Upload 2 files

app_bayesian.py

@@ -117,16 +117,32 @@ st.markdown("---")
 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"
+    # API 選擇
+    api_choice = st.radio(
+        "選擇 LLM API",
+        options=["Google Gemini", "Anthropic Claude"],
+        index=0,
+        help="選擇要使用的 AI 助手"
     )
     
+    # API Key 輸入
+    if api_choice == "Google Gemini":
+        api_key = st.text_input(
+            "Google Gemini API Key",
+            type="password",
+            help="輸入您的 Google Gemini API Key"
+        )
+    else:  # Claude
+        api_key = st.text_input(
+            "Anthropic Claude API Key",
+            type="password",
+            help="輸入您的 Anthropic API Key (https://console.anthropic.com)"
+        )
+    
     if api_key:
         st.session_state.api_key = api_key
-        st.success("✅ API Key 已載入")
+        st.session_state.api_choice = api_choice  # 新增：儲存 API 選擇
+        st.success(f"✅ {api_choice} API Key 已載入")
     
     st.markdown("---")
     
@@ -526,11 +542,13 @@ with tab2:
         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
-            )
+                if 'llm_assistant' not in st.session_state:
+                    api_choice = st.session_state.get('api_choice', 'Google Gemini')
+                    st.session_state.llm_assistant = BayesianLLMAssistant(
+                        api_key=st.session_state.api_key,
+                        session_id=st.session_state.session_id,
+                        api_provider=api_choice  # 新增：傳遞 API 選擇
+                    )
         
         # 聊天容器
         chat_container = st.container()

bayesian_llm_assistant.py

@@ -10,20 +10,32 @@ class BayesianLLMAssistant:
     貝氏階層模型 LLM 問答助手（支援動態 DAG 生成）
     協助用戶理解貝氏分析結果，並可根據描述生成客製化 DAG 圖
     """
-    
-    def __init__(self, api_key, session_id):
+
+    def __init__(self, api_key, session_id, api_provider="Google Gemini"):
         """
         初始化 LLM 助手
         
         Args:
-            api_key: Google Gemini API key
+            api_key: API key (Gemini 或 Claude)
             session_id: 唯一的 session 識別碼
+            api_provider: API 提供商 ("Google Gemini" 或 "Anthropic Claude")
         """
-        genai.configure(api_key=api_key)
-        self.model = genai.GenerativeModel('gemini-2.0-flash-exp')
+        self.api_provider = api_provider
         self.session_id = session_id
         self.conversation_history = []
         
+        if api_provider == "Google Gemini":
+            import google.generativeai as genai
+            genai.configure(api_key=api_key)
+            self.model = genai.GenerativeModel('gemini-2.0-flash-exp')
+            self.client = None
+        else:  # Anthropic Claude
+            import anthropic
+            self.client = anthropic.Anthropic(api_key=api_key)
+            self.model_name = "claude-3-5-sonnet-20241022"
+            self.model = None
+
+        
         # 系統提示詞（加入 DAG 生成能力）
         self.system_prompt = """You are an expert Bayesian statistician specializing in hierarchical models and meta-analysis, particularly in the context of Pokémon battle statistics.
 
@@ -192,16 +204,29 @@ Format responses with proper markdown for better readability.
             # 組合最終提示詞
             final_prompt = full_prompt + conversation_text + f"\nUser: {user_message}\n\nAssistant:"
             
-            # 調用 Gemini API
-            response = self.model.generate_content(
-                final_prompt,
-                generation_config=genai.types.GenerationConfig(
+            
+            # 調用對應的 API
+            if self.api_provider == "Google Gemini":
+                response = self.model.generate_content(
+                    final_prompt,
+                    generation_config=genai.types.GenerationConfig(
+                        temperature=0.7,
+                        max_output_tokens=4000,
+                    )
+                )
+                assistant_message = response.text
+                
+            else:  # Anthropic Claude
+                response = self.client.messages.create(
+                    model=self.model_name,
+                    max_tokens=4000,
                     temperature=0.7,
-                    max_output_tokens=4000,
+                    system=self.system_prompt,
+                    messages=[
+                        {"role": "user", "content": final_prompt}
+                    ]
                 )
-            )
-            
-            assistant_message = response.text
+                assistant_message = response.content[0].text            
             
             # 檢查是否包含 Graphviz 代碼
             dag_image = self._extract_and_render_dag(assistant_message)