Update app.py

app.py

@@ -5,18 +5,7 @@ from __future__ import annotations
 import gradio as gr
 import torch
 
-from app_canny import create_demo as create_demo_canny
-from app_depth import create_demo as create_demo_depth
-from app_ip2p import create_demo as create_demo_ip2p
-from app_lineart import create_demo as create_demo_lineart
-from app_mlsd import create_demo as create_demo_mlsd
-from app_normal import create_demo as create_demo_normal
-from app_openpose import create_demo as create_demo_openpose
-from app_scribble import create_demo as create_demo_scribble
-from app_scribble_interactive import create_demo as create_demo_scribble_interactive
-from app_segmentation import create_demo as create_demo_segmentation
-from app_shuffle import create_demo as create_demo_shuffle
-from app_softedge import create_demo as create_demo_softedge
+from preprocessor import Preprocessor
 
 DESCRIPTION = "# ControlNet v1.1"
 
@@ -31,31 +20,28 @@ with gr.Blocks(css="style.css") as demo:
         visible=False,  # Hiding the duplicate button
     )
 
-    with gr.Accordion(label="Preprocessors", open=False):
-        with gr.TabItem("Canny"):
-            create_demo_canny()
-        with gr.TabItem("MLSD"):
-            create_demo_mlsd()
-        with gr.TabItem("Scribble"):
-            create_demo_scribble()
-        with gr.TabItem("Scribble Interactive"):
-            create_demo_scribble_interactive()
-        with gr.TabItem("SoftEdge"):
-            create_demo_softedge()
-        with gr.TabItem("OpenPose"):
-            create_demo_openpose()
-        with gr.TabItem("Segmentation"):
-            create_demo_segmentation()
-        with gr.TabItem("Depth"):
-            create_demo_depth()
-        with gr.TabItem("Normal map"):
-            create_demo_normal()
-        with gr.TabItem("Lineart"):
-            create_demo_lineart()
-        with gr.TabItem("Content Shuffle"):
-            create_demo_shuffle()
-        with gr.TabItem("Instruct Pix2Pix"):
-            create_demo_ip2p()
-
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch()
+    # Instantiate the Preprocessor class
+    preprocessor = Preprocessor()
+
+    # Function to apply preprocessor to input image
+    def apply_preprocessor(input_image, preprocessor_name):
+        # Load the desired preprocessor
+        preprocessor.load(preprocessor_name)
+        # Apply the preprocessor to the input image
+        processed_image = preprocessor(input_image)
+        # Convert processed image from numpy array to PIL image
+        processed_image_pil = PIL.Image.fromarray(processed_image)
+        return processed_image_pil
+
+    # Gradio interface
+    image = gr.inputs.Image()
+    preprocessor_name = gr.inputs.Dropdown(choices=["Canny", "Midas", "MLSD", "Openpose", "PidiNet", "NormalBae", "Lineart", "LineartAnime", "ContentShuffle", "DPT", "UPerNet"], label="Preprocessor")
+    run_button = gr.Button("Start")
+    output_image = gr.outputs.Image(label="Output")
+
+    # Define processing function for Gradio
+    def process_image(input_image, preprocessor_name):
+        return apply_preprocessor(input_image, preprocessor_name)
+
+    # Link inputs and outputs to function using Gradio's Interface class
+    gr.Interface(fn=process_image, inputs=[image, preprocessor_name], outputs=output_image, title="Preprocessor GUI").launch()