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Commit date
2024-05-28
2024-05-20
import pandas as pd
from datetime import datetime, timedelta
import psycopg2
import json
from flask import Flask
from flask_restx import Api
from apscheduler.schedulers.background import BackgroundScheduler
with open('config_files/MAIN_DB_ENDPOINT.json', 'r') as file:
db_config = json.load(file)
app = Flask(__name__)
print("ITS API Updater START")
api = Api(app,
version='0.1',
title="monitoring",
description="API Server",
terms_url="/",
contact="",
)
def get_hourly_datetime_range():
current_time = datetime.now()
current_hour_start = current_time.replace(minute=0, second=0, microsecond=0)
next_hour_start = current_hour_start + timedelta(hours=1)
# If the current time is past 5 minutes into the hour
if current_time.minute > 5:
return current_hour_start, next_hour_start
else:
previous_hour_start = current_hour_start - timedelta(hours=1)
return previous_hour_start, current_hour_start
def fetch_and_update():
conn = psycopg2.connect(**db_config)
cursor = conn.cursor()
set_schema_query = """
SET search_path TO ai_camera_v0_1;
"""
cursor.execute(set_schema_query)
with conn:
with conn.cursor() as cursor:
# Determine the time window from last full hour to current full hour
now = datetime.now()
previous_hour = (now - timedelta(hours=1)).replace(minute=0, second=0, microsecond=0)
current_hour = now.replace(minute=0, second=0, microsecond=0)
fetch_sql = """
SELECT eqpmn_id, COUNT(*) AS flooding_cnt
FROM flooding_detect_event
WHERE ocrn_dt >= %s AND ocrn_dt < %s AND norm_to_alert_flag = 'True'
GROUP BY eqpmn_id;
"""
cursor.execute(fetch_sql, (previous_hour, current_hour))
rows = cursor.fetchall()
df = pd.DataFrame(rows, columns=['eqpmn_id', 'flooding_cnt'])
insert_sql = """
INSERT INTO flooding_anals_event_data_hr (clct_dt, eqpmn_id, flooding_cnt)
VALUES (%s, %s, %s);
"""
for index, row in df.iterrows():
cursor.execute(insert_sql, (previous_hour, row['eqpmn_id'], row['flooding_cnt']))
conn.commit()
scheduler = BackgroundScheduler()
scheduler.add_job(func=fetch_and_update, trigger='cron', minute=5)
scheduler.start()
@app.route('/')
def home():
return "Flooding analysis service running."
if __name__ == '__main__':
fetch_and_update()
app.run(debug=True, use_reloader=False)