Try readding canny with imports only

app.py

@@ -375,46 +375,45 @@ with gr.Blocks() as beta:
 
 # ----- Canny Edge Tab -----------------------------------------------------------------
 
-# from PIL import Image
-# import gradio as gr
-# import numpy as np
-# import cv2
-
-# # Define a function to process the uploaded image
-# def canny_process_image(input_image, input_low_threshold, input_high_threshold, input_invert):
-#     # Convert the input image to a NumPy array
-#     np_image = np.array(input_image)
+from PIL import Image
+import gradio as gr
+import numpy as np
+import cv2
 
-#     output_image = input_image  # For example, just return the input image
-#     numpy_image = np.array(output_image)
-#     # Return the processed image
+# Define a function to process the uploaded image
+def canny_process_image(input_image, input_low_threshold, input_high_threshold, input_invert):
+   # Convert the input image to a NumPy array
+   np_image = np.array(input_image)
+   output_image = input_image  # For example, just return the input image
+   numpy_image = np.array(output_image)
+   # Return the processed image
 
-#     low_threshold = 100
-#     high_threshold = 200
-#     canny_1 = cv2.Canny(numpy_image, input_low_threshold, input_high_threshold)
-#     canny_1 = canny_1[:, :, None]
-#     canny_1 = np.concatenate([canny_1, canny_1, canny_1], axis=2)
-#     if input_invert:
-#       canny_1 = 255 - canny_1
-#     canny_2 = Image.fromarray(canny_1)
+   # low_threshold = 100
+   # high_threshold = 200
+   canny_1 = cv2.Canny(numpy_image, input_low_threshold, input_high_threshold)
+   canny_1 = canny_1[:, :, None]
+   canny_1 = np.concatenate([canny_1, canny_1, canny_1], axis=2)
+   if input_invert:
+     canny_1 = 255 - canny_1
+   canny_2 = Image.fromarray(canny_1)
 
-#     return np.array(canny_2)
+   return np.array(canny_2)
 
-# # Define the input and output interfaces
-# canny_input_image = gr.inputs.Image()
-# canny_input_low_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Lower Threshold:", default=100)
-# canny_input_high_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Upper Threshold:", default=200)
-# canny_input_invert = gr.inputs.Checkbox(label="Invert Image")
+# Define the input and output interfaces
+canny_input_image = gr.inputs.Image()
+canny_input_low_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Lower Threshold:", default=100)
+canny_input_high_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Upper Threshold:", default=200)
+canny_input_invert = gr.inputs.Checkbox(label="Invert Image")
 
-# canny_outputs = gr.outputs.Image(type="numpy")
+canny_outputs = gr.outputs.Image(type="numpy")
 
-# # Create the Gradio interface
-# canny_interface = gr.Interface(fn=canny_process_image, inputs=[canny_input_image, canny_input_low_threshold, canny_input_high_threshold, canny_input_invert], outputs=canny_outputs, title='Canny Edge Tracing', allow_flagging='never')
+# Create the Gradio interface
+canny_interface = gr.Interface(fn=canny_process_image, inputs=[canny_input_image, canny_input_low_threshold, canny_input_high_threshold, canny_input_invert], outputs=canny_outputs, title='Canny Edge Tracing', allow_flagging='never')
 
 
 
 # ----- Launch Tabs -----------------------------------------------------------------
 
-# tabbed_interface = gr.TabbedInterface([new_welcome, advanced_tab, beta, canny_interface], ["Artists", "Advanced", "Beta", "Edges"])
-tabbed_interface = gr.TabbedInterface([new_welcome, advanced_tab, beta], ["Artists", "Advanced", "Beta"])
+tabbed_interface = gr.TabbedInterface([new_welcome, advanced_tab, beta, canny_interface], ["Artists", "Advanced", "Beta", "Edges"])
+# tabbed_interface = gr.TabbedInterface([new_welcome, advanced_tab, beta], ["Artists", "Advanced", "Beta"])
 tabbed_interface.launch()