Update app.py

app.py

@@ -1,14 +1,35 @@
 import streamlit as st
 import hopsworks
 import joblib
+from datetime import date
 import pandas as pd
-import datetime
-from functions import get_weather_data_weekly, data_encoder, get_aplevel, get_color
+from datetime import timedelta, datetime
+from functions import *
+import numpy as np
+from sklearn.preprocessing import StandardScaler
 
+import folium
+from streamlit_folium import st_folium, folium_static
+import json
+import time
+from branca.element import Figure
 
-today = datetime.date.today()
+
+project = hopsworks.login()
+
+today = date.today()
 city = "Paris"
-weekly_data = get_weather_data_weekly(city, today)
+df_weather = get_weather_data_weekly(city, today)
+df_weather.date = df_weather.date.apply(timestamp_2_time)
+df_weather_x = df_weather.drop(columns=["date"]).fillna(0)
+df_weather_nn=np.array(df_weather_x)
+scaler = StandardScaler()
+scaler.fit(df_weather_x)
+
+df_weather_use=scaler.transform(df_weather_x)
+
+df_weather_use_1= pd.DataFrame(df_weather_use)
+
 
 mr = project.get_model_registry()
 model = mr.get_model("gradient_boost_model", version=1)
@@ -16,17 +37,14 @@ model_dir = model.download()
 model = joblib.load(model_dir + "/model.pkl")
 
 
-preds = model.predict(data_encoder(weekly_data)).astype(int)
+preds = model.predict(df_weather_use_1).astype(int)
+pollution_level = get_aplevel(preds.T.reshape(-1, 1))
 
-air_pollution_level = ['Good', 'Moderate', 'Unhealthy for sensitive Groups','Unhealthy' ,'Very Unhealthy', 'Hazardous']
-poll_level = get_aplevel(preds.T.reshape(-1, 1), air_pollution_level)
+next_week = [f"{(today + timedelta(days=d)).strftime('%Y-%m-%d')},{(today + timedelta(days=d)).strftime('%A')}" for d in range(8)]
 
-next_week_datetime = [today + datetime.timedelta(days=d) for d in range(7)]
-next_week_str = [f"{days.strftime('%A')}, {days.strftime('%Y-%m-%d')}" for days in next_week_datetime]
+df = pd.DataFrame(data=[preds, pollution_level], index=["AQI", "Air pollution level"], columns=next_week)
 
-df = pd.DataFrame(data=[preds, poll_level], index=["AQI", "Air pollution level"], columns=next_week_str)
 
-st.write("Here they are!")
-st.dataframe(df.style.apply(get_color, subset=(["Air pollution level"], slice(None))))
+st.write(df)
 
 st.button("Re-run")