Delete app_bayesian.py.py

app_bayesian.py.py

@@ -1,654 +0,0 @@
-import streamlit as st
-import pandas as pd
-import uuid
-from datetime import datetime, timedelta
-import atexit
-import os
-import sys
-
-# 頁面配置
-st.set_page_config(
-    page_title="Bayesian Hierarchical Model - Pokémon Speed Analysis",
-    page_icon="🎲",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# 自定義 CSS
-st.markdown("""
-<style>
-    .streamlit-expanderHeader {
-        background-color: #e8f1f8;
-        border: 1px solid #b0cfe8;
-        border-radius: 5px;
-        font-weight: 600;
-        color: #1b4f72;
-    }
-    .streamlit-expanderHeader:hover {
-        background-color: #d0e7f8;
-    }
-    .stMetric {
-        background-color: #f8fbff;
-        padding: 10px;
-        border-radius: 5px;
-        border: 1px solid #d0e4f5;
-    }
-    .stButton > button {
-        width: 100%;
-        border-radius: 20px;
-        font-weight: 600;
-        transition: all 0.3s ease;
-    }
-    .stButton > button:hover {
-        transform: translateY(-2px);
-        box-shadow: 0 4px 8px rgba(0,0,0,0.2);
-    }
-    .success-box {
-        background-color: #d4edda;
-        border: 1px solid #c3e6cb;
-        border-radius: 5px;
-        padding: 10px;
-        margin: 10px 0;
-    }
-    .warning-box {
-        background-color: #fff3cd;
-        border: 1px solid #ffeaa7;
-        border-radius: 5px;
-        padding: 10px;
-        margin: 10px 0;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# 導入自定義模組
-from bayesian_core import BayesianHierarchicalAnalyzer
-from bayesian_llm_assistant import BayesianLLMAssistant
-from bayesian_utils import (
-    plot_trace,
-    plot_posterior,
-    plot_forest,
-    plot_model_dag,
-    create_summary_table,
-    create_trial_results_table,
-    export_results_to_text,
-    plot_odds_ratio_comparison
-)
-
-# 清理函數
-def cleanup_old_sessions():
-    """清理超過 1 小時的 session"""
-    current_time = datetime.now()
-    for session_id in list(BayesianHierarchicalAnalyzer._session_results.keys()):
-        result = BayesianHierarchicalAnalyzer._session_results.get(session_id)
-        if result:
-            result_time = datetime.fromisoformat(result['timestamp'])
-            if current_time - result_time > timedelta(hours=1):
-                BayesianHierarchicalAnalyzer.clear_session_results(session_id)
-
-# 註冊清理函數
-atexit.register(cleanup_old_sessions)
-
-# 初始化 session state
-if 'session_id' not in st.session_state:
-    st.session_state.session_id = str(uuid.uuid4())
-if 'analysis_results' not in st.session_state:
-    st.session_state.analysis_results = None
-if 'chat_history' not in st.session_state:
-    st.session_state.chat_history = []
-if 'analyzer' not in st.session_state:
-    st.session_state.analyzer = None
-if 'trace_img' not in st.session_state:
-    st.session_state.trace_img = None
-if 'posterior_img' not in st.session_state:
-    st.session_state.posterior_img = None
-if 'forest_img' not in st.session_state:
-    st.session_state.forest_img = None
-if 'dag_img' not in st.session_state:
-    st.session_state.dag_img = None
-
-# 標題
-st.title("🎲 Bayesian Hierarchical Model Analysis")
-st.markdown("### 寶可夢速度對勝率影響的貝氏階層分析")
-st.markdown("---")
-
-# Sidebar
-with st.sidebar:
-    st.header("⚙️ 配置設定")
-    
-    # Google Gemini API Key
-    api_key = st.text_input(
-        "Google Gemini API Key",
-        type="password",
-        help="輸入您的 Google Gemini API Key 以使用 AI 助手"
-    )
-    
-    if api_key:
-        st.session_state.api_key = api_key
-        st.success("✅ API Key 已載入")
-    
-    st.markdown("---")
-    
-    # MCMC 參數設定
-    st.subheader("🔬 MCMC 參數")
-    
-    n_samples = st.number_input(
-        "抽樣數 (Samples)",
-        min_value=500,
-        max_value=10000,
-        value=2000,
-        step=500,
-        help="每條鏈的抽樣數量"
-    )
-    
-    n_tune = st.number_input(
-        "調整期 (Tune)",
-        min_value=200,
-        max_value=5000,
-        value=1000,
-        step=200,
-        help="調整期的樣本數"
-    )
-    
-    n_chains = st.selectbox(
-        "鏈數 (Chains)",
-        options=[1, 2, 4],
-        index=1,
-        help="平行運行的鏈數"
-    )
-    
-    target_accept = st.slider(
-        "目標接受率",
-        min_value=0.80,
-        max_value=0.99,
-        value=0.95,
-        step=0.01,
-        help="NUTS 採樣器的目標接受率"
-    )
-    
-    st.markdown("---")
-    
-    # 清理按鈕
-    if st.button("🧹 清理過期資料"):
-        cleanup_old_sessions()
-        st.success("✅ 清理完成")
-        st.rerun()
-    
-    st.markdown("---")
-    
-    # 資料來源選擇
-    st.subheader("📊 資料來源")
-    data_source = st.radio(
-        "選擇資料來源：",
-        ["使用預設資料集", "上傳您的資料"]
-    )
-    
-    uploaded_file = None
-    if data_source == "上傳您的資料":
-        uploaded_file = st.file_uploader(
-            "上傳 CSV 檔案",
-            type=['csv'],
-            help="上傳寶可夢速度對戰資料"
-        )
-        
-        with st.expander("📖 資料格式說明"):
-            st.markdown("""
-            **必要欄位格式：**
-            - `Trial_Type`: 屬性名稱（例如：Water, Fire, Grass）
-            - `rc`: 控制組（速度慢）的勝場數
-            - `nc`: 控制組的總場數
-            - `rt`: 實驗組（速度快）的勝場數
-            - `nt`: 實驗組的總場數
-            
-            **範例：**
-            ```
-            Trial_Type,rc,nc,rt,nt
-            Water,45,100,62,100
-            Fire,38,100,55,100
-            Grass,42,100,58,100
-            ```
-            """)
-    
-    st.markdown("---")
-    
-    # 關於系統
-    with st.expander("ℹ️ 關於此系統"):
-        st.markdown("""
-        **貝氏階層模型分析系統**
-        
-        本系統使用貝氏階層模型來分析速度對寶可夢勝率的影響，
-        並考慮不同屬性之間的異質性。
-        
-        **主要功能：**
-        - 🎲 貝氏推論與後驗分佈
-        - 📊 階層模型（借用資訊）
-        - 📈 4 種視覺化圖表
-        - 💬 AI 助手解釋
-        - 🎮 對戰策略建議
-        
-        **適用場景：**
-        - 分析速度對不同屬性的影響
-        - 理解屬性間的異質性
-        - 制定基於統計的對戰策略
-        """)
-
-# 主要內容區 - 雙 Tab
-tab1, tab2 = st.tabs(["📊 貝氏分析", "💬 AI 助手"])
-
-# Tab 1: 貝氏分析
-with tab1:
-    st.header("📊 貝氏階層模型分析")
-    
-    # 載入資料
-    if data_source == "使用預設資料集":
-        # 檢查預設資料是否存在
-        default_data_path = "pokemon_speed_meta_results.csv"
-        if os.path.exists(default_data_path):
-            df = pd.read_csv(default_data_path)
-            st.success(f"✅ 已載入預設資料集（{len(df)} 個屬性）")
-        else:
-            st.warning("⚠️ 找不到預設資料集，請上傳您的資料")
-            df = None
-    else:
-        if uploaded_file is not None:
-            df = pd.read_csv(uploaded_file)
-            st.success(f"✅ 已載入資料（{len(df)} 個屬性）")
-        else:
-            df = None
-            st.info("📁 請在左側上傳 CSV 檔案")
-    
-    if df is not None:
-        # 顯示資料預覽
-        with st.expander("👀 資料預覽"):
-            st.dataframe(df, use_container_width=True)
-        
-        st.markdown("---")
-        
-        # 分析按鈕
-        col1, col2, col3 = st.columns([1, 2, 1])
-        
-        with col2:
-            analyze_button = st.button(
-                "🔬 開始貝氏分析",
-                type="primary",
-                use_container_width=True
-            )
-        
-        # 執行分析
-        if analyze_button:
-            with st.spinner(f"正在執行貝氏分析... (抽樣 {n_samples} × {n_chains} 條鏈)"):
-                try:
-                    # 初始化分析器
-                    if st.session_state.analyzer is None:
-                        st.session_state.analyzer = BayesianHierarchicalAnalyzer(st.session_state.session_id)
-                    
-                    # 載入資料
-                    st.session_state.analyzer.load_data(df)
-                    
-                    # 執行分析
-                    results = st.session_state.analyzer.run_analysis(
-                        n_samples=n_samples,
-                        n_tune=n_tune,
-                        n_chains=n_chains,
-                        target_accept=target_accept
-                    )
-                    
-                    st.session_state.analysis_results = results
-                    
-                    # 生成圖表
-                    with st.spinner("生成視覺化圖表..."):
-                        st.session_state.trace_img = plot_trace(st.session_state.analyzer.trace)
-                        st.session_state.posterior_img = plot_posterior(st.session_state.analyzer.trace)
-                        st.session_state.forest_img = plot_forest(
-                            st.session_state.analyzer.trace,
-                            results['trial_labels']
-                        )
-                        st.session_state.dag_img = plot_model_dag(st.session_state.analyzer)
-                    
-                    st.success("✅ 分析完成！")
-                    st.balloons()
-                    
-                except Exception as e:
-                    st.error(f"❌ 分析失敗: {str(e)}")
-        
-        # 顯示結果
-        if st.session_state.analysis_results is not None:
-            results = st.session_state.analysis_results
-            
-            st.markdown("---")
-            st.subheader("📊 分析結果")
-            
-            # 創建 4 個子頁面
-            result_tabs = st.tabs([
-                "📊 概覽",
-                "📈 Trace & Posterior",
-                "🌲 Forest Plot",
-                "🔍 DAG 模型圖",
-                "📋 詳細報告"
-            ])
-            
-            # Tab: 概覽
-            with result_tabs[0]:
-                st.markdown("### 🎯 整體效應摘要")
-                
-                overall = results['overall']
-                interp = results['interpretation']
-                
-                # 關鍵指標
-                col1, col2, col3 = st.columns(3)
-                
-                with col1:
-                    st.metric(
-                        "d (整體效應)",
-                        f"{overall['d_mean']:.4f}",
-                        delta=f"HDI: [{overall['d_hdi_low']:.3f}, {overall['d_hdi_high']:.3f}]"
-                    )
-                
-                with col2:
-                    st.metric(
-                        "勝算比 (OR)",
-                        f"{overall['or_mean']:.3f}",
-                        delta=f"HDI: [{overall['or_hdi_low']:.3f}, {overall['or_hdi_high']:.3f}]"
-                    )
-                
-                with col3:
-                    st.metric(
-                        "sigma (異質性)",
-                        f"{overall['sigma_mean']:.4f}",
-                        delta=f"HDI: [{overall['sigma_hdi_low']:.3f}, {overall['sigma_hdi_high']:.3f}]"
-                    )
-                
-                st.markdown("---")
-                
-                # 結果解釋
-                st.markdown("### 📖 結果解釋")
-                
-                st.info(f"""
-                **整體效應**: {interp['overall_effect']}
-                
-                **顯著性**: {interp['overall_significance']}
-                
-                **效果大小**: {interp['effect_size']}
-                
-                **異質性**: {interp['heterogeneity']}
-                """)
-                
-                st.markdown("---")
-                
-                # 收斂診斷
-                st.markdown("### 🔍 模型收斂診斷")
-                
-                diag = results['diagnostics']
-                
-                col1, col2 = st.columns(2)
-                
-                with col1:
-                    st.markdown("**R-hat 診斷** (應 < 1.1):")
-                    if diag['rhat_d']:
-                        st.metric("R-hat (d)", f"{diag['rhat_d']:.4f}", 
-                                 delta="✓ 良好" if diag['rhat_d'] < 1.1 else "✗ 需改善")
-                    if diag['rhat_sigma']:
-                        st.metric("R-hat (sigma)", f"{diag['rhat_sigma']:.4f}",
-                                 delta="✓ 良好" if diag['rhat_sigma'] < 1.1 else "✗ 需改善")
-                
-                with col2:
-                    st.markdown("**有效樣本數 (ESS)**:")
-                    if diag['ess_d']:
-                        st.metric("ESS (d)", f"{int(diag['ess_d'])}")
-                    if diag['ess_sigma']:
-                        st.metric("ESS (sigma)", f"{int(diag['ess_sigma'])}")
-                
-                if diag['converged']:
-                    st.success("✅ 模型已收斂，結果可信")
-                else:
-                    st.warning("⚠️ 模型可能未完全收斂，建議增加抽樣數或鏈數")
-                
-                st.markdown("---")
-                
-                # 摘要表格
-                st.markdown("### 📊 統計摘要表")
-                summary_df = create_summary_table(results)
-                st.dataframe(summary_df, use_container_width=True)
-                
-                st.markdown("---")
-                
-                # 各屬性結果
-                st.markdown("### 🎮 各屬性詳細結果")
-                trial_df = create_trial_results_table(results)
-                st.dataframe(trial_df, use_container_width=True)
-                
-                st.markdown("---")
-                
-                # 勝算比比較圖
-                st.markdown("### 📊 各屬性速度效應比較")
-                or_fig = plot_odds_ratio_comparison(results)
-                st.plotly_chart(or_fig, use_container_width=True)
-            
-            # Tab: Trace & Posterior
-            with result_tabs[1]:
-                st.markdown("### 📈 Trace Plot（收斂診斷）")
-                st.markdown("""
-                **Trace Plot 用途**：
-                - 檢查 MCMC 抽樣是否收斂
-                - 左圖：抽樣軌跡（應該像「毛毛蟲」）
-                - 右圖：後驗分佈密度
-                """)
-                
-                if st.session_state.trace_img:
-                    st.image(st.session_state.trace_img, use_column_width=True)
-                else:
-                    st.info("請先執行分析以生成 Trace Plot")
-                
-                st.markdown("---")
-                
-                st.markdown("### 📊 Posterior Plot（後驗分佈）")
-                st.markdown("""
-                **Posterior Plot 用途**：
-                - 顯示參數的後驗分佈
-                - 包含 95% HDI（最高密度區間）
-                - 顯示平均值
-                """)
-                
-                if st.session_state.posterior_img:
-                    st.image(st.session_state.posterior_img, use_column_width=True)
-                else:
-                    st.info("請先執行分析以生成 Posterior Plot")
-            
-            # Tab: Forest Plot
-            with result_tabs[2]:
-                st.markdown("### 🌲 Forest Plot（各屬性效應）")
-                st.markdown("""
-                **Forest Plot 用途**：
-                - 顯示每個屬性的速度效應（delta）
-                - 點：平均效應
-                - 線：95% HDI
-                - ★ 標記：顯著正效應（HDI 不包含 0）
-                - ☆ 標記：顯著負效應
-                """)
-                
-                if st.session_state.forest_img:
-                    st.image(st.session_state.forest_img, use_column_width=True)
-                else:
-                    st.info("請先執行分析以生成 Forest Plot")
-            
-            # Tab: DAG 模型圖
-            with result_tabs[3]:
-                st.markdown("### 🔍 模型結構圖 (DAG)")
-                st.markdown("""
-                **DAG（有向無環圖）用途**：
-                - 視覺化模型的階層結構
-                - 顯示變數之間的依賴關係
-                - 圓形/橢圓：隨機變數
-                - 矩形：觀測資料
-                - 菱形：推導變數
-                """)
-                
-                if st.session_state.dag_img:
-                    st.image(st.session_state.dag_img, use_column_width=True)
-                else:
-                    st.warning("⚠️ 無法生成 DAG 圖（可能需要安裝 Graphviz）")
-                    st.markdown("""
-                    **安裝 Graphviz:**
-                    - Windows: `choco install graphviz`
-                    - Mac: `brew install graphviz`
-                    - Ubuntu: `sudo apt-get install graphviz`
-                    """)
-            
-            # Tab: 詳細報告
-            with result_tabs[4]:
-                st.markdown("### 📋 完整分析報告")
-                
-                # 生成文字報告
-                text_report = export_results_to_text(results)
-                
-                st.text_area(
-                    "報告內容",
-                    text_report,
-                    height=500
-                )
-                
-                # 下載按鈕
-                st.download_button(
-                    label="📥 下載完整報告 (.txt)",
-                    data=text_report,
-                    file_name=f"bayesian_report_{results['timestamp'][:10]}.txt",
-                    mime="text/plain"
-                )
-
-# Tab 2: AI 助手
-with tab2:
-    st.header("💬 AI 分析助手")
-    
-    if not st.session_state.get('api_key'):
-        st.warning("⚠️ 請在左側輸入您的 Google Gemini API Key 以使用 AI 助手")
-    elif st.session_state.analysis_results is None:
-        st.info("ℹ️ 請先在「貝氏分析」頁面執行分析")
-    else:
-        # 初始化 LLM 助手
-        if 'llm_assistant' not in st.session_state:
-            st.session_state.llm_assistant = BayesianLLMAssistant(
-                api_key=st.session_state.api_key,
-                session_id=st.session_state.session_id
-            )
-        
-        # 聊天容器
-        chat_container = st.container()
-        
-        with chat_container:
-            for message in st.session_state.chat_history:
-                with st.chat_message(message["role"]):
-                    st.markdown(message["content"])
-        
-        # 使用者輸入
-        if prompt := st.chat_input("詢問關於分析結果的任何問題..."):
-            # 添加使用者訊息
-            st.session_state.chat_history.append({
-                "role": "user",
-                "content": prompt
-            })
-            
-            with st.chat_message("user"):
-                st.markdown(prompt)
-            
-            # AI 回應
-            with st.chat_message("assistant"):
-                with st.spinner("思考中..."):
-                    try:
-                        response = st.session_state.llm_assistant.get_response(
-                            user_message=prompt,
-                            analysis_results=st.session_state.analysis_results
-                        )
-                        st.markdown(response)
-                    except Exception as e:
-                        error_msg = f"❌ 錯誤: {str(e)}\n\n請檢查 API key 或重新表達問題。"
-                        st.error(error_msg)
-                        response = error_msg
-            
-            # 添加助手回應
-            st.session_state.chat_history.append({
-                "role": "assistant",
-                "content": response
-            })
-        
-        st.markdown("---")
-        
-        # 快速問題按鈕
-        st.subheader("💡 快速問題")
-        
-        quick_questions = [
-            "📊 給我這次分析的總結",
-            "🎯 解釋 d 和勝算比",
-            "🔍 解釋 sigma（異質性）",
-            "❓ 什麼是階層模型？",
-            "🆚 貝氏 vs 頻率論",
-            "⚔️ 對戰策略建議",
-            "🎮 比較不同屬性"
-        ]
-        
-        cols = st.columns(4)
-        for idx, question in enumerate(quick_questions):
-            col_idx = idx % 4
-            if cols[col_idx].button(question, key=f"quick_{idx}"):
-                # 根據問題選擇對應的方法
-                if "總結" in question:
-                    response = st.session_state.llm_assistant.generate_summary(
-                        st.session_state.analysis_results
-                    )
-                elif "d 和勝算比" in question:
-                    response = st.session_state.llm_assistant.explain_metric(
-                        'd',
-                        st.session_state.analysis_results
-                    )
-                elif "sigma" in question or "異質性" in question:
-                    response = st.session_state.llm_assistant.explain_metric(
-                        'sigma',
-                        st.session_state.analysis_results
-                    )
-                elif "階層模型" in question:
-                    response = st.session_state.llm_assistant.explain_hierarchical_model()
-                elif "貝氏" in question and "頻率論" in question:
-                    response = st.session_state.llm_assistant.explain_bayesian_vs_frequentist()
-                elif "策略" in question:
-                    response = st.session_state.llm_assistant.battle_strategy_advice(
-                        st.session_state.analysis_results
-                    )
-                elif "比較" in question:
-                    response = st.session_state.llm_assistant.compare_types(
-                        st.session_state.analysis_results
-                    )
-                else:
-                    response = st.session_state.llm_assistant.get_response(
-                        question,
-                        st.session_state.analysis_results
-                    )
-                
-                st.session_state.chat_history.append({
-                    "role": "user",
-                    "content": question
-                })
-                
-                st.session_state.chat_history.append({
-                    "role": "assistant",
-                    "content": response
-                })
-                
-                st.rerun()
-        
-        # 重置對話按鈕
-        st.markdown("---")
-        if st.button("🔄 重置對話"):
-            st.session_state.llm_assistant.reset_conversation()
-            st.session_state.chat_history = []
-            st.success("✅ 對話已重置")
-            st.rerun()
-
-# Footer
-st.markdown("---")
-st.markdown(
-    f"""
-    <div style='text-align: center'>
-        <p>🎲 Bayesian Hierarchical Model Analysis for Pokémon Speed | Built with Streamlit & PyMC</p>
-        <p>Session ID: {st.session_state.session_id[:8]} | Powered by Google Gemini 2.0 Flash</p>
-    </div>
-    """,
-    unsafe_allow_html=True
-)