fix error

app.py

@@ -28,13 +28,13 @@ DATASET_REPO = "phoebe777777/celltool_feedback"
 
 # ===== 清理缓存目录 =====
 print("===== clearing cache =====")
-cache_path = os.path.expanduser("~/.cache/")
-# cache_path = os.path.expanduser("~/.cache/huggingface/gradio")
+# cache_path = os.path.expanduser("~/.cache/")
+cache_path = os.path.expanduser("~/.cache/huggingface/gradio")
 if os.path.exists(cache_path):
     try:
         shutil.rmtree(cache_path)
-        print("✅ Deleted ~/.cache/")
-        # print("✅ Deleted ~/.cache/huggingface/gradio")
+        # print("✅ Deleted ~/.cache/")
+        print("✅ Deleted ~/.cache/huggingface/gradio")
     except:
         pass
 
@@ -167,6 +167,22 @@ def parse_first_bbox(bboxes):
         return float(b[0]), float(b[1]), float(b[2]), float(b[3])
     return None
 
+def parse_bboxes(bboxes):
+    """解析所有边界框"""
+    if not bboxes:
+        return None
+    
+    result = []
+    for b in bboxes:
+        if isinstance(b, dict):
+            x, y = float(b.get("x", 0)), float(b.get("y", 0))
+            w, h = float(b.get("width", 0)), float(b.get("height", 0))
+            result.append([x, y, x + w, y + h])
+        elif isinstance(b, (list, tuple)) and len(b) >= 4:
+            result.append([float(b[0]), float(b[1]), float(b[2]), float(b[3])])
+    
+    return result
+
 def colorize_mask(mask: np.ndarray, num_colors: int = 512) -> np.ndarray:
     """将实例掩码转换为彩色图像"""
     def hsv_to_rgb(h, s, v):
@@ -206,11 +222,16 @@ def segment_with_choice(use_box_choice, annot_value):
     print(f"🖼️ Image path: {img_path}")
     box_array = None
     if use_box_choice == "Yes" and bboxes:
-        box = parse_first_bbox(bboxes)
+        # box = parse_first_bbox(bboxes)
+        # if box:
+        #     xmin, ymin, xmax, ymax = map(int, box)
+        #     box_array = [[xmin, ymin, xmax, ymax]]
+        #     print(f"📦 Using bounding box: {box_array}")
+        box = parse_bboxes(bboxes)
         if box:
-            xmin, ymin, xmax, ymax = map(int, box)
-            box_array = [[xmin, ymin, xmax, ymax]]
-            print(f"📦 Using bounding box: {box_array}")
+            box_array = box
+            print(f"📦 Using bounding boxes: {box_array}")
+
 
     # 运行分割模型
     try:
@@ -291,11 +312,15 @@ def count_cells_handler(use_box_choice, annot_value):
     print(f"🖼️ Image path: {image_path}")
     box_array = None
     if use_box_choice == "Yes" and bboxes:
-        box = parse_first_bbox(bboxes)
+        # box = parse_first_bbox(bboxes)
+        # if box:
+        #     xmin, ymin, xmax, ymax = map(int, box)
+        #     box_array = [[xmin, ymin, xmax, ymax]]
+        #     print(f"📦 Using bounding box: {box_array}")
+        box = parse_bboxes(bboxes)
         if box:
-            xmin, ymin, xmax, ymax = map(int, box)
-            box_array = [[xmin, ymin, xmax, ymax]]
-            print(f"📦 Using bounding box: {box_array}")
+            box_array = box
+            print(f"📦 Using bounding boxes: {box_array}")
     
     try:
         print(f"🔢 Counting - Image: {image_path}")
@@ -364,6 +389,9 @@ def count_cells_handler(use_box_choice, annot_value):
 
 
         result_text = f"✅ Detected {round(count)} objects"
+        if use_box_choice == "Yes" and box:
+            result_text += f"\n📦 Using bounding box: {box_array}"
+        
 
         print(f"✅ Counting done - Count: {count:.1f}")
 
@@ -703,11 +731,15 @@ def track_video_handler(use_box_choice, first_frame_annot, zip_file_obj):
             if isinstance(first_frame_annot, (list, tuple)) and len(first_frame_annot) > 1:
                 bboxes = first_frame_annot[1]
                 if bboxes:
-                    box = parse_first_bbox(bboxes)
+                    # box = parse_first_bbox(bboxes)
+                    # if box:
+                    #     xmin, ymin, xmax, ymax = map(int, box)
+                    #     box_array = [[xmin, ymin, xmax, ymax]]
+                    #     print(f"📦 Using bounding box: {box_array}")
+                    box = parse_bboxes(bboxes)
                     if box:
-                        xmin, ymin, xmax, ymax = map(int, box)
-                        box_array = [[xmin, ymin, xmax, ymax]]
-                        print(f"📦 Using bounding box: {box_array}")
+                        box_array = box
+                        print(f"📦 Using bounding boxes: {box_array}")
         
         # Extract input ZIP
         temp_dir = tempfile.mkdtemp()
@@ -813,6 +845,9 @@ def track_video_handler(use_box_choice, first_frame_annot, zip_file_obj):
             - README.txt (Results description)
             """
 
+        if use_box_choice == "Yes" and box:
+            result_text += f"\n📦 Using bounding box: {box_array}"
+
         print(f"\n✅ Tracking completed")
 
         # Clean up input temp directory (keep output temp for download)