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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('pipelines.pkl', 'rb') as file_1:
pipelines = pickle.load(file_1)
with open('num_columns.txt', 'r') as file_2:
num_columns = json.load(file_2)
with open('cat_columns.txt', 'r') as file_3:
cat_columns = json.load(file_3)
with open('norm_columns.txt', 'r') as file_4:
norm_columns = json.load(file_4)
with open('skew_columns.txt', 'r') as file_5:
skew_columns = json.load(file_5)
with open('enc_columns.txt', 'r') as file_6:
enc_columns = json.load(file_6)
def run():
with st.form(key='Hepatitis_C_Prediction'):
Category = st.selectbox('Category', ('0=Blood Donor', '1=suspect Blood Donor',
'2=Hepatitis', '3=Fibrosis', '4=Chirrosis'), index=1)
Age = st.number_input('Age', min_value=23, max_value=80, value=23)
Sex = st.selectbox('Sex', ('Male', 'Female'), index=1)
st.markdown('---')
ALB = st.number_input('ALB', min_value=0, max_value=85, value=0)
ALP = st.number_input('ALP', min_value=0, max_value=420, value=0)
ALT = st.number_input('ALT', min_value=0, max_value=325, value=0)
AST = st.number_input('AST', min_value=0, max_value=325, value=0)
BIL = st.number_input('BIL', min_value=0, max_value=210, value=0)
st.markdown('---')
CHE = st.number_input('CHE', min_value=0, max_value=20, value=0)
CHOL = st.number_input('CHOL', min_value=0, max_value=10, value=0)
CREA = st.number_input('CREA', min_value=0, max_value=1080, value=0)
GGT = st.number_input('GGT', min_value=0, max_value=651, value=0)
PROT = st.number_input('PROT', min_value=0, max_value=87, value=0)
submitted = st.form_submit_button('Predict')
data_inf = {
'Category': Category,
'Age': Age,
'Sex': Sex,
'ALB': ALB,
'ALP': ALP,
'ALT': ALT,
'AST': AST,
'BIL': BIL,
'CHE': CHE,
'CHOL': CHOL,
'CREA': CREA,
'GGT': GGT,
'PROT': PROT
}
data_inf = pd.DataFrame([data_inf])
st.dataframe(data_inf)
if submitted:
# Predict using Linear Regression
y_pred_inf = pipelines.predict(data_inf)
st.write('# Diagnose : ', str(int(y_pred_inf)))
if __name__ == '__main__':
run() |