Update app.py

app.py

@@ -7,7 +7,6 @@ import gradio as gr
 import os
 import joblib
 from model import DualStreamTransformer, ArcMarginProduct
-
 css = """
 .scroll-box {
     height: 300px;
@@ -37,30 +36,42 @@ css = """
     word-break: break-all !important;
 }
 """
-
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 FOLD = 5
-model = DualStreamTransformer(n_feat1=25, n_feat2=12, d_model=32).to(DEVICE)
+MODEL_PATH = f"best_model_fold_{FOLD}.pt"
+
+model = DualStreamTransformer(n_feat1=24, n_feat2=10, d_model=32).to(DEVICE)
 metric_fc = ArcMarginProduct(32, 2).to(DEVICE)
-checkpoint = torch.load(f"best_model_fold_{FOLD}.pt", map_location=DEVICE)
-model.load_state_dict(checkpoint['model'])
-metric_fc.load_state_dict(checkpoint['fc'])
 
+if os.path.exists(MODEL_PATH):
+    checkpoint = torch.load(MODEL_PATH, map_location=DEVICE)
+    model.load_state_dict(checkpoint['model'])
+    metric_fc.load_state_dict(checkpoint['fc'])
+    model.eval()
+    metric_fc.eval()
 
 scaler_ccmq = joblib.load(f"scaler_ccmq_fold_{FOLD}.pkl")
 scaler_osdi = joblib.load(f"scaler_osdi_fold_{FOLD}.pkl")
 
-model = DualStreamTransformer(n_feat1=24, n_feat2=10, d_model=32).to(DEVICE)
 def analyze_and_predict(*all_answers):
-    ccmq_ans = all_answers[:24] 
-    osdi_ans = all_answers[25:35]
+    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):
+        raise gr.Error("請完整填寫所有問卷題目！")
+
+    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]]])
 
     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)
-    
+
     with torch.no_grad():
         feats = model(sx1, sx2)
         logits = metric_fc.predict(feats)
@@ -69,18 +80,21 @@ def analyze_and_predict(*all_answers):
         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))
-    sns.barplot(x=[conf, 1-conf], y=["預測類別", "其他"], palette="viridis", ax=ax)
-    ax.set_title(f"診斷信心度: {conf:.2%}")
+    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)
+    ax.set_title(f"AI 診斷信心度: {conf:.2%}")
 
-    table_data = [] 
+    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"
     
-    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"### 診斷結果：{res_label}",
-        "根據 FT-Transformer 的注意力機制分析，您的特徵與臨床乾眼指標有顯著關連。",
         {"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,
@@ -88,9 +102,10 @@ def analyze_and_predict(*all_answers):
     )
 
 def reset_system():
-    return [gr.update(visible=True), gr.update(visible=False), gr.update(selected=0)] + [None] * 37
+    return [gr.update(visible=True), gr.update(visible=False), gr.update(selected=0)] + [None] * 36
+
 with gr.Blocks(theme=gr.themes.Soft(), css=".scroll-box { height: 450px; overflow-y: auto; }") 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:
@@ -98,46 +113,38 @@ with gr.Blocks(theme=gr.themes.Soft(), css=".scroll-box { height: 450px; overflo
                 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", variant="primary")
+                btn_next = gr.Button("下一步：填寫 OSDI", variant="primary")
 
             with gr.Tab("OSDI 症狀評估", id=1):
                 with gr.Group(elem_classes="scroll-box"):
-                    gr.Markdown("#### A. 眼睛症狀")
-                    gr.Markdown("#### 在過去一週中，您是否出現下列任一症狀？")
+                    gr.Markdown("#### 在過去一週中，您是否出現下列症狀？")
                     o1 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="1. 眼睛對光敏感？")
-                    o3 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="3. 眼睛疼痛？")
-                    o4 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="4. 視線模糊？")
-                    o5 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="5. 視力減退？")
-
-                    gr.Markdown("---")
-                    gr.Markdown("#### B. 日常活動限制")
-                    gr.Markdown("#### 下列活動，是否因眼睛問題而受到限制？")
-                    o6 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="6. 閱讀？")
-                    o7 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="7. 夜間駕駛？")
-                    o8 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="8. 操作電腦與提款機？")
-                    o9 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="9. 觀看電視？")
-
-                    gr.Markdown("---")
-                    gr.Markdown("#### C. 環境因素不適感")
-                    gr.Markdown("#### 在過去一週中遇到任一狀況時，您的眼睛是否曾感覺不適？")
-                    o10 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="10. 刮風的狀況？")
-                    o12 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="12. 區域使用空調？")
-
-                    all_osdi = [o1, o2, o3, o4, o5, o6, o7, o8, o9, o10, o11, o12]
-                submit_btn = gr.Button("生成診斷報告", variant="primary")
+                    o2 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="3. 眼睛疼痛？")
+                    o3 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="4. 視線模糊？")
+                    o4 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="5.視力減退？")
+                    o5 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="6. 閱讀？")
+                    o6 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="7. 夜間駕駛？")
+                    o7 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="8. 操作電腦與提款機？")
+                    o8 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="9. 觀看電視？")
+                    o9 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="10. 刮風的狀況？")
+                    o10 = gr.Radio(["總是", "經常","一半一半","偶而","完全不曾"], label="12. 區域使用空調？")
+                    all_osdi = [o1, o2, o3, o4, o5, o6, o7, o8, o9, o10]
+                
+                submit_btn = gr.Button("🚀 生成診斷報告", variant="primary")
 
     with gr.Column(visible=False) as stage_2:
-        gr.Markdown("## 診斷分析報告")
+        gr.Markdown("## 📊 AI 診斷分析報告")
         with gr.Row():
-            res_table = gr.Dataframe(headers=["項目", "回答", "分值"], interactive=False)
+            res_table = gr.Dataframe(headers=["項目", "回答", "狀態"], interactive=False)
             with gr.Column():
                 res_prob = gr.Label(label="預測機率")
                 res_title = gr.Markdown("### 診斷結果")
-                res_desc = gr.Markdown("詳細說明...")
+                res_desc = gr.Markdown("分析中...")
                 plot_1 = gr.Plot()
                 plot_2 = gr.Plot()
-        finish_btn = gr.Button("結束並重新開始", size="lg")
+        finish_btn = gr.Button("結束並重新開始", size="lg", variant="secondary")
 
+    # 互動邏輯
     all_inputs = all_ccmq + all_osdi
     btn_next.click(fn=lambda: gr.Tabs(selected=1), 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])