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app.py
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import streamlit as st
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from streamlit_option_menu import option_menu
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import home
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import eda
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import prediction
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from PIL import Image
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# navigation = st.sidebar.selectbox('Select Page :', ('EDA', 'Predict Credit Card Default'))
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# if navigation == 'EDA':
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# eda.runEDA()
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# else:
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# prediction.runPredictor()
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# Set page title and icon
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# st.set_page_config(page_title='Final Project', page_icon='⭐')
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# Create sidebar navigation
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# st.markdown(
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# f"""
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# <style>
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# [data-testid="stSidebar"] {{
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# background-image: url(https://raw.githubusercontent.com/FTDS-assignment-bay/main/assets/ChurnGuardian-Logo-Transparants.png);
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# background-repeat: no-repeat;
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# padding-top: 20px;
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# background-position: 10px 50px;
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# background-size: 310px 85px;
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# }}
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# </style>
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# """,
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# unsafe_allow_html=True,
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# )
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st.set_page_config(
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page_title='Telco Customer Churn and Segmentation',
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layout='centered', #wide
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initial_sidebar_state='expanded'
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)
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# st.title('Telco Customer Churn and Segmentation')
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# image = Image.open('images\logo_crop_clean.png')
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# image = Image.open('images\logo_grey_clean.png')
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col1, col2, col3 = st.columns([10, 1, 5])
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image_url = 'https://raw.githubusercontent.com/FTDS-assignment-bay/p2-final-project-ftds-001-sby-group-001/main/images/logo_crop_clean.png'
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# qr_url = 'https://raw.githubusercontent.com/FTDS-assignment-bay/p2-final-project-ftds-001-sby-group-001/main/images/qr_link.png'
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col1.image(image_url, width=450)
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# col2.write('')
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# col3.image(qr_url, width=150)
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st.write('# Customer Churn and Segmentation')
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st.subheader('Predict churn and retain your customer!')
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st.markdown('---')
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selected = option_menu(None, ["About", "EDA", "Predict"],
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icons=['house', 'file-earmark-bar-graph', 'search'],
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menu_icon="cast", default_index=0, orientation="horizontal",
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styles={
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"icon": {"color": "orange", "font-size": "15px"},
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"nav-link": {"font-size": "15px", "text-align": "left", "margin":"1px", "--hover-color": "#eee"},
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"nav-link-selected": {"background-color": "grey"},
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}
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)
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if selected == 'About':
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home.run()
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elif selected == 'EDA':
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eda.run()
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else:
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prediction.run()
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#streamlit run app.py
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