Update app.py

app.py

@@ -6,35 +6,6 @@ import os
 import joblib
 from model import DualStreamTransformer, ArcMarginProduct
 
-css = """
-.scroll-box {
-    height: 300px;
-    overflow-y: auto !important;
-    overflow-x: hidden !important;
-    display: block !important;
-    width: 100% !important;
-    max-width: 100% !important;
-}
-.scroll-box * {
-    max-width: 100% !important;
-    box-sizing: border-box !important;
-}
-.vertical-radio {
-    display: block !important;
-    width: 100% !important;
-}
-.vertical-radio .wrap {
-    display: flex !important;
-    flex-direction: column !important;
-    width: 100% !important;
-    min-width: 0 !important;
-}
-.vertical-radio .gradio-radio-item {
-    width: 100% !important;
-    white-space: normal !important;
-    word-break: break-all !important;
-}
-"""
 
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 FOLD = 5
@@ -49,28 +20,30 @@ if os.path.exists(MODEL_PATH):
     model.load_state_dict(checkpoint['model'])
     metric_fc.load_state_dict(checkpoint['fc'])
     model.eval()
-    metric_fc.eval()
-
+    print(" Model 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):
-    # 1. 映射表
+
+    print(f"DEBUG: Received answers count = {len(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}
 
-    print(f"--- 偵錯資訊：收到 {len(all_answers)} 題回答 ---")
-
     try:
-        x1_raw = np.array([[ccmq_map.get(a, 1) for a in all_answers[:24]]])
+
+        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]]
         
-        # .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_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)
+        
         sx1 = torch.tensor(x1_scaled, dtype=torch.float32).to(DEVICE)
         sx2 = torch.tensor(x2_scaled, dtype=torch.float32).to(DEVICE)
 
@@ -81,77 +54,75 @@ def analyze_and_predict(*all_answers):
             pred_idx = torch.argmax(probs, dim=1).item()
             conf = probs[0, pred_idx].item()
             
-    except Exception as 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)}")
+        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"項次 {i+1}", str(all_answers[i]), "已記錄"] for i in range(len(all_answers))]
+    table_data = [[f"項目 {i+1}", str(all_answers[i]), "已紀錄"] for i in range(len(all_answers))]
     
-    report_text = f"### 診斷結論：{res_label}\nAI 信心度：{conf:.2%}"
 
     return (
-        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, 
+        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
     )
 
-def reset_system():
-    return [gr.update(visible=True), gr.update(visible=False), gr.update(selected=0)] + [None] * 34
-
+# --- UI 介面 ---
 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):
-                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}") for i, txt in enumerate(ccmq_labels)]
-                btn_next = gr.Button("下一步：填寫 OSDI", variant="primary")
+                ccmq_labels = ["惡寒惡風", "自汗", "胸悶腹��","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):
-                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. 操作電腦與ATM", "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) for txt in osdi_labels]
+                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 CCMQ")
-                    submit_btn = gr.Button(" 提交診斷", variant="primary")
+                    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")
+        gr.Markdown("## 診斷報告結果")
         with gr.Row():
-            with gr.Column(scale=1):
-                res_table = gr.Dataframe(headers=["項目", "回答", "狀態"], interactive=False,elem_classes="scroll-box")
-                back_to_edit = gr.Button("修改問卷")
-            with gr.Column(scale=1):
-                res_prob = gr.Label(label="預測信心機率")
+            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("說明")
-
+                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)
-    back_to_edit.click(fn=lambda: (gr.update(visible=True), gr.update(visible=False)), outputs=[stage_1, stage_2])
     
+  
     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]
     )
     
-    finish_btn.click(fn=reset_system, outputs=[stage_1, stage_2, survey_tabs] + all_inputs)
+    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
+    )
 
 if __name__ == "__main__":
+
     demo.launch(theme=gr.themes.Soft())