Update app.py

app.py

@@ -139,7 +139,7 @@ def load_model(model_name):
         print(f"Error loading model: {e}")
         return f"❌ Error loading {model_name}: {str(e)}"
 
-def visualize_bbox(image_input, bboxes, classes, scores, id_to_names, alpha=0.3):
+def visualize_bbox(image_input, bboxes, classes, scores, id_to_names, alpha=0.3, show_labels=True):
     """Visualize bounding boxes with OpenCV."""
     if isinstance(image_input, Image.Image):
         image = np.array(image_input)
@@ -176,7 +176,6 @@ def visualize_bbox(image_input, bboxes, classes, scores, id_to_names, alpha=0.3)
             class_id = int(class_id)
             class_name = id_to_names.get(class_id, f"unknown_{class_id}")
 
-            text = f"{class_name}: {score:.3f}"
             color = tuple(int(c) for c in cmap[class_id % len(cmap)])
 
             # Draw filled rectangle on overlay
@@ -184,11 +183,13 @@ def visualize_bbox(image_input, bboxes, classes, scores, id_to_names, alpha=0.3)
             # Draw border on main image
             cv2.rectangle(image, (x_min, y_min), (x_max, y_max), color, 3)
 
-            # Add text label
-            (text_width, text_height), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)
-            cv2.rectangle(image, (x_min, y_min - text_height - baseline - 4), 
-                         (x_min + text_width + 8, y_min), color, -1)
-            cv2.putText(image, text, (x_min + 4, y_min - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
+            # Add text label only if show_labels is True
+            if show_labels:
+                text = f"{class_name}: {score:.3f}"
+                (text_width, text_height), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)
+                cv2.rectangle(image, (x_min, y_min - text_height - baseline - 4), 
+                             (x_min + text_width + 8, y_min), color, -1)
+                cv2.putText(image, text, (x_min + 4, y_min - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
 
         except Exception as e:
             print(f"Skipping box {i} due to error: {e}")
@@ -198,7 +199,7 @@ def visualize_bbox(image_input, bboxes, classes, scores, id_to_names, alpha=0.3)
     
     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-def process_image(input_img, conf_threshold, iou_threshold, nms_method, alpha):
+def process_image(input_img, conf_threshold, iou_threshold, nms_method, alpha, show_labels):
     """Process image with document layout detection."""
     if input_img is None:
         return None, "❌ Please upload an image first."
@@ -252,8 +253,10 @@ def process_image(input_img, conf_threshold, iou_threshold, nms_method, alpha):
             labels = labels[keep_indices]
         
         # Visualize results
-        output = visualize_bbox(input_img, boxes, labels, scores, classes_map, alpha=alpha)
-        info = f"✅ Found {len(boxes)} detections | NMS: {nms_method} | Threshold: {conf_threshold:.2f}"
+        output = visualize_bbox(input_img, boxes, labels, scores, classes_map, alpha=alpha, show_labels=show_labels)
+        
+        labels_status = "with labels" if show_labels else "without labels"
+        info = f"✅ Found {len(boxes)} detections ({labels_status}) | NMS: {nms_method} | Threshold: {conf_threshold:.2f}"
         
         return output, info
             
@@ -325,6 +328,13 @@ if __name__ == "__main__":
     .status-good { color: #28a745; font-weight: bold; }
     .status-error { color: #dc3545; font-weight: bold; }
     .status-info { color: #17a2b8; font-weight: bold; }
+    
+    .toggle-labels {
+        background: linear-gradient(45deg, #667eea, #764ba2) !important;
+        border: none !important;
+        color: white !important;
+        font-weight: bold !important;
+    }
     """
     
     # Create Gradio interface
@@ -420,6 +430,17 @@ if __name__ == "__main__":
                         label="Overlay Transparency",
                         info="Transparency of detection overlays"
                     )
+                
+                # Visualization Options Section
+                with gr.Group(elem_classes=["control-section"]):
+                    gr.HTML("<h3>🎨 Visualization Options</h3>")
+                    
+                    show_labels_checkbox = gr.Checkbox(
+                        value=True,
+                        label="Show Class Labels",
+                        info="Display class names and confidence scores on detections",
+                        interactive=True
+                    )
             
             # RIGHT COLUMN - Results and Output
             with gr.Column(scale=1, elem_classes=["panel-right"]):