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import streamlit as st | |
import pickle | |
import numpy as np | |
model=pickle.load(open("model.pkl","rb")) | |
trans_count=pickle.load(open("trans_count.pkl","rb")) | |
trans_edu=pickle.load(open("trans_edu.pkl","rb")) | |
print(st.__version__) | |
def show_predict_page(): | |
st.title("Software Developer Salary Prediction") | |
st.write("""## we need some information to predict salary""") | |
countries=("United States", | |
"India", | |
"United Kingdom", | |
"Germany", | |
"Canada", | |
"Brazil", | |
"France" , | |
"Spain", | |
"Australia", | |
"Netherlands", | |
"Poland", | |
"Italy", | |
"Russian Federation", | |
"Sweden") | |
education=("Bachelor’s degree", "Master’s degree", "Post grad","Less than a Bachelors") | |
country=st.selectbox("Country" ,countries) | |
education=st.selectbox("Education Level",education) | |
experience=st.slider("Year of EXperience",0,50,3) | |
ok=st.button("Calculate Salary") | |
if ok: | |
test=np.array([[country,education,experience]]) | |
test[:,0]=trans_count.transform(test[:,0]) | |
test[:,1]=trans_edu.transform(test[:,1]) | |
test=test.astype(float) | |
salary=model.predict(test) | |
st.subheader(f"The estimated average salary per year is ${salary[0]:.2f}") |