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