Update app.py

app.py

@@ -33,7 +33,7 @@ transform = Compose([
     ToTensorV2()
 ])
 
-def predict(image, threshold=0.5):
+def predict(image):
     """Process the image and detect camouflage objects."""
     img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     augmented = transform(image=img)
@@ -42,8 +42,8 @@ def predict(image, threshold=0.5):
     with torch.no_grad():
         output = torch.sigmoid(model(img_tensor))
 
-    # Apply threshold
-    mask = (output.squeeze().cpu().numpy() > threshold).astype(np.uint8) * 255
+    # Convert model output to binary mask
+    mask = (output.squeeze().cpu().numpy() > 0.5).astype(np.uint8) * 255
 
     # Resize mask to original image size
     mask_resized = cv2.resize(mask, (image.shape[1], image.shape[0]))
@@ -69,14 +69,13 @@ with gr.Blocks(css=custom_css, theme="soft") as app:
     with gr.Row():
         with gr.Column():
             image_input = gr.Image(type="numpy", label="Upload Image")
-            threshold_slider = gr.Slider(0.1, 1.0, value=0.5, label="Detection Threshold", interactive=True)
             submit_btn = gr.Button("🔍 Detect")
 
         with gr.Column():
             output_overlay = gr.Image(type="numpy", label="Detected Camouflage (Overlay)")
             output_mask = gr.Image(type="numpy", label="Segmentation Mask")
 
-    submit_btn.click(predict, inputs=[image_input, threshold_slider], outputs=[output_overlay, output_mask])
+    submit_btn.click(predict, inputs=[image_input], outputs=[output_overlay, output_mask])
 
     gr.HTML('<div id="footer-text">Made with ❤️ by Jaskaranjeet Singh</div>')