Update prediction.py

prediction.py

@@ -1,79 +1,79 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import pickle
-import json
-
-with open('model.pkl', 'rb') as file_1: #rb =read binary
-  model = pickle.load(file_1)
-
-with open('scaler.pkl', 'rb') as file_2:
-  scaler = pickle.load(file_2)
-
-with open('encoder.pkl', 'rb') as file_3:
-  encoder = pickle.load(file_3)
-
-with open('num.txt', 'r') as file_4:
-  num = json.load(file_4)
-
-with open('cat.txt', 'r') as file_5:
-  cat = json.load(file_5)
-
-def run():
-  # membuat form
-  with st.form(key='form_fifa_2022'):
-    name = st.text_input('Name', placeholder='--nama-pemain--')
-    age = st.number_input('Age', min_value=16, max_value=52, value=24, step=1, help='Usia pemain')
-    height = st.number_input('Height', min_value=100, max_value=220, value=185, step=1, help='Tinggi pemain')
-    weight = st.number_input('Weight', min_value=60, max_value=100, value=80, step=1, help='Berat badan pemain')
-    value_eur = st.slider('ValueEur', min_value=500000, max_value=2000000, value=1000000, step=1, help='Harga pemain')
-    st.markdown('---')
-    attacking_work_rate = st.selectbox('Pilih kategori attacking work rate: ', options=('Low', 'Medium', 'High'), index=0)
-    defensive_work_rate = st.selectbox('Pilih kategori defensive work rate: ', options=('Low', 'Medium', 'High'), index=1)
-    st.markdown('---')
-    pace_total = st.number_input('PaceTotal', min_value=60, max_value=100, value=80, step=1)
-    shooting_total = st.number_input('ShootingTotal', min_value=60, max_value=100, value=80, step=1)
-    passing_total = st.number_input('PassingTotal', min_value=60, max_value=100, value=80, step=1)
-    dribbling_total = st.number_input('DribblingTotal', min_value=60, max_value=100, value=80, step=1)
-    defending_total = st.number_input('DefendingTotal', min_value=60, max_value=100, value=80, step=1)
-    physicality_total = st.number_input('PhysicalityTotal', min_value=60, max_value=100, value=80, step=1)
-    st.markdown('---')
-    submitted = st.form_submit_button('Predict!')
-
-  # Create data inference
-  df_inf = {'Name': name, 
-          'Age':age, 
-          'Height':height, 
-          'Weight':weight, 
-          'ValueEUR':value_eur, 
-          'AttackingWorkRate':attacking_work_rate,
-          'DefensiveWorkRate':defensive_work_rate, 
-          'PaceTotal':pace_total, 
-          'ShootingTotal':shooting_total, 
-          'PassingTotal':passing_total,
-          'DribblingTotal':dribbling_total, 
-          'DefendingTotal':defending_total, 
-          'PhysicalityTotal':physicality_total}
-
-  # Convert to Dataframe pandas
-  df_inf = pd.DataFrame([df_inf])
-  df_inf = df_inf.rename(columns= {'ValueEUR':'Price'})
-  st.dataframe(df_inf)
-  df_inf_num = df_inf[num]
-  df_inf_cat = df_inf[cat]
-
-  # Feature scaling and encoding
-  df_inf_num_scaled = scaler.transform(df_inf_num)
-  df_inf_cat_encoded = encoder.transform(df_inf_cat)
-
-  # Concat
-  df_inf_final = np.concatenate([df_inf_num_scaled,df_inf_cat_encoded],axis=1)
-
-  if submitted:
-    prediction = model.predict(df_inf_final)
-
-    st.write(f'## Prediction score: {'%.2f' % prediction[0]}')
-  
-
-if __name__ == '__main__':
+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import json
+
+with open('model.pkl', 'rb') as file_1: #rb =read binary
+  model = pickle.load(file_1)
+
+with open('scaler.pkl', 'rb') as file_2:
+  scaler = pickle.load(file_2)
+
+with open('encoder.pkl', 'rb') as file_3:
+  encoder = pickle.load(file_3)
+
+with open('num.txt', 'r') as file_4:
+  num = json.load(file_4)
+
+with open('cat.txt', 'r') as file_5:
+  cat = json.load(file_5)
+
+def run():
+  # membuat form
+  with st.form(key='form_fifa_2022'):
+    name = st.text_input('Name', placeholder='--nama-pemain--')
+    age = st.number_input('Age', min_value=16, max_value=52, value=24, step=1, help='Usia pemain')
+    height = st.number_input('Height', min_value=100, max_value=220, value=185, step=1, help='Tinggi pemain')
+    weight = st.number_input('Weight', min_value=60, max_value=100, value=80, step=1, help='Berat badan pemain')
+    value_eur = st.slider('ValueEur', min_value=500000, max_value=2000000, value=1000000, step=1, help='Harga pemain')
+    st.markdown('---')
+    attacking_work_rate = st.selectbox('Pilih kategori attacking work rate: ', options=('Low', 'Medium', 'High'), index=0)
+    defensive_work_rate = st.selectbox('Pilih kategori defensive work rate: ', options=('Low', 'Medium', 'High'), index=1)
+    st.markdown('---')
+    pace_total = st.number_input('PaceTotal', min_value=60, max_value=100, value=80, step=1)
+    shooting_total = st.number_input('ShootingTotal', min_value=60, max_value=100, value=80, step=1)
+    passing_total = st.number_input('PassingTotal', min_value=60, max_value=100, value=80, step=1)
+    dribbling_total = st.number_input('DribblingTotal', min_value=60, max_value=100, value=80, step=1)
+    defending_total = st.number_input('DefendingTotal', min_value=60, max_value=100, value=80, step=1)
+    physicality_total = st.number_input('PhysicalityTotal', min_value=60, max_value=100, value=80, step=1)
+    st.markdown('---')
+    submitted = st.form_submit_button('Predict!')
+
+  # Create data inference
+  df_inf = {'Name': name, 
+          'Age':age, 
+          'Height':height, 
+          'Weight':weight, 
+          'ValueEUR':value_eur, 
+          'AttackingWorkRate':attacking_work_rate,
+          'DefensiveWorkRate':defensive_work_rate, 
+          'PaceTotal':pace_total, 
+          'ShootingTotal':shooting_total, 
+          'PassingTotal':passing_total,
+          'DribblingTotal':dribbling_total, 
+          'DefendingTotal':defending_total, 
+          'PhysicalityTotal':physicality_total}
+
+  # Convert to Dataframe pandas
+  df_inf = pd.DataFrame([df_inf])
+  df_inf = df_inf.rename(columns= {'ValueEUR':'Price'})
+  st.dataframe(df_inf)
+  df_inf_num = df_inf[num]
+  df_inf_cat = df_inf[cat]
+
+  # Feature scaling and encoding
+  df_inf_num_scaled = scaler.transform(df_inf_num)
+  df_inf_cat_encoded = encoder.transform(df_inf_cat)
+
+  # Concat
+  df_inf_final = np.concatenate([df_inf_num_scaled,df_inf_cat_encoded],axis=1)
+
+  if submitted:
+    prediction = model.predict(df_inf_final)
+
+    st.write(f'## Prediction score: {prediction[0]}')
+  
+
+if __name__ == '__main__':
   run()