# 19feb2023
#https://huggingface.co/spaces/keras-io/timeseries_forecasting_for_weather/

import streamlit as st
import datetime
import pandas as pd
import numpy as np

backlogmax = 4
today = datetime.date.today()

ayear = int(today.strftime("%Y"))-1
amonth = int(today.strftime("%m"))
amonthday = int(today.strftime("%d"))

st.write(type(ayear))
st.write(("{}-{}-{}").format(ayear,amonth,amonthday))
adf = pd.DataFrame()

for i in range(ayear-backlogmax,ayear,1):
	alink = ("https://data.weather.gov.hk/weatherAPI/opendata/opendata.php?dataType=CLMTEMP&year={}&rformat=csv&station=HKO").format(str(i))
	df = pd.read_csv(alink, skiprows=[0,1,2], skipfooter=3, engine='python', error_bad_lines=True)
	st.write(i)

#	nparray = np.array([])
	df = df.reset_index()  # make sure indexes pair with number of rows
	for index, row in df.iterrows():
		if (row[1]!=amonth) or (row[2]!=amonthday):
			continue

		st.write(row[0],row[1],row[2],row[3],row[4],amonth,amonthday)
		adate = ("{}.{}.{} 00:00:00").format(row[2], row[1], row[0])
#		np.append([adate,"",row[4],"","","","","","","","","","","",""],nparray)
		nparray = [adate,"",row[4],"","","","","","","","","","","",""]
		adf = pd.concat([adf, pd.DataFrame(nparray)], axis=0)
		break
st.dataframe(adf)