fixed image slider, and relativ imports

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-from gradio_imageslider import ImageSlider  # Replaces gr.ImageCompare
 import torch
 import yaml
 import numpy as np
@@ -492,7 +491,7 @@ def superres_preview_preprocess(pil_image, resolution=768):
 # Dynamically load examples from demo_images directory
 example_list_inp = []
 example_list_sr = []
-demo_images_dir = os.path.join(project_root, "static/demo_images")
+demo_images_dir = os.path.join(project_root, "demo_images")
 
 if os.path.exists(demo_images_dir):
     filenames = sorted(os.listdir(demo_images_dir))
@@ -652,7 +651,7 @@ Use the slider to compare the low resolution input image with the super-resolved
 
             with gr.Column(scale=1):  # Output column
                 output_image_display = gr.Image(type="pil", label="Result")
-                sr_compare_display = ImageSlider(label="Super-Resolution: Input vs Output", visible=False, position=0.5)
+                sr_compare_display = gr.ImageSlider(label="Super-Resolution: Input vs Output", visible=False)
 
 
         
@@ -667,7 +666,7 @@ Use the slider to compare the low resolution input image with the super-resolved
                 sr_scale_slider: gr.update(visible=is_super_resolution),
                 downscale_input: gr.update(visible=is_super_resolution),
                 output_image_display: gr.update(visible=is_inpainting),
-                sr_compare_display: gr.update(visible=is_super_resolution, position=0.5),
+                sr_compare_display: gr.update(visible=is_super_resolution),
                 downscale_input: gr.update(visible=is_super_resolution),
             }
 
@@ -687,7 +686,7 @@ Use the slider to compare the low resolution input image with the super-resolved
                     lr_image_for_sr, prompt_text, fixed_seed_value, use_random_seed,
                     guidance_scale, num_steps, sr_scale_factor_value, downscale_input
                 )
-                return result_images[1], gr.update(value=result_images, position=0.5), seed_val
+                return result_images[1], gr.update(value=result_images), seed_val
             else:
                 raise gr.Error("Unsupported task.")
 

requirements.txt

@@ -15,4 +15,5 @@ opencv-python
 sentencepiece
 protobuf
 accelerate
-gradio
+gradio
+gradio_imageslider