Update api_server.py

api_server.py

@@ -34,7 +34,7 @@ if load_type == 'local':
         
     model = YOLO(model_path)
 
-    print("=============== YOLO DONE =============")
+    print("===============LOAD YOLO MODEL DONE =============")
     #model.eval()  # 設定模型為推理模式
 elif load_type == 'remote_hub_download':
     from huggingface_hub import hf_hub_download
@@ -93,6 +93,7 @@ def check_memory_usage():
 
 # Run the function
 check_memory_usage()
+    
 # Initialize the Flask application
 app = Flask(__name__)
 # Initialize the ClipModel at the start
@@ -119,11 +120,11 @@ def predict():
     except Exception as e:
         return jsonify({'error': str(e)}), 400
         
-    print("***Start YOLO predict***")
+    print("***** Start YOLO predict *****")
     # Make a prediction using YOLO
     results = model(image_data)
-    print ("*****result:",results,"********")
-    print("***YOLO predict DONE***")
+    print ("***** YOLO predict result:",results,"********")
+    print("***** YOLO predict DONE *****")
     check_memory_usage()
     
     # 檢查 YOLO 是否返回了有效的結果
@@ -154,15 +155,15 @@ def predict():
             yolo_path = f"{YOLO_DIR}/{message_id}/{element}"
             yolo_file = get_jpg_files(yolo_path)
 
-            print(yolo_path)
+            print(f"======YOLO result:{yolo_path}======")
 
             element_list.append(element)
             
             for yolo_img in yolo_file: # 每張切圖yolo_img
-                print("yolo _img:", yolo_img)
-                #top_k_words.append(clip_model.clip_result(yolo_img)) # CLIP預測3個結果(top_k_words)
+                print("*****START CLIP *****")
+                top_k_words.append(clip_model.clip_result(yolo_img)) # CLIP預測3個結果(top_k_words)
                 #encoded_images.append(image_to_base64(yolo_img))
-                #print(f"**{yolo_img}:{top_k_words}**\n")
+                print(f"**{yolo_img}:{top_k_words}**\n")
 
             # if element_counts[element] > 1: #某隻角色的數量>1
             #     yolo_path = f"{YOLO_DIR}/{message_id}/{element}"