Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,48 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 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]
+})
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+input_data = pd.get_dummies(input_data, drop_first=True)
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+input_data = input_data.reindex(columns=feature_columns, fill_value=0)
 
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if st.button("Predict Math Score"):
+    prediction = model.predict(input_data)
+    st.subheader("Predicted Math Score")
+    st.write(round(prediction[0], 2))