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