
-    )
-    mask_click_np = np.transpose(mask_click_np, (1, 2, 0)) * 255.0
-    mask_image = HWC3(mask_click_np.astype(np.uint8))
-    mask_image = cv2.resize(mask_image, (W, H), interpolation=cv2.INTER_LINEAR)
-    # print("Mask image prepared.")
-
-    edited_image = input_image
-    for x, y, lab in clicked_points:
-        color = (255, 0, 0) if lab == 1 else (0, 0, 255)
-        edited_image = cv2.circle(edited_image, (x, y), 20, color, -1)
-
-    opacity_mask = 0.75
-    opacity_edited = 1.0
-    overlay_image = cv2.addWeighted(
-        edited_image,
-        opacity_edited,
-        (mask_image * np.array([0 / 255, 255 / 255, 0 / 255])).astype(np.uint8),
-        opacity_mask,
-        0,
-    )
-
-    no_mask_overlay = edited_image.copy()
-
-    return no_mask_overlay, overlay_image, clicked_points, mask_image
-
-
-def image_upload(image, image_resolution):
-    if image is None:
-        return None, None, None, None, None, None
-    else:
-        np_image = np.array(image, dtype=np.uint8)
-        H, W, C = np_image.shape
-        np_image = HWC3(np_image)
-        np_image = resize_image(np_image, image_resolution)
-        features, orig_h, orig_w, input_h, input_w = get_sam_feat(np_image)
-        return image, features, orig_h, orig_w, input_h, input_w
-
-
-def get_inpainted_img(image, mask, image_resolution):
-    lama_config = args.lama_config
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    if len(mask.shape) == 3:
-        mask = mask[:, :, 0]
-    img_inpainted = inpaint_img_with_builded_lama(
-        model["lama"], image, mask, lama_config, device=device
-    )
-    return img_inpainted
-
-
-# get args
-parser = argparse.ArgumentParser()
-setup_args(parser)
-args = parser.parse_args(sys.argv[1:])
-# build models
-model = {}
-# build the sam model
-model_type = "vit_h"
-ckpt_p = args.sam_ckpt
-model_sam = sam_model_registry[model_type](checkpoint=ckpt_p)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_sam.to(device=device)
-model["sam"] = SamPredictor(model_sam)
-
-# build the lama model
-lama_config = args.lama_config
-lama_ckpt = args.lama_ckpt
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model["lama"] = build_lama_model(lama_config, lama_ckpt, device=device)
-
-button_size = (100, 50)
-with gr.Blocks() as demo:
-    clicked_points = gr.State([])
-    # origin_image = gr.State(None)
-    click_mask = gr.State(None)
-    features = gr.State(None)
-    orig_h = gr.State(None)
-    orig_w = gr.State(None)
-    input_h = gr.State(None)
-    input_w = gr.State(None)
-
-    with gr.Row():
-        with gr.Column(variant="panel"):
-            with gr.Row():
-                gr.Markdown("## Upload an image and click the region you want to edit.")
-            with gr.Row():
-                source_image_click = gr.Image(
-                    type="numpy",
-                    interactive=True,
-                    label="Upload and Edit Image",
-                )
-
-                image_edit_complete = gr.Image(
-                    type="numpy",
-                    interactive=False,
-                    label="Editing Complete",
-                )
-            with gr.Row():
-                point_prompt = gr.Radio(
-                    choices=["Foreground Point", "Background Point"],
-                    value="Foreground Point",
-                    label="Point Label",
-                    interactive=True,
-                    show_label=False,
-                )
-                image_resolution = gr.Slider(
-                    label="Image Resolution",
-                    minimum=256,
-                    maximum=768,
-                    value=512,
-                    step=64,
-                )
-                dilate_kernel_size = gr.Slider(
-                    label="Dilate Kernel Size", minimum=0, maximum=30, value=15, step=1
-                )
-        with gr.Column(variant="panel"):
-            with gr.Row():
-                gr.Markdown("## Control Panel")
-            text_prompt = gr.Textbox(label="Text Prompt")
-            lama = gr.Button("Inpaint Image", variant="primary")
-            fill_sd = gr.Button("Fill Anything with SD", variant="primary")
-            replace_sd = gr.Button("Replace Anything with SD", variant="primary")
-            clear_button_image = gr.Button(value="Reset", variant="secondary")
-
-    # todo: maybe we can delete this row, for it's unnecessary to show the original mask for customers
-    with gr.Row(variant="panel"):
-        with gr.Column():
-            with gr.Row():
-                gr.Markdown("## Mask")
-            with gr.Row():
-                click_mask = gr.Image(
-                    type="numpy",
-                    label="Click Mask",
-                    interactive=False,
-                )
-        with gr.Column():
-            with gr.Row():
-                gr.Markdown("## Image Removed with Mask")
-            with gr.Row():
-                img_rm_with_mask = gr.Image(
-                    type="numpy",
-                    label="Image Removed with Mask",
-                    interactive=False,
-                )
-
-        with gr.Column():
-            with gr.Row():
-                gr.Markdown("## Fill Anything with Mask")
-            with gr.Row():
-                img_fill_with_mask = gr.Image(
-                    type="numpy",
-                    label="Image Fill Anything with Mask",
-                    interactive=False,
-                )
-
-        with gr.Column():
-            with gr.Row():
-                gr.Markdown("## Replace Anything with Mask")
-            with gr.Row():
-                img_replace_with_mask = gr.Image(
-                    type="numpy",
-                    label="Image Replace Anything with Mask",
-                    interactive=False,
-                )
-
-    source_image_click.upload(
-        image_upload,
-        inputs=[source_image_click, image_resolution],
-        outputs=[source_image_click, features, orig_h, orig_w, input_h, input_w],
-    )
-
-    source_image_click.select(
-        process_image_click,
-        inputs=[
-            source_image_click,
-            point_prompt,
-            clicked_points,
-            image_resolution,
-            features,
-            orig_h,
-            orig_w,
-            input_h,
-            input_w,
-            dilate_kernel_size,
-        ],
-        outputs=[source_image_click, image_edit_complete, clicked_points, click_mask],
-        show_progress=True,
-        queue=True,
-    )
-
-    lama.click(
-        get_inpainted_img,
-        inputs=[source_image_click, click_mask, image_resolution],
-        outputs=[img_rm_with_mask],
-    )
-
-    fill_sd.click(
-        get_fill_img_with_sd,
-        inputs=[source_image_click, click_mask, image_resolution, text_prompt],
-        outputs=[img_fill_with_mask],
-    )
-
-    replace_sd.click(
-        get_replace_img_with_sd,
-        inputs=[source_image_click, click_mask, image_resolution, text_prompt],
-        outputs=[img_replace_with_mask],
-    )
-
-    def reset(*args):
-        return [None for _ in args]
-
-    clear_button_image.click(
-        reset,
-        inputs=[
-            source_image_click,
-            image_edit_complete,
-            clicked_points,
-            click_mask,
-            features,
-            img_rm_with_mask,
-            img_fill_with_mask,
-            img_replace_with_mask,
-        ],
-        outputs=[
-            source_image_click,
-            image_edit_complete,
-            clicked_points,
-            click_mask,
-            features,
-            img_rm_with_mask,
-            img_fill_with_mask,
-            img_replace_with_mask,
-        ],
-    )
-
-if __name__ == "__main__":
-    demo.launch(debug=False, show_error=True)
+import os
+import sys
+
+sys.path.append(os.path.abspath(os.path.dirname(os.getcwd())))
+# os.chdir("../")
+import cv2
+import gradio as gr
+import numpy as np
+from pathlib import Path
+from matplotlib import pyplot as plt
+import torch
+import tempfile
+
+from stable_diffusion_inpaint import fill_img_with_sd, replace_img_with_sd
+from lama_inpaint import (
+    inpaint_img_with_lama,
+    build_lama_model,
+    inpaint_img_with_builded_lama,
+)
+from utils import (
+    load_img_to_array,
+    save_array_to_img,
+    dilate_mask,
+    show_mask,
+    show_points,
+)
+from PIL import Image
+from segment_anything import SamPredictor, sam_model_registry
+import argparse
+
+
+def setup_args(parser):
+    parser.add_argument(
+        "--lama_config",
+        type=str,
+        default="./lama/configs/prediction/default.yaml",
+        help="The path to the config file of lama model. "
+        "Default: the config of big-lama",
+    )
+    parser.add_argument(
+        "--lama_ckpt",
+        type=str,
+        default="./pretrained_models/big-lama",
+        help="The path to the lama checkpoint.",
+    )
+    parser.add_argument(
+        "--sam_ckpt",
+        type=str,
+        default="./pretrained_models/sam_vit_h_4b8939.pth",
+        help="The path to the SAM checkpoint to use for mask generation.",
+    )
+
+
+def mkstemp(suffix, dir=None):
+    fd, path = tempfile.mkstemp(suffix=f"{suffix}", dir=dir)
+    os.close(fd)
+    return Path(path)
+
+
+def get_sam_feat(img):
+    model["sam"].set_image(img)
+    features = model["sam"].features
+    orig_h = model["sam"].orig_h
+    orig_w = model["sam"].orig_w
+    input_h = model["sam"].input_h
+    input_w = model["sam"].input_w
+    model["sam"].reset_image()
+    return features, orig_h, orig_w, input_h, input_w
+
+
+def get_fill_img_with_sd(image, mask, image_resolution, text_prompt):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    if len(mask.shape) == 3:
+        mask = mask[:, :, 0]
+    np_image = np.array(image, dtype=np.uint8)
+    H, W, C = np_image.shape
+    np_image = HWC3(np_image)
+    np_image = resize_image(np_image, image_resolution)
+    mask = cv2.resize(
+        mask, (np_image.shape[1], np_image.shape[0]), interpolation=cv2.INTER_NEAREST
+    )
+
+    img_fill = fill_img_with_sd(np_image, mask, text_prompt, device=device)
+    img_fill = img_fill.astype(np.uint8)
+    return img_fill
+
+
+def get_replace_img_with_sd(image, mask, image_resolution, text_prompt):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    if len(mask.shape) == 3:
+        mask = mask[:, :, 0]
+    np_image = np.array(image, dtype=np.uint8)
+    H, W, C = np_image.shape
+    np_image = HWC3(np_image)
+    np_image = resize_image(np_image, image_resolution)
+    mask = cv2.resize(
+        mask, (np_image.shape[1], np_image.shape[0]), interpolation=cv2.INTER_NEAREST
+    )
+
+    img_replaced = replace_img_with_sd(np_image, mask, text_prompt, device=device)
+    img_replaced = img_replaced.astype(np.uint8)
+    return img_replaced
+
+
+def HWC3(x):
+    assert x.dtype == np.uint8
+    if x.ndim == 2:
+        x = x[:, :, None]
+    assert x.ndim == 3
+    H, W, C = x.shape
+    assert C == 1 or C == 3 or C == 4
+    if C == 3:
+        return x
+    if C == 1:
+        return np.concatenate([x, x, x], axis=2)
+    if C == 4:
+        color = x[:, :, 0:3].astype(np.float32)
+        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
+        y = color * alpha + 255.0 * (1.0 - alpha)
+        y = y.clip(0, 255).astype(np.uint8)
+        return y
+
+
+def resize_image(input_image, resolution):
+    H, W, C = input_image.shape
+    k = float(resolution) / min(H, W)
+    H = int(np.round(H * k / 64.0)) * 64
+    W = int(np.round(W * k / 64.0)) * 64
+    img = cv2.resize(
+        input_image,
+        (W, H),
+        interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA,
+    )
+    return img
+
+
+def resize_points(clicked_points, original_shape, resolution):
+    original_height, original_width, _ = original_shape
+    original_height = float(original_height)
+    original_width = float(original_width)
+
+    scale_factor = float(resolution) / min(original_height, original_width)
+    resized_points = []
+
+    for point in clicked_points:
+        x, y, lab = point
+        resized_x = int(round(x * scale_factor))
+        resized_y = int(round(y * scale_factor))
+        resized_point = (resized_x, resized_y, lab)
+        resized_points.append(resized_point)
+
+    return resized_points
+
+
+def get_click_mask(
+    clicked_points, features, orig_h, orig_w, input_h, input_w, dilate_kernel_size
+):
+    # model['sam'].set_image(image)
+    model["sam"].is_image_set = True
+    model["sam"].features = features
+    model["sam"].orig_h = orig_h
+    model["sam"].orig_w = orig_w
+    model["sam"].input_h = input_h
+    model["sam"].input_w = input_w
+
+    # Separate the points and labels
+    points, labels = zip(*[(point[:2], point[2]) for point in clicked_points])
+
+    # Convert the points and labels to numpy arrays
+    input_point = np.array(points)
+    input_label = np.array(labels)
+
+    masks, _, _ = model["sam"].predict(
+        point_coords=input_point,
+        point_labels=input_label,
+        multimask_output=False,
+    )
+    if dilate_kernel_size is not None:
+        masks = [dilate_mask(mask, dilate_kernel_size) for mask in masks]
+    else:
+        masks = [mask for mask in masks]
+
+    return masks
+
+
+def process_image_click(
+    original_image,
+    point_prompt,
+    clicked_points,
+    image_resolution,
+    features,
+    orig_h,
+    orig_w,
+    input_h,
+    input_w,
+    dilate_kernel_size,
+    evt: gr.SelectData,
+):
+    if clicked_points is None:
+        clicked_points = []
+
+    # print("Received click event:", evt)
+    if original_image is None:
+        # print("No image loaded.")
+        return None, clicked_points, None
+
+    clicked_coords = evt.index
+    if clicked_coords is None:
+        # print("No valid coordinates received.")
+        return None, clicked_points, None
+
+    x, y = clicked_coords
+    label = point_prompt
+    lab = 1 if label == "Foreground Point" else 0
+    clicked_points.append((x, y, lab))
+    # print("Updated points list:", clicked_points)
+
+    input_image = np.array(original_image, dtype=np.uint8)
+    H, W, C = input_image.shape
+    input_image = HWC3(input_image)
+    img = resize_image(input_image, image_resolution)
+    # print("Processed image size:", img.shape)
+
+    resized_points = resize_points(clicked_points, input_image.shape, image_resolution)
+    mask_click_np = get_click_mask(
+        resized_points, features, orig_h, orig_w, input_h, input_w, dilate_kernel_size
+    )
+    mask_click_np = np.transpose(mask_click_np, (1, 2, 0)) * 255.0
+    mask_image = HWC3(mask_click_np.astype(np.uint8))
+    mask_image = cv2.resize(mask_image, (W, H), interpolation=cv2.INTER_LINEAR)
+    # print("Mask image prepared.")
+
+    edited_image = input_image
+    for x, y, lab in clicked_points:
+        color = (255, 0, 0) if lab == 1 else (0, 0, 255)
+        edited_image = cv2.circle(edited_image, (x, y), 20, color, -1)
+
+    opacity_mask = 0.75
+    opacity_edited = 1.0
+    overlay_image = cv2.addWeighted(
+        edited_image,
+        opacity_edited,
+        (mask_image * np.array([0 / 255, 255 / 255, 0 / 255])).astype(np.uint8),
+        opacity_mask,
+        0,
+    )
+
+    no_mask_overlay = edited_image.copy()
+
+    return no_mask_overlay, overlay_image, clicked_points, mask_image
+
+
+def image_upload(image, image_resolution):
+    if image is None:
+        return None, None, None, None, None, None
+    else:
+        np_image = np.array(image, dtype=np.uint8)
+        H, W, C = np_image.shape
+        np_image = HWC3(np_image)
+        np_image = resize_image(np_image, image_resolution)
+        features, orig_h, orig_w, input_h, input_w = get_sam_feat(np_image)
+        return image, features, orig_h, orig_w, input_h, input_w
+
+
+def get_inpainted_img(image, mask, image_resolution):
+    lama_config = args.lama_config
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    if len(mask.shape) == 3:
+        mask = mask[:, :, 0]
+    img_inpainted = inpaint_img_with_builded_lama(
+        model["lama"], image, mask, lama_config, device=device
+    )
+    return img_inpainted
+
+
+# get args
+parser = argparse.ArgumentParser()
+setup_args(parser)
+args = parser.parse_args(sys.argv[1:])
+# build models
+model = {}
+# build the sam model
+model_type = "vit_h"
+ckpt_p = args.sam_ckpt
+model_sam = sam_model_registry[model_type](checkpoint=ckpt_p)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_sam.to(device=device)
+model["sam"] = SamPredictor(model_sam)
+
+# build the lama model
+lama_config = args.lama_config
+lama_ckpt = args.lama_ckpt
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model["lama"] = build_lama_model(lama_config, lama_ckpt, device=device)
+
+button_size = (100, 50)
+with gr.Blocks() as demo:
+    clicked_points = gr.State([])
+    # origin_image = gr.State(None)
+    click_mask = gr.State(None)
+    features = gr.State(None)
+    orig_h = gr.State(None)
+    orig_w = gr.State(None)
+    input_h = gr.State(None)
+    input_w = gr.State(None)
+
+    with gr.Row():
+        with gr.Column(variant="panel"):
+            with gr.Row():
+                gr.Markdown("## Upload an image and click the region you want to edit.")
+            with gr.Row():
+                source_image_click = gr.Image(
+                    type="numpy",
+                    interactive=True,
+                    label="Upload and Edit Image",
+                )
+
+                image_edit_complete = gr.Image(
+                    type="numpy",
+                    interactive=False,
+                    label="Editing Complete",
+                )
+            with gr.Row():
+                point_prompt = gr.Radio(
+                    choices=["Foreground Point", "Background Point"],
+                    value="Foreground Point",
+                    label="Point Label",
+                    interactive=True,
+                    show_label=False,
+                )
+                image_resolution = gr.Slider(
+                    label="Image Resolution",
+                    minimum=256,
+                    maximum=768,
+                    value=512,
+                    step=64,
+                )
+                dilate_kernel_size = gr.Slider(
+                    label="Dilate Kernel Size", minimum=0, maximum=30, value=15, step=1
+                )
+        with gr.Column(variant="panel"):
+            with gr.Row():
+                gr.Markdown("## Control Panel")
+            text_prompt = gr.Textbox(label="Text Prompt")
+            lama = gr.Button("Inpaint Image", variant="primary")
+            fill_sd = gr.Button("Fill Anything with SD", variant="primary")
+            replace_sd = gr.Button("Replace Anything with SD", variant="primary")
+            clear_button_image = gr.Button(value="Reset", variant="secondary")
+
+    # todo: maybe we can delete this row, for it's unnecessary to show the original mask for customers
+    with gr.Row(variant="panel"):
+        with gr.Column():
+            with gr.Row():
+                gr.Markdown("## Mask")
+            with gr.Row():
+                click_mask = gr.Image(
+                    type="numpy",
+                    label="Click Mask",
+                    interactive=False,
+                )
+        with gr.Column():
+            with gr.Row():
+                gr.Markdown("## Image Removed with Mask")
+            with gr.Row():
+                img_rm_with_mask = gr.Image(
+                    type="numpy",
+                    label="Image Removed with Mask",
+                    interactive=False,
+                )
+
+        with gr.Column():
+            with gr.Row():
+                gr.Markdown("## Fill Anything with Mask")
+            with gr.Row():
+                img_fill_with_mask = gr.Image(
+                    type="numpy",
+                    label="Image Fill Anything with Mask",
+                    interactive=False,
+                )
+
+        with gr.Column():
+            with gr.Row():
+                gr.Markdown("## Replace Anything with Mask")
+            with gr.Row():
+                img_replace_with_mask = gr.Image(
+                    type="numpy",
+                    label="Image Replace Anything with Mask",
+                    interactive=False,
+                )
+
+    gr.Markdown(
+        "Github Source Code: [Link](https://github.com/pg56714/Inpaint-Anything-Gradio)"
+    )
+
+    source_image_click.upload(
+        image_upload,
+        inputs=[source_image_click, image_resolution],
+        outputs=[source_image_click, features, orig_h, orig_w, input_h, input_w],
+    )
+
+    source_image_click.select(
+        process_image_click,
+        inputs=[
+            source_image_click,
+            point_prompt,
+            clicked_points,
+            image_resolution,
+            features,
+            orig_h,
+            orig_w,
+            input_h,
+            input_w,
+            dilate_kernel_size,
+        ],
+        outputs=[source_image_click, image_edit_complete, clicked_points, click_mask],
+        show_progress=True,
+        queue=True,
+    )
+
+    lama.click(
+        get_inpainted_img,
+        inputs=[source_image_click, click_mask, image_resolution],
+        outputs=[img_rm_with_mask],
+    )
+
+    fill_sd.click(
+        get_fill_img_with_sd,
+        inputs=[source_image_click, click_mask, image_resolution, text_prompt],
+        outputs=[img_fill_with_mask],
+    )
+
+    replace_sd.click(
+        get_replace_img_with_sd,
+        inputs=[source_image_click, click_mask, image_resolution, text_prompt],
+        outputs=[img_replace_with_mask],
+    )
+
+    def reset(*args):
+        return [None for _ in args]
+
+    clear_button_image.click(
+        reset,
+        inputs=[
+            source_image_click,
+            image_edit_complete,
+            clicked_points,
+            click_mask,
+            features,
+            img_rm_with_mask,
+            img_fill_with_mask,
+            img_replace_with_mask,
+        ],
+        outputs=[
+            source_image_click,
+            image_edit_complete,
+            clicked_points,
+            click_mask,
+            features,
+            img_rm_with_mask,
+            img_fill_with_mask,
+            img_replace_with_mask,
+        ],
+    )
+
+if __name__ == "__main__":
+    demo.launch(debug=False, show_error=True)