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import joblib
import pandas as pd
import streamlit as st
EDU_DICT = {'some high school': 1,
'some college': 2,
'high school': 3,
"associate's degree": 4,
"bachelor's degree": 5,
"master's degree": 6
}
race = {'group A': 0,
'group B': 1,
'group C': 2,
'group D': 3,
'group E': 4
}
model = joblib.load('modelN.joblib')
unique_values = joblib.load('unique_valuesN.joblib')
unique_gender = unique_values["gender"]
unique_lunch = unique_values["lunch"]
unique_test_preparation_course = unique_values["test preparation course"]
unique_education = unique_values["parental level of education"]
def main():
st.title("Race Prediction")
with st.form("questionaire"):
gender = st.selectbox("gender", options = unique_gender)
lunch = st.selectbox("lunch", options = unique_lunch)
test = st.selectbox("test preparation course", options = unique_test_preparation_course)
education = st.selectbox("parental level of education", options = unique_education)
math_score = st.slider("math score", min_value = 1, max_value = 100)
reading_score = st.slider("reading score", min_value = 1, max_value = 100)
writing_score = st.slider("writing score", min_value = 1, max_value = 100)
# clicked==True only when the button is clicked
clicked = st.form_submit_button("Predict income")
if clicked:
result=model.predict(pd.DataFrame({"gender": [gender],
"lunch": [lunch],
"test preparation course": [test],
"parental level of education": [EDU_DICT[education]],
"math score": [math_score],
"reading score": [reading_score],
"writing score": [writing_score]}))
if result[0] == 0:
result = 'group A'
elif result[0] == 1:
result = 'group B'
elif result[0] == 2:
result = 'group C'
elif result[0] == 3:
result = 'group D'
elif result[0] == 4:
result = 'group E'
else:
'ERROR'
st.success("Race Prediction is "+result)
if __name__ == "__main__":
main()
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