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add st.cache_resource
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‎pages/2_📘_Flood.py‎

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import streamlit as st
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import pandas as pd
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import pickle
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import datetime
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import joblib
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from keras.models import load_model
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from streamlit_extras.metric_cards import style_metric_cards
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from streamlit_extras.stylable_container import stylable_container
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from tools import *
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# page config
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page_config(title="Flood")
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# function
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def plot_tab1(range, data_plot):
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st.caption(f"Here's the height chart for {range}")
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st.area_chart(data=data_plot,
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y="height (cm)")
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def convert_df(df):
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return df.to_csv().encode('utf-8')
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def download_csv(data_df, name):
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st.dataframe(data_df)
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st.caption("Need the data?")
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if st.download_button(label="Download data as CSV",
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data=convert_df(data_df),
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file_name=name,
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mime='text/csv'):
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st.toast('Download complete!', icon = "✅")
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def data_plot_tab2(history, pred):
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# history
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data_history = history.reset_index()
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data_history['index'] = data_history['index'] - 287
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data_history = data_history.set_index('index')
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data_plot_X = data_history[['height (cm)']]
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data_plot_X = data_plot_X.rename(columns = {'height (cm)':'height_history (cm)'})
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# future
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data_pred = pred.reset_index()
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data_pred['index'] = data_pred['index'] + 1
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data_pred = data_pred.set_index('index')
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#concat
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data_plot_2 = pd.concat([data_plot_X, data_pred])
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return data_history, data_pred, data_plot_2
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@st.cache_resource(ttl=3600)
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def load_data_banjir():
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with open("dataset/data_simulasi_banjir.pkl", 'rb') as file:
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data = pickle.load(file)
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return data
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@st.cache_resource(ttl=3600)
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def load_scaler_banjir():
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scaler_X_klas = joblib.load('scaler/scaler_X_klasifikasi_banjir.save')
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scaler_X_pred = joblib.load('scaler/scaler_X_prediksi_banjir.save')
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scaler_y_pred = joblib.load('scaler/scaler_y_prediksi_banjir.save')
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return scaler_X_klas, scaler_X_pred, scaler_y_pred
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@st.cache_resource(ttl=3600)
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def load_model_banjir():
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model_klas = load_model('model/model_klasifikasi_banjir.h5')
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model_pred = load_model('model/model_prediksi_banjir.h5')
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return model_klas, model_pred
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# load data, scaler, model
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data_banjir = load_data_banjir()
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scaler_X_klasifikasi, scaler_X_prediksi, scaler_y_prediksi = load_scaler_banjir()
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model_klasifikasi_banjir, model_prediksi_banjir = load_model_banjir()
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# title
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st.title("Flood")
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# tabs
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tab1, tab2 = st.tabs(["**Dashboard**", "**Prediction**"])
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# konten tab 1 (Dashboard)
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with tab1:
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col_datetime1, col_metric1, col_metric2= st.columns(3)
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# datetime
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with col_datetime1:
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date1 = st.date_input("d",
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value=datetime.datetime(2022,9,30),
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min_value=datetime.datetime(2022,9,9),
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max_value=datetime.datetime(2022,9,30),
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label_visibility="collapsed")
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time1 = st.time_input("t",
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value=datetime.time(10,30),
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step=600,
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label_visibility="collapsed")
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st.caption("Set date and time 👆")
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# data tab 1
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datetime1 = str(date1)+" "+str(time1)
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data_tab1 = data_banjir.loc[data_banjir['datetime']<=datetime1].reset_index(drop=True)
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data_metric = data_tab1[['datetime','height (cm)']].head(2)
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data_metric = data_metric.rename(columns={'datetime':'date',
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'height (cm)':'height'})
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data_metric = klasifikasi_banjir(X=data_metric, # get status
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scaler_X=scaler_X_klasifikasi,
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model=model_klasifikasi_banjir)
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metric1_value = data_metric.height[0].round(2)
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metric1_delta = (data_metric.height[0] - data_metric.height[1]).round(2)
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metric2_value = data_metric.status_pred[0]
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metric2_delta = int(metric2_value - data_metric.status_pred[1])
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# metric 1
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with col_metric1:
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st.metric(label="Last height:",
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value=f"{metric1_value} cm",
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delta=metric1_delta,
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delta_color="inverse")
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# metric 2
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with col_metric2:
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st.metric(label="Last status:",
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value=f"Siaga {metric2_value}",
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delta=metric2_delta,
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delta_color="off")
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# metric style
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style_metric_cards(background_color="#f0f2f6",
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border_left_color="#E84545",
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border_size_px=0,
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border_radius_px=10,
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box_shadow=False)
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# plot
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data_plot1 = data_tab1[['datetime','height (cm)']].set_index("datetime") # set 'date' as index
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col_plot1,col_plot2 = st.columns([2,1])
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col_plot1.subheader("Visualizing height changes:")
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data_plot1_range = col_plot2.selectbox("Plot range:",
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("last day","last week","last month","last year","all data"),
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label_visibility="collapsed")
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if data_plot1_range == "last day": # last day
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plot_tab1(range="the last day",
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data_plot=data_plot1.head(144))
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elif data_plot1_range == "last week": # last week
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plot_tab1(range="the last week",
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data_plot=data_plot1.head(1008))
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elif data_plot1_range == "last month": # last month
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plot_tab1(range="the last month",
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data_plot=data_plot1.head(4320))
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elif data_plot1_range == "last year": # last year
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plot_tab1(range="the last year",
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data_plot=data_plot1.head(52560))
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elif data_plot1_range == "all data": # all data
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plot_tab1(range="all data",
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data_plot=data_plot1)
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# data history
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col_data1,col_data2 = st.columns([2,1])
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col_data1.subheader("Historical data table:")
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data_tab1_range = col_data2.selectbox("Data range:",
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("last day","last week","last month","last year","all data"),
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label_visibility="collapsed")
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if data_tab1_range == "last day": # last day
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download_csv(data_df=data_tab1.head(144),
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name="last_day.csv")
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elif data_tab1_range == "last week": # last week
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download_csv(data_df=data_tab1.head(1008),
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name="last_week.csv")
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elif data_tab1_range == "last month": # last month
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download_csv(data_df=data_tab1.head(4320),
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name="last_month.csv")
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elif data_tab1_range == "last year": # last year
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download_csv(data_df=data_tab1.head(52560),
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name="last_year.csv")
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elif data_tab1_range == "all data": # all data
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download_csv(data_df=data_tab1,
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name="all_data.csv")
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with tab2:
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col_date2, col_time2, col_caption2= st.columns([1,1,1])
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# datetime
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date2 = col_date2.date_input("Date:",
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value=datetime.datetime(2022,9,30),
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min_value=datetime.datetime(2022,9,11),
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max_value=datetime.datetime(2022,9,30),
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label_visibility="collapsed")
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time2 = col_time2.time_input("Time:",
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value=datetime.time(4,30),
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step=600,
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label_visibility="collapsed")
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col_caption2.caption("👈 Set date and time")
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datetime2 = str(date2)+" "+str(time2)
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# proses
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col_button1,col_button2= st.columns([1,2])
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if col_button1.button("Predict ", use_container_width=True,type="primary"):
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col_button2.caption(":green[Predicted!]")
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# klasifikasi
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X_klasifikasi = data_banjir.loc[data_banjir['datetime'] == datetime2].reset_index(drop=True)
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X_klasifikasi = X_klasifikasi[['datetime','height (cm)']].rename(columns={'datetime':'date',
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'height (cm)':'height'})
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y_klasifikasi = klasifikasi_banjir(X=X_klasifikasi,
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scaler_X=scaler_X_klasifikasi,
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model=model_klasifikasi_banjir)
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# prediksi
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X_prediksi = get_X_prediksi(data=data_banjir,
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date=datetime2)
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y_pred = prediksi_banjir(data=data_banjir,
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date=datetime2,
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X=X_prediksi,
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scaler_X=scaler_X_prediksi,
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scaler_y=scaler_y_prediksi,
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model=model_prediksi_banjir)
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y_pred_status = klasifikasi_banjir(X=y_pred,
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scaler_X=scaler_X_klasifikasi,
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model=model_klasifikasi_banjir)
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y_pred_status = y_pred_status.rename(columns = {'height':'height_pred (cm)'})
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# info
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with stylable_container(key="container_with_border",
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css_styles="""{
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border: 3px solid #dedede;
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border-radius: 0.5rem;
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padding: calc(1em - 1px)}"""):
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get_info_banjir(y_klasifikasi=y_klasifikasi,
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y_pred_status=y_pred_status)
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# plot grafik
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st.write("**Plot prediction data 📈**")
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if y_pred_status.shape[1] == 4:
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data_historical,data_future,data_plot2 = data_plot_tab2(history=X_prediksi,
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pred=y_pred_status)
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st.area_chart(data=data_plot2,
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y=["height_history (cm)",
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"height_true (cm)",
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"height_pred (cm)"])
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elif y_pred_status.shape[1] == 2:
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data_historical,data_future,data_plot2 = data_plot_tab2(history=X_prediksi,
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pred=y_pred_status)
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st.area_chart(data=data_plot2,
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y=["height_history (cm)",
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"height_pred (cm)"])
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# data
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with st.expander("**Explore prediction data**"):
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st.write("**Historical data:**")
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st.dataframe(data_historical)
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st.write("**Future/predicted data:**")
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st.dataframe(data_future)
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"---"
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st.button("Click to reset")

‎pages/3_📙_Earthquake.py‎

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import streamlit as st
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import pandas as pd
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import joblib
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import random
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from keras.models import load_model
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from tools import page_config, klasifikasi_gempa, get_info_gempa
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# page config
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page_config(title="Earthquake")
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# initialize state
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if "idx" not in st.session_state:
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st.session_state.idx = random.randint(0,500)
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# function
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def reset_cb():
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st.session_state.cb1 = False
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def reset_num_cb():
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st.session_state.cb1 = False
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st.session_state.v1 = 0.00
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st.session_state.v2 = 0.00
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st.session_state.v3 = 0.00
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st.session_state.v4 = 0.00
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st.session_state.v5 = 0.00
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st.session_state.v6 = 0.00
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def random_num():
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st.session_state.cb1 = False
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st.session_state.v1 = round(random.uniform(-2.00,2.00),2)
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st.session_state.v2 = round(random.uniform(-2.00,2.00),2)
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st.session_state.v3 = round(random.uniform(-2.00,2.00),2)
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st.session_state.v4 = round(random.uniform(-250.00,250.00),2)
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st.session_state.v5 = round(random.uniform(-250.00,250.00),2)
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st.session_state.v6 = round(random.uniform(-250.00,250.00),2)
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def random_idx():
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st.session_state.idx = random.randint(0,500)
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def reset_data_cb():
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st.session_state.cb2 = False
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@st.cache_resource(ttl=3600)
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def load_data_gempa():
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data = pd.read_csv('dataset/data_simulasi_gempa.csv')
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return data
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@st.cache_resource(ttl=3600)
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def load_scaler_gempa():
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scaler = joblib.load('scaler/scaler_X_klasifikasi_gempa.save')
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return scaler
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@st.cache_resource(ttl=3600)
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def load_model_gempa():
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model = load_model('model/model_klasifikasi_gempa.h5')
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return model
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# load data, scaler, model
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data_gempa = load_data_gempa()
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scaler_X_gempa = load_scaler_gempa()
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model_klasifikasi_gempa = load_model_gempa()
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# title and tabs
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st.title("Earthquake")
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tab1_gempa, tab2_gempa= st.tabs(["**Classification (input)**",
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"**Classification (dataset)**"])
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# tab1
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with tab1_gempa:
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# konten
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st.write("**Please input values,**")
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st.button("Click to get random values", on_click=random_num)
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col_value1, col_value2 = st.columns(2)
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with col_value1:
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value1=st.number_input("**aX/accelerometer-X** (g):", key="v1",
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min_value=-2.00,
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max_value=2.00,
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step=0.01,
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on_change=reset_cb)
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value2=st.number_input("**aY/accelerometer-Y** (g):", key="v2",
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min_value=-2.00,
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max_value=2.00,
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step=0.01,
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on_change=reset_cb)
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value3=st.number_input("**aZ/accelerometer-Z** (g):", key="v3",
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min_value=-2.00,
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max_value=2.00,
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step=0.01,
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on_change=reset_cb)
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with col_value2:
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value4=st.number_input("**gX/gyroscope-X** (deg/s):", key="v4",
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min_value=-250.00,
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max_value=250.00,
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step=0.01,
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on_change=reset_cb)
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value5=st.number_input("**gY/gyroscope-Y** (deg/s):", key="v5",
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min_value=-250.00,
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max_value=250.00,
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step=0.01,
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on_change=reset_cb)
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value6=st.number_input("**gZ/gyroscope-Z** (deg/s):", key="v6",
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min_value=-250.00,
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max_value=250.00,
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step=0.01,
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on_change=reset_cb)
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# button
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col_button11,col_button12= st.columns([1,2])
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confirm_checkbox1 = col_button11.checkbox("Confirm values?",
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key="cb1")
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if confirm_checkbox1:
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col_button12.caption(":green[Confirmed!]")
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df_X = pd.DataFrame([[value1,value2,value3,value4,value5,value6]],
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columns=['aX','aY','aZ',
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'gX','gY','gZ'])
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if col_button11.button("Classify", use_container_width=True,type="primary"):
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col_button12.caption(":green[Classified!]")
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y_pred=klasifikasi_gempa(X=df_X,
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scaler_X=scaler_X_gempa,
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model=model_klasifikasi_gempa)
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get_info_gempa(data=y_pred)
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"---"
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st.button("Click to reset", on_click=reset_num_cb)
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# tab2
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with tab2_gempa:
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st.write("**Here's a data sample for you,**")
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st.caption(f"index-{st.session_state.idx}")
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data_gempa_sample = data_gempa[st.session_state.idx:st.session_state.idx+1].reset_index(drop=True)
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col_sample_data, col_sample_button = st.columns([3,1])
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col_sample_data.dataframe(data_gempa_sample, use_container_width=True)
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col_sample_button.caption("Resample data?")
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col_sample_button.button("Resample", on_click=random_idx)
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col_button21,col_button22= st.columns([1,2])
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if col_button21.checkbox("Confirm data?", key="cb2"):
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col_button22.caption(":green[Confirmed!]")
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data_gempa_sample = data_gempa_sample.rename(columns = {'aX (g)':'aX',
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'aY (g)':'aY',
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'aZ (g)':'aZ',
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'gX (deg/s)':'gX',
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'gY (deg/s)':'gY',
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'gZ (deg/s)':'gZ'})
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if col_button21.button("Classify ", use_container_width=True,type="primary"):
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col_button22.caption(":green[Classified!]")
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y_pred2=klasifikasi_gempa(X=data_gempa_sample,
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scaler_X=scaler_X_gempa,
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model=model_klasifikasi_gempa)
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get_info_gempa(data=y_pred2)
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"---"
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st.button("Click to reset", on_click=reset_data_cb)

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