Update app.py

app.py

@@ -5,7 +5,6 @@ import numpy as np
 import cv2 as cv
 from google.cloud import storage
 
-# Constants
 GCS_BUCKET_NAME = "veytel-cloud-store"
 GCS_FOLDER_PATH = "density_mapper"
 service_account_json_str = os.getenv('serviceKey')
@@ -13,19 +12,60 @@ service_account_json = json.loads(service_account_json_str)
 
 if service_account_json:
     print("Secret Value Retrieved Successfully")
+    #print(service_account_json)
 else:
     print("Failed to Retrieve Secret Value")
 
-# GCS Authentication
+count = 0
+fresh_start = False
+
+def set_min_dense_1(max_dense_0):
+    global scaled_thresh1
+    print("max_dense_0", max_dense_0)
+    scaled_thresh1 = int(max_dense_0) * max_val / 255
+    dense_0 = np.where(textured_cxr < scaled_thresh1, textured_cxr, 0)
+    dense_1 = np.where(((textured_cxr < scaled_thresh2) & (textured_cxr >= scaled_thresh1)), textured_cxr, 0)
+    return max_dense_0, dense_0, dense_1
+
+
+def set_min_dense_2(max_dense_1):
+    global scaled_thresh2
+    print("max_dense_1", max_dense_1)
+    scaled_thresh2 = int(max_dense_1) * max_val / 255
+    dense_1 = np.where(((textured_cxr < scaled_thresh2) & (textured_cxr >= scaled_thresh1)), textured_cxr, 0)
+    dense_2 = np.where(((textured_cxr < scaled_thresh3) & (textured_cxr >= scaled_thresh2)), textured_cxr, 0)
+    return max_dense_1, dense_1, dense_2
+
+
+def set_min_dense_3(max_dense_2):
+    global scaled_thresh3
+    print("max_dense_2", max_dense_2)
+    scaled_thresh3 = int(max_dense_2) * max_val / 255
+    dense_2 = np.where(((textured_cxr < scaled_thresh3) & (textured_cxr >= scaled_thresh2)), textured_cxr, 0)
+    dense_3 = np.where(((textured_cxr < 255) & (textured_cxr >= scaled_thresh3)), textured_cxr, 0)
+    return max_dense_2, dense_2, dense_3
+
 def authenticate_gcs():
     return storage.Client.from_service_account_info(service_account_json)
 
-# GCS Operations
+
 def download_csv_from_gcs(filename):
     client = authenticate_gcs()
     bucket = client.get_bucket(GCS_BUCKET_NAME)
     blob = bucket.blob(os.path.join(GCS_FOLDER_PATH, filename))
-    return blob.download_as_text()
+    csv_content = blob.download_as_text()
+    return csv_content
+
+def read_csv_from_gcs(user):
+    filename = f"density_{user}.csv"
+    try:
+        csv_content = download_csv_from_gcs(filename)
+        rows = csv_content.strip().split("\n")
+        csv_data = [row.split(",") for row in rows]
+        return csv_data
+    except Exception as e:
+        print("Error reading CSV from GCS:", e)
+        return []
 
 def upload_csv_to_gcs(csv_content, filename):
     client = authenticate_gcs()
@@ -33,139 +73,333 @@ def upload_csv_to_gcs(csv_content, filename):
     blob = bucket.blob(os.path.join(GCS_FOLDER_PATH, filename))
     blob.upload_from_string(csv_content)
 
-# Load user state
-def read_user_state(user):
-    filename = f"density_{user}.csv"
-    try:
-        csv_content = download_csv_from_gcs(filename)
-        rows = csv_content.strip().split("\n")
-        csv_data = [row.split(",") for row in rows]
+def new__cxr(max_dense_0, max_dense_1, max_dense_2):
+    global textured_cxr, lung_noised, max_val, cxr, mask, scaled_thresh1, scaled_thresh2, scaled_thresh3, image_id, index, count, label1
+    csv_data = read_csv_from_gcs(user)
+    if count < 30:
+        csv_content = ""
+        for row in csv_data:
+            csv_content += ",".join(row) + "\n"
+        if count > 0:
+            csv_content += f"{count},{max_dense_0},{max_dense_1},{max_dense_2}\n"
+        filename = f"density_{user}.csv"
+        upload_csv_to_gcs(csv_content, filename)
 
-        if len(csv_data) > 1:
-            last_row = csv_data[-1]
-            user_state = {
-                "count": int(last_row[0]),
-                "max_dense_0": int(last_row[1]),
-                "max_dense_1": int(last_row[2]),
-                "max_dense_2": int(last_row[3])
-            }
-        else:
-            user_state = {"count": 0, "max_dense_0": 50, "max_dense_1": 100, "max_dense_2": 150}
-        return user_state
-    except Exception as e:
-        print(f"Error reading CSV for {user}: {e}")
-        return {"count": 0, "max_dense_0": 50, "max_dense_1": 100, "max_dense_2": 150}
+    if (count >= 30):
+        csv_content = ""
+        for row in csv_data:
+            csv_content += ",".join(row) + "\n"
+        if count > 0:
+            csv_content += f"{count},{max_dense_0},{max_dense_1},{max_dense_2}\n"
+        filename = f"density_{user}.csv"
+        upload_csv_to_gcs(csv_content, filename)
+        empty_image = np.zeros((256, 256), dtype=np.uint8)
+        label1.update(visible=False)
+        count = 0
+        image_id = 1
+        index = 1
+        fieldnames = ['count', 'thresh_1', 'thresh_2', 'thresh_3']
+        csv_content += ",".join(fieldnames) + "\n"
+        filename = f"density_{user}.csv"
+        upload_csv_to_gcs(csv_content, filename)
+        return empty_image, empty_image, empty_image, empty_image, empty_image, empty_image, empty_image, empty_image, 0
+
+
+    if count >= 0:
+        index += 1
+    if index > 3:
+        index = 1
+        image_id += 1
 
-# Save user state
-def save_user_state(user, count, max_dense_0, max_dense_1, max_dense_2):
-    csv_content = f"{count},{max_dense_0},{max_dense_1},{max_dense_2}\n"
+    count += 1
+    # write count, thresh1, thresh2, thresh3 to csv file
+    cxr_file = "cxr" + str(image_id) + "_cxr.png"
+    mask_file = "cxr" + str(image_id) + "_mask.png"
+    textured_cxr_file = "cxr" + str(image_id) + "_textured_" + str(index) + ".png"
+    lung_noised_file = "cxr" + str(image_id) + "_lung_noised_" + str(index) + ".png"
+    cxr_path = os.path.join(cxr_dir, cxr_file)
+    mask_path = os.path.join(mask_dir, mask_file)
+    textured_cxr_path = os.path.join(textured_cxr_dir, textured_cxr_file)
+    lung_noised_path = os.path.join(lung_noised_dir, lung_noised_file)
+    cxr = cv.imread(cxr_path, cv.IMREAD_GRAYSCALE)
+    mask = cv.imread(mask_path, cv.IMREAD_GRAYSCALE)
+    textured_cxr = cv.imread(textured_cxr_path, cv.IMREAD_GRAYSCALE)
+    lung_noised = cv.imread(lung_noised_path, cv.IMREAD_GRAYSCALE)
+    max_val = np.percentile(cxr, 97)  # To optimize later
+    thresh1 = 50
+    thresh2 = 100
+    thresh3 = 150
+    scaled_thresh1 = thresh1 * max_val / 255
+    scaled_thresh2 = thresh2 * max_val / 255
+    scaled_thresh3 = thresh3 * max_val / 255
+    dense_0 = np.where(textured_cxr < scaled_thresh1, textured_cxr, 0)
+    dense_1 = np.where(((textured_cxr < scaled_thresh2) & (textured_cxr >= scaled_thresh1)), textured_cxr, 0)
+    dense_2 = np.where(((textured_cxr < scaled_thresh3) & (textured_cxr >= scaled_thresh2)), textured_cxr, 0)
+    dense_3 = np.where(((textured_cxr < 255) & (textured_cxr >= scaled_thresh3)), textured_cxr, 0)
+    return cxr, textured_cxr, lung_noised, lung_noised, dense_0, dense_1, dense_2, dense_3, count
+
+
+def create_csv():
+    global count, fieldnames, image_id, index, csv_path, fresh_start
+    fieldnames = ['count', 'thresh_1', 'thresh_2', 'thresh_3']
+    csv_data = read_csv_from_gcs(user)
+    csv_content = ""
+    last_row_count = 0
+    if not csv_data:
+        csv_content = ",".join(fieldnames) + "\n"
+    else:
+        for i, row in enumerate(csv_data):
+            csv_content += ",".join(row) + "\n"
+            if i != 0:
+                if isinstance(row[0], str):
+                    last_row_count = 0
+                else:
+                    last_row_count = int(row[0])
+
+    #csv_content += f"{count+1},{thresh1},{thresh2},{thresh3}\n"
     filename = f"density_{user}.csv"
     upload_csv_to_gcs(csv_content, filename)
+    count = last_row_count
+    if(count>0):
+        image_id, index = get_image_id_index(count)
+    fresh_start = True
 
-# Density calculation functions
-def set_min_dense_1(max_dense_0, textured_cxr, max_val):
-    scaled_thresh1 = int(max_dense_0) * max_val / 255
-    dense_0 = np.where(textured_cxr < scaled_thresh1, textured_cxr, 0)
-    return max_dense_0, dense_0
 
-def set_min_dense_2(max_dense_1, textured_cxr, scaled_thresh1, max_val):
-    scaled_thresh2 = int(max_dense_1) * max_val / 255
-    dense_1 = np.where(((textured_cxr < scaled_thresh2) & (textured_cxr >= scaled_thresh1)), textured_cxr, 0)
-    return max_dense_1, dense_1
+def check_auth(username, password):
+    global user, fresh_start
+    user = username
+    if (user == 'gk' and password == 'upmc2023'):
+        create_csv()
+        return True
+    elif (user == 'veytel' and password == 'pittsburgh'):
+        create_csv()
+        return True
+    elif (user == 'cathy' and password == 'veytel'):
+        create_csv()
+        return True
+    elif (user == 'ellen' and password == 'veytel'):
+        create_csv()
+        return True
+    elif (user == 'kevin' and password == 'veytel'):
+        create_csv()
+        return True
+    elif (user == 'swathi' and password == 'veytel'):
+        create_csv()
+        return True
+    elif (user == 'mike' and password == 'veytel'):
+        create_csv()
+        return True
+    elif (user == 'test' and password == 'test'):
+        create_csv()
+        return True
+    elif (user == 'nischal' and password == 'veytel'):
+        create_csv()
+        return True
+    elif (user == 'vijayakumar' and password == 'veytel'):
+        create_csv()
+        return True
+    elif (user == 'konstantine' and password == 'upmc2023'):
+        create_csv()
+        return True
+    elif (user == 'taaha' and password == 'upmc2023'):
+        create_csv()
+        return True
+    elif (user == 'nameer' and password == 'upmc2023'):
+        create_csv()
+        return True
+    elif (user == 'siddique' and password == 'upmc2023'):
+        create_csv()
+        return True
 
-def set_min_dense_3(max_dense_2, textured_cxr, scaled_thresh2, max_val):
-    scaled_thresh3 = int(max_dense_2) * max_val / 255
+
+def change_vis():
+    global count, fresh_start, button1, button2
+    if fresh_start:
+        fresh_start = not fresh_start
+    if count >= 30:
+        return gr.Label(visible=True), gr.Button(visible=False), gr.Button(visible=False)
+    else:
+        return gr.Label(visible=False), gr.Button(visible=True), gr.Button(visible=False)
+
+
+image_id = 1
+index = 1
+
+def get_image_id_index(count):
+    #global image_id, index
+    if count ==0:
+        return 1, 1
+    image_id = (count - 1) // 3 + 1
+    index = (count - 1) % 3 + 1
+    return image_id, index
+
+def set_layout():
+    global textured_cxr, lung_noised, max_val, cxr, mask, scaled_thresh1, scaled_thresh2, scaled_thresh3, image_id, index, count
+    csv_data = read_csv_from_gcs(user)
+    csv_content = ""
+    last_row_count = 0
+    if csv_data:
+        for i, row in enumerate(csv_data):
+            if i != 0:
+                if str(row[0]) == 'count':
+                    last_row_count = 0
+                else:
+                    last_row_count = int(row[0])
+
+
+    count = last_row_count
+    if (count > 0):
+        image_id, index = get_image_id_index(count)
+
+    if count >= 30:
+        empty_image = np.zeros((256, 256), dtype=np.uint8)
+        return empty_image, empty_image, empty_image, empty_image, empty_image, empty_image, empty_image, empty_image, 30
+    image_id, index = get_image_id_index(count)
+    if count == 0:
+        count = 1
+        image_id = 1
+        index = 1
+    else:
+        count +=1
+        index += 1
+        if index > 3:
+            index = 1
+            image_id += 1
+
+    #new__cxr(max_dense_0, max_dense_1, max_dense_2)
+    cxr_file = "cxr" + str(image_id) + "_cxr.png"
+    mask_file = "cxr" + str(image_id) + "_mask.png"
+    textured_cxr_file = "cxr" + str(image_id) + "_textured_" + str(index) + ".png"
+    lung_noised_file = "cxr" + str(image_id) + "_lung_noised_" + str(index) + ".png"
+    cxr_path = os.path.join(cxr_dir, cxr_file)
+    mask_path = os.path.join(mask_dir, mask_file)
+    textured_cxr_path = os.path.join(textured_cxr_dir, textured_cxr_file)
+    lung_noised_path = os.path.join(lung_noised_dir, lung_noised_file)
+    cxr = cv.imread(cxr_path, cv.IMREAD_GRAYSCALE)
+    mask = cv.imread(mask_path, cv.IMREAD_GRAYSCALE)
+    textured_cxr = cv.imread(textured_cxr_path, cv.IMREAD_GRAYSCALE)
+    lung_noised = cv.imread(lung_noised_path, cv.IMREAD_GRAYSCALE)
+    max_val = np.percentile(cxr, 97)  # To optimize later
+    thresh1 = 50
+    thresh2 = 100
+    thresh3 = 150
+    scaled_thresh1 = thresh1 * max_val / 255
+    scaled_thresh2 = thresh2 * max_val / 255
+    scaled_thresh3 = thresh3 * max_val / 255
+    dense_0 = np.where(textured_cxr < scaled_thresh1, textured_cxr, 0)
+    dense_1 = np.where(((textured_cxr < scaled_thresh2) & (textured_cxr >= scaled_thresh1)), textured_cxr, 0)
     dense_2 = np.where(((textured_cxr < scaled_thresh3) & (textured_cxr >= scaled_thresh2)), textured_cxr, 0)
     dense_3 = np.where(((textured_cxr < 255) & (textured_cxr >= scaled_thresh3)), textured_cxr, 0)
-    return max_dense_2, dense_2, dense_3
+    return cxr, textured_cxr, lung_noised, lung_noised, dense_0, dense_1, dense_2, dense_3, count
+
 
-# Update layout with new images and densities
-def update_layout(user_state, max_dense_0, max_dense_1, max_dense_2):
-    count = user_state['count']
-    
-    # Dummy data (replace with actual image loading code)
-    cxr = np.zeros((256, 256), dtype=np.uint8)
-    textured_cxr = np.zeros((256, 256), dtype=np.uint8)
-    lung_noised = np.zeros((256, 256), dtype=np.uint8)
-    
-    # Calculate scaled thresholds and densities
-    max_val = np.percentile(cxr, 97)
-    scaled_thresh1 = max_dense_0 * max_val / 255
-    scaled_thresh2 = max_dense_1 * max_val / 255
-    scaled_thresh3 = max_dense_2 * max_val / 255
 
+def update_layout(image_id, index):
+    global cxr_dir,mask_dir, textured_cxr_dir,lung_noised_dir,  cxr, mask, textured_cxr, lung_noised, max_val, thresh1, thresh2, thresh3, scaled_thresh1, scaled_thresh2, scaled_thresh3, dense_0, dense_1, dense_2, dense_3, label_title, min_dense_0, max_dense_3, button1, button2, label1
+    executable_path = os.path.dirname(os.path.realpath(__file__))
+    cxr_dir = os.path.join(executable_path, "Images/cxr")
+    mask_dir = os.path.join(executable_path, "Images/mask")
+    textured_cxr_dir = os.path.join(executable_path, "Images/textured_cxr")
+    lung_noised_dir = os.path.join(executable_path, "Images/lung_noised")
+
+    empty_image = np.zeros((256, 256), dtype=np.uint8)
+    cxr = empty_image
+    mask = empty_image
+    textured_cxr = empty_image
+    lung_noised = empty_image
+    max_val = np.percentile(cxr, 97)  # To optimize later
+    thresh1 = 50
+    thresh2 = 100
+    thresh3 = 150
+    scaled_thresh1 = thresh1 * max_val / 255
+    scaled_thresh2 = thresh2 * max_val / 255
+    scaled_thresh3 = thresh3 * max_val / 255
     dense_0 = np.where(textured_cxr < scaled_thresh1, textured_cxr, 0)
     dense_1 = np.where(((textured_cxr < scaled_thresh2) & (textured_cxr >= scaled_thresh1)), textured_cxr, 0)
     dense_2 = np.where(((textured_cxr < scaled_thresh3) & (textured_cxr >= scaled_thresh2)), textured_cxr, 0)
     dense_3 = np.where(((textured_cxr < 255) & (textured_cxr >= scaled_thresh3)), textured_cxr, 0)
+    # open csv in append mode
+    # add title
+    with gr.Row():
+        # label_title = gr.Label("Density Mapper", visible=True)
+        gr.Markdown(
+            """
+        <center>
+        <h1>Density Mapper</h1>
+        </center>
+        <h3>Instructions:</h3>
+        1. Set the brightness of your display to maximum<br>
+        2. Initiate the process by clicking the 'Start' button <br>
+        3. Synthetic density(middle image in top row) is added to "Original CXR" to obtain "Synthetic CXR"<br>
+        4. Adjust the brightness thresholds using the sliders provided \
+            to obtain the correct density maps for each level of RALE density<br>
+        5. If a density level has absence of pixels at the upper limit, please set the Max Value to 255<br>
+        6. Click "Save & continue" to proceed to the next image. The progress is shown in the progress bar<br>
+        7. You may close the window and resume the process later when you reopen the window
+        """
+        )
 
-    return cxr, textured_cxr, lung_noised, dense_0, dense_1, dense_2, dense_3, count
+    with gr.Row():
+        with gr.Column():
+            im1 = gr.Image(cxr, label="Original CXR")
+        with gr.Column():
+            im2 = gr.Image(textured_cxr, label="Combined Synthetic Density 0-3")
+        with gr.Column():
+            im3 = gr.Image(lung_noised, label="Synthetic CXR")
+        with gr.Column():
+            label1 = gr.Label("Completed! Please close window", visible=False)
 
-# Handle new CXR processing
-def new_cxr(user, max_dense_0, max_dense_1, max_dense_2):
-    user_state = read_user_state(user)
-    count = user_state['count'] + 1
-    save_user_state(user, count, max_dense_0, max_dense_1, max_dense_2)
-    return update_layout(user_state, max_dense_0, max_dense_1, max_dense_2)
+    with gr.Row():
+        with gr.Column():
+            dense0 = gr.Image(dense_0, label="Pixels @ Density 0")
+            with gr.Row():
+                min_dense_0 = gr.Textbox(value='0', label="Min")
+                max_dense_0 = gr.Slider(0, 255, value=50, step=1, label="Max")
 
-# User Authentication
-def check_auth(username, password):
-    valid_users = {
-        'gk': 'upmc2023', 'veytel': 'pittsburgh',
-        'cathy': 'veytel', 'ellen': 'veytel', 'kevin': 'veytel',
-        'swathi': 'veytel', 'mike': 'veytel', 'test': 'test',
-        'nischal': 'veytel', 'vijayakumar': 'veytel',
-        'konstantine': 'upmc2023', 'taaha': 'upmc2023',
-        'nameer': 'upmc2023', 'siddique': 'upmc2023'
-    }
-    return valid_users.get(username) == password
-
-# Main Gradio Layout
-def auth_and_start(username, password):
-    if check_auth(username, password):
-        user_state = read_user_state(username)
-        return update_layout(user_state, 50, 100, 150)  # Default threshold values
-    else:
-        return "Invalid login credentials", None, None, None, None, None, None, None, None
+        with gr.Column():
+            dense1 = gr.Image(dense_1, label="Pixels @ Density 1")
+            with gr.Row():
+                min_dense_1 = gr.Textbox(value='50', label="Min")
+                max_dense_1 = gr.Slider(0, 255, value=100, step=1, label="Max")
+                max_dense_0.change(set_min_dense_1, inputs=max_dense_0, outputs=[min_dense_1, dense0, dense1]).then(
+                    set_min_dense_1, inputs=max_dense_0, outputs=[min_dense_1, dense0, dense1])
+
+        with gr.Column():
+            progress_log = gr.Slider(1, 30, value=0, step=1, label="progress")
+            button1 = gr.Button(value="Save & continue", visible=fresh_start)
+            button2 = gr.Button(value="Start", visible=not fresh_start)
+
+    with gr.Row():
+        with gr.Column():
+            dense2 = gr.Image(dense_2, label="Pixels @ Density 2")
+            with gr.Row():
+                min_dense_2 = gr.Textbox(value='100', label="Min")
+                max_dense_2 = gr.Slider(0, 255, value=150, step=1, label="Max")
+                max_dense_1.change(set_min_dense_2, inputs=max_dense_1, outputs=[min_dense_2, dense1, dense2]).then(
+                    set_min_dense_2, inputs=max_dense_1, outputs=[min_dense_2, dense1, dense2])
+
+        with gr.Column():
+            dense3 = gr.Image(dense_3, label="Pixels @ Density 3")
+            with gr.Row():
+                min_dense_3 = gr.Textbox(value='150', label="Min")
+                max_dense_3 = gr.Textbox(value='255', label="Max")
+                max_dense_2.change(set_min_dense_3, inputs=max_dense_2, outputs=[min_dense_3, dense2, dense3]).then(
+                    set_min_dense_3, inputs=max_dense_2, outputs=[min_dense_3, dense2, dense3])
+
+        with gr.Column():  # adding additional for better visualization
+            im3_1 = gr.Image(lung_noised, label="Synthetic CXR")
+    button1.click(new__cxr, inputs=[max_dense_0, max_dense_1, max_dense_2],
+                  outputs=[im1, im2, im3, im3_1, dense0, dense1, dense2, dense3, progress_log])
+    button1.click(change_vis, outputs=[label1, button1, button2])
+    button2.click(set_layout,
+                  outputs=[im1, im2, im3, im3_1, dense0, dense1, dense2, dense3, progress_log])
+    button2.click(change_vis, outputs=[label1, button1, button2])
 
-# Gradio App Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# Density Mapper")
-    
-    # Authentication input fields
-    user_input = gr.Textbox(label="User Name")
-    password_input = gr.Password(label="Password")
-    auth_button = gr.Button("Login")
-    
-    # Output display placeholders
-    cxr_output = gr.Image(label="Original CXR")
-    synthetic_cxr_output = gr.Image(label="Synthetic CXR")
-    combined_output = gr.Image(label="Combined Synthetic Density 0-3")
-    dense0_output = gr.Image(label="Pixels @ Density 0")
-    dense1_output = gr.Image(label="Pixels @ Density 1")
-    dense2_output = gr.Image(label="Pixels @ Density 2")
-    dense3_output = gr.Image(label="Pixels @ Density 3")
-    progress_output = gr.Slider(minimum=0, maximum=30, step=1, label="Progress")
-    
-    # Hook up authentication and UI updates
-    auth_button.click(auth_and_start, inputs=[user_input, password_input], 
-                      outputs=[cxr_output, combined_output, synthetic_cxr_output, 
-                               dense0_output, dense1_output, dense2_output, 
-                               dense3_output, progress_output])
-
-    # Sliders for adjusting densities
-    max_dense_0_slider = gr.Slider(0, 255, value=50, label="Max Dense 0")
-    max_dense_1_slider = gr.Slider(0, 255, value=100, label="Max Dense 1")
-    max_dense_2_slider = gr.Slider(0, 255, value=150, label="Max Dense 2")
-    update_button = gr.Button("Update Densities")
-    
-    def update_density_thresholds(username, max_dense_0, max_dense_1, max_dense_2):
-        user_state = read_user_state(username)
-        save_user_state(username, user_state['count'], max_dense_0, max_dense_1, max_dense_2)
-        return update_layout(user_state, max_dense_0, max_dense_1, max_dense_2)
-    
-    update_button.click(update_density_thresholds, inputs=[user_input, max_dense_0_slider, max_dense_1_slider, max_dense_2_slider],
-                        outputs=[cxr_output, synthetic_cxr_output, combined_output, dense0_output, dense1_output, dense2_output, dense3_output, progress_output])
+    update_layout(image_id, index)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(auth=check_auth)
+
+
+