Upload app.py

app.py

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+import streamlit as st
+import hopsworks
+import joblib
+from datetime import date
+import pandas as pd
+from datetime import timedelta, datetime
+from functions import *
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+import folium
+from streamlit_folium import st_folium, folium_static
+import json
+import time
+from branca.element import Figure
+
+
+def fancy_header(text, font_size=24):
+    res = f'<p style="color:#ff5f72; 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🌩')
+
+st.write(36 * "-")
+fancy_header('\n Connecting to Hopsworks Feature Store...')
+
+project = hopsworks.login()
+
+st.write("Successfully connected!✔️")
+
+st.write(36 * "-")
+fancy_header('\n Getting data from Feature Store...')
+
+today = date.today()
+##########################城市####################
+city = "Guangzhou"
+df_weather = get_weather_data_weekly(city, today)
+df_weather.date = df_weather.date.apply(timestamp_2_time)
+df_weather_x = df_weather.drop(columns=["date"]).fillna(0)
+df_weather_nn=np.array(df_weather_x)
+scaler = StandardScaler()
+scaler.fit(df_weather_x)
+
+df_weather_use=scaler.transform(df_weather_x)
+
+df_weather_use_1= pd.DataFrame(df_weather_use)
+
+#preds_zzz = model.predict(df_weather_use_1).astype(int)
+
+st.write(36 * "-")
+
+########################根据模型名称进行修改#####################
+mr = project.get_model_registry()
+model = mr.get_model("AIR_Forecast_Model", version=9)
+model_dir = model.download()
+model = joblib.load(model_dir + "/AIR_Forecast_Model.pkl")
+
+st.write("-" * 36)
+
+
+preds = model.predict(df_weather_use_1).astype(int)
+pollution_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(8)]
+
+df = pd.DataFrame(data=[preds, pollution_level], index=["AQI", "Air pollution level"], columns=next_week)
+###########如果报错AQI这个修改成preds的标签##################
+
+st.write(df)
+
+st.button("Re-run")
+
+