Update app.py

app.py

@@ -1,8 +1,6 @@
 import torch
 import pandas as pd
 import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
 import gradio as gr
 import os
 import joblib
@@ -37,15 +35,10 @@ 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"
 
-plt.rcParams['font.sans-serif'] = ['Noto Sans CJK TC', 'Droid Sans Fallback', 'Arial Unicode MS']
-plt.rcParams['axes.unicode_minus'] = False
-
 model = DualStreamTransformer(feat_num_1=24, feat_num_2=10, d_model=32).to(DEVICE)
 metric_fc = ArcMarginProduct(32, 2).to(DEVICE)
 
@@ -61,14 +54,11 @@ 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 = all_answers[24:34] # 確保只取 10 題
+    osdi_ans = all_answers[24:34] 
 
     x1_raw = np.array([[ccmq_map[a] for a in ccmq_ans]])
     x2_raw = np.array([[osdi_map[a] for a in osdi_ans]])
@@ -85,31 +75,34 @@ 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))
-    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"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"
+    # 準備純文字回傳內容
+    table_data = [[f"問卷項目 {i+1}", all_answers[i], "已記錄"] for i in range(len(all_answers))]
+    res_label = " 乾眼風險 (SJS/DES)" if pred_idx == 1 else " 正常/健康"
+    
+  
+    detail_text = f"""
+    ### 🧬 AI 模型分析詳情
+    - **診斷信心度**：{conf:.2%}
+    - **預測類別**：{res_label}
+    - **核心演算法**：Dual-Stream FT-Transformer
+    - **數據來源**：中醫體質辨識量表 (24項) + OSDI 症狀量表 (10項)
+    """
 
     return (
         gr.update(visible=False), 
         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。",
+        detail_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)},
         table_data,
-        fig,
-        fig
+        gr.update(visible=False), 
+        gr.update(visible=False)  
     )
 
 def reset_system():
     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:
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 中醫 AI 診斷系統")
 
     with gr.Column(visible=True) as stage_1:
@@ -126,29 +119,27 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
                     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("🚀 生成分析報告", variant="primary")
+                    back_to_ccmq = 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():
             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")
+                res_table = gr.Dataframe(headers=["項目", "回答", "狀態"], interactive=False,elem_classes="scroll-box")
             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("報告說明")
-                plot_1 = gr.Plot()
-                plot_2 = gr.Plot()
+                res_desc = gr.Markdown("分析中...")
+                plot_1 = gr.Plot(visible=False)
+                plot_2 = gr.Plot(visible=False)
+        
         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,