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import streamlit as st |
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import hopsworks |
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import joblib |
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from datetime import date |
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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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import numpy as np |
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from sklearn.preprocessing import StandardScaler |
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def fancy_header(text, font_size=24): |
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res = f'<p style="color:#ff5f72; font-size: {font_size}px; text-align:center;">{text}</p>' |
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st.markdown(res, unsafe_allow_html=True) |
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st.set_page_config(layout="wide") |
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st.title('Air Quality Prediction Project🌩') |
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st.write(9 * "-") |
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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(18 * "-") |
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fancy_header('\n Getting data from Feature Store...') |
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today = date.today() |
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city = "Beijing" |
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df_weather = get_weather_data_weekly(city, today) |
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df_weather.date = df_weather.date.apply(timestamp_2_time) |
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df_weather_x = df_weather.drop(columns=["date"]).fillna(0) |
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df_weather_nn=np.array(df_weather_x) |
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scaler = StandardScaler() |
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scaler.fit(df_weather_x) |
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df_weather_use=scaler.transform(df_weather_x) |
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df_weather_use_1= pd.DataFrame(df_weather_use) |
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st.write(27 * "-") |
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mr = project.get_model_registry() |
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model = mr.get_model("air_quality_modal_choosed", version=1) |
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model_dir = model.download() |
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model = joblib.load(model_dir + "/air_quality_model_choosed.pkl") |
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st.write("-" * 36) |
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preds = model.predict(df_weather_use_1).astype(int) |
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poll_level = get_aplevel(preds.T.reshape(-1, 1)) |
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next_week = [f"{(today + timedelta(days=d)).strftime('%Y-%m-%d')},{(today + timedelta(days=d)).strftime('%A')}" for d in range(8)] |
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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") |
