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import streamlit as st
import pandas as pd
import numpy as np
import pickle
import json
with open('model_lin_reg.pkl', 'rb') as file_1:
model_lin_reg = pickle.load(file_1)
with open('model_scaler.pkl', 'rb') as file_2:
model_scaler = pickle.load(file_2)
with open('model_encoder.pkl','rb') as file_3:
model_encoder = pickle.load(file_3)
with open('list_num_cols.txt', 'r') as file_4:
list_num_cols = json.load(file_4)
with open('list_cat_cols.txt', 'r') as file_5:
list_cat_cols = json.load(file_5)
def run():
st.title("FIFA Player Rating Prediction")
with st.form("form_fifa"):
name = st.text_input("Name", value="")
age = st.number_input("Age", min_value=16, max_value=60, value=25, step=1, help="Usia Pemain")
weight = st.number_input("Weight", min_value=50, max_value=150, value=75)
height = st.slider("Height", 140, 250, 170)
price = st.number_input("Price", min_value=0, max_value=1000000000, value=0)
st.markdown("---")
attackingWorkRate = st.selectbox("AttackingWorkRate", ("Low", "Medium", "High"), index=1)
defensiveWorkRate = st.selectbox("DefensiveWorkRate", ("Low", "Medium", "High"), index=1)
st.markdown("---")
pace = st.number_input("Pace", min_value=0, max_value=100, value=50)
shooting = st.number_input("Shooting", min_value=0, max_value=100, value=50)
passing = st.number_input("Passing", min_value=0, max_value=100, value=50)
dribbling = st.number_input("Dribbling", min_value=0, max_value=100, value=50)
defending = st.number_input("Defending", min_value=0, max_value=100, value=50)
physicality = st.number_input("Physicality", min_value=0, max_value=100, value=50)
submitted = st.form_submit_button("Predict")
dataInference = {
"Name": name,
"Age": age,
"Height": height,
"Weight": weight,
"Price": price,
"AttackingWorkRate": attackingWorkRate,
"DefensiveWorkRate": defensiveWorkRate,
"PaceTotal": pace,
"ShootingTotal": shooting,
"PassingTotal": passing,
"DribblingTotal": dribbling,
"DefendingTotal": defending,
"PhysicalityTotal": physicality
}
dfInference = pd.DataFrame([dataInference])
st.dataframe(dfInference)
if submitted:
#Split between Numerical and Categorical Columns
data_inf_num = dfInference[list_num_cols]
data_inf_cat = dfInference[list_cat_cols]
#Feature Preprocessing (Scaling and 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)
#Prediction
y_pred_inf = model_lin_reg.predict(data_inf_final)
st.write("## Rating: ", str(int(y_pred_inf)))
if __name__ == "__main__":
run()
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