Update app.py

app.py

@@ -20,29 +20,20 @@ if os.path.exists(MODEL_PATH):
     model.load_state_dict(checkpoint['model'], strict=False)
     metric_fc.load_state_dict(checkpoint['fc'], strict=False)
     model.eval()
-    print("Model weights loaded successfully.")
-
 
 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):
-
     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}
 
     try:
-
         x1_vals = [ccmq_map.get(a, 1) for a in all_answers[:24]]
         x2_vals = [osdi_map.get(a, 0) for a in all_answers[24:34]]
         
-        x1_raw = np.array([x1_vals])
-        x2_raw = np.array([x2_vals])
-
-
-        x1_scaled = scaler_ccmq.transform(x1_raw)
-        x2_scaled = scaler_osdi.transform(x2_raw)
+        x1_scaled = scaler_ccmq.transform(np.array([x1_vals]))
+        x2_scaled = scaler_osdi.transform(np.array([x2_vals]))
         
         sx1 = torch.tensor(x1_scaled, dtype=torch.float32).to(DEVICE)
         sx2 = torch.tensor(x2_scaled, dtype=torch.float32).to(DEVICE)
@@ -56,73 +47,79 @@ def analyze_and_predict(*all_answers):
             
         print(f"DEBUG: Prediction successful! Pred: {pred_idx}")
 
-    except Exception as e:
-        print(f" ERROR in inference: {e}")
-        raise gr.Error(f"計算出錯：{str(e)}")
 
-    res_label = " 乾眼風險 (SJS/DES)" if pred_idx == 1 else " 正常/健康"
-    table_data = [[f"項目 {i+1}", str(all_answers[i]), "已紀錄"] for i in range(len(all_answers))]
-    
+        if pred_idx == 1:
+            res_label = "正常 / 健康"
+        elif pred_idx == 0:
+            res_label = "乾眼風險 (DES)"
+        else:
+            res_label = "修格蘭氏症風險 (SJS)"
 
-    return (
-        gr.update(visible=False), 
-        gr.update(visible=True),
-        f"### {res_label}",
-        f"根據雙流 Transformer 模型分析，您的信心度為 {conf:.2%}。這代表系統觀察到您的中醫體質與眼表症狀具備相關特徵。",
-        {"Risk": conf if pred_idx==1 else 1-conf, "Healthy": 1 - (conf if pred_idx==1 else 1-conf)},
-        table_data,
-        None, 
-        None
-    )
+        prob_dict = {
+            "健康": probs[0, 0].item(),
+            "乾眼 (DES)": probs[0, 1].item(),
+            "修格蘭氏 (SJS)": probs[0, 2].item()
+        }
+        
+        return (
+            f"## 診斷結果：{res_label}",
+            f"**分析完成**：AI 信心度為 **{conf:.2%}**。本系統整合了中醫 24 項體質特徵與西醫 10 項 OSDI 症狀進行多模態計算。",
+            {"Risk": conf if pred_idx==1 else 1-conf, "Healthy": 1 - (conf if pred_idx==1 else 1-conf)}
+        )
+
+    except Exception as e:
+        print(f"Error: {e}")
+        return f"### 計算出錯", str(e), {}
+
+with gr.Blocks(theme=gr.themes.Soft(), css=".scroll-box { height: 400px; overflow-y: auto; border: 1px solid #ddd; padding: 15px; border-radius: 8px; }") as demo:
+    gr.Markdown("# 中西醫 AI 診斷系統")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            gr.Markdown("### 第一步：填寫問卷")
+            with gr.Tabs() as survey_tabs:
+                with gr.Tab("CCMQ 體質評估", id=0):
+                    with gr.Group(elem_classes="scroll-box"):
+                        ccmq_labels = ["惡寒惡風", "自汗", "胸悶腹脹","咽喉痰梗感","多愁善感","易受驚","面部暗沉","黑眼圈","健忘","唇色暗","身熱、面熱","膚乾口乾","唇紅","便祕","兩顴紅","眼乾澀","四肢冷","惡寒、腰膝冷","飲冷腹瀉","口苦口臭","帶下色黃/下陰潮濕","鼻塞流涕","變天咳喘","過敏"]
+                        all_ccmq = [gr.Radio(["總是", "經常", "有時", "很少", "沒有"], label=f"{i+1}. {txt}", value="沒有") for i, txt in enumerate(ccmq_labels)]
+                    btn_next = gr.Button("下一步：填寫 OSDI")
+
+                with gr.Tab("OSDI 症狀評估", id=1):
+                    with gr.Group(elem_classes="scroll-box"):
+                        osdi_labels = ["1. 對光敏感", "2. 眼睛疼痛", "3. 視線模糊", "4. 視力減退", "5. 閱讀限制", "6. 夜間駕駛", "7. 電腦操作", "8. 觀看電視", "9. 刮風不適", "10. 空調不適"]
+                        all_osdi = [gr.Radio(["總是", "經常", "一半一半", "偶而", "完全不曾"], label=txt, value="完全不曾") for txt in osdi_labels]
+                    
+                    with gr.Row():
+                        back_btn = gr.Button("返回")
+                        submit_btn = gr.Button("🚀 生成分析報告", variant="primary")
+
+
+        with gr.Column(scale=1):
+            gr.Markdown("### 第二步：AI 診斷結果")
+            res_title = gr.Markdown("### 點擊按鈕開始分析")
+            res_desc = gr.Markdown("請先完成左側問卷並點擊「生成分析報告」。")
+            res_prob = gr.Label(label="模��信心分佈")
+            
+            gr.Markdown("---")
+            reset_btn = gr.Button("🏁 清除重新開始")
 
 
-with gr.Blocks() as demo:
-    gr.Markdown("# 中醫 AI 診斷系統")
-
-    with gr.Column(visible=True) as stage_1:
-        with gr.Tabs() as survey_tabs:
-            with gr.Tab("CCMQ 體質評估", id=0):
-                ccmq_labels = ["惡寒惡風", "自汗", "胸悶腹脹","咽喉痰梗感","多愁善感","易受驚","面部暗沉","黑眼圈","健忘","唇色暗","身熱、面熱","膚乾口乾","唇紅","便祕","兩顴紅","眼乾澀","四肢冷","惡寒、腰膝冷","飲冷腹瀉","口苦口臭","帶下色黃/下陰潮濕","鼻塞流涕","變天咳喘","過敏"]
-                all_ccmq = [gr.Radio(["總是", "經常", "有時", "很少", "沒有"], label=f"{i+1}. {txt}", value="沒有") for i, txt in enumerate(ccmq_labels)]
-                btn_next = gr.Button("下一步", variant="primary")
-
-            with gr.Tab("OSDI 症狀評估", id=1):
-                osdi_labels = ["1. 對光敏感", "2. 眼睛疼痛", "3. 視線模糊", "4. 視力減退", "5. 閱讀限制", "6. 夜間駕駛", "7. 電腦操作", "8. 觀看電視", "9. 刮風不適", "10. 空調不適"]
-                all_osdi = [gr.Radio(["總是", "經常", "一半一半", "偶而", "完全不曾"], label=txt, value="完全不曾") for txt in osdi_labels]
-                
-                with gr.Row():
-                    back_btn = gr.Button("返回")
-                    submit_btn = gr.Button("生成診斷報告", variant="primary")
-
-    with gr.Column(visible=False) as stage_2:
-        gr.Markdown("## 診斷報告結果")
-        with gr.Row():
-            res_table = gr.Dataframe(headers=["項目", "回答", "狀態"], interactive=False)
-            with gr.Column():
-                res_prob = gr.Label(label="分析機率")
-                res_title = gr.Markdown("### 結論")
-                res_desc = gr.Markdown("分析中...")
-                plot_1 = gr.Plot(visible=False)
-                plot_2 = gr.Plot(visible=False)
-        finish_btn = gr.Button(" 重新開始", variant="secondary")
-
-   
     all_inputs = all_ccmq + all_osdi
     btn_next.click(fn=lambda: gr.Tabs(selected=1), outputs=survey_tabs)
     back_btn.click(fn=lambda: gr.Tabs(selected=0), outputs=survey_tabs)
     
-  
+
     submit_btn.click(
         fn=analyze_and_predict, 
         inputs=all_inputs, 
-        outputs=[stage_1, stage_2, res_title, res_desc, res_prob, res_table, plot_1, plot_2]
+        outputs=[res_title, res_desc, res_prob]
     )
     
-    finish_btn.click(
-        fn=lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(selected=0)] + ["沒有"]*24 + ["完全不曾"]*10,
-        outputs=[stage_1, stage_2, survey_tabs] + all_inputs
+
+    reset_btn.click(
+        fn=lambda: ["### 點擊按鈕開始分析", "請先完成左側問卷。", {}] + ["沒有"]*24 + ["完全不曾"]*10,
+        outputs=[res_title, res_desc, res_prob] + all_inputs
     )
 
 if __name__ == "__main__":
-
-    demo.launch(theme=gr.themes.Soft())
+    demo.launch(ssr_mode=False)