更新の頻度を、3回/day -> 1回/day に、AI model を、"gpt-4o" -> "gpt-4o-mini" に変更しました。頻度については、plist 設定ファイルで。
$ pwd
/Library/LaunchDaemons
AI model の変更は、下記の Python スクリプトで一行だけ変更。ところで、最新の、API 用の、Credit balance を記録しようと思っていたのですが、表示されず。どのくらい残金があるのだろう。
import os import time import gzip import shutil import logging from datetime import datetime, timedelta, timezone import smtplib from email.mime.image import MIMEImage from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText import requests import matplotlib.pyplot as plt import matplotlib.dates as mdates from dotenv import load_dotenv import openai # 環境変数の読み込み load_dotenv() # 環境変数から設定を取得 log_dir = os.getenv('LOG_DIR') api_key = os.getenv('OPENWEATHERMAP_API_KEY') openai_api_key = os.getenv('OPENAI_API_KEY') smtp_server = os.getenv('SMTP_SERVER') smtp_port = int(os.getenv('SMTP_PORT')) from_email = os.getenv('FROM_EMAIL') to_addr = os.getenv('TO_ADDR') email_password = os.getenv('EMAIL_PASSWORD') # 日付ごとにログファイルを生成 current_date = datetime.now().strftime("%Y-%m-%d") log_file_path = os.path.join(log_dir, f"{current_date}.log") compressed_log_file_path = f"{log_file_path}.gz" # ログファイルを展開する関数 def decompress_log_file(): if os.path.exists(compressed_log_file_path): try: with gzip.open(compressed_log_file_path, 'rb') as f_in: with open(log_file_path, 'wb') as f_out: shutil.copyfileobj(f_in, f_out) except gzip.BadGzipFile: shutil.copy(compressed_log_file_path, log_file_path) else: open(log_file_path, 'w').close() # ログファイルを圧縮する関数 def compress_log_file(): try: with open(log_file_path, 'rb') as f_in: with gzip.open(compressed_log_file_path, 'wb') as f_out: shutil.copyfileobj(f_in, f_out) os.remove(log_file_path) logging.debug("Log file compressed successfully") except Exception as e: logging.error(f"Error compressing log file: {e}") # gzファイルの展開または新規作成 decompress_log_file() # ログの設定 logging.basicConfig(filename=log_file_path, level=logging.DEBUG, format='%(asctime)s %(levelname)s %(message)s') logging.debug("Starting script") # 定数の設定 CITY_NAME = 'Shibuya,JP' API_URL = f'http://api.openweathermap.org/data/2.5/forecast?q={CITY_NAME}&appid={api_key}&units=metric' THRESHOLD = -0.1 # 極端な下降トレンドとみなす閾値 MIN_INTERVAL = 2 * 3600 # 強調表示する最小間隔（2時間） # OpenAI APIキーの設定 openai.api_key = openai_api_key # 気圧データを取得する関数 def get_pressure_data(): logging.debug("Starting to fetch pressure data") max_retries = 3 for attempt in range(max_retries): try: response = requests.get(API_URL) response.raise_for_status() logging.debug("Pressure data fetched successfully") return response.json() except requests.RequestException as e: logging.error(f"Error fetching data: {e}, retrying ({attempt + 1}/{max_retries})...") time.sleep(5) # 5秒待ってからリトライ return None # 気圧データを解析する関数（OpenAI APIを使用） def analyze_pressure_data(timestamps, pressures): logging.debug("Starting to analyze pressure data") try: data_text = "\n".join([f"{t.strftime('%Y-%m-%d %H:%M')}: {p}" for t, p in zip(timestamps, pressures)]) prompt = ( "以下は東京渋谷の気圧データです。このデータに基づいて、気圧と気象病についてコメントを生成してください。" "コメントの最初に結論をやや長めに述べてください。その後、コメントは短いパラグラフを用い、見出しや箇条書き、番号付きリストなどを活用して読みやすくしてください。\n\n" + data_text ) response = openai.ChatCompletion.create( model="gpt-4o-mini", messages=[ {"role": "system", "content": "You are an assistant that analyzes pressure data trends."}, {"role": "user", "content": prompt} ], max_tokens=1000 ) comment = response['choices'][0]['message']['content'].strip() logging.debug("Comment from OpenAI: %s", comment) return comment except Exception as e: logging.error(f"Error analyzing pressure data: {e}") return None # 気圧グラフを作成し、極端な下降トレンドをハイライトする関数 def highlight_extreme_downward_trend(timestamps, pressures, threshold, min_interval): logging.debug("Starting to create pressure graph") plt.figure(figsize=(12, 6)) plt.plot(timestamps, pressures, marker='o', linestyle='-') highlight_start = None for i in range(len(pressures) - 1): rate_of_change = (pressures[i + 1] - pressures[i]) / ((timestamps[i + 1] - timestamps[i]).total_seconds() / 3600) if rate_of_change < threshold: if highlight_start is None: highlight_start = i else: if highlight_start is not None: interval = (timestamps[i] - timestamps[highlight_start]).total_seconds() if interval >= min_interval: alpha = max(0.1, min(1.0, abs(rate_of_change) / 2)) plt.axvspan(timestamps[highlight_start], timestamps[i], color=(1.0, 0.0, 0.0, alpha)) highlight_start = None plt.title('Pressure Data in Shibuya, Tokyo') plt.xlabel('Date') plt.ylabel('Pressure (hPa)') plt.xticks(rotation=45) ax = plt.gca() ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) ax.xaxis.set_major_locator(mdates.DayLocator()) ax.xaxis.set_minor_locator(mdates.HourLocator(interval=6)) plt.grid(True) plt.tight_layout() output_dir = os.getenv('OUTPUT_DIR', os.path.join(os.path.dirname(__file__), "png")) os.makedirs(output_dir, exist_ok=True) file_name = os.path.join(output_dir, f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_fig.png") plt.savefig(file_name) logging.debug("Pressure graph saved as %s", file_name) return file_name # メール送信関数 def send_email(file_path, comment): logging.debug("Starting to send email") current_time = datetime.now().strftime("%I %p %d %b") msg = MIMEMultipart() msg['Subject'] = f'[meteoropathy]Tokyo Pres - {current_time}' msg['From'] = from_email msg['To'] = to_addr # メール本文をMarkdown形式で作成 if comment: email_body = f""" **Current time:** {current_time} {comment} """ msg.attach(MIMEText(email_body, 'plain')) # Markdown形式で送信 else: email_body = f""" **Current time:** {current_time} No analysis comment available. """ msg.attach(MIMEText(email_body, 'plain')) # Markdown形式で送信 with open(file_path, 'rb') as f: img_data = f.read() image = MIMEImage(img_data, name=os.path.basename(file_path)) msg.attach(image) try: with smtplib.SMTP(smtp_server, smtp_port) as server: server.starttls() server.login(from_email, email_password) server.send_message(msg) logging.debug("Email sent successfully") except smtplib.SMTPException as e: logging.error(f"Error sending email: {e}") # メイン関数 def main(): logging.debug("Main function started") try: data = get_pressure_data() if data: timestamps, pressures = [], [] jst = timezone(timedelta(hours=9), 'JST') current_time = datetime.now(jst) start_time = current_time - timedelta(hours=24) for item in data.get('list', []): timestamp = datetime.utcfromtimestamp(item.get('dt', 0)).replace(tzinfo=timezone.utc).astimezone(jst) if timestamp >= start_time: timestamps.append(timestamp) pressures.append(item.get('main', {}).get('pressure', 0)) # 画像データのBase64エンコード file_name = highlight_extreme_downward_trend(timestamps, pressures, THRESHOLD, MIN_INTERVAL) # 気圧データの解析コメント取得 comment = analyze_pressure_data(timestamps, pressures) # メール送信 send_email(file_name, comment) else: logging.error("Failed to fetch pressure data after retries.") except Exception as e: logging.error(f"Error in main function: {e}") # ログファイルをgzipで再圧縮 compress_log_file() if __name__ == "__main__": main()
