Refactor datetime handling in app.py: Ensure UTC conversion for sensor timestamps and adjust filtering logic for recent sensor data.

app.py

@@ -27,8 +27,7 @@ sensor_df = pd.DataFrame(sensor_data.data)
 troubleshooting_df = pd.DataFrame(troubleshooting_data.data)
 
 # Convert 'datetime' column to datetime objects (初回のみ実行)
-sensor_df['datetime'] = pd.to_datetime(sensor_df['datetime'])
-
+sensor_df['datetime'] = pd.to_datetime(sensor_df['datetime'], utc=True)
 
 # 閾値チェック関数の定義
 def check_thresholds(sensor_df_filtered, threshold_df): # Renamed parameter to clarify it's the filtered data
@@ -96,13 +95,22 @@ import datetime # Import datetime here as it's used in run_troubleshooting
 def run_troubleshooting():
     try:
         # Get current time and calculate the time 24 hours ago
-        current_time = datetime.datetime.now(datetime.timezone.utc)
-        time_24_hours_ago = current_time - datetime.timedelta(hours=24)
+        #current_time = datetime.datetime.now(datetime.timezone.utc)
+        #time_24_hours_ago = current_time - datetime.timedelta(hours=24)
+        # 現在のUTC時刻
+        current_time_utc = datetime.datetime.now(datetime.timezone.utc)
+
+        # 24時間前のUTC
+        time_24_hours_ago_utc = current_time_utc - datetime.timedelta(hours=24)
 
         # Filter sensor data for the last 24 hours
         # Use the globally available sensor_df and filter it each time the function is called
         global sensor_df
-        recent_sensor_df = sensor_df[sensor_df['datetime'] >= time_24_hours_ago].copy()
+        #recent_sensor_df = sensor_df[sensor_df['datetime'] < time_24_hours_ago].copy()
+        recent_sensor_df = sensor_df[
+        (sensor_df['datetime'] >= time_24_hours_ago) &
+        (sensor_df['datetime'] <= current_time)
+        ].copy()
 
         # Ensure other dataframes are accessible (they are loaded globally once)
         global threshold_df