Update app.py

app.py

@@ -1,56 +1,42 @@
 import streamlit as st
+import gradio as gr
 import hopsworks
 import joblib
 import pandas as pd
-import datetime
-from datetime import timedelta
-from functions import *
-from PIL import Image
-
+import numpy as np
+import json
+import time
+from datetime import timedelta, datetime
 
-def fancy_header(text, font_size=24):
-    res = f'<p style="color:#ff5f27; font-size: {font_size}px;text-align:center">{text}</p>'
-    st.markdown(res, unsafe_allow_html=True)
 
-st.set_page_config(layout="wide")
-st.title('Air Quality Prediction Project for Helsinki! 🌩')
+from functions import *
 
-fancy_header('\n Connecting to Hopsworks Feature Store...')
 
 project = hopsworks.login()
+fs = project.get_feature_store()
+	
 
-st.write("Successfully connected!✔️")
-
-st.write(36 * "-")
-fancy_header('\n Collecting the weather data from Vienna...')
-
-today = datetime.now()
-city = "helsinki"
 weather_df = pd.DataFrame()
 for i in range(8):
     weather_data = get_weather_df([get_weather_data((datetime.now() + timedelta(days=i)).strftime("%Y-%m-%d"))])
     weather_df = weather_df.append(weather_data)
         
 weather_df = weather_df.drop(columns=["precipprob", "uvindex", "date","city","conditions"]).fillna(0)
-st.write("Successfully collected!✔️")
-
-st.write(36 * "-")
-
-fancy_header("Loading the fitted XGBoost model...")
+	    
 mr = project.get_model_registry()
 model = mr.get_model("lond_gradient_boost_model", version=1)
 model_dir = model.download()
 model = joblib.load(model_dir + "/lond_model.pkl")
-
-st.write("Succesfully loaded!✔️")
-st.sidebar.write("-" * 36)
-
-fancy_header("Making AQI predictions for the next 7 days")
-
+	    
 preds = model.predict(weather_df)
+	
+predictions = ''
+for k in range(7):
+    predictions += "Predicted AQI on  " + (datetime.now() + timedelta(days=k)).strftime('%Y-%m-%d') + ":      " + str(int(preds[k]))+"\n"
+	    
 
 air_pollution_level = ['Good', 'Moderate', 'Unhealthy for sensitive Groups','Unhealthy' ,'Very Unhealthy', 'Hazardous']
-poll_level = get_aplevel(preds.T.reshape(-1, 1))
+poll_level = get_aplevel(preds.T.reshape(-1, 1), air_pollution_level)
 
 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]
@@ -61,3 +47,4 @@ st.write("Here they are!")
 st.dataframe(df.style.apply(get_color, subset=(["Air pollution level"], slice(None))))
 
 st.button("Re-run")
+