Update app.py

app.py

@@ -1,5 +1,3 @@
-
-
 import streamlit as st
 import pandas as pd
 import numpy as np
@@ -36,10 +34,18 @@ def asubmit(aparam):
 	aparam["acontainer"].map(adf3)
 
 	abacklogmax = 10
+
+	atimezone = os.environ['TZ']
+#	os.environ['TZ'] = 'Europe/London'
+	os.environ['TZ'] = 'Asia/Hong Kong'
+	time.tzset()
 	atoday = datetime.date.today()
 	ayear = int(atoday.strftime("%Y"))-0
 	amonth = int(atoday.strftime("%m"))
 	amonthday = int(atoday.strftime("%d"))
+	os.environ['TZ'] = atimezone
+	time.tzset()
+
 	atitles = aparam["acolumntitles"]
 
 	csvString = ""
@@ -62,13 +68,13 @@ def asubmit(aparam):
 	adf3 = pd.read_csv(StringIO(csvString), sep=",")
 	aparam["acontainer"].dataframe(adf3)
 
+# Trend projection for same date of current year with past year references of same date
 	from sklearn.linear_model import LinearRegression
-# Creating a Linear Regression model on our data
-	lin = LinearRegression()
-	lin.fit(adf3[['Date']].astype('float').astype('int32'), adf3['Celsius'])
+	lr = LinearRegression()
+	lr.fit(adf3[['Date']].astype('float').astype('int32'), adf3['Celsius'])
 
 	atrendproject = [20230223,]
-	st.dataframe(pd.merge(pd.DataFrame([atrendproject]), pd.DataFrame(lin.predict(np.array([atrendproject]))), left_index=True, right_index=True))
+	st.dataframe(pd.merge(pd.DataFrame([atrendproject]), pd.DataFrame(lr.predict(np.array([atrendproject]))), left_index=True, right_index=True))
 
 adf = pd.DataFrame(
 	astations,