DrugUse_LineBot_backup/app.py

import os
import sqlite3
import requests
import tempfile
import logging
from io import BytesIO

from flask import Flask, request, abort, send_from_directory
from PIL import Image

from linebot.v3.webhook import WebhookParser, WebhookHandler
from linebot.v3.webhooks import MessageEvent, TextMessageContent, ImageMessageContent
from linebot.v3.messaging import MessagingApi, Configuration, ApiClient, MessagingApiBlob
from linebot.v3.messaging.models import (
    TextMessage, ReplyMessageRequest,
    FlexMessage, FlexBubble, FlexBox, FlexText, FlexButton, URIAction,
    QuickReply, QuickReplyItem, LocationAction, ImageMessage
)
from linebot.v3.exceptions import InvalidSignatureError

import google.generativeai as genai
import io
import json
from dotenv import load_dotenv

# --- Google Drive API 相關導入 ---
from google.oauth2 import service_account # 修改這裡
from googleapiclient.discovery import build
from googleapiclient.http import MediaIoBaseDownload
# --- END Google Drive API 相關導入 ---

load_dotenv()

app = Flask(__name__)

# 從環境變數讀取 LINE Bot 設定
LINE_CHANNEL_SECRET = os.environ.get("LINE_CHANNEL_SECRET")
LINE_CHANNEL_ACCESS_TOKEN = os.environ.get("LINE_CHANNEL_ACCESS_TOKEN")

if not LINE_CHANNEL_SECRET or not LINE_CHANNEL_ACCESS_TOKEN:
    raise RuntimeError("Missing essential environment variables")

print(f"LINE_CHANNEL_SECRET: {LINE_CHANNEL_SECRET}")
print(f"LINE_CHANNEL_ACCESS_TOKEN: {LINE_CHANNEL_ACCESS_TOKEN}")
#print(f"GOOGLE_MAP_API_KEY: {GOOGLE_MAP_API_KEY}")

# --- 資料庫路徑設定 ---
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DB_FILENAME = "medicine.db"
DB_PATH = os.path.join(BASE_DIR, DB_FILENAME) # 確保 DB_PATH 指向容器內的預期路徑
# --- END 資料庫路徑設定 ---

# --- Google Drive 下載函式 ---
def download_db_from_google_drive():
    print("Attempting to download database from Google Drive...")
    google_creds_json_str = os.getenv("GOOGLE_CREDENTIALS_JSON")
    file_id = os.getenv("GOOGLE_DRIVE_FILE_ID")

    if not google_creds_json_str:
        print("Error: GOOGLE_CREDENTIALS_JSON secret not found.")
        return False
    if not file_id:
        print("Error: GOOGLE_DRIVE_FILE_ID secret not found.")
        return False

    try:
        # 將 JSON 字串轉換為字典
        creds_info = json.loads(google_creds_json_str)
        # 從服務帳戶資訊建立憑證
        creds = service_account.Credentials.from_service_account_info(
            creds_info,
            scopes=['https://www.googleapis.com/auth/drive.readonly'] # 只需要唯讀權限
        )
        drive_service = build('drive', 'v3', credentials=creds)

        request_dl = drive_service.files().get_media(fileId=file_id)
        # fh = io.BytesIO() # 記憶體中處理
        # downloader = MediaIoBaseDownload(fh, request_dl)
        
        # 直接寫入檔案
        with open(DB_PATH, 'wb') as fh:
            downloader = MediaIoBaseDownload(fh, request_dl)
            done = False
            while done is False:
                status, done = downloader.next_chunk()
                print(f"Download {int(status.progress() * 100)}%.")
        print(f"Database '{DB_FILENAME}' downloaded successfully to '{DB_PATH}'.")
        return True
    except Exception as e:
        print(f"Error downloading database from Google Drive: {e}")
        return False
# --- END Google Drive 下載函式 ---

# --- 在應用程式啟動時執行資料庫下載 ---
DOWNLOAD_SUCCESS = False # Initialize
# Check if running in Hugging Face Space or if secrets are generally available
# In HF Spaces, secrets are environment variables. Locally, .env would be used.
if os.getenv("GOOGLE_CREDENTIALS_JSON") and os.getenv("GOOGLE_DRIVE_FILE_ID"):
    print("Found Google Drive credentials, attempting download...")
    DOWNLOAD_SUCCESS = download_db_from_google_drive()
else:
    print("Warning: GOOGLE_CREDENTIALS_JSON or GOOGLE_DRIVE_FILE_ID not found in environment. Skipping DB download.")
    print("If running locally, ensure they are in your .env file or environment.")
    print("If on Hugging Face, ensure secrets are set in Space settings.")

if not DOWNLOAD_SUCCESS:
    print("CRITICAL: Database download failed or was skipped. The application might not function as expected if the database is required at startup.")
# --- END 應用程式啟動時執行資料庫下載 ---

configuration = Configuration(access_token=LINE_CHANNEL_ACCESS_TOKEN)
parser = WebhookParser(LINE_CHANNEL_SECRET)
handler = WebhookHandler(LINE_CHANNEL_SECRET)

genai.configure(api_key=GOOGLE_API_KEY)
chat = genai.GenerativeModel(model_name="gemini-1.5-flash")

logging.basicConfig(level=logging.INFO)
app.logger.setLevel(logging.INFO)

@app.route("/images/<filename>")
def serve_image(filename):
    return send_from_directory(static_tmp_path, filename)

@app.route("/")
def home():
    return {"message": "Line Webhook Server"}

@app.route("/callback", methods=["POST"])
def callback():
    
    # 檢查資料庫是否已成功下載 (可選，但建議)
    if not DOWNLOAD_SUCCESS and not os.path.exists(DB_PATH):
        print("Database not available, aborting callback.")
        abort(500) # 或返回一個提示用戶稍後再試的訊息

    signature = request.headers.get("X-Line-Signature", "")
    body = request.get_data(as_text=True)

    try:
        events = parser.parse(body, signature)
    except InvalidSignatureError:
        abort(400)
    except Exception as e:
        print("Webhook parse error:", e)
        abort(400)

    with ApiClient(configuration) as api_client:
        messaging_api = MessagingApi(api_client)
        blob_api = MessagingApiBlob(api_client)

        for event in events:
            if event.type == "message":
                # 文字訊息
                if event.message.type == "text":
                    user_input = event.message.text.strip()
                    print("📨 收到訊息：", user_input)

                    # AI 問答
                    if user_input.startswith("AI "):
                        prompt = "你是一個中文的AI助手，請用繁體中文回答。\n" + user_input[3:].strip()
                        try:
                            response = chat.generate_content(prompt)
                            reply_text = response.text
                        except Exception as e:
                            reply_text = f"AI 回答失敗：{e}"

                        reply_request = ReplyMessageRequest(
                            reply_token=event.reply_token,
                            messages=[TextMessage(text=reply_text)]
                        )
                        messaging_api.reply_message(reply_message_request=reply_request)

                    # 查詢藥品
                    elif user_input == "查詢藥品":
                        reply_text = "請輸入藥品名稱，例如：口服感冒藥"
                        reply_request = ReplyMessageRequest(
                            reply_token=event.reply_token,
                            messages=[TextMessage(text=reply_text)]
                        )
                        messaging_api.reply_message(reply_message_request=reply_request)

                    # 查詢藥局
                    elif "查詢藥局" in user_input:
                        quick_reply = QuickReply(
                            items=[QuickReplyItem(action=LocationAction(label="傳送我的位置"))]
                        )
                        reply_request = ReplyMessageRequest(
                            reply_token=event.reply_token,
                            messages=[TextMessage(text="請點選下方按鈕傳送你的位置，我才能幫你找附近藥局喔～", quick_reply=quick_reply)]
                        )
                        messaging_api.reply_message(reply_message_request=reply_request)

                    # 其他：查詢藥品資料庫，副作用永遠由AI產生
                    else:
                        medicine_name = user_input.lower()
                        try:
                            conn = sqlite3.connect(DB_PATH)
                            cursor = conn.cursor()
                            query = """
                                SELECT DISTINCT 中文品名, 英文品名, 適應症
                                FROM drugs
                                WHERE LOWER(中文品名) LIKE ?
                                LIMIT 3
                            """
                            like_param = f'%{medicine_name}%'
                            cursor.execute(query, (like_param,))
                            rows = cursor.fetchall()
                            conn.close()
                            
                            if rows:
                                zh_name, en_name, indication = rows[0]
                                # 副作用由 AI 產生
                                prompt = (
                                    f"請用簡短條列式，僅列出副作用，針對藥品「{zh_name}」(英文名：{en_name})，"
                                    "請用繁體中文回答，若無法判斷請推測。"
                                )
                                try:
                                    ai_resp = chat.generate_content(prompt)
                                    side_effects = ai_resp.text.strip()
                                except Exception as e:
                                    side_effects = f"AI 回答失敗：{e}"
                                reply_text = (
                                    f"🔹 中文品名：{zh_name}\n"
                                    f"📌 英文品名：{en_name}\n"
                                    f"📄 適應症：{indication}\n"
                                    f"⚠️ 副作用：{side_effects}"
                                )
                            else:
                                # 全部請AI生成
                                prompt = (
                                    f"請用以下格式，幫我介紹藥品「{medicine_name}」，若無法查到請盡量推測：\n"
                                    "🔹 中文品名：\n"
                                    "📌 英文品名：\n"
                                    "📄 適應症：\n"
                                    "⚠️ 副作用："
                                )
                                try:
                                    ai_resp = chat.generate_content(prompt)
                                    reply_text = ai_resp.text
                                except Exception as e:
                                    reply_text = f"AI 回答失敗：{e}"

                        except Exception as e:
                            reply_text = f"⚠️ 查詢資料時發生錯誤：{str(e)}"

                        reply_request = ReplyMessageRequest(
                            reply_token=event.reply_token,
                            messages=[TextMessage(text=reply_text.strip())]
                        )
                        messaging_api.reply_message(reply_message_request=reply_request)

                # 處理位置訊息（查詢附近藥局）
                elif event.message.type == "location":
                    user_lat = event.message.latitude
                    user_lng = event.message.longitude

                    nearby_url = (
                        f"https://maps.googleapis.com/maps/api/place/nearbysearch/json?"
                        f"location={user_lat},{user_lng}&radius=1000&type=pharmacy&key={GOOGLE_MAP_API_KEY}"
                    )
                    nearby_res = requests.get(nearby_url).json()

                    if not nearby_res.get('results'):
                        reply_request = ReplyMessageRequest(
                            reply_token=event.reply_token,
                            messages=[TextMessage(text="附近找不到藥局")]
                        )
                        messaging_api.reply_message(reply_message_request=reply_request)
                        continue

                    place = nearby_res['results'][0]
                    place_id = place['place_id']
                    name = place.get('name', '藥局名稱未知')
                    location = place['geometry']['location']
                    dest_lat, dest_lng = location['lat'], location['lng']

                    details_url = (
                        f"https://maps.googleapis.com/maps/api/place/details/json?"
                        f"place_id={place_id}&fields=name,formatted_phone_number&key={GOOGLE_MAP_API_KEY}"
                    )
                    details_res = requests.get(details_url).json()
                    phone = details_res.get('result', {}).get('formatted_phone_number', '電話不詳')

                    dist_url = (
                        f"https://maps.googleapis.com/maps/api/distancematrix/json?"
                        f"origins={user_lat},{user_lng}&destinations={dest_lat},{dest_lng}&key={GOOGLE_MAP_API_KEY}"
                    )
                    dist_res = requests.get(dist_url).json()
                    distance = dist_res['rows'][0]['elements'][0]['distance']['text']

                    map_url = f"https://www.google.com/maps/search/?api=1&query={dest_lat},{dest_lng}"

                    bubble = FlexBubble(
                        body=FlexBox(
                            layout="vertical",
                            contents=[
                                FlexText(text=name, weight="bold", size="lg"),
                                FlexText(text=f"電話：{phone}", size="sm", color="#555555"),
                                FlexText(text=f"距離：{distance}", size="sm", color="#777777"),
                            ],
                        ),
                        footer=FlexBox(
                            layout="vertical",
                            contents=[
                                FlexButton(
                                    style="link",
                                    height="sm",
                                    action=URIAction(label="地圖導航", uri=map_url),
                                )
                            ],
                        ),
                    )

                    flex_message = FlexMessage(
                        alt_text="附近藥局推薦",
                        contents=bubble
                    )

                    reply_request = ReplyMessageRequest(
                        reply_token=event.reply_token,
                        messages=[flex_message]
                    )
                    messaging_api.reply_message(reply_message_request=reply_request)

                # 圖片訊息：用 Gemini AI 以藥品格式解釋圖片
                elif event.message.type == "image":
                    try:
                        content = blob_api.get_message_content(message_id=event.message.id)
                        with tempfile.NamedTemporaryFile(dir=static_tmp_path, suffix=".jpg", delete=False) as tf:
                            tf.write(content)
                            filename = os.path.basename(tf.name)
                        image_url = f"https://{base_url}/images/{filename}"
                        image = Image.open(tf.name)

                        # Gemini 圖片說明（指定格式，四欄都AI產生）
                        prompt = (
                            "請根據這張圖片判斷藥品資訊，並用以下格式回答，若無法判斷請盡量推測：\n"
                            "🔹 中文品名：\n"
                            "📌 英文品名：\n"
                            "📄 適應症：\n"
                            "⚠️ 副作用："
                        )
                        response = chat.generate_content([image, prompt])
                        description = response.text

                        reply_request = ReplyMessageRequest(
                            reply_token=event.reply_token,
                            messages=[
                                ImageMessage(
                                    original_content_url=image_url,
                                    preview_image_url=image_url
                                ),
                                TextMessage(text=description)
                            ]
                        )
                        messaging_api.reply_message(reply_message_request=reply_request)
                    except Exception as e:
                        reply_request = ReplyMessageRequest(
                            reply_token=event.reply_token,
                            messages=[TextMessage(text=f"圖片處理失敗：{e}")]
                        )
                        messaging_api.reply_message(reply_message_request=reply_request)

    return "OK"

if __name__ == "__main__":
    port = int(os.environ.get("PORT", 7860)) # 讀取環境變數 PORT，預設為 7860
    app.run(host="0.0.0.0", port=port, debug=False)