Upload app.py

app.py

@@ -369,11 +369,11 @@ def process_image(img_rgb, model_name, text_prompt="", morph_cleanup=False):
     run_color = random.choice(PASTEL_COLORS)
     
     try:
-        if model_name == "YOLOv8x-seg (Custom Windows)":
+        if model_name == "YOLOv8x-seg (Custom Window)":
             return run_yolo_generic(img_rgb, "best.pt", target_classes=[0, 1], color=run_color, morph_cleanup=morph_cleanup)
-        elif model_name == "YOLOv8x-seg (Fine-tuned)":
+        elif model_name == "YOLOv8x-seg (Fine-tuned · 86.77% mAP50)":
             return run_yolo_generic(img_rgb, "best.pt", target_classes=[0, 1], color=(255, 215, 0), morph_cleanup=morph_cleanup)
-        elif model_name == "SAM + YOLO (Custom Windows)":
+        elif model_name == "SAM + YOLO (Custom Window)":
             return run_sam_generic(img_rgb, "best.pt", target_classes=[0, 1], color=run_color, morph_cleanup=morph_cleanup)
         elif model_name == "Grounding DINO (Zero-Shot Detection)":
             return run_grounding_dino(img_rgb, text_prompt)
@@ -391,30 +391,30 @@ def process_image(img_rgb, model_name, text_prompt="", morph_cleanup=False):
 # ═══════════════════════════════════════════════════════════════════════════════
 theme = gr.themes.Soft(primary_hue="blue", secondary_hue="indigo")
 
-with gr.Blocks(theme=theme, title="Car Windows Segmentation") as demo:
+with gr.Blocks(theme=theme, title="Car Window Segmentation") as demo:
     gr.Markdown("""
-    # Car Windows Segmentation
+    # Car Window Segmentation
     Compare your custom trained YOLOv8 model against state-of-the-art Zero-Shot models!
     """)
 
     # ── TAB 1: Custom Models ──
-    with gr.Tab("🎯 Test Custom Models"):
+    with gr.Tab("Test Custom Models"):
         with gr.Row():
             with gr.Column(scale=1):
-                input_image_custom = gr.Image(type="numpy", label="Upload Mirror Image")
+                input_image_custom = gr.Image(type="numpy", label="Upload Window Image")
                 model_dropdown_custom = gr.Dropdown(
                     choices=[
-                        "YOLOv8x-seg (Custom Windows)",
-                        "YOLOv8x-seg (Fine-tuned)",
-                        "SAM + YOLO (Custom Windows)",
+                        "YOLOv8x-seg (Custom Window)",
+                        "YOLOv8x-seg (Fine-tuned · 86.77% mAP50)",
+                        "SAM + YOLO (Custom Window)",
                     ],
-                    value="YOLOv8x-seg (Fine-tuned)",
+                    value="YOLOv8x-seg (Fine-tuned · 86.77% mAP50)",
                     label="Select Custom Model",
-                    info="Fine-tuned model achieves"
+                    info="Fine-tuned model achieves 86.77% Mask mAP50 on the test set"
                 )
                 morph_checkbox = gr.Checkbox(
                     value=False,
-                    label="🔬 Apply Morphological Cleanup",
+                    label="Apply Morphological Cleanup",
                     info="Fills holes inside mask (Closing) and removes tiny noise blobs (Opening). Visual only — does not affect mAP metrics."
                 )
                 submit_btn_custom = gr.Button("Run Segmentation", variant="primary", size="lg")
@@ -424,7 +424,7 @@ with gr.Blocks(theme=theme, title="Car Windows Segmentation") as demo:
                 output_stats_custom = gr.Textbox(label="Detection Statistics", interactive=False)
 
         if mirror_examples:
-            gr.Markdown("### Click any image below to load it")
+            gr.Markdown("### Click any window image below to load it")
             custom_gallery = gr.Gallery(value=mirror_examples, columns=10, height=120, object_fit="cover", allow_preview=False, show_label=False)
             
             def load_mirror_img_custom(evt: gr.SelectData):
@@ -439,10 +439,10 @@ with gr.Blocks(theme=theme, title="Car Windows Segmentation") as demo:
         )
 
     # ── TAB 2: Pretrained Zero-Shot Models ──
-    with gr.Tab("🔍 Pretrained Zero-Shot Models"):
+    with gr.Tab("Pretrained Zero-Shot Models"):
         with gr.Row():
             with gr.Column(scale=1):
-                input_image_pretrained = gr.Image(type="numpy", label="Upload Mirror Image")
+                input_image_pretrained = gr.Image(type="numpy", label="Upload Window Image")
                 model_dropdown_pretrained = gr.Dropdown(
                     choices=[
                         "Grounding DINO (Zero-Shot Detection)", 
@@ -451,21 +451,21 @@ with gr.Blocks(theme=theme, title="Car Windows Segmentation") as demo:
                     ],
                     value="Grounded SAM (Zero-Shot Segmentation)",
                     label="Select Pretrained Model",
-                    info="Finds mirrors purely based on the text prompt you provide below!"
+                    info="Finds windows purely based on the text prompt you provide below!"
                 )
                 text_prompt = gr.Textbox(
-                    value="side mirror. rearview mirror. wing mirror.",
+                    value="car window. car glass. windshield.",
                     label="What to search for? (Text Prompt)",
                     info="Be sure to separate terms with a period."
                 )
-                submit_btn_pretrained = gr.Button("🚀 Run Zero-Shot Detection", variant="primary", size="lg")
+                submit_btn_pretrained = gr.Button("Run Zero-Shot Detection", variant="primary", size="lg")
             with gr.Column(scale=1):
                 output_image_pretrained = gr.Image(label="Segmentation Result", interactive=False)
                 output_mask_pretrained = gr.Image(label="Binary Mask", interactive=False)
                 output_stats_pretrained = gr.Textbox(label="Detection Statistics", interactive=False)
 
         if mirror_examples:
-            gr.Markdown("### 📸 Click any image below to load it")
+            gr.Markdown("### Click any window image below to load it")
             pretrained_gallery = gr.Gallery(value=mirror_examples, columns=10, height=120, object_fit="cover", allow_preview=False, show_label=False)
             
             def load_mirror_img_pretrained(evt: gr.SelectData):