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import pandas as pd
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import numpy as np
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import streamlit as st
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import pickle
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with open('model_v2.pkl', 'rb') as file:
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model = pickle.load(file)
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def run():
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st.title('FIFA Overall Rating Score')
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st.write('---')
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link_gambar = 'data:image/jpeg;base64,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'
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st.image(link_gambar,
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caption = 'source: google.com')
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st.write('Halaman ini berisi model prediksi yang mampu menghasilkan overall rating dari seorang pemain')
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with st.form(key='form parameters'):
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nama = st.text_input('Nama Pemain: ')
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age = st.number_input('Usia Pemain: ', min_value=15, max_value=54, value=20)
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height = st.slider('Tinggi Pemain: ', min_value=155, max_value=210, value=175)
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weight = st.slider('Berat Pemain: ', min_value=49, max_value=110, value=75)
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ValueEUR = st.number_input('Harga Pemain: ', value=1500, step=100)
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attack_work_rate = st.selectbox('Attack Level: ', ('Low','Medium', 'High'))
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defensive_work_rate = st.selectbox('Defense Level: ', ('Low','Medium', 'High'))
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pace_total=st.number_input('Pace Total: ', min_value=0, max_value=100)
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shootingtotal = st.number_input('Shooting Total: ', min_value=0, max_value=100)
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passingtotal = st.number_input('Pasing Total: ', min_value=0, max_value=100)
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dribblingtotal = st.number_input('Dribbling Total: ', min_value=0, max_value=100)
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deffendingtotal = st.number_input('Deffending Total: ', min_value=0, max_value=100)
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physicalitytotal = st.number_input('Physicality Total: ', min_value=0, max_value=100)
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submit=st.form_submit_button('Prediksi')
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data_raw={
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'Name': nama,
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'Age': age,
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'Height': height,
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'Weight': weight,
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'ValueEUR': ValueEUR,
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'AttackingWorkRate': attack_work_rate,
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'DefensiveWorkRate': defensive_work_rate,
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'PaceTotal': pace_total,
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'ShootingTotal': shootingtotal,
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'PassingTotal': passingtotal,
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'DribblingTotal': dribblingtotal,
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'DefendingTotal': deffendingtotal,
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'PhysicalityTotal': physicalitytotal
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}
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data = pd.DataFrame([data_raw])
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st.dataframe(data)
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if submit:
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result = model.predict(data)
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st.write(f'### Hasil Overall Score Pemain {nama}: {result[0]:.2f}')
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if __name__ == '__main__':
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run() |