Update app.py

app.py

@@ -2,59 +2,49 @@ import streamlit as st
 import hopsworks
 import joblib
 import pandas as pd
-import numpy as np
 from datetime import timedelta, datetime
-
 from functions import *
 
+
 def fancy_header(text, font_size=24):
     res = f'<span style="color:#ff5f27; font-size: {font_size}px;">{text}</span>'
-    st.markdown(res, unsafe_allow_html=True )
+    st.markdown(res, unsafe_allow_html=True)
+
 
 st.title('Air Quality Prediction Project🌩')
 
-progress_bar = st.sidebar.header('Working Progress')
-progress_bar = st.sidebar.progress(0)
 st.write(36 * "-")
 fancy_header('\n Connecting to Hopsworks Feature Store...')
 
 project = hopsworks.login()
 
 st.write("Successfully connected!✔️")
-progress_bar.progress(20)
 
 st.write(36 * "-")
-fancy_header('\n Getting data from thee weather API...')
+fancy_header('\n Getting data from Feature Store...')
 
 today = datetime.date.today()
 city = "vienna"
 weekly_data = get_weather_data_weekly(city, today)
 
-fancy_header('\n Acquired data!')
-progress_bar.progress(60)
 
 st.write(36 * "-")
 
-fancy_header('\n Loading the XGBoost model from the Hopsworks Model Registry')
-
 mr = project.get_model_registry()
 model = mr.get_best_model("aqi_model", "rmse", "min")
 model_dir = model.download()
 model = joblib.load(model_dir + "/aqi_model.pkl")
 
-fancy_header('\n Model loaded. Let\'s make predictions!')
-
-progress_bar.progress(80)
-
 st.sidebar.write("-" * 36)
 
+
 preds = model.predict(data_encoder(weekly_data)).astype(int)
 poll_level = get_aplevel(preds.T.reshape(-1, 1))
 
-next_week = [f"{(today + timedelta(days=d)).strftime('%Y-%m-%d')}, {(today + timedelta(days=d)).strftime('%A')}" for d in range(7)]
+next_week = [[(today + timedelta(days=d)).strftime('%Y-%m-%d'),(today + timedelta(days=d)).strftime('%A')] for d in range(1, 7)]
 
-df = pd.DataFrame(data=[preds, poll_level], index=["AQI", "Air pollution level"], columns=next_week)
+df = pd.DataFrame(data=[preds, poll_level], index=["AQI", "Air pollution level"], columns=[f"AQI Predictions for {next_day}" for next_day in next_week])
 
 st.write(df)
-progress_bar.progress(100)
+
 st.button("Re-run")