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Update command_handler.py
Browse files- command_handler.py +40 -34
command_handler.py
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# command_handler.py (
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import pandas as pd
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from linebot.v3.messaging import TextMessage, ImageMessage
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# 匯入所有服務函式
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from cwa_service import fetch_cwa_alarm_list, fetch_significant_earthquakes, fetch_latest_significant_earthquake
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from usgs_service import fetch_global_last24h_text, fetch_taiwan_df_this_year
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from plotting_service import create_and_save_map
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from ai_service import generate_ai_text
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# (pws_service 的匯入已被移除)
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from config import CURRENT_YEAR, MCP_SERVER_URL
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def get_help_message() -> TextMessage:
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"• 6 - CWA 最近7天顯著有感地震\n\n"
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"【AI 與工具】\n"
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"• 7 <問題> - 與 AI 助理對話\n"
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" (範例: 7
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"
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"• 8 - 關於此機器人\n"
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"• 9 - 顯示此說明"
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)
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return TextMessage(text=text)
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text = (
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"🤖 關於我\n\n"
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"我是一個多功能助理機器人,提供地震查詢與 AI 對話功能。\n\n"
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"• 版本: 5.
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"• 資料來源: CWA, USGS, Google Gemini\n"
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"• 開發者: dayichen"
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)
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@@ -54,23 +51,18 @@ def get_taiwan_earthquake_list() -> TextMessage:
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f"地點: {row['place']}\n"
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f"報告連結: {row.get('url', '無')}"
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)
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if count > 15:
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lines.append(f"... (還有 {count-15} 筆資料)")
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reply_text = "\n\n".join(lines)
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else:
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reply_text = result
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return TextMessage(text=reply_text)
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def get_latest_earthquake_reply() -> list:
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"""取得 CWA 最新一筆地震,並組合文字與圖片訊息。"""
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try:
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latest_eq = fetch_latest_significant_earthquake()
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if not latest_eq:
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return [TextMessage(text="✅ 近期無顯著有感地震報告。")]
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mag_str = f"{latest_eq['Magnitude']:.1f}" if latest_eq.get('Magnitude') is not None else "—"
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depth_str = f"{latest_eq['Depth']:.0f}" if latest_eq.get('Depth') is not None else "—"
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text_message_content = (
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f"🚨 CWA 最新顯著有感地震\n"
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f"----------------------------------\n"
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f"報告: {latest_eq.get('URL', '無')}"
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)
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reply_messages = [TextMessage(text=text_message_content)]
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if latest_eq.get("ImageURL"):
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image_url = latest_eq["ImageURL"]
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reply_messages.append(
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ImageMessage(original_content_url=image_url, preview_image_url=image_url)
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)
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return reply_messages
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except Exception as e:
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return [TextMessage(text=f"❌ 查詢最新地震失敗:{e}")]
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def
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"""
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"""
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parts = user_message.split(' ', 1)
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command_key = parts[0]
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arg = parts[1].strip() if len(parts) > 1 else ""
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# 根據數字指令執行對應的動作
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if command_key == '1': return get_latest_earthquake_reply()
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if command_key == '2': return [TextMessage(text=fetch_global_last24h_text())]
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if command_key == '3': return [get_taiwan_earthquake_list()]
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if command_key == '4': return [TextMessage(text=f"🗺️ 外部地震查詢服務\n\n請點擊以下連結:\n{MCP_SERVER_URL}")]
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if command_key == '5': return [TextMessage(text=fetch_cwa_alarm_list(limit=5))]
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if command_key == '6': return [TextMessage(text=fetch_significant_earthquakes(limit=5))]
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if command_key == '7':
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prompt = arg
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if not prompt: return [TextMessage(text="請輸入問題,例如:7 台灣最高的山是哪座?")]
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return [TextMessage(text=generate_ai_text(prompt))]
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if command_key == '8': return [get_info_message()]
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if command_key == '9': return [get_help_message()]
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#
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# command_handler.py (Added keyword pre-processing for AI queries)
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import pandas as pd
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import re
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from linebot.v3.messaging import TextMessage, ImageMessage
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# 匯入所有服務函式
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from cwa_service import fetch_cwa_alarm_list, fetch_significant_earthquakes, fetch_latest_significant_earthquake
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from usgs_service import fetch_global_last24h_text, fetch_taiwan_df_this_year
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from ai_service import generate_ai_text
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from config import CURRENT_YEAR, MCP_SERVER_URL
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def get_help_message() -> TextMessage:
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"• 6 - CWA 最近7天顯著有感地震\n\n"
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"【AI 與工具】\n"
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"• 7 <問題> - 與 AI 助理對話\n"
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" (範例: 7 昨天花蓮有地震嗎?)\n"
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" (範例: 7 2023年臺灣最大地震)"
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)
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return TextMessage(text=text)
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text = (
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"🤖 關於我\n\n"
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"我是一個多功能助理機器人,提供地震查詢與 AI 對話功能。\n\n"
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"• 版本: 5.2 (Gemini Advanced Edition)\n"
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"• 資料來源: CWA, USGS, Google Gemini\n"
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"• 開發者: dayichen"
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)
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f"地點: {row['place']}\n"
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f"報告連結: {row.get('url', '無')}"
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)
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if count > 15: lines.append(f"... (還有 {count-15} 筆資料)")
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reply_text = "\n\n".join(lines)
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else: reply_text = result
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return TextMessage(text=reply_text)
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def get_latest_earthquake_reply() -> list:
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"""取得 CWA 最新一筆地震,並組合文字與圖片訊息。"""
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try:
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latest_eq = fetch_latest_significant_earthquake()
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if not latest_eq: return [TextMessage(text="✅ 近期無顯著有感地震報告。")]
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mag_str = f"{latest_eq['Magnitude']:.1f}" if latest_eq.get('Magnitude') is not None else "—"
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depth_str = f"{latest_eq['Depth']:.0f}" if latest_eq.get('Depth') is not None else "—"
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text_message_content = (
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f"🚨 CWA 最新顯著有感地震\n"
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f"----------------------------------\n"
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f"報告: {latest_eq.get('URL', '無')}"
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)
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reply_messages = [TextMessage(text=text_message_content)]
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if latest_eq.get("ImageURL"):
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image_url = latest_eq["ImageURL"]
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reply_messages.append(ImageMessage(original_content_url=image_url, preview_image_url=image_url))
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return reply_messages
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except Exception as e: return [TextMessage(text=f"❌ 查詢最新地震失敗:{e}")]
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def preprocess_ai_prompt(prompt: str) -> str:
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"""
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[新增] 預處理使用者發給 AI 的問題。
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如果偵測到特定關鍵字組合,就將問題改寫為更明確的指令。
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"""
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# 尋找 "YYYY年" 的格式
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year_match = re.search(r"(\d{4})年", prompt)
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# 如果同時找到年份和 "最大地震"
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if year_match and "最大地震" in prompt:
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year = year_match.group(1)
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# 改寫成一個非常明確的指令
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new_prompt = f"請幫我呼叫地震搜尋工具,找出{year}年1月1日至{year}年12月31日之間,規模最大的地震。"
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print(f"--- AI Prompt Rewritten ---\nOriginal: {prompt}\nNew: {new_prompt}\n---------------------------")
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return new_prompt
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# 如果沒有匹配,回傳原始問題
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return prompt
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def process_message(user_message_raw: str, request_base_url: str) -> list:
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"""處理使用者輸入的主函式。"""
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user_message = (user_message_raw or "").strip()
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parts = user_message.split(' ', 1)
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command_key = parts[0]
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arg = parts[1].strip() if len(parts) > 1 else ""
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if command_key == '1': return get_latest_earthquake_reply()
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if command_key == '2': return [TextMessage(text=fetch_global_last24h_text())]
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if command_key == '3': return [get_taiwan_earthquake_list()]
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if command_key == '4': return [TextMessage(text=f"🗺️ 外部地震查詢服務\n\n請點擊以下連結:\n{MCP_SERVER_URL}")]
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if command_key == '5': return [TextMessage(text=fetch_cwa_alarm_list(limit=5))]
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if command_key == '6': return [TextMessage(text=fetch_significant_earthquakes(limit=5))]
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if command_key == '8': return [get_info_message()]
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if command_key == '9': return [get_help_message()]
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# [核心修改]
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# 如果是 AI 指令 (7) 或其他無法識別的指令,先經過預處理
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final_prompt = user_message
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if command_key == '7':
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if not arg: return [TextMessage(text="請輸入問題,例如:7 台灣最高的山是哪座?")]
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final_prompt = arg # 如果是指令7,只取後面的問題部分
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# 將最終要發給 AI 的問題傳入預處理函式
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processed_prompt = preprocess_ai_prompt(final_prompt)
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return [TextMessage(text=generate_ai_text(processed_prompt))]
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