Delete strApp.py

strApp.py

@@ -1,110 +0,0 @@
-import streamlit as st
-import hopsworks
-import os
-import joblib
-import pandas as pd
-import dataframe_image as dfi
-import streamlit as st
-import joblib
-import numpy as np
-import folium
-from streamlit_folium import st_folium, folium_static
-import json
-import time
-from datetime import timedelta, datetime
-from branca.element import Figure
-from API_call_visual_crossing import *
-from API_call_AQI  import *
-#----------------------------------HEADER + TITLE
-def fancy_header(text, font_size=24):
-    res = f'<span style="color:#ff5f27; font-size: {font_size}px;">{text}</span>'
-    st.markdown(res, unsafe_allow_html=True )
-st.title('⛅️Air Quality Prediction Project🌩')
-
-progress_bar = st.sidebar.header('⚙️ Working Progress')
-progress_bar = st.sidebar.progress(0)
-st.write(36 * "-")
-
-#-----------------------------------CONNECTING TO HOPSWORKS 
-fancy_header('\n📡 Connecting to Hopsworks Feature Store...')
-
-project = hopsworks.login()
-
-st.write("Successfully connected!✔️")
-progress_bar.progress(20)
-#------------------------------------  PREDICTION
-
-st.write(36 * "-")
-fancy_header('\n☁️ Getting data for predict ...')
-city = 'Paris'
-date = ''
-
-
-WEATHER_API_KEY ="V7VHELUHHCY7JPQ8332XPR8A9"# = os.getenv('WEATHER_API_KEY')
-
-df_weather = get_weather_df(city, date, WEATHER_API_KEY)
-next_7_days = df_weather["date"]
-data_7_days = df_weather.drop('date', axis=1)
-
-#mr = project.get_model_registry()
-#model = mr.get_best_model("aqi_model", "r2", "max")
-#model_dir = model.download()
-#model = joblib.load(model_dir + "/aqi_model.pkl")
-
-
-
-from urllib.request import urlopen
-#from sklearn.externals import joblib
-import joblib
-
-model = joblib.load(urlopen('https://github.com/reyrobs/Scalable_ML_Project_AQ/raw/main/aqi_model.pkl'))
-
-pred_7_days = model.predict(data_7_days)
-
-df = pd.DataFrame(data=pred_7_days, index=next_7_days, columns=[f"AQI Predictions for the next 7 days"], dtype=int)
-
-
-
-progress_bar.progress(60)
-#----------------------------------
-
-st.write(36 * "-")
-fancy_header(f"🗺 Processing the map...")
-
-fig = Figure(width=550,height=350)
-
-my_map = folium.Map(location=[58, 20], zoom_start=3.71)
-fig.add_child(my_map)
-folium.TileLayer('Stamen Terrain').add_to(my_map)
-folium.TileLayer('Stamen Toner').add_to(my_map)
-folium.TileLayer('Stamen Water Color').add_to(my_map)
-folium.TileLayer('cartodbpositron').add_to(my_map)
-folium.TileLayer('cartodbdark_matter').add_to(my_map)
-folium.LayerControl().add_to(my_map)
-
-#Determining Paris
-coords = [48.862, 2.346]
-#-----
-city = "Pris"
-country="France"
-text=""
-
-#-----
-text +=str(pred_7_days[0])
-#-----
-st.sidebar.write(df)
-
-folium.Marker(
-        location=coords, popup=text, tooltip=f"<strong>{city}</strong>"
-    ).add_to(my_map)
-
-
-# call to render Folium map in Streamlit
-folium_static(my_map)
-progress_bar.progress(80)
-st.sidebar.write("-" * 36)
-
-
-st.sidebar.write(df)
-progress_bar.progress(100)
-st.button("Re-run")