Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,68 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import joblib
+import os
 
-"""
-# Welcome to Streamlit!
+# ======================
+# LOAD MODEL
+# ======================
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+model = joblib.load(os.path.join(BASE_DIR, "house_price_model.pkl"))
 
-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).
+# ======================
+# PAGE CONFIG
+# ======================
+st.set_page_config(
+    page_title="House Price Prediction",
+    page_icon="🏡",
+    layout="centered"
+)
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+st.title("🏡 House Price Prediction")
+st.write("Predict the house price based on key features")
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+# ======================
+# SIDEBAR INPUTS
+# ======================
+st.sidebar.header("House Features")
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+OverallQual = st.sidebar.slider("Overall Quality", 1, 10, 5)
+GrLivArea = st.sidebar.number_input("Above Ground Living Area (sq ft)", 300, 5000, 1500)
+GarageCars = st.sidebar.slider("Garage Capacity (cars)", 0, 4, 2)
+TotalBsmtSF = st.sidebar.number_input("Total Basement Area (sq ft)", 0, 3000, 800)
+FullBath = st.sidebar.slider("Full Bathrooms", 0, 4, 2)
+YearBuilt = st.sidebar.slider("Year Built", 1900, 2024, 2000)
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+Neighborhood = st.sidebar.selectbox(
+    "Neighborhood",
+    [
+        "NAmes", "CollgCr", "OldTown", "Edwards", "Somerst",
+        "Gilbert", "NridgHt", "Sawyer", "NWAmes", "SawyerW"
+    ]
+)
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
+# ======================
+# DATAFRAME
+# ======================
+input_df = pd.DataFrame({
+    "OverallQual": [OverallQual],
+    "GrLivArea": [GrLivArea],
+    "GarageCars": [GarageCars],
+    "TotalBsmtSF": [TotalBsmtSF],
+    "FullBath": [FullBath],
+    "YearBuilt": [YearBuilt],
+    "Neighborhood": [Neighborhood]
 })
 
-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])),
-    ))
+st.subheader("Input Data")
+st.write(input_df)
+
+# ======================
+# PREDICTION
+# ======================
+if st.button("Predict Price"):
+
+    prediction = model.predict(input_df)[0]
+
+    st.subheader("Estimated House Price 💰")
+    st.success(f"${prediction:,.0f}")