first deploy

app.py

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+import streamlit as st
+import eda1
+import prediction
+
+page = st.sidebar.selectbox('Pilih Halaman : ', ('EDA', 'Prediction'))
+
+if page == 'EDA' : 
+    eda1.run()
+else:
+    prediction.run()

eda1.py

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+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import plotly.express as px
+from PIL import Image
+
+def run():
+    #Membuat title
+    st.title('FIFA 2022 Player Rating Prediction')
+
+    #Membuat subheader
+    st.subheader('EDA untuk Analisa Dataset FIFA 2022')
+
+    #Tambahkan gambar
+    image = Image.open('bola.jpg')
+    st.image(image, caption = 'FIFA 2022')
+
+    #Menambahkan deskripsi
+    st.write('Page ini dibuat oleh Bani')
+    st.write('**Mardhya Malik**')
+    st.write('*Mardhya Malik*')
+    st.write('# Mardhya Malik')
+    st.write('## Mardhya Malik')
+    st.write('### Mardhya Malik')
+
+    #Membuat garis
+    st.markdown('----')
+
+    #Masukkan pandas dataframe
+
+    #Show dataframe
+    df = pd.read_csv('https://raw.githubusercontent.com/FTDS-learning-materials/phase-1/master/w1/P1W1D1PM%20-%20Machine%20Learning%20Problem%20Framing.csv')
+    st.dataframe(df)
+
+    #Membuat bar plot
+    st.write('#### Plot AttackingWorkRate')
+    fig = plt.figure(figsize=(15,5))
+    sns.countplot(x='AttackingWorkRate', data = df)
+    st.pyplot(fig)
+
+    #Membuat histogram
+    st.write('#### Histogram of Rating')
+    fig = plt.figure(figsize=(15,5))
+    sns.histplot(df['Overall'], bins = 30, kde = True)
+    st.pyplot(fig)
+
+    #membuat histogram berdasarkan inputan user
+    st.write('#### Histogram berdasarkan input user')
+    #kalo mau pake radio button, ganti selectbox jadi radio
+    option = st.selectbox('Pilih Column : ', ('Age', 'Weight', 'Height', 'ShootingTotal'))
+    fig = plt.figure(figsize= (15,5))
+    sns.histplot(df[option], bins = 30, kde = True)
+    st.pyplot(fig)
+
+    #Membuat Plotly plot
+
+    st.write('#### Plotly Plot - ValueEUR vs Overall')
+    fig = px.scatter(df, x = 'ValueEUR', y = 'Overall', hover_data = ['Name', 'Age'])
+    st.plotly_chart(fig)
+
+if __name__ == '__main__':
+    run()

list_cat_cols.txt

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+["AttackingWorkRate", "DefensiveWorkRate"]

list_num_cols.txt

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+["Age", "Height", "Weight", "Price", "PaceTotal", "ShootingTotal", "PassingTotal", "DribblingTotal", "DefendingTotal", "PhysicalityTotal"]

model_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e95575e4f4325a8b2cc3751e09de7f29dec00be64588df3e060c44b17ef7e3d
+size 572

model_lin_reg.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:61b5177c7282ce0ad6f60601b1b9c4b0e3b25ea7fb558db8f240077726a5b47a
+size 595

model_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:886a94e22bb659265592ec555c491e70ab234b9e3aa33b0f2546b5d69ea2f0e6
+size 1096

prediction.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import json
+
+#Load All Files
+
+with open('list_num_cols.txt', 'r') as file_1:
+  list_num_col = json.load(file_1)
+
+with open('list_cat_cols.txt', 'r') as file_2:
+  list_cat_col = json.load(file_2)
+
+with open('model_scaler.pkl', 'rb') as file_3:
+  model_scaler = pickle.load(file_3)
+
+with open('model_encoder.pkl', 'rb') as file_4:
+  model_encoder = pickle.load(file_4)
+
+with open('model_lin_reg.pkl', 'rb') as file_5:
+  model_lin_reg = pickle.load(file_5)
+
+def run():
+
+    with st.form('form_fifa_2022'):
+        #Field Nama
+        name = st.text_input('Name', value = '')
+        #Field Umur
+        age = st.number_input('Age', min_value = 16, max_value = 60, value = 25, step = 1, help = 'Usia Pemain')
+        #Field Tinggi badan
+        height = st.slider('Height', 100, 250, 170)
+        #Field Weight
+        weight = st.number_input('weight', 50, 150, 70)
+        #field price
+        price = st.number_input('Price', value = 0)
+
+        st.markdown('----')
+
+        #Field Attacking Work Rate
+        attacking_work_rate = st.selectbox('Attacking Work Rate', ('Low', 'Medium', 'High'), index = 1)
+        #Field Defensive Work Rate
+        devensive_work_rate = st.selectbox('Devensive Work Rate', ('Low', 'Medium', 'High'), index = 1)
+        #Field Pace Total
+        pace_total = st.number_input('Pace', min_value = 0, max_value=100, value = 50)
+        #Field Shooting Total
+        shooting_total = st.number_input('Shooting', min_value = 0, max_value=100, value = 50)
+        #Field Passing Total
+        passing_total = st.number_input('Passing', min_value = 0, max_value=100, value = 50)
+        #Field Dribbling Total
+        dribbling_total = st.number_input('Dribbling', min_value = 0, max_value=100, value = 50)
+        #Field Defending Total
+        defending_total = st.number_input('Defending', min_value = 0, max_value=100, value = 50)
+        #Field Physicality Total
+        physicality = st.number_input('Physicality', min_value = 0, max_value=100, value = 50)
+
+        #bikin submit button
+        submitted = st.form_submit_button('Predict')
+
+    #Inference
+    data_inf = {
+        'Name' : name,
+        'Age' : age,
+        'Height' : height,
+        'Weight' : weight,
+        'Price' : price,
+        'AttackingWorkRate' : attacking_work_rate,
+        'DefensiveWorkRate' : devensive_work_rate,
+        'PaceTotal' :pace_total,
+        'ShootingTotal': shooting_total,
+        'PassingTotal' : passing_total,
+        'DribblingTotal' :dribbling_total,
+        'DefendingTotal' :defending_total,
+        'PhysicalityTotal':physicality,
+    }
+
+    data_inf = pd.DataFrame([data_inf])
+    st.dataframe(data_inf)
+
+    #Logic ketika predic button ditekan
+
+    if submitted:
+        #split between numerical and categorical columns
+        data_inf_num = data_inf[list_num_col]
+        data_inf_cat = data_inf[list_cat_col]
+        
+        #Scaling & Encoding
+        data_inf_num_scaled = model_scaler.transform(data_inf_num)
+        data_inf_cat_encoded = model_encoder.transform(data_inf_cat)
+        data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis = 1)
+
+        #predict using linear reg model
+        y_pred_inf = model_lin_reg.predict(data_inf_final)
+        
+        st.write('## Rating : ', str(int(y_pred_inf)))
+
+if __name__ == '__main__':
+   run()

requirements.txt

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+streamlit
+pandas
+seaborn
+matplotlib
+numpy
+plotly
+pillow
+scikit-learn==1.2.2