predict_page.py

import streamlit as st
import pickle
import numpy as np

def load_model():
    with open('saved_steps.pkl', 'rb') as file:
        data = pickle.load(file)
    return data

data = load_model()

regressor = data["model"]
le_country = data["le_country"]
le_education = data["le_education"] 


# show predicition page
def show_predict_page():
    st.title('Software Developer Salary Prediction')
    
    st.write('''We need some information to predict the salary''')
    
    countries = ("United States of America", "India", "Germany", 
                 "United Kingdom of Great Britain and Northern Ireland",
                 'Canada',
                 'France', 
                 'Brazil',
                 'Poland', 
                 'Netherlands',
                 'Spain', 
                 'Australia',
                 'Italy', 
                 'Russian Federation',
                 'Sweden',
                 'Switzerland',
                 'Turkey')
    
    education = (
        'Less than a Bachelors',
        "Bachelor’s degree",
        'Master’s degree',
        'Post grad',
        )
    
    country = st.selectbox("Country", countries)
    education = st.selectbox("Education Level", education)
    
    experience= st.slider('Years of Experience', 0,50, 3)
    
    ok = st.button('Calculate Salary')
    
    if ok:
        X = np.array([[country, education, experience ]])
        X[:, 0] = le_country.transform(X[:,0])
        X[:, 1] = le_education.transform(X[:,1])
        X = X.astype(float)
        
        
        salary = regressor.predict(X)
        st.subheader(f"The estimated salary is ${salary[0]:.2f}")