index

app.py

@@ -19,9 +19,6 @@ MODELS = {
 
 
 def load_example(index:int=0, useGta:bool=True)-> gr.Image:
-    print(type(index))
-    print(index)
-    print(useGta)
     example_img = loadPreloadedImages(gta_image_dir if useGta else city_image_dir)
     return gr.update(value=example_img[index if 0 <= index < len(example_img) else 0], visible=True)
 
@@ -67,7 +64,7 @@ def run_prediction(image: gr.Image, selected_model: str)-> tuple[torch.Tensor]:
 # Gradio UI
 with gr.Blocks(title="Semantic Segmentation Predictors") as demo:
     gr.Markdown("# Semantic Segmentation with Real-Time Networks")
-    gr.Markdown('A small user interface created to run semantic segmentation on images using city scapes like predictions and real time segmentation networks.')
+    gr.Markdown('A small user interface created to run semantic segmentation on images using Cityscapes-like predictions and real-time segmentation networks.')
     gr.Markdown("Upload an image and choose your preferred model for segmentation, or otherwise use one of the preloaded images.")
 
     with gr.Row():
@@ -82,25 +79,37 @@ with gr.Blocks(title="Semantic Segmentation Predictors") as demo:
         with gr.Column():
             result_display = gr.Image(label="Model prediction", visible=True)
             error_text = gr.Markdown("", visible=False)
-    
+
     with gr.Row():
         gr.Markdown("## Preloaded GTA V images to be used for testing the model")
     with gr.Row():
-        gta_gallery = gr.Gallery(value=loadPreloadedImages(gta_image_dir),
-            label="GTA V Examples", show_label=False, columns=5, type="index",
-            rows=1, height=200, allow_preview=False
+        gta_gallery = gr.Gallery(
+            value=loadPreloadedImages(gta_image_dir),
+            label="GTA V Examples",
+            show_label=False,
+            columns=5,
+            type="index",
+            rows=1,
+            height=200,
+            allow_preview=False
         )
 
     with gr.Row():
         gr.Markdown("## Preloaded Cityscapes images to be used for testing the model")
     with gr.Row():
-        city_gallery = gr.Gallery(value=loadPreloadedImages(city_image_dir),
-            label="Cityscapes Examples", show_label=False, columns=5, type="index",
-            rows=1, height=256, allow_preview=False
-    )
-    
-    gta_gallery.select(fn=lambda x: load_example(x, True), inputs=[gta_gallery], outputs=[image_input])
-    city_gallery.select(fn=lambda x: load_example(x, False), inputs=[city_gallery], outputs=[image_input])
+        city_gallery = gr.Gallery(
+            value=loadPreloadedImages(city_image_dir),
+            label="Cityscapes Examples",
+            show_label=False,
+            columns=5,
+            type="index",
+            rows=1,
+            height=256,
+            allow_preview=False
+        )
+
+    gta_gallery.select(fn=lambda i: load_example(i, True), inputs=[], outputs=image_input)
+    city_gallery.select(fn=lambda i: load_example(i, False), inputs=[], outputs=image_input)
 
     submit_btn.click(
         fn=run_prediction,
@@ -108,7 +117,7 @@ with gr.Blocks(title="Semantic Segmentation Predictors") as demo:
         outputs=[result_display, error_text],
     )
 
-    gr.Markdown("Made by group 21 semantic segmentation project at Politecnico di Torino 2024-2025")
+    gr.Markdown("Made by group 21 semantic segmentation project at Politecnico di Torino 2024–2025")
 
 demo.launch()