Update app.py

app.py

@@ -56,16 +56,19 @@ scaler_ccmq = joblib.load(f"scaler_ccmq_fold_{FOLD}.pkl")
 scaler_osdi = joblib.load(f"scaler_osdi_fold_{FOLD}.pkl")
 
 def analyze_and_predict(*all_answers):
-   
+    # 1. 映射表
     ccmq_map = {"總是": 5, "經常": 4, "有時": 3, "很少": 2, "沒有": 1}
     osdi_map = {"總是": 4, "經常": 3, "一半一半": 2, "偶而": 1, "完全不曾": 0}
 
-   
+    print(f"--- 偵錯資訊：收到 {len(all_answers)} 題回答 ---")
+
     try:
-        x1_raw = np.array([[ccmq_map[a] for a in all_answers[:24]]])
-        x2_raw = np.array([[osdi_map[a] for a in all_answers[24:34]]])
+        x1_raw = np.array([[ccmq_map.get(a, 1) for a in all_answers[:24]]])
+        
+        # .get(a, 0) 代表：如果 a 是 None 或找不到，就預設給 0 (OSDI 的 '完全不曾')
+        x2_raw = np.array([[osdi_map.get(a, 0) for a in all_answers[24:34]]])
+
 
-     
         x1_scaled = scaler_ccmq.transform(x1_raw)
         x2_scaled = scaler_osdi.transform(x2_raw)
         sx1 = torch.tensor(x1_scaled, dtype=torch.float32).to(DEVICE)
@@ -77,30 +80,26 @@ def analyze_and_predict(*all_answers):
             probs = torch.softmax(logits, dim=1)
             pred_idx = torch.argmax(probs, dim=1).item()
             conf = probs[0, pred_idx].item()
+            
     except Exception as e:
-        raise gr.Error(f"填寫不完整或轉換錯誤: {str(e)}")
+        print(f"❌ 推論發生錯誤: {e}")
+        raise gr.Error(f"系統轉換異常，請重新整理頁面。錯誤原因: {str(e)}")
 
 
     res_label = " 乾眼風險 (SJS/DES)" if pred_idx == 1 else " 正常/健康"
     table_data = [[f"項次 {i+1}", str(all_answers[i]), "已記錄"] for i in range(len(all_answers))]
     
-    report_text = f"""
-    ### 🧬 深度學習模型分析說明
-    - **信心度評估**：{conf:.2%}
-    - **診斷結論**：{res_label}
-    - **模型架構**：雙流 Transformer 融合模型
-    - **分析結果**：系統檢測到您的中醫體質訊號與 OSDI 症狀分佈呈現強關聯。
-    """
+    report_text = f"### 診斷結論：{res_label}\nAI 信心度：{conf:.2%}"
 
     return (
-        gr.update(visible=False), 
-        gr.update(visible=True),  
-        f"### {res_label}",       
-        report_text,              
-        {"風險機率": conf if pred_idx==1 else 1-conf, "健康機率": 1-(conf if pred_idx==1 else 1-conf)}, # res_prob
-        table_data,               
-        None,                     
-        None                      
+        gr.update(visible=False), # stage_1
+        gr.update(visible=True),  # stage_2
+        f"### {res_label}", 
+        report_text, 
+        {"風險機率": conf if pred_idx==1 else 1-conf, "健康機率": 1-(conf if pred_idx==1 else 1-conf)}, 
+        table_data, 
+        None, 
+        None
     )
 
 def reset_system():