Update api_server.py

api_server.py

@@ -135,6 +135,11 @@ def predict():
     
     # 儲存辨識後的圖片到指定資料夾
     for result in results:
+
+        encoded_images=[]
+        element_list =[]
+        top_k_words =[]
+        
         # 保存圖片
         result.save_crop(f"{YOLO_DIR}/{message_id}")
         
@@ -146,10 +151,6 @@ def predict():
 
         element_counts = Counter(label_names)
 
-        encoded_images=[]
-        element_list =[]
-        top_k_words =[]
-
         for element, count in element_counts.items():
             
             yolo_path = f"{YOLO_DIR}/{message_id}/{element}"
@@ -157,6 +158,10 @@ def predict():
 
             print(f"======YOLO result:{yolo_path}======")
 
+            if len(yolo_file) == 0:
+                print(f"警告：{element} 沒有找到相關的 JPG 檔案")
+                continue
+
             element_list.append(element)
             
             for yolo_img in yolo_file: # 每張切圖yolo_img
@@ -177,24 +182,24 @@ def predict():
             #     encoded_images.append(image_to_base64(yolo_path))
                 
         # 建立回應資料
-        response_data = {
-            'message_id': message_id,
-            'description': element_list,
-            'images': [
-                {
-                    'encoded_image': encoded_image,
-                    'description_list': top_k_words
-                }
-                for encoded_image, elements in zip(encoded_images, element_list)
-            ]
-        }
+        # response_data = {
+        #     'message_id': message_id,
+        #     'description': element_list,
+        #     'images': [
+        #         {
+        #             'encoded_image': encoded_image,
+        #             'description_list': top_k_words
+        #         }
+        #         for encoded_image, elements in zip(encoded_images, element_list)
+        #     ]
+        # }
         # response_data = {
         #     'message_id': message_id,
         #     'images': encoded_images,
         #     'description': element_list
         # }
         
-        return jsonify(response_data)
+        return top_k_words#jsonify(response_data)
             
         # for label_name in label_names:
         #     yolo_file=f"{YOLO_DIR}/{message_id}/{label_name}/im.jpg.jpg"