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README (2).md

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+---
+title: COVID-19 Precision Prevention - Digital Twin Model
+emoji: 🦠
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+sdk_version: 1.32.0
+app_file: app_combined.py
+pinned: false
+---
+
+# COVID-19 Precision Prevention: Digital Twin Model
+
+Interactive 3D visualization system for precision COVID-19 prevention strategies using viral load (Ct value) data.
+
+## Features
+
+- **Tab 1: Contact Tracing (Alpha Variant)** - Determine optimal retrospective tracing duration based on confirmed case Ct values
+- **Tab 2: Quarantine & Isolation (Omicron Variant)** - Optimize quarantine duration based on Ct values and booster vaccination status
+- **AI Assistant** - GPT-4 powered intelligent analysis and recommendations
+- **3D Visualization** - Interactive exploration of effectiveness across different scenarios
+
+## Data Source
+
+Based on research from: "A Bayesian Digital Twin Model for Precision Control of Emerging Infectious Diseases Using Viral Load Data" (Lin et al., 2025)
+
+## Usage
+
+1. Enter your OpenAI API Key in the sidebar
+2. Adjust parameters (Ct value, days, vaccination status)
+3. Explore 3D visualizations
+4. Get AI-powered strategy recommendations

app_combined (2).py

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+import streamlit as st
+import os  
+
+# 設置頁面配置
+st.set_page_config(
+    page_title="精準傳染病防疫-元宇宙時間數位雙胞胎模型",
+    page_icon="🦠",
+    layout="wide"
+)
+
+# 初始化 session state - 直接從環境變數讀取
+if 'api_key' not in st.session_state:
+    st.session_state.api_key = os.getenv("OPENAI_API_KEY", "")  # 從環境變數讀取
+
+
+# 主標題
+st.title("🦠 精準傳染病防疫-元宇宙時間數位雙胞胎模型")
+st.markdown("透過 AI 深度學習模型，根據確診者的病毒量(Ct值)操縱最佳追蹤策略")
+
+# 側邊欄 - API Key 設定（兩個分頁共用）
+with st.sidebar:
+    
+    
+    st.markdown("---")
+    st.markdown("""
+    ### 📖 系統說明
+    
+    **分頁1: 接觸者追蹤**
+    - 基於 Alpha 變異株
+    - 問題：確診者要往回追蹤幾天？
+    
+    **分頁2: 隔離檢疫**
+    - 基於 Omicron 變異株
+    - 問題：接觸者要隔離幾天？
+    """)
+
+# 創建分頁
+tab1, tab2 = st.tabs(["📊 接觸者追蹤 ", "🏥 隔離檢疫 "])
+
+with tab1:
+    # 導入原有的 Table 1 系統
+    import tab1_contact_tracing
+    tab1_contact_tracing.render()
+
+with tab2:
+    # 導入新的 Table 2 系統
+    import tab2_quarantine
+    tab2_quarantine.render()
+
+# 頁腳
+st.markdown("---")
+st.markdown("""
+<div style='text-align: center; color: gray; font-size: 12px;'>
+    <p>📖 數據來源: Lin et al. (2025) - A Bayesian Digital Twin Model for Precision Control of Emerging Infectious Diseases</p>
+    <p>🤖 AI 功能: 使用 OpenAI GPT-4 模型提供智能分析</p>
+    <p>⚠️ 本工具僅供參考,實際防疫策略需由專業人員評估</p>
+</div>
+""", unsafe_allow_html=True)

requirements (2).txt

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+streamlit==1.29.0
+plotly==5.18.0
+numpy==1.24.3
+pandas==2.0.3
+openai>=1.30.0 
+python-dotenv

tab1_contact_tracing (1).py

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+import streamlit as st
+import plotly.graph_objects as go
+import numpy as np
+import pandas as pd
+from openai import OpenAI
+
+# 初始化 tab1 專屬的 session state
+def init_tab1_session_state():
+    if 'tab1_chat_history' not in st.session_state:
+        st.session_state.tab1_chat_history = []
+    if 'tab1_selected_ct' not in st.session_state:
+        st.session_state.tab1_selected_ct = 18.0
+    if 'tab1_tracing_days' not in st.session_state:
+        st.session_state.tab1_tracing_days = 7
+
+# 效益計算函數（保持原樣）
+def get_effectiveness_for_ct18(days):
+    if days <= 1: return 0.06
+    elif days <= 2: return 0.18
+    elif days <= 3: return 0.31
+    elif days <= 4: return 0.42
+    elif days <= 5: return 0.52
+    elif days <= 6: return 0.60
+    elif days <= 7: return 0.67
+    elif days <= 8: return 0.72
+    elif days <= 9: return 0.77
+    elif days <= 10: return 0.81
+    elif days <= 11: return 0.84
+    elif days <= 12: return 0.87
+    elif days <= 13: return 0.89
+    elif days <= 14: return 0.91
+    elif days <= 15: return 0.92
+    elif days <= 16: return 0.93
+    elif days <= 17: return 0.95
+    elif days <= 20: return 0.97
+    elif days <= 34: return 0.97 + (days - 20) * 0.03 / 14
+    else: return 1.0
+
+def get_effectiveness_for_ct22(days):
+    if days <= 1: return 0.01
+    elif days <= 2: return 0.03
+    elif days <= 3: return 0.07
+    elif days <= 4: return 0.12
+    elif days <= 5: return 0.18
+    elif days <= 6: return 0.24
+    elif days <= 7: return 0.30
+    elif days <= 8: return 0.36
+    elif days <= 9: return 0.41
+    elif days <= 10: return 0.47
+    elif days <= 11: return 0.52
+    elif days <= 12: return 0.57
+    elif days <= 13: return 0.62
+    elif days <= 14: return 0.66
+    elif days <= 17: return 0.66 + (days - 14) * 0.10 / 3
+    elif days <= 21: return 0.76 + (days - 17) * 0.10 / 4
+    elif days <= 28: return 0.86 + (days - 21) * 0.08 / 7
+    elif days <= 48: return 0.94 + (days - 28) * 0.06 / 20
+    else: return 1.0
+
+def get_effectiveness_for_ct30(days):
+    if days <= 8: return 0
+    elif days <= 9: return 0.01
+    elif days <= 10: return 0.02
+    elif days <= 11: return 0.03
+    elif days <= 12: return 0.05
+    elif days <= 13: return 0.08
+    elif days <= 14: return 0.11
+    elif days <= 15: return 0.15
+    elif days <= 16: return 0.20
+    elif days <= 17: return 0.26
+    elif days <= 18: return 0.32
+    elif days <= 19: return 0.37
+    elif days <= 20: return 0.43
+    elif days <= 21: return 0.48
+    elif days <= 24: return 0.48 + (days - 21) * 0.14 / 3
+    elif days <= 28: return 0.62 + (days - 24) * 0.14 / 4
+    elif days <= 54: return 0.76 + (days - 28) * 0.24 / 26
+    else: return 1.0
+
+def get_effectiveness(ct_value, days):
+    if ct_value <= 18:
+        ratio = (ct_value - 10) / 8
+        higher_eff = get_effectiveness_for_ct18(days) * 1.1
+        effectiveness = higher_eff * (1 - ratio) + get_effectiveness_for_ct18(days) * ratio
+    elif ct_value <= 22:
+        ratio = (ct_value - 18) / 4
+        effectiveness = get_effectiveness_for_ct18(days) * (1 - ratio) + get_effectiveness_for_ct22(days) * ratio
+    elif ct_value <= 30:
+        ratio = (ct_value - 22) / 8
+        effectiveness = get_effectiveness_for_ct22(days) * (1 - ratio) + get_effectiveness_for_ct30(days) * ratio
+    else:
+        ratio = min((ct_value - 30) / 5, 1)
+        effectiveness = get_effectiveness_for_ct30(days) * (1 - ratio * 0.3)
+    
+    return min(max(effectiveness, 0), 1.0)
+
+def find_days_for_target_effectiveness(ct_value, target_effectiveness):
+    for days in range(1, 61):
+        eff = get_effectiveness(ct_value, days)
+        if eff >= target_effectiveness:
+            return days
+    return 60
+
+def get_virus_level(ct_value):
+    if ct_value <= 18:
+        return "高病毒量"
+    elif ct_value <= 25:
+        return "中病毒量"
+    else:
+        return "低病毒量"
+
+def generate_summary_prompt(ct_value, days, effectiveness):
+    virus_level = get_virus_level(ct_value)
+    
+    comparison_data = []
+    for test_days in [3, 7, 14, 21, 30]:
+        eff = get_effectiveness(ct_value, test_days)
+        comparison_data.append(f"追蹤{test_days}天: {eff*100:.1f}%")
+    
+    prompt = f"""你是一位 COVID-19 防疫專家。請根據以下數據生成一份專業的防疫策略效益分析報告:
+
+**當前參數:**
+- Ct 值:{ct_value}
+- 病毒量等級:{virus_level}
+- 回溯追蹤天數:{days} 天
+- 追蹤效益:{effectiveness*100:.1f}%
+
+**不同追蹤天數的效益比較:**
+{chr(10).join(comparison_data)}
+
+請提供:
+1. 當前策略的效益評估(是否充足?)
+2. 基於 Ct 值的感染階段判斷
+3. 具體的追蹤建議(是否需要調整天數?)
+4. 可能遺漏的接觸者風險評估
+5. 實務操作建議
+
+請用繁體中文回答,使用清晰的結構和專業但易懂的語言。"""
+    
+    return prompt
+
+def extract_query_params(text):
+    import re
+    
+    text_lower = text.lower()
+    
+    # 提取 Ct 值
+    ct_patterns = [
+        r'ct\s*[=值]?\s*(\d+\.?\d*)',
+        r'(\d+\.?\d*)\s*ct',
+    ]
+    
+    ct_value = None
+    for pattern in ct_patterns:
+        match = re.search(pattern, text_lower)
+        if match:
+            try:
+                ct_value = float(match.group(1))
+                if 10 <= ct_value <= 35:
+                    break
+            except:
+                continue
+    
+    # 提取天數
+    day_patterns = [
+        r'(\d+)\s*天',
+        r'追[蹤跡]\s*(\d+)',
+        r'回[朔溯]\s*(\d+)',
+    ]
+    
+    days = None
+    for pattern in day_patterns:
+        match = re.search(pattern, text)
+        if match:
+            try:
+                days = int(match.group(1))
+                if 1 <= days <= 60:
+                    break
+            except:
+                continue
+    
+    # 檢查是否在詢問「要幾天才能達到X%效益」
+    target_eff = None
+    reverse_query_patterns = [
+        r'要.*?(\d+).*?%',
+        r'達到.*?(\d+).*?%',
+        r'(\d+).*?%.*?效益',
+        r'100%',
+        r'百分之百',
+    ]
+    
+    for pattern in reverse_query_patterns:
+        match = re.search(pattern, text)
+        if match:
+            if pattern in ['100%', '百分之百']:
+                target_eff = 1.0
+                break
+            else:
+                try:
+                    target_eff = float(match.group(1)) / 100
+                    if 0 <= target_eff <= 1:
+                        break
+                except:
+                    continue
+    
+    return ct_value, days, target_eff
+
+def call_gpt4(prompt, api_key, tools=None, tool_choice=None):
+    try:
+        client = OpenAI(api_key=api_key)
+        
+        messages = [
+            {"role": "system", "content": "你是一位專業的 COVID-19 防疫策略分析專家,擅長解釋接觸者追蹤效益,並提供清晰實用的建議。當用戶詢問特定 Ct 值和追蹤天數的效益時,你必須使用 calculate_effectiveness 函數來獲取精確數據。當用戶詢問需要多少天才能達到某個效益時,你必須使用 find_required_days 函數。"},
+            {"role": "user", "content": prompt}
+        ]
+        
+        if tools:
+            response = client.chat.completions.create(
+                model="gpt-4",
+                messages=messages,
+                tools=tools,
+                tool_choice=tool_choice,
+                temperature=0.7,
+                max_tokens=1500
+            )
+        else:
+            response = client.chat.completions.create(
+                model="gpt-4",
+                messages=messages,
+                temperature=0.7,
+                max_tokens=1500
+            )
+        
+        return response
+    except Exception as e:
+        return f"❌ API 呼叫失敗: {str(e)}"
+
+def process_gpt4_response(response, api_key):
+    if isinstance(response, str):
+        return response, None, None
+    
+    message = response.choices[0].message
+    
+    if message.tool_calls:
+        import json
+        
+        tool_call = message.tool_calls[0]
+        function_name = tool_call.function.name
+        arguments = json.loads(tool_call.function.arguments)
+        
+        result_content = ""
+        extracted_ct = None
+        extracted_days = None
+        
+        if function_name == "calculate_effectiveness":
+            ct_value = arguments.get("ct_value")
+            days = arguments.get("days")
+            
+            extracted_ct = ct_value
+            extracted_days = days
+            
+            effectiveness = get_effectiveness(ct_value, days)
+            virus_level = get_virus_level(ct_value)
+            
+            result_content = json.dumps({
+                "ct_value": ct_value,
+                "days": days,
+                "effectiveness_percentage": round(effectiveness * 100, 1),
+                "virus_level": virus_level
+            }, ensure_ascii=False)
+        
+        elif function_name == "find_required_days":
+            ct_value = arguments.get("ct_value")
+            target_effectiveness = arguments.get("target_effectiveness")
+            
+            required_days = find_days_for_target_effectiveness(ct_value, target_effectiveness)
+            actual_effectiveness = get_effectiveness(ct_value, required_days)
+            virus_level = get_virus_level(ct_value)
+            
+            extracted_ct = ct_value
+            extracted_days = required_days
+            
+            result_content = json.dumps({
+                "ct_value": ct_value,
+                "target_effectiveness_percentage": round(target_effectiveness * 100, 1),
+                "required_days": required_days,
+                "actual_effectiveness_percentage": round(actual_effectiveness * 100, 1),
+                "virus_level": virus_level
+            }, ensure_ascii=False)
+        
+        client = OpenAI(api_key=api_key)
+        second_response = client.chat.completions.create(
+            model="gpt-4",
+            messages=[
+                {"role": "system", "content": "你是一位專業的 COVID-19 防疫策略分析專家,擅長解釋接觸者追蹤效益,並提供清晰實用的建議。"},
+                {"role": "user", "content": message.content if hasattr(message, 'content') and message.content else "請分析效益"},
+                {
+                    "role": "assistant",
+                    "content": None,
+                    "tool_calls": [tool_call.model_dump()]
+                },
+                {
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "content": result_content
+                }
+            ],
+            temperature=0.7,
+            max_tokens=1500
+        )
+        
+        return second_response.choices[0].message.content, extracted_ct, extracted_days
+    
+    return message.content, None, None
+
+def render():
+    """渲染 Tab1 的完整內容"""
+    init_tab1_session_state()
+    
+    # 側邊欄 - 控制面板
+    with st.sidebar:
+        st.header("📊 防疫決策工具 (接觸者追蹤)")
+        
+        # 互動式參數調整
+        st.subheader("🎛️ 參數設定")
+        
+        selected_ct = st.slider(
+            "🦠 確診者Ct值",
+            min_value=10.0,
+            max_value=35.0,
+            value=st.session_state.tab1_selected_ct,
+            step=0.5,
+            help="Ct值越低代表病毒量越高",
+            key="tab1_ct_slider"
+        )
+        st.session_state.tab1_selected_ct = selected_ct
+        st.caption("10 (高病毒量) ← → 35 (低病毒量)")
+        
+        tracing_days = st.slider(
+            "📅 接觸者往回追蹤天數",
+            min_value=1,
+            max_value=54,
+            value=st.session_state.tab1_tracing_days,
+            help="需要追溯過去幾天的接觸者",
+            key="tab1_days_slider"
+        )
+        st.session_state.tab1_tracing_days = tracing_days
+        st.caption("1天 ← → 54天")
+        
+        # 當前效益顯示
+        current_eff = get_effectiveness(selected_ct, tracing_days)
+        st.metric(
+            label="📊 當前情境效益",
+            value=f"{current_eff * 100:.0f}%",
+            delta="追蹤效益"
+        )
+        
+        # AI 分析按鈕
+        st.markdown("---")
+        if st.button("🤖 生成 AI 效益分析報告", type="primary", use_container_width=True, key="tab1_ai_report"):
+            if not st.session_state.api_key:
+                st.error("❌ 請先輸入 OpenAI API Key")
+            else:
+                with st.spinner("AI 正在分析中..."):
+                    prompt = generate_summary_prompt(selected_ct, tracing_days, current_eff)
+                    response = call_gpt4(prompt, st.session_state.api_key)
+                    final_response, _, _ = process_gpt4_response(response, st.session_state.api_key)
+                    st.session_state.tab1_chat_history.append({
+                        "type": "auto_summary",
+                        "ct": selected_ct,
+                        "days": tracing_days,
+                        "effectiveness": current_eff,
+                        "response": final_response
+                    })
+        
+        st.markdown("---")
+        
+        # 情境說明
+        with st.expander("🎯 使用情境", expanded=False):
+            st.markdown("""
+            當發現確診者時,防疫人員需要決定:
+            **「要往回追蹤多少天的接觸者?」**
+            """)
+        
+        with st.expander("✅ 關鍵指標:Ct值"):
+            st.markdown("""
+            RT-PCR檢測的「循環閾值」
+            - **Ct值 ≤ 18** = 病毒量很高
+            - **Ct值 18-25** = 病毒量中等
+            - **Ct值 > 25** = 病毒量較低
+            """)
+        
+        with st.expander("📈 決策邏輯"):
+            st.markdown("""
+            **低Ct值**(高病毒量)
+            → 最近才感染
+            → 追蹤「近期」接觸者即可
+            
+            **高Ct值**(低病毒量)
+            → 可能感染較久
+            → 需追溯「更早」的接觸者
+            """)
+
+    # 主要內容區 - 使用兩欄布局
+    col1, col2 = st.columns([2, 1])
+
+    with col1:
+        # 生成3D數據
+        ct_range = np.arange(10, 35.5, 0.5)
+        day_range = np.arange(1, 61, 1)
+        ct_mesh, day_mesh = np.meshgrid(ct_range, day_range)
+
+        # 計算效益值
+        effectiveness_mesh = np.zeros_like(ct_mesh)
+        for i in range(len(day_range)):
+            for j in range(len(ct_range)):
+                effectiveness_mesh[i, j] = get_effectiveness(ct_mesh[i, j], day_mesh[i, j])
+
+        # 創建3D曲面圖
+        fig = go.Figure()
+
+        # 添加曲面
+        fig.add_trace(go.Surface(
+            x=ct_mesh,
+            y=day_mesh,
+            z=effectiveness_mesh,
+            colorscale=[
+                [0.0, 'rgb(239, 68, 68)'],
+                [0.33, 'rgb(245, 158, 11)'],
+                [0.67, 'rgb(16, 185, 129)'],
+                [1.0, 'rgb(59, 130, 246)']
+            ],
+            showscale=True,
+            colorbar=dict(
+                title="追蹤效益",
+                tickvals=[0, 0.25, 0.5, 0.75, 1.0],
+                ticktext=['0%', '25%', '50%', '75%', '100%']
+            ),
+            opacity=0.9,
+            name='效益曲面',
+            contours=dict(
+                x=dict(show=True, color='white', width=1, highlightwidth=2),
+                y=dict(show=True, color='white', width=1, highlightwidth=2),
+                z=dict(show=True, color='white', width=1, highlightwidth=2)
+            )
+        ))
+
+        # 添加當前天數的切片線
+        ct_line = np.arange(10, 35.1, 0.3)
+        eff_line = [get_effectiveness(ct, tracing_days) for ct in ct_line]
+        day_line = [tracing_days] * len(ct_line)
+
+        fig.add_trace(go.Scatter3d(
+            x=ct_line,
+            y=day_line,
+            z=eff_line,
+            mode='lines',
+            line=dict(color='yellow', width=8),
+            name=f'第{tracing_days}天'
+        ))
+
+        # 添加用戶選擇的點
+        user_eff = get_effectiveness(selected_ct, tracing_days)
+        fig.add_trace(go.Scatter3d(
+            x=[selected_ct],
+            y=[tracing_days],
+            z=[user_eff],
+            mode='markers+text',
+            marker=dict(size=10, color='blue', symbol='circle', 
+                        line=dict(color='white', width=2)),
+            text=[f'{user_eff*100:.0f}%'],
+            textposition='top center',
+            textfont=dict(size=14, color='white'),
+            name='當前情境'
+        ))
+
+        # 設置佈局
+        fig.update_layout(
+            title={
+                'text': '精準追蹤接觸者效益 3D 圖 (Alpha 變異株)',
+                'x': 0.5,
+                'xanchor': 'center'
+            },
+            scene=dict(
+                xaxis=dict(
+                    title='X-病毒量(Ct值)',
+                    range=[10, 35],
+                    tickvals=[10, 15, 20, 25, 30, 35],
+                    showgrid=True,
+                    gridwidth=2,
+                    gridcolor='rgb(200, 200, 200)',
+                    showbackground=True,
+                    backgroundcolor='rgba(240, 240, 240, 0.9)'
+                ),
+                yaxis=dict(
+                    title='Y-回朔追蹤接觸者天數',
+                    range=[1, 60],
+                    tickvals=[1, 10, 20, 30, 40, 50, 60],
+                    showgrid=True,
+                    gridwidth=2,
+                    gridcolor='rgb(200, 200, 200)',
+                    showbackground=True,
+                    backgroundcolor='rgba(240, 240, 240, 0.9)'
+                ),
+                zaxis=dict(
+                    title='Z-效益',
+                    range=[0, 1],
+                    tickvals=[0, 0.25, 0.5, 0.75, 1.0],
+                    ticktext=['0%', '25%', '50%', '75%', '100%'],
+                    showgrid=True,
+                    gridwidth=2,
+                    gridcolor='rgb(200, 200, 200)',
+                    showbackground=True,
+                    backgroundcolor='rgba(240, 240, 240, 0.9)'
+                ),
+                camera=dict(
+                    eye=dict(x=1.5, y=-1.5, z=1.3),
+                    center=dict(x=0, y=0, z=0)
+                ),
+                aspectmode='manual',
+                aspectratio=dict(x=1, y=1.2, z=0.8)
+            ),
+            height=700,
+            showlegend=True,
+            legend=dict(
+                x=0.02,
+                y=0.98,
+                bgcolor='rgba(255, 255, 255, 0.8)'
+            ),
+            margin=dict(l=0, r=0, t=40, b=0)
+        )
+
+        st.plotly_chart(fig, use_container_width=True)
+
+        # 情境狀態顯示
+        if selected_ct <= 18:
+            status = "⚠️ 高病毒量 - 近期感染"
+            color = "red"
+        elif selected_ct <= 25:
+            status = "⚡ 中病毒量 - 感染數天"
+            color = "orange"
+        else:
+            status = "💊 低病毒量 - 感染較久"
+            color = "blue"
+
+        st.info(f"**情境:** 確診者 Ct值 = **{selected_ct}**,追蹤 **{tracing_days}** 天 | {status}")
+
+        # 效益解釋
+        effectiveness = get_effectiveness(selected_ct, tracing_days)
+        if effectiveness >= 0.9:
+            st.success("💡 **意義:** 追蹤時間充足,可以找到大部分接觸者")
+        elif effectiveness >= 0.7:
+            st.warning("💡 **意義:** 追蹤效果良好,但仍可能遺漏部分接觸者")
+        elif effectiveness >= 0.5:
+            st.warning("💡 **意義:** 追蹤效果一般,建議延長追蹤時間")
+        else:
+            st.error("💡 **意義:** 追蹤效果不足,需要大幅延長追蹤時間")
+
+    with col2:
+        st.subheader("🤖 AI 助手對話")
+        
+        # 聊天輸入區
+        st.markdown("##### 💬 快速查詢")
+        user_input = st.text_input(
+            "輸入問題(例如:Ct��20追蹤14天的效益如何?)",
+            placeholder="例如: Ct 20, 要回溯幾天才能達到100%?",
+            label_visibility="collapsed",
+            key="tab1_user_input"
+        )
+        
+        if st.button("📤 發送", use_container_width=True, key="tab1_send"):
+            if not st.session_state.api_key:
+                st.error("❌ 請先在側邊欄輸入 OpenAI API Key")
+            elif user_input.strip():
+                with st.spinner("AI 思考中..."):
+                    # 提取參數
+                    extracted_ct, extracted_days, target_eff = extract_query_params(user_input)
+                    
+                    # 構建對話 prompt
+                    chat_prompt = f"""用戶問題:{user_input}
+
+當前系統參數作為參考:
+- Ct 值:{selected_ct}
+- 追蹤天數:{tracing_days}
+- 當前效益:{current_eff*100:.1f}%
+
+請根據用戶的具體問題,使用相應的工具函數來獲取精確數據並給出專業建議。
+
+請用繁體中文回答,簡潔專業。只需要:
+1. 直接回答數據結果(Ct值、天數、效益)
+2. 提供2-3點簡短的策略調整建議即可
+不要解釋接觸者追蹤的背景知識或一般性原理。"""
+                    
+                    # 定義 function calling tools
+                    tools = [
+                        {
+                            "type": "function",
+                            "function": {
+                                "name": "calculate_effectiveness",
+                                "description": "計算特定 Ct 值和追蹤天數組合的接觸者追蹤效益。當用戶詢問具體的 Ct 值和天數時,必須使用此函數獲取精確數據。",
+                                "parameters": {
+                                    "type": "object",
+                                    "properties": {
+                                        "ct_value": {
+                                            "type": "number",
+                                            "description": "確診者的 Ct 值,範圍 10-35。Ct 值越低代表病毒量越高。"
+                                        },
+                                        "days": {
+                                            "type": "integer",
+                                            "description": "需要回溯追蹤的天數,範圍 1-60 天。"
+                                        }
+                                    },
+                                    "required": ["ct_value", "days"]
+                                }
+                            }
+                        },
+                        {
+                            "type": "function",
+                            "function": {
+                                "name": "find_required_days",
+                                "description": "反向查詢:給定 Ct 值和目標效益百分比,計算需要回溯多少天才能達到該效益。當用戶詢問「要幾天才能達到X%效益」時使用。",
+                                "parameters": {
+                                    "type": "object",
+                                    "properties": {
+                                        "ct_value": {
+                                            "type": "number",
+                                            "description": "確診者的 Ct 值,範圍 10-35。"
+                                        },
+                                        "target_effectiveness": {
+                                            "type": "number",
+                                            "description": "目標效益,範圍 0-1 (例如 0.9 代表 90%, 1.0 代表 100%)。"
+                                        }
+                                    },
+                                    "required": ["ct_value", "target_effectiveness"]
+                                }
+                            }
+                        }
+                    ]
+                    
+                    # 根據問題類型決定 tool_choice
+                    tool_choice = "auto"
+                    if target_eff is not None and extracted_ct is not None:
+                        tool_choice = {"type": "function", "function": {"name": "find_required_days"}}
+                    elif extracted_ct and extracted_days:
+                        tool_choice = {"type": "function", "function": {"name": "calculate_effectiveness"}}
+                    
+                    response = call_gpt4(chat_prompt, st.session_state.api_key, tools=tools, tool_choice=tool_choice)
+                    final_response, result_ct, result_days = process_gpt4_response(response, st.session_state.api_key)
+                    
+                    # 更新參數
+                    if result_ct is not None and result_days is not None:
+                        st.session_state.tab1_selected_ct = float(result_ct)
+                        st.session_state.tab1_tracing_days = int(result_days)
+                    
+                    st.session_state.tab1_chat_history.append({
+                        "type": "user_query",
+                        "question": user_input,
+                        "response": final_response
+                    })
+                    
+                    # 如果有更新參數,重新渲染頁面
+                    if result_ct is not None and result_days is not None:
+                        st.rerun()
+        
+        # 顯示對話歷史
+        st.markdown("---")
+        st.markdown("##### 📜 對話記錄")
+        
+        if st.session_state.tab1_chat_history:
+            # 反向顯示(最新的在上面)
+            for i, chat in enumerate(reversed(st.session_state.tab1_chat_history)):
+                with st.container():
+                    if chat["type"] == "auto_summary":
+                        st.markdown(f"**🤖 AI 報告** (Ct={chat['ct']}, {chat['days']}天, 效益={chat['effectiveness']*100:.0f}%)")
+                        st.markdown(chat["response"])
+                    else:
+                        st.markdown(f"**👤 問題:** {chat['question']}")
+                        st.markdown(f"**🤖 回答:** {chat['response']}")
+                    st.markdown("---")
+            
+            if st.button("🗑️ 清除對話記錄", use_container_width=True, key="tab1_clear_chat"):
+                st.session_state.tab1_chat_history = []
+                st.rerun()
+        else:
+            st.info("💡 點擊「生成 AI 報告」或在上方輸入問題開始對話")
+
+    # 底部說明區域
+    st.markdown("---")
+
+    col_a, col_b = st.columns(2)
+
+    with col_a:
+        with st.expander("💡 操作說明", expanded=False):
+            st.markdown("""
+            **3D 圖表操作:**
+            - 🖱️ **拖曳旋轉**:用滑鼠拖曳3D圖表可以旋轉視角
+            - 🔍 **縮放**:使用滑鼠滾輪可以縮放
+            - 🎚️ **調整參數**:使用左側滑桿調整Ct值和追蹤天數
+            - 💡 **黃色線**:顯示當前追蹤天數下,不同Ct值的效益
+            - 🔵 **藍色點**:您當前選擇的情境
+            
+            **AI 助手功能:**
+            - 📊 **自動報告**:點擊「生成 AI 報告」獲得當前策略的完整分析
+            - 💬 **快速查詢**:直接輸入 Ct 值和天數快速獲得效益評估
+            - 🔄 **反向查詢**:詢問「Ct X 要幾天達到 Y% 效益」獲得所需天數
+            - 🔗 **參數連動**:對話查詢後左側滑桿會自動更新到對應數值
+            """)
+
+    with col_b:
+        with st.expander("📊 查看詳細數據", expanded=False):
+            # 創建示例數據表
+            data = {
+                'Ct值': [15, 18, 22, 28, 33],
+                f'追蹤{tracing_days}天的效益': [
+                    f"{get_effectiveness(15, tracing_days)*100:.1f}%",
+                    f"{get_effectiveness(18, tracing_days)*100:.1f}%",
+                    f"{get_effectiveness(22, tracing_days)*100:.1f}%",
+                    f"{get_effectiveness(28, tracing_days)*100:.1f}%",
+                    f"{get_effectiveness(33, tracing_days)*100:.1f}%"
+                ],
+                '病毒量等級': ['高', '高', '中', '低', '低']
+            }
+            
+            df = pd.DataFrame(data)
+            st.dataframe(df, use_container_width=True)

tab2_quarantine (4).py

@@ -0,0 +1,442 @@
+import streamlit as st
+import plotly.graph_objects as go
+import numpy as np
+import pandas as pd
+from openai import OpenAI
+
+# 初始化 tab2 專屬的 session state
+def init_tab2_session_state():
+    if 'tab2_chat_history' not in st.session_state:
+        st.session_state.tab2_chat_history = []
+    # 有加強劑的參數
+    if 'tab2_booster_ct' not in st.session_state:
+        st.session_state.tab2_booster_ct = 18.0
+    if 'tab2_booster_days' not in st.session_state:
+        st.session_state.tab2_booster_days = 7
+    # 沒有加強劑的參數
+    if 'tab2_no_booster_ct' not in st.session_state:
+        st.session_state.tab2_no_booster_ct = 18.0
+    if 'tab2_no_booster_days' not in st.session_state:
+        st.session_state.tab2_no_booster_days = 7
+
+# 基礎數據：從 Table 2 論文原始數據
+BASE_DATA = {
+    # Ct <= 18, 無加強劑
+    'ct18_no_booster': {
+        1: 0.10, 2: 0.25, 3: 0.39, 4: 0.51, 5: 0.61,
+        6: 0.70, 7: 0.76, 8: 0.81, 9: 0.84, 10: 0.87,
+        11: 0.89, 12: 0.91, 13: 0.93, 14: 0.94, 15: 0.95,
+        16: 0.96, 17: 0.96, 18: 0.97, 19: 0.97, 20: 0.98, 21: 0.98
+    },
+    # Ct <= 18, 有加強劑
+    'ct18_booster': {
+        1: 0.44, 2: 0.65, 3: 0.77, 4: 0.84, 5: 0.89,
+        6: 0.92, 7: 0.94, 8: 0.95, 9: 0.97, 10: 0.97,
+        11: 0.97, 12: 0.98, 13: 0.98, 14: 0.99, 15: 0.99,
+        16: 0.99, 17: 0.99, 18: 0.99, 19: 0.99, 20: 1.00, 21: 1.00
+    },
+    # Ct 18-25, 無加強劑
+    'ct22_no_booster': {
+        1: 0.49, 2: 0.57, 3: 0.65, 4: 0.72, 5: 0.78,
+        6: 0.83, 7: 0.86, 8: 0.89, 9: 0.91, 10: 0.92,
+        11: 0.94, 12: 0.95, 13: 0.96, 14: 0.97, 15: 0.97,
+        16: 0.98, 17: 0.98, 18: 0.98, 19: 0.98, 20: 0.99, 21: 0.99
+    },
+    # Ct 18-25, 有加強劑
+    'ct22_booster': {
+        1: 0.68, 2: 0.80, 3: 0.87, 4: 0.91, 5: 0.94,
+        6: 0.95, 7: 0.96, 8: 0.97, 9: 0.98, 10: 0.98,
+        11: 0.98, 12: 0.99, 13: 0.99, 14: 0.99, 15: 0.99,
+        16: 1.00, 17: 1.00, 18: 1.00, 19: 1.00, 20: 1.00, 21: 1.00
+    }
+}
+
+def get_effectiveness_from_data(data_key, days):
+    """從基礎數據中獲取效益值（帶線性插值）"""
+    data = BASE_DATA[data_key]
+    
+    if days < 1:
+        return 0.0
+    
+    if days > 21:
+        return data[21]
+    
+    if days in data:
+        return data[days]
+    
+    # 線性插值
+    day_lower = int(days)
+    day_upper = day_lower + 1
+    
+    if day_upper > 21:
+        return data[21]
+    
+    eff_lower = data[day_lower]
+    eff_upper = data[day_upper]
+    
+    ratio = days - day_lower
+    effectiveness = eff_lower * (1 - ratio) + eff_upper * ratio
+    
+    return effectiveness
+
+def get_quarantine_effectiveness(ct_value, days, has_booster):
+    """獲取隔離效益"""
+    if ct_value > 25:
+        return 0.0
+    
+    booster_suffix = 'booster' if has_booster else 'no_booster'
+    
+    if ct_value <= 18:
+        data_key = f'ct18_{booster_suffix}'
+        effectiveness = get_effectiveness_from_data(data_key, days)
+        
+        if ct_value < 10:
+            boost_factor = 1.0 + (10 - ct_value) * 0.005
+            effectiveness = min(effectiveness * boost_factor, 1.0)
+        
+        return effectiveness
+    
+    else:
+        ct18_key = f'ct18_{booster_suffix}'
+        ct22_key = f'ct22_{booster_suffix}'
+        
+        eff_ct18 = get_effectiveness_from_data(ct18_key, days)
+        eff_ct22 = get_effectiveness_from_data(ct22_key, days)
+        
+        ratio = (ct_value - 18) / 7
+        effectiveness = eff_ct18 * (1 - ratio) + eff_ct22 * ratio
+        
+        return min(effectiveness, 1.0)
+
+def get_ct_category(ct_value):
+    """獲取 Ct 值分類"""
+    if ct_value <= 18:
+        return "高病毒量 (Ct ≤ 18)"
+    elif ct_value <= 25:
+        return "中病毒量 (18 < Ct ≤ 25)"
+    else:
+        return "低病毒量 (Ct > 25，假設無傳染性)"
+
+def create_3d_plot(ct_mesh, day_mesh, effectiveness_mesh, selected_ct, quarantine_days, 
+                   user_eff, has_booster, title, colorscale, marker_color):
+    """創建單一 3D 曲面圖"""
+    fig = go.Figure()
+
+    fig.add_trace(go.Surface(
+        x=ct_mesh,
+        y=day_mesh,
+        z=effectiveness_mesh,
+        colorscale=colorscale,
+        showscale=True,
+        colorbar=dict(
+            title="防疫效益",
+            tickvals=[0, 0.25, 0.5, 0.75, 1.0],
+            ticktext=['0%', '25%', '50%', '75%', '100%']
+        ),
+        opacity=0.9,
+        name=title,
+        contours=dict(
+            x=dict(show=True, color='white', width=1),
+            y=dict(show=True, color='white', width=1),
+            z=dict(show=True, color='white', width=1)
+        )
+    ))
+
+    fig.add_trace(go.Scatter3d(
+        x=[selected_ct],
+        y=[quarantine_days],
+        z=[user_eff],
+        mode='markers+text',
+        marker=dict(size=12, color=marker_color, symbol='circle',
+                    line=dict(color='white', width=3)),
+        text=[f'{user_eff*100:.0f}%'],
+        textposition='top center',
+        textfont=dict(size=14, color='white', family='Arial Black'),
+        name='當前情境',
+        showlegend=True
+    ))
+
+    fig.update_layout(
+        title={
+            'text': title,
+            'x': 0.5,
+            'xanchor': 'center',
+            'font': {'size': 16}
+        },
+        scene=dict(
+            xaxis=dict(
+                title='X-病毒量(Ct值)',
+                range=[10, 25],
+                tickvals=[10, 15, 18, 20, 25],
+                showgrid=True,
+                gridwidth=2,
+                gridcolor='rgb(200, 200, 200)',
+                showbackground=True,
+                backgroundcolor='rgba(240, 240, 240, 0.9)'
+            ),
+            yaxis=dict(
+                title='Y-隔離檢疫天數',
+                range=[1, 21],
+                tickvals=[1, 5, 7, 10, 14, 21],
+                showgrid=True,
+                gridwidth=2,
+                gridcolor='rgb(200, 200, 200)',
+                showbackground=True,
+                backgroundcolor='rgba(240, 240, 240, 0.9)'
+            ),
+            zaxis=dict(
+                title='Z-防疫效益',
+                range=[0, 1],
+                tickvals=[0, 0.25, 0.5, 0.75, 1.0],
+                ticktext=['0%', '25%', '50%', '75%', '100%'],
+                showgrid=True,
+                gridwidth=2,
+                gridcolor='rgb(200, 200, 200)',
+                showbackground=True,
+                backgroundcolor='rgba(240, 240, 240, 0.9)'
+            ),
+            camera=dict(
+                eye=dict(x=1.5, y=-1.5, z=1.3),
+                center=dict(x=0, y=0, z=0)
+            ),
+            aspectmode='manual',
+            aspectratio=dict(x=1, y=1.2, z=0.8)
+        ),
+        height=600,
+        showlegend=True,
+        legend=dict(
+            x=0.02,
+            y=0.98,
+            bgcolor='rgba(255, 255, 255, 0.9)',
+            bordercolor='black',
+            borderwidth=1
+        ),
+        margin=dict(l=0, r=0, t=40, b=0)
+    )
+    
+    return fig
+
+def render():
+    """渲染 Tab2 的完整內容"""
+    init_tab2_session_state()
+    
+    # 側邊欄 - 控制面板
+    with st.sidebar:
+        st.header("🏥 隔離檢疫決策工具")
+        
+        st.markdown("---")
+        
+        # 有加強劑的參數設定
+        st.subheader("💉 有加強劑情境")
+        
+        booster_ct = st.slider(
+            "🦠 接觸者Ct值",
+            min_value=10.0,
+            max_value=25.0,
+            value=st.session_state.tab2_booster_ct,
+            step=0.5,
+            help="Ct值越低代表病毒量越高 (Ct>25視為無傳染性)",
+            key="tab2_booster_ct_slider"
+        )
+        st.session_state.tab2_booster_ct = booster_ct
+        
+        booster_days = st.slider(
+            "📅 隔離天數",
+            min_value=1,
+            max_value=21,
+            value=st.session_state.tab2_booster_days,
+            help="需要隔離檢疫的天數",
+            key="tab2_booster_days_slider"
+        )
+        st.session_state.tab2_booster_days = booster_days
+        
+        booster_eff = get_quarantine_effectiveness(booster_ct, booster_days, True)
+        st.metric(
+            label="📊 防疫效益",
+            value=f"{booster_eff * 100:.0f}%",
+            delta="有加強劑"
+        )
+        
+        st.markdown("---")
+        
+        # 沒有加強劑的參數設定
+        st.subheader("⚠️ 無加強劑情境")
+        
+        no_booster_ct = st.slider(
+            "🦠 接觸者Ct值",
+            min_value=10.0,
+            max_value=25.0,
+            value=st.session_state.tab2_no_booster_ct,
+            step=0.5,
+            help="Ct值越低代表病毒量越高 (Ct>25視為無傳染性)",
+            key="tab2_no_booster_ct_slider"
+        )
+        st.session_state.tab2_no_booster_ct = no_booster_ct
+        
+        no_booster_days = st.slider(
+            "📅 隔離天數",
+            min_value=1,
+            max_value=21,
+            value=st.session_state.tab2_no_booster_days,
+            help="需要隔離檢疫的天數",
+            key="tab2_no_booster_days_slider"
+        )
+        st.session_state.tab2_no_booster_days = no_booster_days
+        
+        no_booster_eff = get_quarantine_effectiveness(no_booster_ct, no_booster_days, False)
+        st.metric(
+            label="📊 防疫效益",
+            value=f"{no_booster_eff * 100:.0f}%",
+            delta="無加強劑"
+        )
+        
+        st.markdown("---")
+        
+        # 情境說明
+        with st.expander("🎯 使用情境", expanded=False):
+            st.markdown("""
+            當找到疑似接觸者時,防疫人員需要決定:
+            **「要隔離/檢疫多少天?」**
+            
+            考量因素:
+            - 接觸者的病毒量 (Ct值)
+            - 是否已接種加強劑
+            """)
+        
+        with st.expander("✅ Omicron 特性"):
+            st.markdown("""
+            相較於 Alpha 變異株:
+            - **傳播更快** 但症狀較輕
+            - **疫苗保護** 顯著縮短隔離時間
+            - **Ct > 25** 視為無傳染性
+            """)
+        
+        with st.expander("💉 疫苗影響"):
+            st.markdown("""
+            **加強劑的效益:**
+            - 大幅縮短所需隔離天數
+            - 相同天數下效益更高
+            - 例: 達到90%效益
+              - 有加強劑: 5天
+              - 無加強劑: 11天
+            """)
+
+    # 主要內容區
+    st.markdown("### 📊 隔離檢疫效益 3D 視覺化")
+    
+    # 生成 3D 數據
+    ct_range = np.arange(10, 25.5, 0.5)
+    day_range = np.arange(1, 22, 1)
+    ct_mesh, day_mesh = np.meshgrid(ct_range, day_range)
+
+    # 計算兩組效益值
+    effectiveness_with_booster = np.zeros_like(ct_mesh)
+    effectiveness_without_booster = np.zeros_like(ct_mesh)
+    
+    for i in range(len(day_range)):
+        for j in range(len(ct_range)):
+            effectiveness_with_booster[i, j] = get_quarantine_effectiveness(ct_mesh[i, j], day_mesh[i, j], True)
+            effectiveness_without_booster[i, j] = get_quarantine_effectiveness(ct_mesh[i, j], day_mesh[i, j], False)
+
+    # 創建兩個並排的圖表
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.markdown("#### 💉 有加強劑情境")
+        fig1 = create_3d_plot(
+            ct_mesh, day_mesh, effectiveness_with_booster,
+            booster_ct, booster_days, 
+            booster_eff,
+            True,
+            "隔離檢疫效益 - 有加強劑 (Omicron 變異株)",
+            [
+                [0.0, 'rgb(239, 68, 68)'],
+                [0.33, 'rgb(245, 158, 11)'],
+                [0.67, 'rgb(16, 185, 129)'],
+                [1.0, 'rgb(59, 130, 246)']
+            ],
+            'blue'
+        )
+        st.plotly_chart(fig1, use_container_width=True)
+        
+        ct_category_booster = get_ct_category(booster_ct)
+        st.info(f"**情境:** Ct值 = **{booster_ct}** ({ct_category_booster}), 隔離 **{booster_days}** 天 | 💉 已接種加強劑")
+        
+        if booster_eff >= 0.9:
+            st.success("💡 **意義:** 隔離時間充足,可有效防止疫情傳播")
+        elif booster_eff >= 0.7:
+            st.warning("💡 **意義:** 隔離效果良好,但建議視情況延長")
+        elif booster_eff >= 0.5:
+            st.warning("💡 **意義:** 隔離效果一般,建議延長隔離時間")
+        else:
+            st.error("💡 **意義:** 隔離效果不足,需要大幅延長隔離時間")
+    
+    with col2:
+        st.markdown("#### ⚠️ 無加強劑情境")
+        fig2 = create_3d_plot(
+            ct_mesh, day_mesh, effectiveness_without_booster,
+            no_booster_ct, no_booster_days,
+            no_booster_eff,
+            False,
+            "隔離檢疫效益 - 無加強劑 (Omicron 變異株)",
+            [
+                [0.0, 'rgb(239, 68, 68)'],
+                [0.33, 'rgb(245, 158, 11)'],
+                [0.67, 'rgb(16, 185, 129)'],
+                [1.0, 'rgb(59, 130, 246)']
+            ],
+            'red'
+        )
+        st.plotly_chart(fig2, use_container_width=True)
+        
+        ct_category_no_booster = get_ct_category(no_booster_ct)
+        st.info(f"**情境:** Ct值 = **{no_booster_ct}** ({ct_category_no_booster}), 隔離 **{no_booster_days}** 天 | ⚠️ 未接種加強劑")
+        
+        if no_booster_eff >= 0.9:
+            st.success("💡 **意義:** 隔離時間充足,可有效防止疫情傳播")
+        elif no_booster_eff >= 0.7:
+            st.warning("💡 **意義:** 隔離效果良好,但建議視情況延長")
+        elif no_booster_eff >= 0.5:
+            st.warning("💡 **意義:** 隔離效果一般,建議延長隔離時間")
+        else:
+            st.error("💡 **意義:** 隔離效果不足,需要大幅延長隔離時間")
+        
+        # 疫苗建議
+        improvement = (booster_eff - no_booster_eff) * 100
+        if improvement > 0:
+            st.info(f"💉 **提示:** 若接種加強劑,在相同參數下可提升 {improvement:.0f}% 效益")
+
+    # 底部說明區域
+    st.markdown("---")
+
+    col_a, col_b = st.columns(2)
+
+    with col_a:
+        with st.expander("💡 操作說明", expanded=False):
+            st.markdown("""
+            **3D 圖表說明:**
+            - 🔵 **左側圖表**: 有加強劑的隔離效益 (藍綠色)
+            - 🔴 **右側圖表**: 無加強劑的隔離效益 (橙紅色)
+            - 💎 **菱形標記**: 您當前選擇的情境
+            - 兩圖對比可清楚看出**疫苗的效益差異**
+            
+            **互動操作:**
+            - 🖱️ 拖曳旋轉視角
+            - 🔍 滾輪縮放
+            - 🎚️ 使用左側滑桿調整參數
+            """)
+
+    with col_b:
+        with st.expander("📊 查看詳細數據", expanded=False):
+            # 創建對比表格
+            test_days_list = [3, 5, 7, 10, 14]
+            
+            data = {
+                '隔離天數': test_days_list,
+                '有加強劑 (Ct=' + str(booster_ct) + ')': [f"{get_quarantine_effectiveness(booster_ct, d, True)*100:.0f}%" for d in test_days_list],
+                '無加強劑 (Ct=' + str(no_booster_ct) + ')': [f"{get_quarantine_effectiveness(no_booster_ct, d, False)*100:.0f}%" for d in test_days_list],
+                '效益差異': [f"+{(get_quarantine_effectiveness(booster_ct, d, True) - get_quarantine_effectiveness(no_booster_ct, d, False))*100:.0f}%" for d in test_days_list]
+            }
+            
+            df = pd.DataFrame(data)
+            st.dataframe(df, use_container_width=True)