src/streamlit_app.py

import streamlit as st
import pandas as pd
import joblib

st.title("Student Performance Prediction")

st.write("This app predicts a student's math score using regression model.")

model = joblib.load("src/student_performance_model.pkl")
feature_columns = joblib.load("src/feature_columns.pkl")

gender = st.selectbox("Gender", ["female", "male"])
race = st.selectbox("Race/Ethnicity", ["group A", "group B", "group C", "group D", "group E"])
parent_education = st.selectbox(
    "Parental Level of Education",
    [
        "some high school",
        "high school",
        "some college",
        "associate's degree",
        "bachelor's degree",
        "master's degree"
    ]
)
lunch = st.selectbox("Lunch", ["standard", "free/reduced"])
test_prep = st.selectbox("Test Preparation Course", ["none", "completed"])

reading_score = st.number_input("Reading Score", min_value=0, max_value=100, value=70)
writing_score = st.number_input("Writing Score", min_value=0, max_value=100, value=70)

input_data = pd.DataFrame({
    "gender": [gender],
    "race/ethnicity": [race],
    "parental level of education": [parent_education],
    "lunch": [lunch],
    "test preparation course": [test_prep],
    "reading score": [reading_score],
    "writing score": [writing_score]
})

input_data = pd.get_dummies(input_data, drop_first=True)

input_data = input_data.reindex(columns=feature_columns, fill_value=0)

if st.button("Predict Math Score"):
    prediction = model.predict(input_data)
    st.subheader("Predicted Math Score")
    st.write(round(prediction[0], 2))