Update app.py

app.py

@@ -7,6 +7,7 @@ import gradio as gr
 import os
 import joblib
 from model import DualStreamTransformer, ArcMarginProduct
+
 css = """
 .scroll-box {
     height: 300px;
@@ -36,11 +37,14 @@ css = """
     word-break: break-all !important;
 }
 """
+
+
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 FOLD = 5
 MODEL_PATH = f"best_model_fold_{FOLD}.pt"
 
-model = DualStreamTransformer(feat_num_1=24, feat_num_2=10, d_model=32)
+
+model = DualStreamTransformer(feat_num_1=24, feat_num_2=10, d_model=32).to(DEVICE)
 metric_fc = ArcMarginProduct(32, 2).to(DEVICE)
 
 if os.path.exists(MODEL_PATH):
@@ -53,25 +57,22 @@ if os.path.exists(MODEL_PATH):
 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):
+
+    if any(a is None for a in all_answers):
+        missing = [i+1 for i, a in enumerate(all_answers) if a is None]
+        raise gr.Error(f"還有題目沒填完！索引：{missing}")
     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}
 
     ccmq_ans = all_answers[:24]
-    osdi_ans_raw = all_answers[24:] 
-    
-    if any(a is None for a in all_answers):
-      missing_indices = [i+1 for i, a in enumerate(all_answers) if a is None]
-      print(f"DEBUG - 未填寫題目索引: {missing_indices}")
-      raise gr.Error(f"還有題目沒填完喔！未填索引: {missing_indices}")
+    osdi_ans = all_answers[24:34] # 確保只取 10 題
 
     x1_raw = np.array([[ccmq_map[a] for a in ccmq_ans]])
-    x2_raw = np.array([[osdi_map[a] for a in osdi_ans_raw[:10]]])
+    x2_raw = np.array([[osdi_map[a] for a in osdi_ans]])
 
     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)
 
@@ -82,34 +83,32 @@ def analyze_and_predict(*all_answers):
         pred_idx = torch.argmax(probs, dim=1).item()
         conf = probs[0, pred_idx].item()
 
+
     plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei', 'DejaVu Sans']
     plt.rcParams['axes.unicode_minus'] = False
-    
     fig, ax = plt.subplots(figsize=(6, 4))
-    labels = ["健康/正常", "乾眼風險"]
-    sns.barplot(x=[conf if i == pred_idx else 1-conf for i in range(2)], y=labels, palette="viridis", ax=ax)
+    sns.barplot(x=[conf if i == pred_idx else 1-conf for i in range(2)], y=["健康", "風險"], palette="viridis", ax=ax)
     ax.set_title(f"AI 診斷信心度: {conf:.2%}")
 
-    table_data = [[f"題目 {i+1}", all_answers[i], "已記錄"] for i in range(10)]
-    
-    res_label = "乾眼風險 (SJS/DES)" if pred_idx == 1 else "正常/健康"
-    
+    table_data = [[f"問卷項目 {i+1}", all_answers[i], "OK"] for i in range(len(all_answers))]
+    res_label = "🔴 乾眼風險 (SJS/DES)" if pred_idx == 1 else "🟢 正常/健康"
+
     return (
         gr.update(visible=False), 
-        gr.update(visible=True),  
-        f"### {res_label}",       
-        advice_text,              
-        {"風險機率": conf if pred_idx==1 else 1-conf, "健康程度": 1 - (conf if pred_idx==1 else 1-conf)}, # res_prob
-        table_data,              
-        fig,                      
-        fig                       
+        gr.update(visible=True),
+        f"### {res_label}",
+        "分析報告：系統已根據您的中醫體質與西醫症狀完成多模態融合計算。",
+        {"風險機率": conf if pred_idx==1 else 1-conf, "健康程度": 1 - (conf if pred_idx==1 else 1-conf)},
+        table_data,
+        fig,
+        fig
     )
 
 def reset_system():
-    return [gr.update(visible=True), gr.update(visible=False), gr.update(selected=0)] + [None] * 36
+    return [gr.update(visible=True), gr.update(visible=False), gr.update(selected=0)] + [None] * 34
 
 with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
-    gr.Markdown("# 中醫AI診斷系統")
+    gr.Markdown("# 中醫 AI 診斷系統")
 
     with gr.Column(visible=True) as stage_1:
         with gr.Tabs() as survey_tabs:
@@ -117,40 +116,43 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
                 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")
+                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. 電腦操作", "8. 觀看電視", "9. 刮風不適", "10. 空調不適"]
-                    # 注意：這裡使用列表生成式，避免變數名稱重複覆蓋
                     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]
+                
                 with gr.Row():
                     back_to_ccmq = gr.Button("返回 CCMQ")
-                    submit_btn = gr.Button("送出診斷並生成報告", variant="primary")              
+                    submit_btn = gr.Button("🚀 生成分析報告", variant="primary")
 
     with gr.Column(visible=False) as stage_2:
-        gr.Markdown("## AI 診斷分析報告")
+        gr.Markdown("## 診斷報告結果")
         with gr.Row():
-            res_table = gr.Dataframe(headers=["項目", "回答", "狀態"], interactive=False)
-            back_to_edit = gr.Button("重新修改問卷")
-            with gr.Column():
+            with gr.Column(scale=1):
+                res_table = gr.Dataframe(headers=["項目", "回答", "狀態"], interactive=False)
+                back_to_edit = gr.Button("修改問卷")
+            with gr.Column(scale=1):
                 res_prob = gr.Label(label="預測機率")
                 res_title = gr.Markdown("### 診斷結果")
-                res_desc = gr.Markdown("分析中...")
+                res_desc = gr.Markdown("報告說明")
                 plot_1 = gr.Plot()
                 plot_2 = gr.Plot()
-        finish_btn = gr.Button("結束並重新開始", size="lg", variant="secondary")
-
+        finish_btn = gr.Button("結束並重新開始", variant="secondary")
 
     all_inputs = all_ccmq + all_osdi
+    
     btn_next.click(fn=lambda: gr.Tabs(selected=1), outputs=survey_tabs)
     back_to_ccmq.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)
 
 if __name__ == "__main__":