Update app.py
Browse files
app.py
CHANGED
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@@ -13,20 +13,17 @@ import tempfile
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import uuid
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import time
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import threading
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torch.set_float32_matmul_precision("medium")
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# Load both BiRefNet models
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birefnet = AutoModelForImageSegmentation.from_pretrained(
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"ZhengPeng7/BiRefNet", trust_remote_code=True
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)
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birefnet.to(device)
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birefnet_lite = AutoModelForImageSegmentation.from_pretrained(
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"ZhengPeng7/BiRefNet_lite", trust_remote_code=True
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)
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birefnet_lite.to(device)
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transform_image = transforms.Compose(
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@@ -37,7 +34,6 @@ transform_image = transforms.Compose(
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]
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)
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# Function to delete files older than 10 minutes in the temp directory
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def cleanup_temp_files():
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while True:
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@@ -55,11 +51,29 @@ def cleanup_temp_files():
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print(f"Error deleting file {filepath}: {e}")
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time.sleep(60) # Check every minute
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# Start the cleanup thread
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cleanup_thread = threading.Thread(target=cleanup_temp_files, daemon=True)
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cleanup_thread.start()
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@spaces.GPU
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def fn(vid, bg_type="Color", bg_image=None, bg_video=None, color="#00FF00", fps=0, video_handling="slow_down", fast_mode=True):
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@@ -77,7 +91,7 @@ def fn(vid, bg_type="Color", bg_image=None, bg_video=None, color="#00FF00", fps=
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audio = video.audio
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# Extract frames at the specified FPS
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frames = video.iter_frames(fps=fps)
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# Process each frame for background removal
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processed_frames = []
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@@ -96,29 +110,14 @@ def fn(vid, bg_type="Color", bg_image=None, bg_video=None, color="#00FF00", fps=
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bg_frame_index = 0 # Initialize background frame index
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background_frame = background_frames[bg_frame_index % len(background_frames)]
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bg_frame_index += 1
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background_image = Image.fromarray(background_frame)
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processed_image = process(pil_image, background_image, fast_mode)
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else: # video_handling == "loop"
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background_frame = background_frames[bg_frame_index % len(background_frames)]
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bg_frame_index += 1
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background_image = Image.fromarray(background_frame)
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processed_image = process(pil_image, background_image, fast_mode)
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else:
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processed_image = pil_image # Default to original image if no background is selected
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processed_frames.append(np.array(processed_image))
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elapsed_time = time.time() - start_time
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yield processed_image, None, f"Processing frame {i+1}... Elapsed time: {elapsed_time:.2f} seconds"
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# Create a new video from the processed frames
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processed_video = mp.ImageSequenceClip(processed_frames, fps=fps)
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@@ -135,8 +134,9 @@ def fn(vid, bg_type="Color", bg_image=None, bg_video=None, color="#00FF00", fps=
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elapsed_time = time.time() - start_time
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yield gr.update(visible=False), gr.update(visible=True), f"Processing complete! Elapsed time: {elapsed_time:.2f} seconds"
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# Return the path to the temporary file
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yield
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except Exception as e:
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print(f"Error: {e}")
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@@ -144,7 +144,6 @@ def fn(vid, bg_type="Color", bg_image=None, bg_video=None, color="#00FF00", fps=
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yield gr.update(visible=False), gr.update(visible=True), f"Error processing video: {e}. Elapsed time: {elapsed_time:.2f} seconds"
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yield None, f"Error processing video: {e}", f"Error processing video: {e}. Elapsed time: {elapsed_time:.2f} seconds"
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def process(image, bg, fast_mode=False):
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image_size = image.size
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input_images = transform_image(image).unsqueeze(0).to("cuda")
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@@ -169,12 +168,10 @@ def process(image, bg, fast_mode=False):
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# Composite the image onto the background using the mask
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image = Image.composite(image, background, mask)
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return image
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with gr.Blocks(theme=gr.themes.Ocean()) as demo:
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gr.Markdown("# Video Background Remover & Changer\n### You can replace image background with any color, image or video.\nNOTE: As this Space is running on ZERO GPU it has limit. It can handle approx
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with gr.Row():
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in_video = gr.Video(label="Input Video", interactive=True)
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stream_image = gr.Image(label="Streaming Output", visible=False)
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@@ -196,8 +193,7 @@ with gr.Blocks(theme=gr.themes.Ocean()) as demo:
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with gr.Column(visible=False) as video_handling_options:
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video_handling_radio = gr.Radio(["slow_down", "loop"], label="Video Handling", value="slow_down", interactive=True)
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fast_mode_checkbox = gr.Checkbox(label="Fast Mode (Use BiRefNet_lite)", value=True, interactive=True)
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time_textbox = gr.Textbox(label="Time Elapsed", interactive=False) # Add time textbox
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def update_visibility(bg_type):
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if bg_type == "Color":
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import uuid
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import time
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import threading
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from concurrent.futures import ThreadPoolExecutor
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torch.set_float32_matmul_precision("medium")
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# Load both BiRefNet models
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birefnet = AutoModelForImageSegmentation.from_pretrained(
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"ZhengPeng7/BiRefNet", trust_remote_code=True)
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birefnet.to(device)
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birefnet_lite = AutoModelForImageSegmentation.from_pretrained(
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"ZhengPeng7/BiRefNet_lite", trust_remote_code=True)
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birefnet_lite.to(device)
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transform_image = transforms.Compose(
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]
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)
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# Function to delete files older than 10 minutes in the temp directory
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def cleanup_temp_files():
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while True:
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print(f"Error deleting file {filepath}: {e}")
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time.sleep(60) # Check every minute
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# Start the cleanup thread
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cleanup_thread = threading.Thread(target=cleanup_temp_files, daemon=True)
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cleanup_thread.start()
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# Function to process a single frame
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def process_frame(frame, bg_type, bg, fast_mode, bg_frame_index, background_frames, color):
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try:
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pil_image = Image.fromarray(frame)
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if bg_type == "Color":
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processed_image = process(pil_image, color, fast_mode)
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elif bg_type == "Image":
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processed_image = process(pil_image, bg, fast_mode)
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elif bg_type == "Video":
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background_frame = background_frames[bg_frame_index % len(background_frames)]
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bg_frame_index += 1
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background_image = Image.fromarray(background_frame)
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processed_image = process(pil_image, background_image, fast_mode)
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else:
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processed_image = pil_image # Default to original image if no background is selected
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return np.array(processed_image), bg_frame_index
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except Exception as e:
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print(f"Error processing frame: {e}")
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return frame, bg_frame_index
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@spaces.GPU
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def fn(vid, bg_type="Color", bg_image=None, bg_video=None, color="#00FF00", fps=0, video_handling="slow_down", fast_mode=True):
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audio = video.audio
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# Extract frames at the specified FPS
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frames = list(video.iter_frames(fps=fps))
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# Process each frame for background removal
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processed_frames = []
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bg_frame_index = 0 # Initialize background frame index
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# Use ThreadPoolExecutor for parallel processing
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with ThreadPoolExecutor(max_workers=4) as executor:
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futures = [executor.submit(process_frame, frames[i], bg_type, bg_image, fast_mode, bg_frame_index, background_frames, color) for i in range(len(frames))]
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for future in futures:
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result, bg_frame_index = future.result()
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processed_frames.append(result)
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elapsed_time = time.time() - start_time
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yield result, None, f"Processing frame {len(processed_frames)}... Elapsed time: {elapsed_time:.2f} seconds"
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# Create a new video from the processed frames
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processed_video = mp.ImageSequenceClip(processed_frames, fps=fps)
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elapsed_time = time.time() - start_time
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yield gr.update(visible=False), gr.update(visible=True), f"Processing complete! Elapsed time: {elapsed_time:.2f} seconds"
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# Return the path to the temporary file
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yield processed_frames[-1], temp_filepath, f"Processing complete! Elapsed time: {elapsed_time:.2f} seconds"
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except Exception as e:
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print(f"Error: {e}")
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yield gr.update(visible=False), gr.update(visible=True), f"Error processing video: {e}. Elapsed time: {elapsed_time:.2f} seconds"
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yield None, f"Error processing video: {e}", f"Error processing video: {e}. Elapsed time: {elapsed_time:.2f} seconds"
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def process(image, bg, fast_mode=False):
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image_size = image.size
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input_images = transform_image(image).unsqueeze(0).to("cuda")
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# Composite the image onto the background using the mask
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image = Image.composite(image, background, mask)
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return image
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with gr.Blocks(theme=gr.themes.Ocean()) as demo:
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gr.Markdown("# Video Background Remover & Changer\n### You can replace image background with any color, image or video.\nNOTE: As this Space is running on ZERO GPU it has limit. It can handle approx 200 frames at once. So, if you have a big video than use small chunks or Duplicate this space.")
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with gr.Row():
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in_video = gr.Video(label="Input Video", interactive=True)
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stream_image = gr.Image(label="Streaming Output", visible=False)
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with gr.Column(visible=False) as video_handling_options:
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video_handling_radio = gr.Radio(["slow_down", "loop"], label="Video Handling", value="slow_down", interactive=True)
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fast_mode_checkbox = gr.Checkbox(label="Fast Mode (Use BiRefNet_lite)", value=True, interactive=True)
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time_textbox = gr.Textbox(label="Time Elapsed", interactive=False) # Add time textbox
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def update_visibility(bg_type):
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if bg_type == "Color":
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