Update ai_service.py

ai_service.py

@@ -1,4 +1,4 @@
-# ai_service.py (已修正 function_call 處理邏輯)
+# ai_service.py (Final version, removed api_name for PWS tool)
 import json
 from datetime import datetime
 import google.generativeai as genai
@@ -26,13 +26,15 @@ def call_mcp_earthquake_search(
         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, param_6=119.0, param_7=123.0,
+            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"
         )
         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。"
+        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:
@@ -43,7 +45,10 @@ def call_mcp_pws_search() -> str:
     try:
         print(f"--- 正在呼叫遠端 PWS MCP 伺服器 (由 Gemini 觸發) ---")
         client = Client("cwadayi/MCP-pws")
-        result = client.predict(api_name="/MCP_pws_get_disaster_warnings")
+        
+        # [核心修正] 移除 api_name 參數，讓 gradio_client 自動尋找預設的 API 端點。
+        result = client.predict()
+        
         return result[0] if isinstance(result, tuple) and len(result) > 0 else str(result)
     except Exception as e:
         return f"工具執行失敗，錯誤訊息: {e}"
@@ -51,17 +56,25 @@ def call_mcp_pws_search() -> str:
 # --- 3. 向 Gemini 定義工具 (Tool Declarations) ---
 
 earthquake_search_tool_declaration = {
-    "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": {
+    "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')。"},
-        }, "required": ["start_date", "end_date"]
+        },
+        "required": ["start_date", "end_date"]
     }
 }
 
 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": {}
+    }
 }
 
 available_tools = {
@@ -84,15 +97,13 @@ if GEMINI_API_KEY and "YOUR_GEMINI_API_KEY" not in GEMINI_API_KEY:
 
 # --- 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:
         chat = model.start_chat()
         response = chat.send_message(user_prompt)
         
-        # [*** 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)
@@ -101,13 +112,11 @@ def generate_ai_text(user_prompt: str) -> str:
                 
                 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
         
-        # 如果沒有函式呼叫，直接回傳文字結果
         return response.text
 
     except Exception as e: