Update app.py

app.py

@@ -1,47 +1,68 @@
-import gradio as gr
+import streamlit as st
 import hopsworks
 import joblib
 import pandas as pd
-import numpy as np
-import json
-import time
-from datetime import timedelta, datetime
+import datetime
+from functions import get_weather_data_weekly, data_encoder, get_aplevel, get_color
+from PIL import Image
 
 
-from functions import *
+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 Vienna! 🌩')
+vienna_image = Image.open('vienna.jpg')
+st.image(vienna_image, use_column_width='auto')
+st.write(36 * "-")
+
+st.markdown("# This is a final project in the course ID2223 Scalable Machine Learning and Deep Learning :computer:")
+st.markdown("My task was to predict the Air Quality Index (AQI) for one city (I choose Vienna) based on different weather data (pressure, snow-and cloud-coverage, temperature, etc.).")
+st.markdown("For the full list of weather data, please click [here](https://visualcrossing.com/resources/documentation/weather-api/timeline-weather-api)")
+fancy_header('\n Connecting to Hopsworks Feature Store...')
 
 project = hopsworks.login()
-fs = project.get_feature_store()
-	
-
-def air_quality(city):
-    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)
+
+st.write("Successfully connected!✔️")
+
+st.write(36 * "-")
+fancy_header('\n Collecting the weather data from Vienna...')
+
+today = datetime.date.today()
+city = "vienna"
+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)
-	    
-    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")
-	    
-    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"
-	        
-        print(predictions)
-    return predictions
-	
-    
-demo = gr.Interface(fn=air_quality, title="Air quality predictor",
-description="Input a value to get next weeks AQI prediction for Helsinki", inputs="text", outputs="text")
-	
-
-	    
-if __name__ == "__main__":
-	demo.launch()
+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)
+
+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_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, 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.button("Re-run")