Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,83 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pickle
+import numpy as np
+
+# ---------------------------------------------------
+# Load the trained model
+# ---------------------------------------------------
+
+model = pickle.load(open("housing_model.pkl", "rb"))
+
+# ---------------------------------------------------
+# App title and description
+# ---------------------------------------------------
+
+st.title("🏠 House Price Estimator")
 
+st.write(
 """
-# Welcome to Streamlit!
+This app estimates the value of a property in Île-de-France  
+based on a simple regression model trained on real estate data.
+"""
+)
+
+# ---------------------------------------------------
+# Sidebar inputs
+# ---------------------------------------------------
+
+st.sidebar.header("Property information")
+
+surface = st.sidebar.number_input(
+    "Surface (m²)",
+    min_value=10,
+    max_value=400,
+    value=70
+)
+
+rooms = st.sidebar.number_input(
+    "Number of rooms",
+    min_value=1,
+    max_value=10,
+    value=3
+)
 
-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).
+neighborhood_score = st.sidebar.slider(
+    "Neighborhood score",
+    min_value=1,
+    max_value=10,
+    value=5
+)
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+# ---------------------------------------------------
+# Prediction
+# ---------------------------------------------------
+
+if st.sidebar.button("Estimate price"):
+
+    features = np.array([[surface, rooms, neighborhood_score]])
+
+    prediction = model.predict(features)
+
+    estimated_price = int(prediction[0])
+
+    st.subheader("Estimated property price")
+
+    st.success(f"{estimated_price:,.0f} €")
+
+# ---------------------------------------------------
+# Extra explanation
+# ---------------------------------------------------
+
+st.write("---")
+
+st.write(
 """
+**Model inputs**
 
-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)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-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])),
-    ))
+- Surface of the property (m²)  
+- Number of rooms  
+- Neighborhood attractiveness score  
+
+The model predicts the approximate **market price** of the property.
+"""
+)