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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") | |