Delete app.py

app.py

@@ -1,71 +0,0 @@
-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()
-city = "Beijing"
-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 * "-")
-
-mr = project.get_model_registry()
-model = mr.get_model("air_quality_modal_choosed_xgb", version=1)
-model_dir = model.download()
-model = joblib.load(model_dir + "/air_quality_model_choosed_xgb.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)
-
-
-st.write(df)
-
-st.button("Re-run")