running

app.py

@@ -3,39 +3,6 @@ from model.BiSeNet.build_bisenet import BiSeNet
 import gradio as gr
 from utils.imageHandling import hfImageToTensor, preprocessing
 
-
-def run_prediction(image: gr.Image, selected_model: str)-> tuple[torch.Tensor]:
-    device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    image = hfImageToTensor(image, width=1024, height=512)
-    return image, predict(image, loadModel(selected_model, device))
-
-# Gradio UI
-with gr.Blocks(title="🔀 BiSeNet | BiSeNetV2 Predictor") as demo:
-    gr.Markdown("## 🧠 Image Segmentation with BiSeNet and BiSeNetV2")
-    gr.Markdown("Upload an image and choose your preferred model for segmentation.")
-
-    with gr.Row():
-        with gr.Column():
-            model_selector = gr.Radio(
-                choices=["BiSeNet", "BiSeNetV2"],
-                value="BiSeNet",
-                label="Select model"
-            )
-            image_input = gr.Image(type="pil", label="Upload image")
-            submit_btn = gr.Button("🧪 Run prediction")
-        with gr.Column():
-            original_display = gr.Image(label="Original image")
-            result_display = gr.Image(label="Model prediction")
-
-    submit_btn.click(
-        fn=run_prediction,
-        inputs=[image_input, model_selector],
-        outputs=[original_display, result_display]
-    )
-
-demo.launch()
-
-
 # %% prediction on an image
 
 def predict(inputImage: torch.Tensor, model: BiSeNet) -> torch.Tensor:
@@ -92,3 +59,35 @@ def loadBiSeNet(device: str = 'cpu') -> BiSeNet:
     model.eval()
     
     return model
+
+
+# %% Gradio interface
+def run_prediction(image: gr.Image, selected_model: str)-> tuple[torch.Tensor]:
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    image = hfImageToTensor(image, width=1024, height=512)
+    return image, predict(image, loadModel(selected_model, device))
+
+# Gradio UI
+with gr.Blocks(title="🔀 BiSeNet | BiSeNetV2 Predictor") as demo:
+    gr.Markdown("## 🧠 Image Segmentation with BiSeNet and BiSeNetV2")
+    gr.Markdown("Upload an image and choose your preferred model for segmentation.")
+
+    with gr.Row():
+        with gr.Column():
+            model_selector = gr.Radio(
+                choices=["BiSeNet", "BiSeNetV2"],
+                value="BiSeNet",
+                label="Select model"
+            )
+            image_input = gr.Image(type="pil", label="Upload image")
+            submit_btn = gr.Button("🧪 Run prediction")
+        with gr.Column():
+            result_display = gr.Image(label="Model prediction")
+
+    submit_btn.click(
+        fn=run_prediction,
+        inputs=[image_input, model_selector],
+        outputs=[result_display]
+    )
+
+demo.launch()