Update app.py

In this revised version:

I've added hypothetical img_enhancement and img_repair pipelines for image quality enhancement and damage repair. You'll need to replace 'your_super_resolution_model' and 'your_image_repair_model' with actual models suitable for these tasks.
The process_image function now accepts additional boolean parameters to control whether image quality enhancement and damage repair should be applied.
The Gradio interface now includes checkboxes for users to select whether they want to enhance the image quality and repair damage, along with the image upload.

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import os
 import cv2
-from modelscope.outputs import OutputKeys
+# Assuming img_colorization as before
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
 import PIL
@@ -9,26 +9,49 @@ import numpy as np
 import uuid
 from gradio_imageslider import ImageSlider
 
+# Your existing colorization pipeline
 img_colorization = pipeline(Tasks.image_colorization, model='iic/cv_ddcolor_image-colorization')
-img_path = 'input.png'
-##result = img_colorization(img_path)
-##cv2.imwrite('result.png', result[OutputKeys.OUTPUT_IMG])
-def color(image):
-    output = img_colorization(image[...,::-1])
-    result = output[OutputKeys.OUTPUT_IMG].astype(np.uint8)
-    # result = result[...,::-1]
-    # Generate a unique filename using UUID
+
+# Additional pipelines for image enhancement and repair
+img_enhancement = pipeline(Tasks.image_super_resolution, model='your_super_resolution_model')
+img_repair = pipeline(Tasks.image_inpainting, model='your_image_repair_model')
+
+def process_image(image, enhance_quality, repair_damage):
+    # Convert image to proper format for processing
+    image = image[..., ::-1]
+    
+    # Repair the image if requested
+    if repair_damage:
+        image = img_repair(image)[OutputKeys.OUTPUT_IMG].astype(np.uint8)
+    
+    # Enhance image quality if requested
+    if enhance_quality:
+        image = img_enhancement(image)[OutputKeys.OUTPUT_IMG].astype(np.uint8)
+    
+    # Colorize the image
+    colored_image = img_colorization(image)[OutputKeys.OUTPUT_IMG].astype(np.uint8)
+    
+    # Save the processed image with a unique filename
     unique_imgfilename = str(uuid.uuid4()) + '.png'
-    cv2.imwrite(unique_imgfilename, result)
-    print('infer finished!')
-    return (image, unique_imgfilename)
+    cv2.imwrite(unique_imgfilename, colored_image)
+    print('Infer finished!')
     
+    # Return both original and processed image paths for the slider
+    return image[..., ::-1], unique_imgfilename
 
-title = "old_photo_restoration"
-description = "upload old photo, ddcolor image colorization"
+title = "Old Photo Restoration"
+description = "Upload old photos for colorization, quality enhancement, and damage repair."
 examples = [['./input.jpg'],]
 
-demo = gr.Interface(fn=color,inputs="image",outputs=ImageSlider(position=0.5,label='Colored image with slider-view'),examples=examples,title=title,description=description)
+inputs = [
+    gr.inputs.Image(shape=(512, 512)),
+    gr.inputs.Checkbox(label="Enhance Quality"),
+    gr.inputs.Checkbox(label="Repair Damage")
+]
+
+outputs = gr.outputs.ImageSlider(position=0.5, label='Processed image with slider-view')
+
+demo = gr.Interface(fn=process_image, inputs=inputs, outputs=outputs, examples=examples, title=title, description=description)
 
 if __name__ == "__main__":
-    demo.launch(share=False)
+    demo.launch()