Upload app.py

app.py

@@ -647,11 +647,11 @@ with gr.Blocks(theme=theme, title="Car Window Segmentation") as demo:
 **Custom Trained Models:**
 
 1. SegFormer
-2. YOLO11x-seg
-3. YOLOv8x-seg
-4. Mask R-CNN
-5. BiRefNet
-6. SegFormer + Morphological
+2. SegFormer + Morphological
+3. YOLO11x-seg
+4. YOLOv8x-seg
+5. Mask R-CNN
+6. BiRefNet
 7. SAM + YOLO (Strategy 1: Bbox + 5 Points)
 8. SAM + YOLO (Strategy 2: Mask + 5 Points)
 9. SAM + YOLO (Strategy 3: Direct Mask Prompting)
@@ -687,40 +687,40 @@ with gr.Blocks(theme=theme, title="Car Window Segmentation") as demo:
         seq_segf_stats = gr.Textbox(label="SegFormer Stats", interactive=False)
 
         gr.Markdown("---")
-        gr.Markdown("### 2️⃣ YOLO11x-seg")
+        gr.Markdown("### 2️⃣ SegFormer + Morphological Cleanup (Holes Filled + Sharp Borders)")
+        with gr.Row():
+            seq_segf_morph_img = gr.Image(label="SegFormer + Morph Overlay", interactive=False)
+            seq_segf_morph_bw = gr.Image(label="SegFormer + Morph Binary Mask", interactive=False, image_mode="L")
+        seq_segf_morph_stats = gr.Textbox(label="SegFormer + Morph Stats", interactive=False)
+
+        gr.Markdown("---")
+        gr.Markdown("### 3️⃣ YOLO11x-seg")
         with gr.Row():
             seq_yolo11_img = gr.Image(label="YOLO11x Overlay", interactive=False)
             seq_yolo11_bw = gr.Image(label="YOLO11x Binary Mask", interactive=False, image_mode="L")
         seq_yolo11_stats = gr.Textbox(label="YOLO11x Stats", interactive=False)
 
         gr.Markdown("---")
-        gr.Markdown("### 3️⃣ YOLOv8x-seg")
+        gr.Markdown("### 4️⃣ YOLOv8x-seg")
         with gr.Row():
             seq_yolo_img = gr.Image(label="YOLO Overlay", interactive=False)
             seq_yolo_bw = gr.Image(label="YOLO Binary Mask", interactive=False, image_mode="L")
         seq_yolo_stats = gr.Textbox(label="YOLO Stats", interactive=False)
 
         gr.Markdown("---")
-        gr.Markdown("### 4️⃣ Mask R-CNN (ResNet50-FPN)")
+        gr.Markdown("### 5️⃣ Mask R-CNN (ResNet50-FPN)")
         with gr.Row():
             seq_mrcnn_img = gr.Image(label="Mask R-CNN Overlay", interactive=False)
             seq_mrcnn_bw = gr.Image(label="Mask R-CNN Binary Mask", interactive=False, image_mode="L")
         seq_mrcnn_stats = gr.Textbox(label="Mask R-CNN Stats", interactive=False)
 
         gr.Markdown("---")
-        gr.Markdown("### 5️⃣ BiRefNet (Boundary-Aware Model)")
+        gr.Markdown("### 6️⃣ BiRefNet (Boundary-Aware Model)")
         with gr.Row():
             seq_biref_img = gr.Image(label="BiRefNet Overlay", interactive=False)
             seq_biref_bw = gr.Image(label="BiRefNet Binary Mask", interactive=False, image_mode="L")
         seq_biref_stats = gr.Textbox(label="BiRefNet Stats", interactive=False)
 
-        gr.Markdown("---")
-        gr.Markdown("### 6️⃣ SegFormer + Morphological Cleanup (Holes Filled + Sharp Borders)")
-        with gr.Row():
-            seq_segf_morph_img = gr.Image(label="SegFormer + Morph Overlay", interactive=False)
-            seq_segf_morph_bw = gr.Image(label="SegFormer + Morph Binary Mask", interactive=False, image_mode="L")
-        seq_segf_morph_stats = gr.Textbox(label="SegFormer + Morph Stats", interactive=False)
-
         gr.Markdown("---")
         gr.Markdown("### 7️⃣ SAM + YOLO (Strategy 1: Bbox + 5 Points)")
         with gr.Row():
@@ -770,31 +770,36 @@ with gr.Blocks(theme=theme, title="Car Window Segmentation") as demo:
                 yield tuple([None]*36)
                 return
             
-            # Initialize empty array for all 36 outputs
+            # ── Step 0: Show "Processing..." in ALL textboxes immediately ──
+            PENDING = "⏳ Processing..."
             results = [None] * 36
+            # Set all stats textboxes to pending state
+            for i in [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35]:
+                results[i] = PENDING
+            yield tuple(results)
             
             # 1. SegFormer
             results[0], results[1], results[2] = run_segformer(img, morph_cleanup=False)
             yield tuple(results)
             
-            # 2. YOLO11x-seg
-            results[3], results[4], results[5] = process_image(img, "YOLO11x-seg", "", False)
+            # 2. SegFormer + Morphology
+            results[3], results[4], results[5] = run_segformer(img, morph_cleanup=True)
             yield tuple(results)
             
-            # 3. YOLOv8x-seg
-            results[6], results[7], results[8] = process_image(img, "YOLOv8x-seg", "", False)
+            # 3. YOLO11x-seg
+            results[6], results[7], results[8] = process_image(img, "YOLO11x-seg", "", False)
             yield tuple(results)
             
-            # 4. Mask R-CNN
-            results[9], results[10], results[11] = process_image(img, "Mask R-CNN", "", False)
+            # 4. YOLOv8x-seg
+            results[9], results[10], results[11] = process_image(img, "YOLOv8x-seg", "", False)
             yield tuple(results)
             
-            # 5. BiRefNet
-            results[12], results[13], results[14] = run_birefnet(img)
+            # 5. Mask R-CNN
+            results[12], results[13], results[14] = process_image(img, "Mask R-CNN", "", False)
             yield tuple(results)
             
-            # 6. SegFormer + Morphology
-            results[15], results[16], results[17] = run_segformer(img, morph_cleanup=True)
+            # 6. BiRefNet
+            results[15], results[16], results[17] = run_birefnet(img)
             yield tuple(results)
             
             # 7. SAM + YOLO Strat 1
@@ -825,11 +830,11 @@ with gr.Blocks(theme=theme, title="Car Window Segmentation") as demo:
             fn=run_all_models,
             inputs=[input_image_seq],
             outputs=[seq_segf_img, seq_segf_bw, seq_segf_stats,
+                     seq_segf_morph_img, seq_segf_morph_bw, seq_segf_morph_stats,
                      seq_yolo11_img, seq_yolo11_bw, seq_yolo11_stats,
                      seq_yolo_img, seq_yolo_bw, seq_yolo_stats, 
                      seq_mrcnn_img, seq_mrcnn_bw, seq_mrcnn_stats, 
                      seq_biref_img, seq_biref_bw, seq_biref_stats,
-                     seq_segf_morph_img, seq_segf_morph_bw, seq_segf_morph_stats,
                      seq_sam1_img, seq_sam1_bw, seq_sam1_stats,
                      seq_sam2_img, seq_sam2_bw, seq_sam2_stats,
                      seq_sam3_img, seq_sam3_bw, seq_sam3_stats,