File size: 3,069 Bytes
3ebb57c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
import streamlit as st
import pandas as pd
import numpy as np
import seaborn as sns
import json
import transformers
# Load model
with open('list_num_columns.txt', 'r') as file_1:
list_num_cols = json.load(file_1)
with open('list_cat_columns.txt', 'r') as file_2:
list_cat_cols = 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)
with st.form('form_fifa_2022'):
name = st.text_input('Name', value='', help='Player name')
age = st.number_input('Age',
min_value=12,
max_value=48,
value=28,
step=2,
help='Player age')
weight = st.number_input('Weight',
min_value=30,
max_value=100,
value=80,
help='Player weight')
height = st.number_input('Height',
min_value=140,
max_value=210,
value=180,
help='Player height')
price = st.slider('Price', 0, 200000000, 0)
st.markdown('---')
defense = st.radio('Defending Work Rate',
('Low','Medium','High'),
index=1)
attack = st.radio('Attacking Work Rate',
('Low','Medium','High'),
index=1)
st.markdown('---')
pace = st.number_input('Pace', 0, 100, 50)
shoot = st.number_input('Shooting', 0, 100, 50)
passing = st.number_input('Passing', 0, 100, 50)
dribble = st.number_input('Dribbling', 0, 100, 50)
defend = st.number_input('Defending', 0, 100, 50)
physical = st.number_input('Phisicality', 0, 100, 50)
st.markdown('---')
submitted = st.form_submit_button('Predict')
data_inf = {
'Name': name,
'Age': age,
'Weight': weight,
'Height': height,
'Price': price,
'AttackRate': attack,
'DefenseRate': defense,
'PaceTotal': pace,
'ShootingTotal': shoot,
'PassingTotal': passing,
'DribblingTotal': dribble,
'DefendingTotal': defend,
'PhysicalityTotal': physical
}
data_inf = pd.DataFrame([data_inf])
st.dataframe(data_inf)
if submitted:
# Numeric-Categoric split
data_inf_num = data_inf[list_num_cols]
data_inf_cat = data_inf[list_cat_cols]
# Numeric
data_inf_num_scaled = model_scaler.transform(data_inf_num)
# Categoric
data_inf_cat_encoded = model_encoder.transform(data_inf_cat)
# Concatenate
data_inf_final = np.concatenate([data_inf_num_scaled,data_inf_cat_encoded], axis=1)
# Predict
y_inf_pred = model_lin_reg.predict(data_inf_final)
# Show prediction
st.write('# Rating: ', str(int(y_inf_pred))) |