Update app.py

app.py

@@ -10,18 +10,22 @@ app = FastAPI()
 THRESHOLD = 0.35
 
 print("正在加载 BGE-Large-ZH-v1.5...")
+# 这里会自动下载模型，如果日志卡在这里请耐心等待
 model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
 print("模型加载完成")
 
 def load_data():
     if not os.path.exists('emoji_labels.json'):
+        print("警告: 找不到 emoji_labels.json")
         return [], None
     with open('emoji_labels.json', 'r', encoding='utf-8') as f:
         data = json.load(f)
     texts = [item['text'] for item in data]
+    # 预计算向量
     embeddings = model.encode(texts, normalize_embeddings=True, convert_to_tensor=True)
     return data, embeddings
 
+# 初始化数据
 emoji_data, emoji_embeddings = load_data()
 
 @app.get("/")
@@ -38,11 +42,11 @@ async def match_emoji(request: Request):
         body = await request.json()
         user_text = body.get("text", "")
         
-        # 兜底：空输入返回 neutral
+        # 兜底：空输入或者数据没加载好，返回 neutral
         if not user_text or emoji_embeddings is None:
             return {"label": "neutral", "score": 0.0}
 
-        # 构造查询
+        # 构造查询 (BGE模型建议加前缀)
         query_text = "为这个句子分类情感：" + user_text
         query_emb = model.encode(query_text, normalize_embeddings=True, convert_to_tensor=True)
         
@@ -63,8 +67,13 @@ async def match_emoji(request: Request):
                 "score": best_score             # 例如 0.8512
             }
         else:
-            # 分数太低，返回 neutral，分数保留原值以便调试
+            # 分数太低，返回 neutral
             return {
                 "label": "neutral", 
                 "score": best_score
-            }
+            }
+            
+    except Exception as e:
+        print(f"Error: {e}")
+        # 发生任何报错都返回 neutral，保证程序不崩
+        return {"label": "neutral", "score": 0.0}