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Update app.py
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app.py
CHANGED
@@ -2,72 +2,90 @@ import streamlit as st
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import hopsworks
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import joblib
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import pandas as pd
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import datetime
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from functions import *
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import pytz
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st.set_page_config(layout="wide")
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st.title('AQI prediction for Beijing in next week')
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project = hopsworks.login()
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# get Hopsworks Model Registry
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#mr = project.get_model_registry()
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# get model object
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#model = mr.get_model("gradient_boost_model", version=1)
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#model_dir = model.download()
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#model = joblib.load(model_dir + "/model.pkl")
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feature_view = fs.get_feature_view(
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name = 'hel_air_fv1',
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version = 1
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)
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#start_time = int(start_date.timestamp()) * 1000
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X = feature_view.get_batch_data(start_time=1670194800000)
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print(X.tail(10))
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preds=model.predict(X)
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#preds=model.predict(weekly_data)
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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(7)]
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print(
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print(preds)
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aqi_level = encoder_range(preds.T.reshape(-1, 1))
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#df = pd.DataFrame(data=[map(int,preds), aqi_level], index=["aqi","Air Pollution Level"], columns=next_week)
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print(aqi_level)
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st
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st.button("Re-run")
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import hopsworks
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import joblib
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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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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 = datetime.date.today()
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weekly_data = get_weather_data_weekly(today)
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st.write(27 * "-")
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mr = project.get_model_registry()
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# get Hopsworks Model Registry
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mr = project.get_model_registry()
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# get model object
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model = mr.get_model("aqi_model_gb", version=1)
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model_dir = model.download()
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model = joblib.load(model_dir + "/gb_model.pkl")
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weekly_data['aqi'] = 0
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weekly_data['city'] = 0
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weekly_data = data_encoder(weekly_data)
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weekly_data.drop(['tempmax'], inplace = True, axis = 1)
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weekly_data.drop('tempmin', inplace = True, axis = 1)
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weekly_data.drop('feelslikemax', inplace = True, axis = 1)
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weekly_data.drop('feelslikemin', inplace = True, axis = 1)
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weekly_data.drop('feelslike', inplace = True, axis = 1)
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weekly_data.drop('dew', inplace = True, axis = 1)
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weekly_data.drop('precipprob', inplace = True, axis = 1)
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weekly_data.drop('precipcover', inplace = True, axis = 1)
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weekly_data.drop('snow', inplace = True, axis = 1)
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weekly_data.drop('snowdepth', inplace = True, axis = 1)
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weekly_data.drop('windgust', inplace = True, axis = 1)
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weekly_data.drop('windspeed', inplace = True, axis = 1)
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weekly_data.drop('winddir', inplace = True, axis = 1)
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weekly_data.drop('solarradiation', inplace = True, axis = 1)
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weekly_data.drop('solarenergy', inplace = True, axis = 1)
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weekly_data.drop('pressure', inplace = True, axis = 1)
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print(weekly_data)
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preds=model.predict(weekly_data)
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#model = get_model(project=project,
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# model_name="gradient_boost_model",
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# evaluation_metric="f1_score",
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# sort_metrics_by="max")
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#print("here")
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#print(model)
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#model_dir = model.download()
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#model = joblib.load(model_dir + "/model.pkl")
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st.write("-" * 36)
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#preds = model.predict(data_encoder(weekly_data)).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(7)]
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print(next_week)
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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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print(st)
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#st.button("Re-run")
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