Update ai_service.py

ai_service.py

@@ -1,4 +1,4 @@
-# ai_service.py (已修正 PWS 工具呼叫目標)
+# ai_service.py (已修正 function_call 處理邏輯)
 import json
 from datetime import datetime
 import google.generativeai as genai
@@ -13,7 +13,6 @@ if GEMINI_API_KEY and "YOUR_GEMINI_API_KEY" not in GEMINI_API_KEY:
 
 # --- 2. 工具函式 (Tool Functions) ---
 
-# [既有] 地震查詢工具函式
 def call_mcp_earthquake_search(
     start_date: str,
     end_date: str,
@@ -23,82 +22,48 @@ def call_mcp_earthquake_search(
     """根據指定的條件（時間、規模）從遠端伺服器搜尋地震事件。"""
     try:
         print(f"--- 正在呼叫遠端地震 MCP 伺服器 (由 Gemini 觸發) ---")
-        print(f"    查詢條件: {start_date} 到 {end_date}, 規模 {min_magnitude} 以上")
-
         client = Client(src=MCP_SERVER_URL)
         result = client.predict(
             param_0=start_date, param_1="00:00:00",
             param_2=end_date, param_3="23:59:59",
-            param_4=21.0, param_5=26.0, # 預設台灣緯度
-            param_6=119.0, param_7=123.0, # 預設台灣經度
+            param_4=21.0, param_5=26.0, param_6=119.0, param_7=123.0,
             param_8=0.0, param_9=100.0,
             param_10=min_magnitude, param_11=max_magnitude,
             api_name="/gradio_fetch_and_plot_data"
         )
-        dataframe_dict = result[0]
-        data = dataframe_dict.get('data', [])
-
-        if not data:
-            print("--- MCP 伺服器回傳：未找到符合條件的地震 ---")
-            return "查詢完成，但未找到任何符合條件的地震資料。"
-
-        headers = dataframe_dict.get('headers', [])
-        formatted_results = [dict(zip(headers, row)) for row in data]
-        print(f"--- MCP 伺服器成功回傳 {len(data)} 筆資料 ---")
-        return json.dumps(formatted_results, indent=2, ensure_ascii=False)
+        data = result[0].get('data', [])
+        if not data: return "查詢完成，但未找到任何符合條件的地震資料。"
+        headers = result[0].get('headers', [])
+        return json.dumps([dict(zip(headers, row)) for row in data], indent=2, ensure_ascii=False)
     except Exception as e:
-        print(f"呼叫 MCP 伺服器失敗: {e}")
         return f"工具執行失敗，錯誤訊息: {e}"
 
-# [修改] PWS 查詢工具函式
 def call_mcp_pws_search() -> str:
     """從遠端伺服器查詢最新的 PWS (Public Weather Service) 發布情形。"""
     try:
         print(f"--- 正在呼叫遠端 PWS MCP 伺服器 (由 Gemini 觸發) ---")
         client = Client("cwadayi/MCP-pws")
-        
-        # [*** 核心修正 ***]
-        # 明確指定要呼叫的 API 名稱，對應到 MCP_pws_get_disaster_warnings
         result = client.predict(api_name="/MCP_pws_get_disaster_warnings")
-        
-        if isinstance(result, tuple) and len(result) > 0:
-            report = result[0]
-        else:
-            report = str(result)
-
-        print(f"--- PWS MCP 伺服器成功回傳 ---")
-        return report
+        return result[0] if isinstance(result, tuple) and len(result) > 0 else str(result)
     except Exception as e:
-        print(f"呼叫 PWS MCP 伺服器失敗: {e}")
         return f"工具執行失敗，錯誤訊息: {e}"
 
 # --- 3. 向 Gemini 定義工具 (Tool Declarations) ---
 
-# [既有] 地震查詢工具定義
 earthquake_search_tool_declaration = {
-    "name": "call_earthquake_search_tool",
-    "description": "根據指定的條件（時間、地點、規模等）從台灣中央氣象署的資料庫中搜尋地震事件。預設搜尋台灣周邊地區。",
-    "parameters": {
-        "type": "OBJECT", "properties": {
+    "name": "call_earthquake_search_tool", "description": "從台灣中央氣象署的資料庫中搜尋地震事件。",
+    "parameters": { "type": "OBJECT", "properties": {
             "start_date": {"type": "STRING", "description": "搜尋的開始日期 (格式 'YYYY-MM-DD')。"},
             "end_date": {"type": "STRING", "description": "搜尋的結束日期 (格式 'YYYY-MM-DD')。"},
-            "min_magnitude": {"type": "NUMBER", "description": "要搜尋的最小地震規模。"},
-            "max_magnitude": {"type": "NUMBER", "description": "要搜尋的最大地震規模。"},
         }, "required": ["start_date", "end_date"]
     }
 }
 
-# [維持不變] PWS 查詢工具定義
 pws_search_tool_declaration = {
-    "name": "call_mcp_pws_search",
-    "description": "查詢最新的 PWS (Public Weather Service) 公共天氣服務發布情形。",
-    "parameters": {
-        "type": "OBJECT",
-        "properties": {}
-    }
+    "name": "call_mcp_pws_search", "description": "查詢最新的 PWS (Public Weather Service) 公共天氣服務發布情形。",
+    "parameters": {"type": "OBJECT", "properties": {}}
 }
 
-# [維持不變] 註冊所有工具
 available_tools = {
     "call_earthquake_search_tool": call_mcp_earthquake_search,
     "call_mcp_pws_search": call_mcp_pws_search
@@ -108,11 +73,7 @@ available_tools = {
 model = None
 if GEMINI_API_KEY and "YOUR_GEMINI_API_KEY" not in GEMINI_API_KEY:
     try:
-        system_instruction = (
-            "You are a helpful AI assistant. You must answer in Traditional Chinese."
-            "You have access to tools. When a tool returns data, "
-            "you must analyze the data to fully answer the user's question."
-        )
+        system_instruction = "You are a helpful AI assistant. You must answer in Traditional Chinese. You have access to tools. When a tool returns data, you must analyze the data to fully answer the user's question."
         model = genai.GenerativeModel(
             model_name="gemini-1.5-flash",
             tools=[earthquake_search_tool_declaration, pws_search_tool_declaration],
@@ -121,36 +82,34 @@ if GEMINI_API_KEY and "YOUR_GEMINI_API_KEY" not in GEMINI_API_KEY:
     except Exception as e:
         print(f"建立 Gemini 模型失敗: {e}")
 
-# --- 5. 主要的 AI 文字生成函式 (維持不變) ---
+# --- 5. 主要的 AI 文字生成函式 ---
 def generate_ai_text(user_prompt: str) -> str:
-    if not model:
-        return "🤖 AI (Gemini) 服務尚未設定 API 金鑰，或金鑰無效。"
+    if not model: return "🤖 AI (Gemini) 服務尚未設定 API 金鑰，或金鑰無效。"
     try:
-        print(f"--- 開始 Gemini 對話，使用者輸入: '{user_prompt}' ---")
         chat = model.start_chat()
         response = chat.send_message(user_prompt)
-        try:
-            function_call = response.candidates[0].content.parts[0].function_call
-        except (IndexError, AttributeError):
-            function_call = None
-        if not function_call:
-            print("--- Gemini 直接回覆文字 ---")
-            return response.text
-        
-        print(f"--- Gemini 要求呼叫工具: {function_call.name} ---")
-        tool_function = available_tools.get(function_call.name)
-        if not tool_function:
-            return f"錯誤：模型嘗試呼叫一個不存在的工具 '{function_call.name}'。"
-        
-        tool_result = tool_function(**dict(function_call.args))
-        print("--- 將工具結果回傳給 Gemini ---")
         
-        response = chat.send_message(
-            {"function_response": {"name": function_call.name, "response": {"result": tool_result}}}
-        )
+        # [*** 核心修正 ***]
+        # 迭代檢查模型的回應部分，而不是直接存取
+        for part in response.parts:
+            # 如果回應部分是函式呼叫，則執行它
+            if part.function_call:
+                function_call = part.function_call
+                tool_function = available_tools.get(function_call.name)
+                if not tool_function:
+                    return f"錯誤：模型嘗試呼叫一個不存在的工具 '{function_call.name}'。"
+                
+                tool_result = tool_function(**dict(function_call.args))
+                
+                # 將工具執行的結果送回給模型
+                final_response = chat.send_message(
+                    {"function_response": {"name": function_call.name, "response": {"result": tool_result}}}
+                )
+                return final_response.text
         
-        print("--- Gemini 根據工具結果生成最終回覆 ---")
+        # 如果沒有函式呼叫，直接回傳文字結果
         return response.text
+
     except Exception as e:
         print(f"與 Gemini AI 互動時發生錯誤: {e}")
-        return f"🤖 AI 服務發生錯誤: {e}"
+        return f"🤖 AI 服務發生錯誤: {e}"