better code

app.py

@@ -1,6 +1,5 @@
-import torch, uuid
+import torch
 import gradio as gr
-from PIL import Image
 
 from utils.imageHandling import hfImageToTensor, preprocessing, postprocessing, loadPreloadedImages
 from model.modelLoading import loadBiSeNet, loadBiSeNetV2
@@ -18,8 +17,6 @@ MODELS = {
     "BISENETV2": loadBiSeNetV2(device)
 }
 
-
-    
 image_list = loadPreloadedImages(gta_image_dir, city_image_dir, turin_image_dir)
 
 
@@ -83,16 +80,14 @@ with gr.Blocks(title="Semantic Segmentation Predictors") as demo:
     with gr.Row():
         gr.Markdown("## Preloaded images to be used for testing the model")
         gr.Markdown("You can use images from the Grand Theft Auto V video game, the Cityscapes dataset or even from Turin")
-    def set_image(image):
-            return image
 
     # Mostriamo 4 righe da 5 immagini
     for i in range(0, len(image_list), 5):
         with gr.Row():
-            for img, _ in image_list[i:i+5]:
-                img_comp = gr.Image(value=img, interactive=False, show_label=False, show_download_button=False, height=196, width=256,
+            for img in image_list[i:i+5]:
+                img_comp = gr.Image(value=img, interactive=False, show_label=False, show_download_button=False, height=180, width=256,
                                     show_fullscreen_button=False, show_share_button=False, mirror_webcam=False)
-                img_comp.select(fn=set_image, inputs=img_comp, outputs=image_input)
+                img_comp.select(fn=lambda x:x, inputs=img_comp, outputs=image_input)
     
     submit_btn.click(
         fn=run_prediction,

utils/imageHandling.py

@@ -95,5 +95,5 @@ def loadPreloadedImages(*args:str) -> list[tuple[Image.Image, str]]:
     Returns:
         images (list[tuple[Image.Image, str]]): List of loaded images with their original paths.
     """
-    return sorted([[Image.open(os.path.join(imageDir, image)).convert("RGB"), os.path.join(imageDir, image)]
-                    for imageDir in args for image in os.listdir(imageDir) if image.endswith((".png", ".jpg", "jpeg"))], key=lambda x: x[1])
+    return list(map(lambda x:x[0], sorted([[Image.open(os.path.join(imageDir, image)).convert("RGB"), os.path.join(imageDir, image)]
+                    for imageDir in args for image in os.listdir(imageDir) if image.endswith((".png", ".jpg", "jpeg"))], key=lambda x: x[1])))